From d4e70185fa7e16b1d0b07dfb731897ef06bb3067 Mon Sep 17 00:00:00 2001 From: Simon Brugman Date: Tue, 5 Jan 2021 16:37:35 +0100 Subject: [PATCH] Visions integration (#570) * Visions integration Co-authored-by: ieaves --- .github/workflows/ci_test.yml | 8 +- .pre-commit-config.yaml | 4 +- MANIFEST.in | 2 + Makefile | 4 +- README.md | 2 + examples/features/images_cats_and_dogs.py | 1 + examples/features/urls.py | 2 +- examples/meteorites/meteorites.ipynb | 434 +- examples/meteorites/meteorites.py | 3 + examples/titanic/titanic.ipynb | 108334 +++++++------- .../tutorials/modify_report_structure.ipynb | 758 +- mypy.ini | 28 + requirements-test.txt | 9 +- requirements.txt | 6 +- setup.py | 3 + src/pandas_profiling/config.py | 8 +- src/pandas_profiling/config_default.yaml | 17 +- src/pandas_profiling/config_minimal.yaml | 18 +- .../controller/pandas_decorator.py | 2 +- src/pandas_profiling/model/base.py | 294 - src/pandas_profiling/model/correlations.py | 319 +- src/pandas_profiling/model/describe.py | 37 +- src/pandas_profiling/model/duplicates.py | 28 + src/pandas_profiling/model/handler.py | 31 + src/pandas_profiling/model/imghdr_patch.py | 31 + src/pandas_profiling/model/messages.py | 260 +- src/pandas_profiling/model/sample.py | 4 + src/pandas_profiling/model/summarizer.py | 120 + src/pandas_profiling/model/summary.py | 559 +- .../model/summary_algorithms.py | 425 + src/pandas_profiling/model/summary_helpers.py | 364 + .../model/summary_helpers_image.py | 114 + src/pandas_profiling/model/typeset.py | 229 + .../model/typeset_relations.py | 122 + src/pandas_profiling/profile_report.py | 84 +- .../report/structure/report.py | 75 +- .../report/structure/variables/__init__.py | 1 + .../structure/variables/render_boolean.py | 7 +- .../structure/variables/render_categorical.py | 7 +- .../structure/variables/render_common.py | 7 +- .../report/structure/variables/render_date.py | 1 - .../report/structure/variables/render_url.py | 2 +- tests/__init__.py | 0 tests/conftest.py | 12 + tests/issues/test_issue120.py | 11 +- tests/issues/test_issue249.py | 3 +- tests/issues/test_issue351.py | 8 +- tests/issues/test_issue353.py | 2 +- tests/issues/test_issue377.py | 2 +- tests/issues/test_issue397.py | 4 +- tests/issues/test_issue416.py | 17 +- tests/issues/test_issue437.py | 8 +- tests/issues/test_issue502.py | 16 +- tests/issues/test_issue51.py | 71 +- tests/issues/test_issue523.py | 2 +- tests/issues/test_issue537.py | 11 +- tests/issues/test_issue587.py | 21 - tests/issues/test_issue72.py | 23 +- tests/issues/test_issue85.py | 36 - tests/notebooks/lazy_pipeline.ipynb | 108393 ++++++++------- tests/notebooks/meteorites.ipynb | 340 +- tests/notebooks/titanic.ipynb | 108348 +++++++------- tests/unit/__init__.py | 0 tests/unit/test_console.py | 3 + tests/unit/test_date.py | 14 - tests/unit/test_decorator.py | 2 +- tests/unit/test_describe.py | 291 +- tests/unit/test_modular.py | 14 +- tests/unit/test_summarizer.py | 69 + tests/unit/test_summary_algos.py | 26 + tests/unit/test_type_inference.py | 9 - tests/unit/test_typeset_custom.py | 301 + tests/unit/test_typeset_default.py | 332 + tests/unit/test_url.py | 4 +- tests/unit/test_utils.py | 1 + 75 files changed, 166303 insertions(+), 164855 deletions(-) create mode 100644 mypy.ini delete mode 100644 src/pandas_profiling/model/base.py create mode 100644 src/pandas_profiling/model/duplicates.py create mode 100644 src/pandas_profiling/model/handler.py create mode 100644 src/pandas_profiling/model/imghdr_patch.py create mode 100644 src/pandas_profiling/model/summarizer.py create mode 100644 src/pandas_profiling/model/summary_algorithms.py create mode 100644 src/pandas_profiling/model/summary_helpers.py create mode 100644 src/pandas_profiling/model/summary_helpers_image.py create mode 100644 src/pandas_profiling/model/typeset.py create mode 100644 src/pandas_profiling/model/typeset_relations.py create mode 100644 tests/__init__.py delete mode 100644 tests/issues/test_issue587.py delete mode 100644 tests/issues/test_issue85.py create mode 100644 tests/unit/__init__.py delete mode 100644 tests/unit/test_date.py create mode 100644 tests/unit/test_summarizer.py create mode 100644 tests/unit/test_summary_algos.py delete mode 100644 tests/unit/test_type_inference.py create mode 100644 tests/unit/test_typeset_custom.py create mode 100644 tests/unit/test_typeset_default.py diff --git a/.github/workflows/ci_test.yml b/.github/workflows/ci_test.yml index 724c9cf1d..0b3cbd761 100644 --- a/.github/workflows/ci_test.yml +++ b/.github/workflows/ci_test.yml @@ -9,14 +9,14 @@ jobs: matrix: os: [ ubuntu-latest ] python-version: [ 3.8 ] - pandas: ["<1", "==1.0.5", ">1.1"] + pandas: ["==0.25.3", "==1.0.5", ">1.1"] include: - os: macos-latest python-version: 3.6 pandas: ">1.1" - - os: windows-latest - python-version: 3.6 - pandas: ">1.1" +# - os: windows-2016 +# python-version: 3.6 +# pandas: ">1.1" - os: ubuntu-latest python-version: 3.6 pandas: ">1.1" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 87786f23a..52545ebcd 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -35,4 +35,6 @@ repos: rev: "3.8.4" hooks: - id: flake8 - args: [ "--select=E9,F63,F7,F82" ] + args: [ "--select=E9,F63,F7,F82"] #,T001 +# additional_dependencies: +# - flake8-print diff --git a/MANIFEST.in b/MANIFEST.in index 22598f088..b34ca3063 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -4,6 +4,8 @@ include requirements*.txt # Include license, Readme, etc. include LICENSE include *.md +include mypy.ini +include src/pandas_profiling/py.typed # Styles include src/pandas_profiling/visualisation/*.mplstyle diff --git a/Makefile b/Makefile index 1081606d8..bbcd539e9 100644 --- a/Makefile +++ b/Makefile @@ -19,8 +19,8 @@ test: test_cov: pytest --cov=. tests/unit/ - pytest --cov=. tests/issues/ - pytest --cov=. --nbval tests/notebooks/ + pytest --cov=. --cov-append tests/issues/ + pytest --cov=. --cov-append --nbval tests/notebooks/ pandas_profiling -h make typing diff --git a/README.md b/README.md index f082f4597..b7bff79cc 100644 --- a/README.md +++ b/README.md @@ -81,6 +81,8 @@ The following examples can give you an impression of what the package can do: * [Stata Auto](https://pandas-profiling.github.io/pandas-profiling/examples/master/stata_auto/stata_auto_report.html) (1978 Automobile data) * [Vektis](https://pandas-profiling.github.io/pandas-profiling/examples/master/vektis/vektis_report.html) (Vektis Dutch Healthcare data) * [Colors](https://pandas-profiling.github.io/pandas-profiling/examples/master/colors/colors_report.html) (a simple colors dataset) +* [UCI Bank Dataset](https://pandas-profiling.github.io/pandas-profiling/examples/master/cbank_marketing_data/uci_bank_marketing_report.html) (banking marketing dataset) + Specific features: diff --git a/examples/features/images_cats_and_dogs.py b/examples/features/images_cats_and_dogs.py index 518a8a13e..fcb53c211 100644 --- a/examples/features/images_cats_and_dogs.py +++ b/examples/features/images_cats_and_dogs.py @@ -50,6 +50,7 @@ profile.set_variable("plot.scatter_threshold", 25000) # Enable files and images (off by default, as it uses relatively expensive computations when not interested) +profile.set_variable("vars.path.active", True) profile.set_variable("vars.file.active", True) profile.set_variable("vars.image.active", True) diff --git a/examples/features/urls.py b/examples/features/urls.py index 916367ee7..e56a506a3 100644 --- a/examples/features/urls.py +++ b/examples/features/urls.py @@ -15,6 +15,6 @@ profile = ProfileReport( df, title="Website Inaccessibility Test Lists", - correlations={"cramers": {"calculate": False}}, + vars=dict(url=dict(active=True)), ) profile.to_file(Path("./website_inaccessibility_report.html")) diff --git a/examples/meteorites/meteorites.ipynb b/examples/meteorites/meteorites.ipynb index cbdfc9375..d2feb2ef5 100644 --- a/examples/meteorites/meteorites.ipynb +++ b/examples/meteorites/meteorites.ipynb @@ -1,217 +1,217 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pandas Profiling: NASA Meteorites example\n", - "Source of data: https://data.nasa.gov/Space-Science/Meteorite-Landings/gh4g-9sfh" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The autoreload instruction reloads modules automatically before code execution, which is helpful for the update below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make sure that we have the latest version of pandas-profiling." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "!{sys.executable} -m pip install -U pandas-profiling[notebook]\n", - "!jupyter nbextension enable --py widgetsnbextension" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You might want to restart the kernel now." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import requests\n", - "\n", - "import pandas_profiling\n", - "from pandas_profiling.utils.cache import cache_file" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load and prepare example dataset\n", - "We add some fake variables for illustrating pandas-profiling capabilities" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "file_name = cache_file(\n", - " \"meteorites.csv\",\n", - " \"https://data.nasa.gov/api/views/gh4g-9sfh/rows.csv?accessType=DOWNLOAD\",\n", - ")\n", - "\n", - "df = pd.read_csv(file_name)\n", - "\n", - "# Note: Pandas does not support dates before 1880, so we ignore these for this analysis\n", - "df[\"year\"] = pd.to_datetime(df[\"year\"], errors=\"coerce\")\n", - "\n", - "# Example: Constant variable\n", - "df[\"source\"] = \"NASA\"\n", - "\n", - "# Example: Boolean variable\n", - "df[\"boolean\"] = np.random.choice([True, False], df.shape[0])\n", - "\n", - "# Example: Mixed with base types\n", - "df[\"mixed\"] = np.random.choice([1, \"A\"], df.shape[0])\n", - "\n", - "# Example: Highly correlated variables\n", - "df[\"reclat_city\"] = df[\"reclat\"] + np.random.normal(scale=5, size=(len(df)))\n", - "\n", - "# Example: Duplicate observations\n", - "duplicates_to_add = pd.DataFrame(df.iloc[0:10])\n", - "duplicates_to_add[\"name\"] = duplicates_to_add[\"name\"] + \" copy\"\n", - "\n", - "df = df.append(duplicates_to_add, ignore_index=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Inline report without saving object" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "report = df.profile_report(\n", - " sort=\"None\", html={\"style\": {\"full_width\": True}}, progress_bar=False\n", - ")\n", - "report" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Save report to file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "profile_report = df.profile_report(html={\"style\": {\"full_width\": True}})\n", - "profile_report.to_file(\"/tmp/example.html\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### More analysis (Unicode) and Print existing ProfileReport object inline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "profile_report = df.profile_report(\n", - " explorative=True, html={\"style\": {\"full_width\": True}}\n", - ")\n", - "profile_report" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Notebook Widgets" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "profile_report.to_widgets()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas Profiling: NASA Meteorites example\n", + "Source of data: https://data.nasa.gov/Space-Science/Meteorite-Landings/gh4g-9sfh" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The autoreload instruction reloads modules automatically before code execution, which is helpful for the update below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure that we have the latest version of pandas-profiling." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "!{sys.executable} -m pip install -U pandas-profiling[notebook]\n", + "!jupyter nbextension enable --py widgetsnbextension" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might want to restart the kernel now." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import requests\n", + "\n", + "import pandas_profiling\n", + "from pandas_profiling.utils.cache import cache_file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load and prepare example dataset\n", + "We add some fake variables for illustrating pandas-profiling capabilities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file_name = cache_file(\n", + " \"meteorites.csv\",\n", + " \"https://data.nasa.gov/api/views/gh4g-9sfh/rows.csv?accessType=DOWNLOAD\",\n", + ")\n", + "\n", + "df = pd.read_csv(file_name)\n", + "\n", + "# Note: Pandas does not support dates before 1880, so we ignore these for this analysis\n", + "df[\"year\"] = pd.to_datetime(df[\"year\"], errors=\"coerce\")\n", + "\n", + "# Example: Constant variable\n", + "df[\"source\"] = \"NASA\"\n", + "\n", + "# Example: Boolean variable\n", + "df[\"boolean\"] = np.random.choice([True, False], df.shape[0])\n", + "\n", + "# Example: Mixed with base types\n", + "df[\"mixed\"] = np.random.choice([1, \"A\"], df.shape[0])\n", + "\n", + "# Example: Highly correlated variables\n", + "df[\"reclat_city\"] = df[\"reclat\"] + np.random.normal(scale=5, size=(len(df)))\n", + "\n", + "# Example: Duplicate observations\n", + "duplicates_to_add = pd.DataFrame(df.iloc[0:10])\n", + "duplicates_to_add[\"name\"] = duplicates_to_add[\"name\"] + \" copy\"\n", + "\n", + "df = df.append(duplicates_to_add, ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inline report without saving object" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "report = df.profile_report(\n", + " sort=\"None\", html={\"style\": {\"full_width\": True}}, progress_bar=False\n", + ")\n", + "report" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save report to file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "profile_report = df.profile_report(html={\"style\": {\"full_width\": True}})\n", + "profile_report.to_file(\"/tmp/example.html\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### More analysis (Unicode) and Print existing ProfileReport object inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "profile_report = df.profile_report(\n", + " explorative=True, html={\"style\": {\"full_width\": True}}\n", + ")\n", + "profile_report" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Notebook Widgets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "profile_report.to_widgets()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/meteorites/meteorites.py b/examples/meteorites/meteorites.py index 8d1c5157b..f4c30c7a2 100644 --- a/examples/meteorites/meteorites.py +++ b/examples/meteorites/meteorites.py @@ -28,6 +28,9 @@ # Example: Mixed with base types df["mixed"] = np.random.choice([1, "A"], df.shape[0]) + # Example: Unhashable + df["unhashable"] = [[1]] * df.shape[0] + # Example: Highly correlated variables df["reclat_city"] = df["reclat"] + np.random.normal(scale=5, size=(len(df))) diff --git a/examples/titanic/titanic.ipynb b/examples/titanic/titanic.ipynb index ce7cec6f4..9f64d9481 100644 --- a/examples/titanic/titanic.ipynb +++ b/examples/titanic/titanic.ipynb @@ -1,54167 +1,54167 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The autoreload instruction reloads modules automatically before code execution, which is helpful for the update below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make sure that we have the latest version of pandas-profiling." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "!{sys.executable} -m pip install -U pandas-profiling[notebook]\n", - "!jupyter nbextension enable --py widgetsnbextension" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You might want to restart the kernel now." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Standard Library Imports\n", - "from pathlib import Path\n", - "\n", - "# Installed packages\n", - "import pandas as pd\n", - "from ipywidgets import widgets\n", - "\n", - "# Our package\n", - "from pandas_profiling import ProfileReport\n", - "from pandas_profiling.utils.cache import cache_file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read the Titanic Dataset\n", - "file_name = cache_file(\n", - " \"titanic.csv\",\n", - " \"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\",\n", - ")\n", - "df = pd.read_csv(file_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate the Profiling Report\n", - "profile = ProfileReport(\n", - " df, title=\"Titanic Dataset\", html={\"style\": {\"full_width\": True}}, sort=\"None\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# The Notebook Widgets Interface\n", - "profile.to_widgets()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Or use the HTML report in an iframe\n", - "profile" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - 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AgeCabinEmbarkedFareNameParchPassengerIdPclassSexSibSpSurvivedTicket
022.0NaNS7.2500Braund, Mr. Owen Harris013male10A/5 21171
138.0C85C71.2833Cumings, Mrs. John Bradley (Florence Briggs Thayer)021female11PC 17599
226.0NaNS7.9250Heikkinen, Miss. Laina033female01STON/O2. 3101282
335.0C123S53.1000Futrelle, Mrs. Jacques Heath (Lily May Peel)041female11113803
435.0NaNS8.0500Allen, Mr. William Henry053male00373450
5NaNNaNQ8.4583Moran, Mr. James063male00330877
654.0E46S51.8625McCarthy, Mr. Timothy J071male0017463
72.0NaNS21.0750Palsson, Master. Gosta Leonard183male30349909
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null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure that we have the latest version of pandas-profiling." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "!{sys.executable} -m pip install -U pandas-profiling[notebook]\n", + "!jupyter nbextension enable --py widgetsnbextension" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might want to restart the kernel now." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Standard Library Imports\n", + "from pathlib import Path\n", + "\n", + "# Installed packages\n", + "import pandas as pd\n", + "from ipywidgets import widgets\n", + "\n", + "# Our package\n", + "from pandas_profiling import ProfileReport\n", + "from pandas_profiling.utils.cache import cache_file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the Titanic Dataset\n", + "file_name = cache_file(\n", + " \"titanic.csv\",\n", + " \"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\",\n", + ")\n", + "df = pd.read_csv(file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate the Profiling Report\n", + "profile = ProfileReport(\n", + " df, title=\"Titanic Dataset\", html={\"style\": {\"full_width\": True}}, sort=\"None\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# The Notebook Widgets Interface\n", + "profile.to_widgets()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Or use the HTML report in an iframe\n", 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-1,379 +1,379 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Modify the report's structure\n", - "In this notebook we have a look at two use cases in which we modify the existing report structure: splitting up large reports and reordering the report sections. Both use cases are based on actual user inquiries. The datasets used in this notebook are obtained using the `kaggle` api. If you haven't done so already, you should set up the [api credentials](https://github.com/Kaggle/kaggle-api#api-credentials)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The autoreload instruction reloads modules automatically before code execution, which is helpful for the update below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make sure that we have the latest version of pandas-profiling." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "!{sys.executable} -m pip install -U pandas-profiling[notebook]\n", - "!jupyter nbextension enable --py widgetsnbextension" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You might want to restart the kernel now." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "import kaggle\n", - "\n", - "from pandas_profiling.utils.common import extract_zip\n", - "\n", - "kaggle.api.authenticate()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reorder Sections\n", - "\n", - "We can leverage the same approach to reorder sections. First, we need to generate a profile report." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We are using the Craigslist Carstrucks Data\n", - "vehicles_dataset = Path(\"craigslist-carstrucks-data/vehicles.csv\")\n", - "\n", - "# Download and extract (~228M)\n", - "if not vehicles_dataset.exists():\n", - " kaggle.api.dataset_download_files(\n", - " \"austinreese/craigslist-carstrucks-data\",\n", - " path=\"craigslist-carstrucks-data\",\n", - " quiet=False,\n", - " )\n", - "\n", - " extract_zip(\n", - " \"craigslist-carstrucks-data/craigslist-carstrucks-data.zip\",\n", - " \"craigslist-carstrucks-data/\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "from pandas_profiling import ProfileReport\n", - "\n", - "# For our demonstration, we only take a fraction of the dataset\n", - "df = pd.read_csv(vehicles_dataset, nrows=100)\n", - "\n", - "# Generate the profile report\n", - "vehicles_report = ProfileReport(df)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The structure of the report is stored in the `report` attribute. The report is essentially a tree object. We inspect the root of the report." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(repr(vehicles_report.report))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that the root object is of the type \"Sequence\". Sequence types have at least the attributes `name` and `items`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(vehicles_report.report.content)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example, we would like to pull up the samples section, so that the reordered sequence items are:\n", - "- Overview\n", - "- Samples\n", - "- Missing\n", - "- Variables\n", - "- Interactions\n", - "- Correlations\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Reorder the sections\n", - "vehicles_report.report.content[\"items\"] = [\n", - " vehicles_report.report.content[\"items\"][i] for i in [0, 5, 1, 2, 3, 4]\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can render the report and see that the changes have taken place:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vehicles_report.to_notebook_iframe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Split Profile Reports\n", - "\n", - "When profiling large datasets, a monolithic HTML file can become enormous. Using the report structure generated by `pandas-profiling`, we create a modular report. In this notebook we demonstrate how to split up a profile report in multiple different titles. We start with generating the report's structure in the usual way. The `minimal` mode is set to `True`. This step may take a few minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We are using the IEEE Fraud Detection transaction training data\n", - "ieee_dataset = Path(\"ieee-fraud-detection/train_transaction.csv\")\n", - "\n", - "# Download and extract (~118M)\n", - "if not ieee_dataset.exists():\n", - " kaggle.api.competition_download_files(\n", - " \"ieee-fraud-detection\", path=\"ieee-fraud-detection\", quiet=False\n", - " )\n", - "\n", - " extract_zip(\n", - " \"ieee-fraud-detection/ieee-fraud-detection.zip\",\n", - " \"ieee-fraud-detection/\",\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "from pandas_profiling import ProfileReport\n", - "\n", - "# Read the dataset\n", - "df = pd.read_csv(ieee_dataset)\n", - "\n", - "# Generate the profile report\n", - "ieee_fraud_report = ProfileReport(df, minimal=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(repr(ieee_fraud_report.report))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(repr(ieee_fraud_report.report.content))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from copy import deepcopy\n", - "\n", - "# Make a copy for the original report structure\n", - "original_report_structure = deepcopy(ieee_fraud_report.report)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Loop over each section\n", - "for section in original_report_structure.content[\"items\"]:\n", - " # Only consider sections that contain items\n", - " # if len(section.content['items']) > 0:\n", - " # Set the report structure\n", - " ieee_fraud_report.report = deepcopy(original_report_structure)\n", - " # Overwrite the section lists with the section we would like to print\n", - " ieee_fraud_report.report.content[\"items\"] = [section]\n", - " # Output the report to HTML\n", - " ieee_fraud_report.to_file(f\"ieee_fraud_report_section_{section.name.lower()}.html\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Paginate variables\n", - "We can use the same approach to paginate variables:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ieee_fraud_report.report = original_report_structure\n", - "\n", - "# Number of variables per page\n", - "page_size = 25\n", - "\n", - "# The Root node, which is a sequence of sections\n", - "print(repr(ieee_fraud_report.report.content[\"items\"]))\n", - "\n", - "# The variables\n", - "variable_section = ieee_fraud_report.report.content[\"items\"][1]\n", - "variables = variable_section.content[\"items\"]\n", - "variable_count = len(variables)\n", - "print(f\"Number of variables: {variable_count}\")\n", - "\n", - "# Reset the report structure\n", - "ieee_fraud_report.report = deepcopy(original_report_structure)\n", - "\n", - "# Only keep the variables section\n", - "ieee_fraud_report.report.content[\"items\"] = [\n", - " ieee_fraud_report.report.content[\"items\"][1]\n", - "]\n", - "\n", - "for page_num, variable_page in enumerate(\n", - " [variables[i : i + page_size] for i in range(0, variable_count, page_size)]\n", - "):\n", - " print(f\"Write page {page_num}\")\n", - " # Set the report title\n", - " ieee_fraud_report.title = (\n", - " f\"IEEE Fraud Detection Dataset, Variables, Page {page_num}\"\n", - " )\n", - "\n", - " # Overwrite the variables lists with the variables we would like to print\n", - " ieee_fraud_report.report.content[\"items\"][0].content[\"items\"] = variable_page\n", - "\n", - " # Output the report to HTML\n", - " ieee_fraud_report.to_file(f\"ieee_fraud_report_variables_page_{page_num}.html\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook we have seen two ways of manipulating the report structure. Advanced users may alter the structure in other ways we have not touched, such as exploring deeper parts of the tree structure or inserting and deleting objects." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - }, - "pycharm": { - "stem_cell": { - "cell_type": "raw", - "metadata": { - "collapsed": false - }, - "source": [] - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Modify the report's structure\n", + "In this notebook we have a look at two use cases in which we modify the existing report structure: splitting up large reports and reordering the report sections. Both use cases are based on actual user inquiries. The datasets used in this notebook are obtained using the `kaggle` api. If you haven't done so already, you should set up the [api credentials](https://github.com/Kaggle/kaggle-api#api-credentials)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The autoreload instruction reloads modules automatically before code execution, which is helpful for the update below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make sure that we have the latest version of pandas-profiling." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "!{sys.executable} -m pip install -U pandas-profiling[notebook]\n", + "!jupyter nbextension enable --py widgetsnbextension" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might want to restart the kernel now." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "import kaggle\n", + "\n", + "from pandas_profiling.utils.common import extract_zip\n", + "\n", + "kaggle.api.authenticate()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reorder Sections\n", + "\n", + "We can leverage the same approach to reorder sections. First, we need to generate a profile report." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We are using the Craigslist Carstrucks Data\n", + "vehicles_dataset = Path(\"craigslist-carstrucks-data/vehicles.csv\")\n", + "\n", + "# Download and extract (~228M)\n", + "if not vehicles_dataset.exists():\n", + " kaggle.api.dataset_download_files(\n", + " \"austinreese/craigslist-carstrucks-data\",\n", + " path=\"craigslist-carstrucks-data\",\n", + " quiet=False,\n", + " )\n", + "\n", + " extract_zip(\n", + " \"craigslist-carstrucks-data/craigslist-carstrucks-data.zip\",\n", + " \"craigslist-carstrucks-data/\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "from pandas_profiling import ProfileReport\n", + "\n", + "# For our demonstration, we only take a fraction of the dataset\n", + "df = pd.read_csv(vehicles_dataset, nrows=100)\n", + "\n", + "# Generate the profile report\n", + "vehicles_report = ProfileReport(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The structure of the report is stored in the `report` attribute. The report is essentially a tree object. We inspect the root of the report." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(repr(vehicles_report.report))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the root object is of the type \"Sequence\". Sequence types have at least the attributes `name` and `items`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(vehicles_report.report.content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we would like to pull up the samples section, so that the reordered sequence items are:\n", + "- Overview\n", + "- Samples\n", + "- Missing\n", + "- Variables\n", + "- Interactions\n", + "- Correlations\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reorder the sections\n", + "vehicles_report.report.content[\"items\"] = [\n", + " vehicles_report.report.content[\"items\"][i] for i in [0, 5, 1, 2, 3, 4]\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can render the report and see that the changes have taken place:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "vehicles_report.to_notebook_iframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Split Profile Reports\n", + "\n", + "When profiling large datasets, a monolithic HTML file can become enormous. Using the report structure generated by `pandas-profiling`, we create a modular report. In this notebook we demonstrate how to split up a profile report in multiple different titles. We start with generating the report's structure in the usual way. The `minimal` mode is set to `True`. This step may take a few minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# We are using the IEEE Fraud Detection transaction training data\n", + "ieee_dataset = Path(\"ieee-fraud-detection/train_transaction.csv\")\n", + "\n", + "# Download and extract (~118M)\n", + "if not ieee_dataset.exists():\n", + " kaggle.api.competition_download_files(\n", + " \"ieee-fraud-detection\", path=\"ieee-fraud-detection\", quiet=False\n", + " )\n", + "\n", + " extract_zip(\n", + " \"ieee-fraud-detection/ieee-fraud-detection.zip\",\n", + " \"ieee-fraud-detection/\",\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "from pandas_profiling import ProfileReport\n", + "\n", + "# Read the dataset\n", + "df = pd.read_csv(ieee_dataset)\n", + "\n", + "# Generate the profile report\n", + "ieee_fraud_report = ProfileReport(df, minimal=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(repr(ieee_fraud_report.report))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(repr(ieee_fraud_report.report.content))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from copy import deepcopy\n", + "\n", + "# Make a copy for the original report structure\n", + "original_report_structure = deepcopy(ieee_fraud_report.report)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Loop over each section\n", + "for section in original_report_structure.content[\"items\"]:\n", + " # Only consider sections that contain items\n", + " # if len(section.content['items']) > 0:\n", + " # Set the report structure\n", + " ieee_fraud_report.report = deepcopy(original_report_structure)\n", + " # Overwrite the section lists with the section we would like to print\n", + " ieee_fraud_report.report.content[\"items\"] = [section]\n", + " # Output the report to HTML\n", + " ieee_fraud_report.to_file(f\"ieee_fraud_report_section_{section.name.lower()}.html\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Paginate variables\n", + "We can use the same approach to paginate variables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ieee_fraud_report.report = original_report_structure\n", + "\n", + "# Number of variables per page\n", + "page_size = 25\n", + "\n", + "# The Root node, which is a sequence of sections\n", + "print(repr(ieee_fraud_report.report.content[\"items\"]))\n", + "\n", + "# The variables\n", + "variable_section = ieee_fraud_report.report.content[\"items\"][1]\n", + "variables = variable_section.content[\"items\"]\n", + "variable_count = len(variables)\n", + "print(f\"Number of variables: {variable_count}\")\n", + "\n", + "# Reset the report structure\n", + "ieee_fraud_report.report = deepcopy(original_report_structure)\n", + "\n", + "# Only keep the variables section\n", + "ieee_fraud_report.report.content[\"items\"] = [\n", + " ieee_fraud_report.report.content[\"items\"][1]\n", + "]\n", + "\n", + "for page_num, variable_page in enumerate(\n", + " [variables[i : i + page_size] for i in range(0, variable_count, page_size)]\n", + "):\n", + " print(f\"Write page {page_num}\")\n", + " # Set the report title\n", + " ieee_fraud_report.title = (\n", + " f\"IEEE Fraud Detection Dataset, Variables, Page {page_num}\"\n", + " )\n", + "\n", + " # Overwrite the variables lists with the variables we would like to print\n", + " ieee_fraud_report.report.content[\"items\"][0].content[\"items\"] = variable_page\n", + "\n", + " # Output the report to HTML\n", + " ieee_fraud_report.to_file(f\"ieee_fraud_report_variables_page_{page_num}.html\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook we have seen two ways of manipulating the report structure. Advanced users may alter the structure in other ways we have not touched, such as exploring deeper parts of the tree structure or inserting and deleting objects." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/mypy.ini b/mypy.ini new file mode 100644 index 000000000..cd84f624c --- /dev/null +++ b/mypy.ini @@ -0,0 +1,28 @@ +[mypy] + +[mypy-numpy.*] +ignore_missing_imports = True + +[mypy-pandas.*] +ignore_missing_imports = True + +[mypy-scipy.*] +ignore_missing_imports = True + +[mypy-matplotlib.*] +ignore_missing_imports = True + +[mypy-tqdm.*] +ignore_missing_imports = True + +[mypy-google.*] +ignore_missing_imports = True + +[mypy-PyQt5.*] +ignore_missing_imports = True + +[mypy-IPython.*] +ignore_missing_imports = True + +[mypy-htmlmin.*] +ignore_missing_imports = True \ No newline at end of file diff --git a/requirements-test.txt b/requirements-test.txt index f10fbcfa0..980bae930 100644 --- a/requirements-test.txt +++ b/requirements-test.txt @@ -1,11 +1,12 @@ pytest coverage<5 codecov -pytest-mypy +pytest-mypy>=0.7.0 pytest-cov -nbval -fastparquet>=0.3.3 +nbval>=0.9.6 +ipykernel>=5.3.4 +fastparquet==0.4.1 flake8 -check-manifest>=0.41 +check-manifest>=0.45 twine>=3.1.1 kaggle \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 0baee5b33..34181cbc0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -13,13 +13,13 @@ htmlmin>=0.1.12 # Missing values missingno>=0.4.2 # Correlations -phik>=0.9.10 +phik>=0.10.0 # Text analysis tangled-up-in-unicode>=0.0.6 # Examples -requests>=2.23.0 +requests>=2.24.0 # Progress bar -tqdm>=4.43.0 +tqdm>=4.48.2 # Jupyter notebook ipywidgets>=7.5.1 seaborn>=0.10.1 \ No newline at end of file diff --git a/setup.py b/setup.py index dce4172bf..be0f30a05 100644 --- a/setup.py +++ b/setup.py @@ -39,6 +39,9 @@ extras_require={ "notebook": ["jupyter-client>=6.0.0", "jupyter-core>=4.6.3"], }, + package_data={ + "pandas_profiling": ["py.typed"], + }, include_package_data=True, classifiers=[ "Development Status :: 5 - Production/Stable", diff --git a/src/pandas_profiling/config.py b/src/pandas_profiling/config.py index 138537539..2a5fe78cb 100644 --- a/src/pandas_profiling/config.py +++ b/src/pandas_profiling/config.py @@ -58,10 +58,10 @@ def set_file(self, file_name: Union[str, Path]) -> None: "explorative": { "vars": { "cat": {"characters": True, "words": True}, + "url": {"active": True}, + "path": {"active": True}, "file": {"active": True}, - "image": { - "active": True, - }, + "image": {"active": True}, }, "n_obs_unique": 10, "n_extreme_obs": 10, @@ -105,7 +105,7 @@ def _set_kwargs(self, reference, values: dict): copyright_year="", url="", ), - "samples": {"head": 0, "tail": 0}, + "samples": {"head": 0, "tail": 0, "random": 0}, "duplicates": {"head": 0}, "interactions": {"targets": [], "continuous": False}, "missing_diagrams": { diff --git a/src/pandas_profiling/config_default.yaml b/src/pandas_profiling/config_default.yaml index aa098ffdd..bf1831a7f 100644 --- a/src/pandas_profiling/config_default.yaml +++ b/src/pandas_profiling/config_default.yaml @@ -47,12 +47,22 @@ vars: redact: False bool: n_obs: 3 +# string to boolean mappings pairs (true, false) + mappings: + - ["t", "f"] + - ["yes", "no"] + - ["y", "n"] + - ["true", "false"] file: active: False image: active: False exif: True hash: True + path: + active: False + url: + active: False # Sort the variables. Possible values: ascending, descending or None (leaves original sorting) sort: None @@ -85,11 +95,7 @@ correlations: calculate: True warn_high_correlations: True threshold: 0.9 - # Deprecated: do not use - recoded: - calculate: False - warn_high_correlations: False - threshold: 0.0 + # Bivariate / Pairwise relations interactions: @@ -146,6 +152,7 @@ duplicates: samples: head: 10 tail: 10 + random: 0 # Configuration related to the rejection of variables reject_variables: True diff --git a/src/pandas_profiling/config_minimal.yaml b/src/pandas_profiling/config_minimal.yaml index 5143072c8..c8209852a 100644 --- a/src/pandas_profiling/config_minimal.yaml +++ b/src/pandas_profiling/config_minimal.yaml @@ -47,12 +47,23 @@ vars: redact: False bool: n_obs: 3 +# string to boolean mappings pairs (true, false) + mappings: + - ["t", "f"] + - ["yes", "no"] + - ["y", "n"] + - ["true", "false"] + path: + active: False file: active: False image: active: False exif: False hash: False + url: + active: False + # Sort the variables. Possible values: ascending, descending or None (leaves original sorting) sort: None @@ -85,11 +96,7 @@ correlations: calculate: False warn_high_correlations: True threshold: 0.9 - # Deprecated: do not use - recoded: - calculate: False - warn_high_correlations: False - threshold: 0.0 + # Bivariate / Pairwise relations interactions: @@ -146,6 +153,7 @@ duplicates: samples: head: 0 tail: 0 + random: 0 # Configuration related to the rejection of variables reject_variables: True diff --git a/src/pandas_profiling/controller/pandas_decorator.py b/src/pandas_profiling/controller/pandas_decorator.py index 0c03ce258..d98bd763c 100644 --- a/src/pandas_profiling/controller/pandas_decorator.py +++ b/src/pandas_profiling/controller/pandas_decorator.py @@ -1,7 +1,7 @@ """This file add the decorator on the DataFrame object.""" from pandas import DataFrame -from pandas_profiling.__init__ import ProfileReport +from pandas_profiling.profile_report import ProfileReport def profile_report(df, **kwargs) -> ProfileReport: diff --git a/src/pandas_profiling/model/base.py b/src/pandas_profiling/model/base.py deleted file mode 100644 index de3c6ab36..000000000 --- a/src/pandas_profiling/model/base.py +++ /dev/null @@ -1,294 +0,0 @@ -"""Common parts to all other modules, mainly utility functions.""" -import imghdr -import os -from enum import Enum, unique -from urllib.parse import ParseResult, urlparse - -import numpy as np -import pandas as pd -from pandas.api.types import is_categorical_dtype - -from pandas_profiling.config import config - - -@unique -class Variable(Enum): - """The possible types of variables in the Profiling Report.""" - - TYPE_CAT = "CAT" - """A categorical variable""" - - TYPE_BOOL = "BOOL" - """A boolean variable""" - - TYPE_NUM = "NUM" - """A numeric variable""" - - TYPE_DATE = "DATE" - """A date variable""" - - TYPE_URL = "URL" - """A URL variable""" - - TYPE_COMPLEX = "COMPLEX" - - TYPE_PATH = "PATH" - """Absolute path""" - - TYPE_FILE = "FILE" - """File (i.e. existing path)""" - - TYPE_IMAGE = "IMAGE" - """Images""" - - S_TYPE_UNSUPPORTED = "UNSUPPORTED" - """An unsupported variable""" - - -# Temporary mapping -Boolean = Variable.TYPE_BOOL -Real = Variable.TYPE_NUM -Count = Variable.TYPE_NUM -Complex = Variable.TYPE_COMPLEX -Date = Variable.TYPE_DATE -Categorical = Variable.TYPE_CAT -Url = Variable.TYPE_URL -AbsolutePath = Variable.TYPE_PATH -FilePath = Variable.TYPE_FILE -ImagePath = Variable.TYPE_IMAGE -Generic = Variable.S_TYPE_UNSUPPORTED - - -def get_counts(series: pd.Series) -> dict: - """Counts the values in a series (with and without NaN, distinct). - - Args: - series: Series for which we want to calculate the values. - - Returns: - A dictionary with the count values (with and without NaN, distinct). - """ - if is_categorical_dtype(series): - series = series.cat.remove_unused_categories() - - value_counts_with_nan = series.value_counts(dropna=False) - value_counts_without_nan = ( - value_counts_with_nan.reset_index().dropna().set_index("index").iloc[:, 0] - ) - distinct_count_with_nan = value_counts_with_nan.count() - distinct_count_without_nan = value_counts_without_nan.count() - - return { - "value_counts": value_counts_without_nan, # Alias - "value_counts_with_nan": value_counts_with_nan, - "value_counts_without_nan": value_counts_without_nan, - "distinct_count_with_nan": distinct_count_with_nan, - "distinct_count_without_nan": distinct_count_without_nan, - } - - -def is_boolean(series: pd.Series, series_description: dict) -> bool: - """Is the series boolean type? - - Args: - series: Series - series_description: Series description - - Returns: - True is the series is boolean type in the broad sense (e.g. including yes/no, NaNs allowed). - """ - keys = series_description["value_counts_without_nan"].keys() - if pd.api.types.is_bool_dtype(keys): - return True - elif ( - 1 <= series_description["distinct_count_without_nan"] <= 2 - and pd.api.types.is_numeric_dtype(series) - and series[~series.isnull()].between(0, 1).all() - ): - return True - elif 1 <= series_description["distinct_count_without_nan"] <= 4: - unique_values = {str(value).lower() for value in keys.values} - accepted_combinations = [ - ["y", "n"], - ["yes", "no"], - ["true", "false"], - ["t", "f"], - ] - - if len(unique_values) == 2 and any( - [unique_values == set(bools) for bools in accepted_combinations] - ): - return True - - return False - - -def is_numeric(series: pd.Series, series_description: dict) -> bool: - """Is the series numeric type? - - Args: - series: Series - series_description: Series description - - Returns: - True is the series is numeric type (NaNs allowed). - """ - return pd.api.types.is_numeric_dtype(series) and ( - series_description["distinct_count_without_nan"] - >= config["vars"]["num"]["low_categorical_threshold"].get(int) - or series.isin({np.inf, -np.inf}).any() - ) - - -def is_url(series: pd.Series, series_description: dict) -> bool: - """Is the series url type? - - Args: - series: Series - series_description: Series description - - Returns: - True is the series is url type (NaNs allowed). - """ - - def is_url_item(x): - return isinstance(x, ParseResult) and all((x.netloc, x.scheme, x.path)) - - if series_description["distinct_count_without_nan"] > 0: - try: - result = series[~series.isnull()].astype(str) - return all(is_url_item(urlparse(x)) for x in result) - except ValueError: - return False - else: - return False - - -def is_path(series, series_description) -> bool: - """Is the series of the path type (i.e. absolute path)? - - Args: - series: Series - series_description: Series description - - Returns: - True is the series is path type (NaNs allowed). - """ - if series_description["distinct_count_without_nan"] == 0: - return False - - try: - result = series[~series.isnull()].astype(str) - return all(os.path.isabs(p) for p in result) - except (ValueError, TypeError): - return False - - -def is_file(series, series_description) -> bool: - """Is the series of the type "file" (i.e. existing paths)? - - Args: - series: Series - series_description: Series description - - Returns: - True is the series is of the file type (NaNs allowed). - """ - if series_description["distinct_count_without_nan"] == 0: - return False - - try: - result = series[~series.isnull()].astype(str) - return all(os.path.exists(p) for p in result) - except (ValueError, TypeError): - return False - - -def is_image(series, series_description) -> bool: - """Is the series of the image type (i.e. "file" with image extensions)? - - Args: - series: Series - series_description: Series description - - Returns: - True is the series is of the image type (NaNs allowed). - """ - if series_description["distinct_count_without_nan"] > 0: - try: - result = series[~series.isnull()].astype(str) - return all(imghdr.what(p) for p in result) - except (TypeError, ValueError): - return False - else: - return False - - -def is_date(series) -> bool: - """Is the variable of type datetime? Throws a warning if the series looks like a datetime, but is not typed as - datetime64. - - Args: - series: Series - - Returns: - True if the variable is of type datetime. - """ - is_date_value = pd.api.types.is_datetime64_dtype(series) - - return is_date_value - - -def get_var_type(series: pd.Series) -> dict: - """Get the variable type of a series. - - Args: - series: Series for which we want to infer the variable type. - - Returns: - The series updated with the variable type included. - """ - - series_description = {} - - try: - series_description = get_counts(series) - - # When the inferred type of the index is just "mixed" probably the types within the series are tuple, dict, - # list and so on... - if series_description[ - "value_counts_without_nan" - ].index.inferred_type.startswith("mixed"): - raise TypeError("Not supported mixed type") - - if series_description["distinct_count_without_nan"] == 0: - # Empty - var_type = Variable.S_TYPE_UNSUPPORTED - elif is_boolean(series, series_description): - var_type = Variable.TYPE_BOOL - elif is_numeric(series, series_description): - var_type = Variable.TYPE_NUM - elif is_date(series): - var_type = Variable.TYPE_DATE - elif is_url(series, series_description): - var_type = Variable.TYPE_URL - elif is_path(series, series_description): - if config["vars"]["file"]["active"].get(bool) and is_file( - series, series_description - ): - if config["vars"]["image"]["active"].get(bool) and is_image( - series, series_description - ): - var_type = Variable.TYPE_IMAGE - else: - var_type = Variable.TYPE_FILE - else: - var_type = Variable.TYPE_PATH - else: - var_type = Variable.TYPE_CAT - except TypeError: - var_type = Variable.S_TYPE_UNSUPPORTED - - series_description.update({"type": var_type}) - - return series_description diff --git a/src/pandas_profiling/model/correlations.py b/src/pandas_profiling/model/correlations.py index 834351805..99756ed71 100644 --- a/src/pandas_profiling/model/correlations.py +++ b/src/pandas_profiling/model/correlations.py @@ -1,103 +1,135 @@ """Correlations between variables.""" import itertools import warnings -from contextlib import suppress -from functools import partial -from typing import Callable, Dict, List, Optional, Union +from typing import Dict, List, Optional import numpy as np import pandas as pd -from confuse import NotFoundError from pandas.core.base import DataError from scipy import stats from pandas_profiling.config import config -from pandas_profiling.model.base import Variable +from pandas_profiling.model.typeset import Boolean, Categorical, Numeric, Unsupported + + +class Correlation: + @staticmethod + def compute(df, summary) -> Optional[pd.DataFrame]: + return NotImplemented + + +class Spearman(Correlation): + @staticmethod + def compute(df, summary) -> Optional[pd.DataFrame]: + return df.corr(method="spearman") + + +class Pearson(Correlation): + @staticmethod + def compute(df, summary) -> Optional[pd.DataFrame]: + return df.corr(method="pearson") + + +class Kendall(Correlation): + @staticmethod + def compute(df, summary) -> Optional[pd.DataFrame]: + return df.corr(method="kendall") + + +class Cramers(Correlation): + @staticmethod + def _cramers_corrected_stat(confusion_matrix, correction: bool) -> float: + """Calculate the Cramer's V corrected stat for two variables. + + Args: + confusion_matrix: Crosstab between two variables. + correction: Should the correction be applied? + + Returns: + The Cramer's V corrected stat for the two variables. + """ + chi2 = stats.chi2_contingency(confusion_matrix, correction=correction)[0] + n = confusion_matrix.sum().sum() + phi2 = chi2 / n + r = confusion_matrix.shape[0] + k = confusion_matrix.shape[1] if len(confusion_matrix.shape) > 1 else 1 + + # Deal with NaNs later on + with np.errstate(divide="ignore", invalid="ignore"): + phi2corr = max(0.0, phi2 - ((k - 1.0) * (r - 1.0)) / (n - 1.0)) + rcorr = r - ((r - 1.0) ** 2.0) / (n - 1.0) + kcorr = k - ((k - 1.0) ** 2.0) / (n - 1.0) + rkcorr = min((kcorr - 1.0), (rcorr - 1.0)) + if rkcorr == 0.0: + corr = 1.0 + else: + corr = np.sqrt(phi2corr / rkcorr) + return corr + + @staticmethod + def compute(df, summary) -> Optional[pd.DataFrame]: + threshold = config["categorical_maximum_correlation_distinct"].get(int) + + categoricals = { + key + for key, value in summary.items() + if value["type"] in {Categorical, Boolean} + and value["n_distinct"] <= threshold + } + + if len(categoricals) <= 1: + return None + + matrix = np.zeros((len(categoricals), len(categoricals))) + np.fill_diagonal(matrix, 1.0) + correlation_matrix = pd.DataFrame( + matrix, + index=categoricals, + columns=categoricals, + ) + + for name1, name2 in itertools.combinations(categoricals, 2): + confusion_matrix = pd.crosstab(df[name1], df[name2]) + correlation_matrix.loc[name2, name1] = Cramers._cramers_corrected_stat( + confusion_matrix, correction=True + ) + correlation_matrix.loc[name1, name2] = correlation_matrix.loc[name2, name1] + return correlation_matrix + + +class PhiK(Correlation): + @staticmethod + def compute(df, summary) -> Optional[pd.DataFrame]: + threshold = config["categorical_maximum_correlation_distinct"].get(int) + intcols = { + key + for key, value in summary.items() + # DateTime currently excluded + # In some use cases, it makes sense to convert it to interval + # See https://github.com/KaveIO/PhiK/issues/7 + if value["type"] == Numeric and 1 < value["n_distinct"] + } + + selcols = { + key + for key, value in summary.items() + if value["type"] != Unsupported and 1 < value["n_distinct"] <= threshold + } + selcols = selcols.union(intcols) + + if len(selcols) <= 1: + return None + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + import phik -def cramers_corrected_stat(confusion_matrix, correction: bool) -> float: - """Calculate the Cramer's V corrected stat for two variables. - - Args: - confusion_matrix: Crosstab between two variables. - correction: Should the correction be applied? - - Returns: - The Cramer's V corrected stat for the two variables. - """ - chi2 = stats.chi2_contingency(confusion_matrix, correction=correction)[0] - n = confusion_matrix.sum().sum() - phi2 = chi2 / n - r, k = confusion_matrix.shape - - # Deal with NaNs later on - with np.errstate(divide="ignore", invalid="ignore"): - phi2corr = max(0.0, phi2 - ((k - 1.0) * (r - 1.0)) / (n - 1.0)) - rcorr = r - ((r - 1.0) ** 2.0) / (n - 1.0) - kcorr = k - ((k - 1.0) ** 2.0) / (n - 1.0) - corr = np.sqrt(phi2corr / min((kcorr - 1.0), (rcorr - 1.0))) - return corr - - -def cramers_matrix(df: pd.DataFrame, variables: dict): - """Calculate the Cramer's V correlation matrix. - - Args: - df: The pandas DataFrame. - variables: A dict with column names mapped to variable type. - - Returns: - A Cramer's V matrix for categorical variables. - """ - return categorical_matrix( - df, variables, partial(cramers_corrected_stat, correction=True) - ) - - -def categorical_matrix( - df: pd.DataFrame, variables: dict, correlation_function: Callable -) -> Optional[pd.DataFrame]: - """Calculate a correlation matrix for categorical variables. - - Args: - df: The pandas DataFrame. - variables: A dict with column names mapped to variable type. - correlation_function: A function to calculate the correlation between two variables. - - Returns: - A correlation matrix for categorical variables. - """ - categoricals = { - column_name: df[column_name] - for column_name, variable_type in variables.items() - if variable_type == Variable.TYPE_CAT - # TODO: solve in type system - and config["categorical_maximum_correlation_distinct"].get(int) - >= df[column_name].nunique() - > 1 - } - - if len(categoricals) <= 1: - return None - - correlation_matrix = pd.DataFrame( - np.ones((len(categoricals), len(categoricals))), - index=categoricals.keys(), - columns=categoricals.keys(), - ) - - for (name1, data1), (name2, data2) in itertools.combinations( - categoricals.items(), 2 - ): - confusion_matrix = pd.crosstab(data1, data2) - correlation_matrix.loc[name2, name1] = correlation_matrix.loc[ - name1, name2 - ] = correlation_function(confusion_matrix) + correlation = df[selcols].phik_matrix(interval_cols=intcols) - return correlation_matrix + return correlation -def warn_correlation(correlation_name, error): +def warn_correlation(correlation_name: str, error): warnings.warn( f"""There was an attempt to calculate the {correlation_name} correlation, but this failed. To hide this warning, disable the calculation @@ -109,91 +141,35 @@ def warn_correlation(correlation_name, error): def calculate_correlation( - df: pd.DataFrame, variables: dict, correlation_name: str -) -> Union[pd.DataFrame, None]: + df: pd.DataFrame, correlation_name: str, summary: dict +) -> Optional[pd.DataFrame]: """Calculate the correlation coefficients between variables for the correlation types selected in the config (pearson, spearman, kendall, phi_k, cramers). Args: - variables: A dict with column names and variable types. df: The DataFrame with variables. correlation_name: + summary: summary dictionary Returns: The correlation matrices for the given correlation measures. Return None if correlation is empty. """ - categorical_correlations = {"cramers": cramers_matrix} - correlation = None - - if correlation_name in ["pearson", "spearman", "kendall"]: - try: - correlation = df.corr(method=correlation_name) + correlation_measures = { + "pearson": Pearson, + "spearman": Spearman, + "kendall": Kendall, + "cramers": Cramers, + "phi_k": PhiK, + } - except (ValueError, AssertionError, TypeError) as e: - warn_correlation(correlation_name, e) - elif correlation_name in ["phi_k"]: - import phik + correlation = None + try: + correlation = correlation_measures[correlation_name].compute(df, summary) + except (ValueError, AssertionError, TypeError, DataError, IndexError) as e: + warn_correlation(correlation_name, e) - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - # Phi_k does not filter non-numerical with high cardinality - selcols = [] - intcols = [] - for col in df.columns.tolist(): - try: - tmp = ( - df[col] - .value_counts(dropna=False) - .reset_index() - .dropna() - .set_index("index") - .iloc[:, 0] - ) - if tmp.index.inferred_type == "mixed": - continue - - if pd.api.types.is_numeric_dtype(df[col]): - intcols.append(col) - selcols.append(col) - elif df[col].nunique() <= config[ - "categorical_maximum_correlation_distinct" - ].get(int): - selcols.append(col) - except (TypeError, ValueError): - continue - - if len(selcols) > 1: - try: - correlation = df[selcols].phik_matrix(interval_cols=intcols) - - # Only do this if the column_order is set - with suppress(NotFoundError): - # Get the preferred order - column_order = config["column_order"].get(list) - - # Get the Phi_k sorted order - current_order = correlation.index.get_level_values( - "var1" - ).tolist() - - # Intersection (some columns are not used in correlation) - column_order = [x for x in column_order if x in current_order] - - # Override the Phi_k sorting - correlation = correlation.reindex( - index=column_order, columns=column_order - ) - except (ValueError, DataError, IndexError, TypeError) as e: - warn_correlation("phi_k", e) - elif correlation_name in ["cramers"]: - try: - get_matrix = categorical_correlations[correlation_name] - correlation = get_matrix(df, variables) - except (ValueError, AssertionError) as e: - warn_correlation(correlation_name, e) - - if correlation is None or len(correlation) <= 0: + if correlation is not None and len(correlation) <= 0: correlation = None return correlation @@ -212,24 +188,11 @@ def perform_check_correlation( The variables that are highly correlated. """ - corr = correlation_matrix.copy() - - # TODO: use matrix logic - # correlation_tri = correlation.where(np.triu(np.ones(correlation.shape),k=1).astype(np.bool)) - # drop_cols = [i for i in correlation_tri if any(correlation_tri[i]>threshold)] - - mapping: Dict[str, List[str]] = {} - for x, corr_x in corr.iterrows(): - for y, corr in corr_x.iteritems(): - if x == y: - break - - if corr >= threshold or corr <= -1 * threshold: - if x not in mapping: - mapping[x] = [] - if y not in mapping: - mapping[y] = [] - - mapping[x].append(y) - mapping[y].append(x) - return mapping + cols = correlation_matrix.columns + bool_index = abs(correlation_matrix.values) >= threshold + np.fill_diagonal(bool_index, False) + return { + col: cols[bool_index[i]].values.tolist() + for i, col in enumerate(cols) + if any(bool_index[i]) + } diff --git a/src/pandas_profiling/model/describe.py b/src/pandas_profiling/model/describe.py index 918a514d6..80b53d253 100644 --- a/src/pandas_profiling/model/describe.py +++ b/src/pandas_profiling/model/describe.py @@ -7,21 +7,23 @@ from tqdm.auto import tqdm from pandas_profiling.config import config as config -from pandas_profiling.model.base import Variable from pandas_profiling.model.correlations import calculate_correlation +from pandas_profiling.model.duplicates import get_duplicates from pandas_profiling.model.sample import Sample, get_sample from pandas_profiling.model.summary import ( - get_duplicates, get_messages, get_missing_diagrams, get_scatter_matrix, get_series_descriptions, get_table_stats, ) +from pandas_profiling.model.typeset import Numeric, Unsupported from pandas_profiling.version import __version__ -def describe(title: str, df: pd.DataFrame, sample: Optional[dict] = None) -> dict: +def describe( + title: str, df: pd.DataFrame, summarizer, typeset, sample: Optional[dict] = None +) -> dict: """Calculate the statistics for each series in this DataFrame. Args: @@ -64,23 +66,26 @@ def describe(title: str, df: pd.DataFrame, sample: Optional[dict] = None) -> dic if config["correlations"][correlation_name]["calculate"].get(bool) ] - number_of_tasks = 9 + len(df.columns) + len(correlation_names) + number_of_tasks = 8 + len(df.columns) + len(correlation_names) with tqdm( total=number_of_tasks, desc="Summarize dataset", disable=disable_progress_bar ) as pbar: - series_description = get_series_descriptions(df, pbar) + series_description = get_series_descriptions(df, summarizer, typeset, pbar) pbar.set_postfix_str("Get variable types") variables = { column: description["type"] for column, description in series_description.items() } - pbar.update() - - # Transform the series_description in a DataFrame - pbar.set_postfix_str("Get variable statistics") - variable_stats = pd.DataFrame(series_description) + supported_columns = [ + column + for column, type_name in variables.items() + if type_name != Unsupported + ] + interval_columns = [ + column for column, type_name in variables.items() if type_name == Numeric + ] pbar.update() # Get correlations @@ -88,7 +93,7 @@ def describe(title: str, df: pd.DataFrame, sample: Optional[dict] = None) -> dic for correlation_name in correlation_names: pbar.set_postfix_str(f"Calculate {correlation_name} correlation") correlations[correlation_name] = calculate_correlation( - df, variables, correlation_name + df, correlation_name, series_description ) pbar.update() @@ -99,12 +104,12 @@ def describe(title: str, df: pd.DataFrame, sample: Optional[dict] = None) -> dic # Scatter matrix pbar.set_postfix_str("Get scatter matrix") - scatter_matrix = get_scatter_matrix(df, variables) + scatter_matrix = get_scatter_matrix(df, interval_columns) pbar.update() # Table statistics pbar.set_postfix_str("Get table statistics") - table_stats = get_table_stats(df, variable_stats) + table_stats = get_table_stats(df, series_description) pbar.update() # missing diagrams @@ -129,12 +134,6 @@ def describe(title: str, df: pd.DataFrame, sample: Optional[dict] = None) -> dic # Duplicates pbar.set_postfix_str("Locating duplicates") - supported_columns = [ - key - for key, value in series_description.items() - if value["type"] != Variable.S_TYPE_UNSUPPORTED - ] - duplicates = get_duplicates(df, supported_columns) pbar.update() diff --git a/src/pandas_profiling/model/duplicates.py b/src/pandas_profiling/model/duplicates.py new file mode 100644 index 000000000..b81fc232b --- /dev/null +++ b/src/pandas_profiling/model/duplicates.py @@ -0,0 +1,28 @@ +from typing import Optional + +import pandas as pd + +from pandas_profiling.config import config + + +def get_duplicates(df: pd.DataFrame, supported_columns) -> Optional[pd.DataFrame]: + """Obtain the most occurring duplicate rows in the DataFrame. + + Args: + df: the Pandas DataFrame. + supported_columns: the columns to consider + + Returns: + A subset of the DataFrame, ordered by occurrence. + """ + n_head = config["duplicates"]["head"].get(int) + + if n_head > 0 and supported_columns: + return ( + df[df.duplicated(subset=supported_columns, keep=False)] + .groupby(supported_columns) + .size() + .reset_index(name="count") + .nlargest(n_head, "count") + ) + return None diff --git a/src/pandas_profiling/model/handler.py b/src/pandas_profiling/model/handler.py new file mode 100644 index 000000000..3c87dad0c --- /dev/null +++ b/src/pandas_profiling/model/handler.py @@ -0,0 +1,31 @@ +from pandas_profiling.model import typeset as ppt + + +def get_render_map(): + from pandas_profiling.report.structure.variables import ( + render_boolean, + render_categorical, + render_complex, + render_date, + render_file, + render_generic, + render_image, + render_path, + render_real, + render_url, + ) + + render_map = { + ppt.Boolean: render_boolean, + ppt.Numeric: render_real, + ppt.Complex: render_complex, + ppt.DateTime: render_date, + ppt.Categorical: render_categorical, + ppt.URL: render_url, + ppt.Path: render_path, + ppt.File: render_file, + ppt.Image: render_image, + ppt.Unsupported: render_generic, + } + + return render_map diff --git a/src/pandas_profiling/model/imghdr_patch.py b/src/pandas_profiling/model/imghdr_patch.py new file mode 100644 index 000000000..d4de809ff --- /dev/null +++ b/src/pandas_profiling/model/imghdr_patch.py @@ -0,0 +1,31 @@ +# Monkeypatch bug in imagehdr +from imghdr import tests + + +def test_jpeg1(h, f): + """JPEG data in JFIF format""" + if b"JFIF" in h[:23]: + return "jpeg" + + +JPEG_MARK = ( + b"\xff\xd8\xff\xdb\x00C\x00\x08\x06\x06" + b"\x07\x06\x05\x08\x07\x07\x07\t\t\x08\n\x0c\x14\r\x0c\x0b\x0b\x0c\x19\x12\x13\x0f" +) + + +def test_jpeg2(h, f): + """JPEG with small header""" + if len(h) >= 32 and 67 == h[5] and h[:32] == JPEG_MARK: + return "jpeg" + + +def test_jpeg3(h, f): + """JPEG data in JFIF or Exif format""" + if h[6:10] in (b"JFIF", b"Exif") or h[:2] == b"\xff\xd8": + return "jpeg" + + +tests.append(test_jpeg1) +tests.append(test_jpeg2) +tests.append(test_jpeg3) diff --git a/src/pandas_profiling/model/messages.py b/src/pandas_profiling/model/messages.py index d0ab019f2..3330c049e 100644 --- a/src/pandas_profiling/model/messages.py +++ b/src/pandas_profiling/model/messages.py @@ -1,13 +1,13 @@ """Logic for alerting the user on possibly problematic patterns in the data (e.g. high number of zeros , constant values, high correlations).""" from enum import Enum, auto, unique -from typing import List, Union +from typing import Dict, List, Optional, Set, Union import numpy as np from pandas_profiling.config import config -from pandas_profiling.model.base import Variable from pandas_profiling.model.correlations import perform_check_correlation +from pandas_profiling.model.typeset import Categorical, Numeric, Unsupported @unique @@ -63,10 +63,12 @@ class Message: def __init__( self, message_type: MessageType, - values: dict, + values: Optional[Dict] = None, column_name: Union[str, None] = None, - fields=None, + fields: Optional[Set] = None, ): + if values is None: + values = {} if fields is None: fields = set() @@ -74,7 +76,13 @@ def __init__( self.message_type = message_type self.values = values self.column_name = column_name - self.anchor_id = hash(column_name) + self._anchor_id = None + + @property + def anchor_id(self): + if self._anchor_id is None: + self._anchor_id = hash(self.column_name) + return self._anchor_id def fmt(self): # TODO: render in template @@ -112,162 +120,170 @@ def check_table_messages(table: dict) -> List[Message]: return messages -def check_variable_messages(col: str, description: dict) -> List[Message]: - """Checks individual variables for warnings. +def numeric_warnings(summary: dict) -> List[Message]: + messages = [] - Args: - col: The column name that is checked. - description: The series description. + chi_squared_threshold_num = config["vars"]["num"]["chi_squared_threshold"].get( + float + ) - Returns: - A list of messages. - """ - messages = [] + # Skewness + if warning_skewness(summary["skewness"]): + messages.append( + Message( + message_type=MessageType.SKEWED, + fields={"skewness"}, + ) + ) - # Missing - if warning_value(description["p_missing"]): + # Infinite values + if warning_value(summary["p_infinite"]): messages.append( Message( - column_name=col, - message_type=MessageType.MISSING, - values=description, - fields={"p_missing", "n_missing"}, + message_type=MessageType.INFINITE, + fields={"p_infinite", "n_infinite"}, ) ) - if description["type"] == Variable.S_TYPE_UNSUPPORTED: + # Zeros + if warning_value(summary["p_zeros"]): messages.append( Message( - column_name=col, - message_type=MessageType.UNSUPPORTED, - values=description, - fields={}, + message_type=MessageType.ZEROS, + fields={"n_zeros", "p_zeros"}, ) ) - elif description["distinct_count_with_nan"] <= 1: - description["mode"] = description["value_counts"].iloc[0] + + if ( + "chi_squared" in summary + and summary["chi_squared"]["pvalue"] > chi_squared_threshold_num + ): + messages.append(Message(message_type=MessageType.UNIFORM)) + + return messages + + +def categorical_warnings(summary: dict) -> List[Message]: + messages = [] + + cardinality_threshold_cat = config["vars"]["cat"]["cardinality_threshold"].get(int) + chi_squared_threshold_cat = config["vars"]["cat"]["chi_squared_threshold"].get( + float + ) + + # High cardinality + if summary["n_distinct"] > cardinality_threshold_cat: messages.append( Message( - column_name=col, - message_type=MessageType.CONSTANT, - values=description, + message_type=MessageType.HIGH_CARDINALITY, fields={"n_distinct"}, ) ) if ( - description["type"] == Variable.S_TYPE_UNSUPPORTED - or description["distinct_count_with_nan"] <= 1 + "chi_squared" in summary + and summary["chi_squared"]["pvalue"] > chi_squared_threshold_cat ): + messages.append(Message(message_type=MessageType.UNIFORM)) + + if "date_warning" in summary and summary["date_warning"]: + messages.append(Message(message_type=MessageType.TYPE_DATE)) + + # Constant length + if "composition" in summary and summary["min_length"] == summary["max_length"]: messages.append( Message( - column_name=col, - message_type=MessageType.REJECTED, - values=description, - fields={}, + message_type=MessageType.CONSTANT_LENGTH, + fields={"composition_min_length", "composition_max_length"}, + ) + ) + + return messages + + +def generic_warnings(summary: dict) -> List[Message]: + messages = [] + + # Missing + if warning_value(summary["p_missing"]): + messages.append( + Message( + message_type=MessageType.MISSING, + fields={"p_missing", "n_missing"}, ) ) - elif description["distinct_count_without_nan"] == description["n"]: + + return messages + + +def supported_warnings(summary: dict) -> List[Message]: + messages = [] + + if summary["n_distinct"] == summary["n"]: messages.append( Message( - column_name=col, message_type=MessageType.UNIQUE, - values=description, fields={"n_distinct", "p_distinct", "n_unique", "p_unique"}, ) ) - elif description["type"] in [ - Variable.TYPE_NUM, - Variable.TYPE_CAT, - Variable.TYPE_DATE, - ]: - # Uniformity - if description["type"] == Variable.TYPE_CAT: - # High cardinality - if description["n_distinct"] > config["vars"]["cat"][ - "cardinality_threshold" - ].get(int): - messages.append( - Message( - column_name=col, - message_type=MessageType.HIGH_CARDINALITY, - values=description, - fields={"n_distinct"}, - ) - ) - - chi_squared_threshold = config["vars"]["cat"]["chi_squared_threshold"].get( - float + if summary["n_distinct"] == 1: + summary["mode"] = summary["value_counts_without_nan"].index[0] + messages.append( + Message( + message_type=MessageType.CONSTANT, + fields={"n_distinct"}, ) - else: - chi_squared_threshold = config["vars"]["num"]["chi_squared_threshold"].get( - float + ) + messages.append( + Message( + message_type=MessageType.REJECTED, + fields=set(), ) + ) + return messages - if ( - "chi_squared" in description - and description["chi_squared"][1] > chi_squared_threshold - ): - messages.append( - Message(column_name=col, message_type=MessageType.UNIFORM, values={}) - ) - # Categorical - if description["type"] == Variable.TYPE_CAT: - if "date_warning" in description and description["date_warning"]: - messages.append( - Message(column_name=col, message_type=MessageType.TYPE_DATE, values={}) - ) +def unsupported_warnings(summary): + messages = [ + Message( + message_type=MessageType.UNSUPPORTED, + fields=set(), + ), + Message( + message_type=MessageType.REJECTED, + fields=set(), + ), + ] + return messages - # Constant length - if ( - "composition" in description - and description["min_length"] == description["max_length"] - ): - messages.append( - Message( - column_name=col, - message_type=MessageType.CONSTANT_LENGTH, - values=description, - fields={"composition_min_length", "composition_max_length"}, - ) - ) - # Numerical - if description["type"] == Variable.TYPE_NUM: - # Skewness - if warning_skewness(description["skewness"]): - messages.append( - Message( - column_name=col, - message_type=MessageType.SKEWED, - values=description, - fields={"skewness"}, - ) - ) +def check_variable_messages(col: str, description: dict) -> List[Message]: + """Checks individual variables for warnings. - # Infinite values - if warning_value(description["p_infinite"]): - messages.append( - Message( - column_name=col, - message_type=MessageType.INFINITE, - values=description, - fields={"p_infinite", "n_infinite"}, - ) - ) + Args: + col: The column name that is checked. + description: The series description. - # Zeros - if warning_value(description["p_zeros"]): - messages.append( - Message( - column_name=col, - message_type=MessageType.ZEROS, - values=description, - fields={"n_zeros", "p_zeros"}, - ) - ) + Returns: + A list of messages. + """ + messages = [] + + messages += generic_warnings(description) + + if description["type"] == Unsupported: + messages += unsupported_warnings(description) + else: + messages += supported_warnings(description) + + if description["type"] == Categorical: + messages += categorical_warnings(description) + if description["type"] == Numeric: + messages += numeric_warnings(description) + for idx in range(len(messages)): + messages[idx].column_name = col + messages[idx].values = description return messages diff --git a/src/pandas_profiling/model/sample.py b/src/pandas_profiling/model/sample.py index 20bd27084..1df2acc78 100644 --- a/src/pandas_profiling/model/sample.py +++ b/src/pandas_profiling/model/sample.py @@ -30,4 +30,8 @@ def get_sample(df: pd.DataFrame) -> list: if n_tail > 0: samples.append(Sample("tail", df.tail(n=n_tail), "Last rows")) + n_random = config["samples"]["random"].get(int) + if n_random > 0: + samples.append(Sample("random", df.sample(n=n_random), "Random sample")) + return samples diff --git a/src/pandas_profiling/model/summarizer.py b/src/pandas_profiling/model/summarizer.py new file mode 100644 index 000000000..114e59b72 --- /dev/null +++ b/src/pandas_profiling/model/summarizer.py @@ -0,0 +1,120 @@ +from functools import reduce +from typing import Type + +import networkx as nx +import numpy as np +import pandas as pd +from visions import VisionsBaseType + +from pandas_profiling.model.summary_algorithms import ( + describe_categorical_1d, + describe_counts, + describe_date_1d, + describe_file_1d, + describe_generic, + describe_image_1d, + describe_numeric_1d, + describe_path_1d, + describe_supported, + describe_url_1d, +) +from pandas_profiling.model.typeset import ( + URL, + Boolean, + Categorical, + DateTime, + File, + Image, + Numeric, + Path, + Unsupported, +) + + +def compose(functions): + """ + Compose a sequence of functions + :param functions: sequence of functions + :return: combined functions, e.g. [f(x), g(x)] -> g(f(x)) + """ + return reduce(lambda f, g: lambda *x: f(*g(*x)), reversed(functions), lambda *x: x) + + +class BaseSummarizer: + def __init__(self, summary_map, typeset, *args, **kwargs): + self.summary_map = summary_map + self.typeset = typeset + + self._complete_summaries() + + def _complete_summaries(self): + for from_type, to_type in nx.topological_sort( + nx.line_graph(self.typeset.base_graph) + ): + self.summary_map[to_type] = ( + self.summary_map[from_type] + self.summary_map[to_type] + ) + + def summarize(self, series: pd.Series, dtype: Type[VisionsBaseType]) -> dict: + """ + + Returns: + object: + """ + summarizer_func = compose(self.summary_map.get(dtype, [])) + _, summary = summarizer_func(series, {"type": dtype}) + return summary + + +class PandasProfilingSummarizer(BaseSummarizer): + def __init__(self, typeset, *args, **kwargs): + summary_map = { + Unsupported: [ + describe_counts, + describe_generic, + describe_supported, + ], + Numeric: [ + describe_numeric_1d, + ], + DateTime: [ + describe_date_1d, + ], + Categorical: [ + describe_categorical_1d, + ], + Boolean: [], + URL: [ + describe_url_1d, + ], + Path: [ + describe_path_1d, + ], + File: [ + describe_file_1d, + ], + Image: [ + describe_image_1d, + ], + } + super().__init__(summary_map, typeset, *args, **kwargs) + + +def format_summary(summary): + def fmt(v): + if isinstance(v, dict): + return {k: fmt(va) for k, va in v.items()} + else: + if isinstance(v, pd.Series): + return fmt(v.to_dict()) + elif ( + isinstance(v, tuple) + and len(v) == 2 + and all(isinstance(x, np.ndarray) for x in v) + ): + return {"counts": v[0].tolist(), "bin_edges": v[1].tolist()} + else: + return v + + summary = {k: fmt(v) for k, v in summary.items()} + return summary diff --git a/src/pandas_profiling/model/summary.py b/src/pandas_profiling/model/summary.py index ff76185a0..decd1e1e2 100644 --- a/src/pandas_profiling/model/summary.py +++ b/src/pandas_profiling/model/summary.py @@ -2,36 +2,21 @@ import multiprocessing import multiprocessing.pool -import string import warnings -from pathlib import Path +from collections import Counter from typing import Callable, Mapping, Optional, Tuple -from urllib.parse import urlsplit import numpy as np import pandas as pd -from pandas.core.arrays.integer import _IntegerDtype -from scipy.stats.stats import chisquare -from visions.application.summaries.series import ( - file_summary, - image_summary, - path_summary, - url_summary, -) -from visions.application.summaries.series.text_summary import ( - length_summary, - unicode_summary, -) from pandas_profiling.config import config as config -from pandas_profiling.model import base -from pandas_profiling.model.base import Variable from pandas_profiling.model.messages import ( check_correlation_messages, check_table_messages, check_variable_messages, - warning_type_date, ) +from pandas_profiling.model.summarizer import BaseSummarizer +from pandas_profiling.model.typeset import Unsupported from pandas_profiling.visualisation.missing import ( missing_bar, missing_dendrogram, @@ -41,27 +26,7 @@ from pandas_profiling.visualisation.plot import scatter_pairwise -def word_summary(series: pd.Series) -> dict: - # TODO: preprocess (stopwords) - # TODO: configurable lowercase/punctuation etc. - word_lists = series.str.lower().str.split() - words = word_lists.explode() - words = words.str.strip(string.punctuation) - return {"word_counts": words.value_counts()} - - -def sort_column_names(dct: Mapping, sort: str): - sort = sort.lower() - if sort.startswith("asc"): - dct = dict(sorted(dct.items(), key=lambda x: x[0].casefold())) - elif sort.startswith("desc"): - dct = dict(reversed(sorted(dct.items(), key=lambda x: x[0].casefold()))) - elif sort != "none": - raise ValueError('"sort" should be "ascending", "descending" or "None".') - return dct - - -def describe_1d(series: pd.Series) -> dict: +def describe_1d(series: pd.Series, summarizer: BaseSummarizer, typeset) -> dict: """Describe a series (infer the variable type, then calculate type-specific values). Args: @@ -71,437 +36,28 @@ def describe_1d(series: pd.Series) -> dict: A Series containing calculated series description values. """ - def describe_supported(series: pd.Series, series_description: dict) -> dict: - """Describe a supported series. - Args: - series: The Series to describe. - series_description: The dict containing the series description so far. - Returns: - A dict containing calculated series description values. - """ - - # number of observations in the Series - length = len(series) - - # number of non-NaN observations in the Series - count = series.count() - - distinct_count = series_description["distinct_count_without_nan"] - value_counts = series_description["value_counts_without_nan"] - unique_count = value_counts.where(value_counts == 1).count() - - stats = { - "n": length, - "count": count, - "n_distinct": distinct_count, - "p_distinct": distinct_count / count, - "p_missing": 1 - (count / length), - "n_missing": length - count, - "is_unique": unique_count == count, - "n_unique": unique_count, - "p_unique": unique_count / count, - "memory_size": series.memory_usage(config["memory_deep"].get(bool)), - } - - return stats - - def describe_unsupported(series: pd.Series, series_description: dict): - """Describe an unsupported series. - Args: - series: The Series to describe. - series_description: The dict containing the series description so far. - Returns: - A dict containing calculated series description values. - """ - - # number of observations in the Series - length = len(series) - - # number of non-NaN observations in the Series - count = series.count() - - results_data = { - "n": length, - "count": count, - "p_missing": 1 - count / length, - "n_missing": length - count, - "memory_size": series.memory_usage(deep=config["memory_deep"].get(bool)), - } - - return results_data - - def histogram_compute(finite_values, n_unique, name="histogram"): - stats = {} - bins = config["plot"]["histogram"]["bins"].get(int) - bins = "auto" if bins == 0 else min(bins, n_unique) - stats[name] = np.histogram(finite_values, bins) - - max_bins = config["plot"]["histogram"]["max_bins"].get(int) - if bins == "auto" and len(stats[name][1]) > max_bins: - stats[name] = np.histogram(finite_values, max_bins) - - return stats - - def numeric_stats_pandas(series: pd.Series): - return { - "mean": series.mean(), - "std": series.std(), - "variance": series.var(), - "min": series.min(), - "max": series.max(), - # Unbiased kurtosis obtained using Fisher's definition (kurtosis of normal == 0.0). Normalized by N-1. - "kurtosis": series.kurt(), - # Unbiased skew normalized by N-1 - "skewness": series.skew(), - "sum": series.sum(), - } - - def numeric_stats_numpy(present_values): - return { - "mean": np.mean(present_values), - "std": np.std(present_values, ddof=1), - "variance": np.var(present_values, ddof=1), - "min": np.min(present_values), - "max": np.max(present_values), - # Unbiased kurtosis obtained using Fisher's definition (kurtosis of normal == 0.0). Normalized by N-1. - "kurtosis": series.kurt(), - # Unbiased skew normalized by N-1 - "skewness": series.skew(), - "sum": np.sum(present_values), - "n_zeros": (series_description["count"] - np.count_nonzero(present_values)), - } - - def describe_numeric_1d(series: pd.Series, series_description: dict) -> dict: - """Describe a numeric series. - Args: - series: The Series to describe. - series_description: The dict containing the series description so far. - Returns: - A dict containing calculated series description values. - Notes: - When 'bins_type' is set to 'bayesian_blocks', astropy.stats.bayesian_blocks is used to determine the number of - bins. Read the docs: - https://docs.astropy.org/en/stable/visualization/histogram.html - https://docs.astropy.org/en/stable/api/astropy.stats.bayesian_blocks.html - This method might print warnings, which we suppress. - https://github.com/astropy/astropy/issues/4927 - """ - - def mad(arr): - """Median Absolute Deviation: a "Robust" version of standard deviation. - Indices variability of the sample. - https://en.wikipedia.org/wiki/Median_absolute_deviation - """ - return np.median(np.abs(arr - np.median(arr))) - - quantiles = config["vars"]["num"]["quantiles"].get(list) - - n_infinite = ((series == np.inf) | (series == -np.inf)).sum() - - if isinstance(series.dtype, _IntegerDtype): - stats = numeric_stats_pandas(series) - present_values = series.loc[series.notnull()].astype( - str(series.dtype).lower() - ) - stats["n_zeros"] = series_description["count"] - np.count_nonzero( - present_values - ) - stats["histogram_data"] = present_values - finite_values = present_values - else: - values = series.values - present_values = values[~np.isnan(values)] - finite_values = values[np.isfinite(values)] - stats = numeric_stats_numpy(present_values) - stats["histogram_data"] = finite_values - - stats.update( - { - "mad": mad(present_values), - "scatter_data": series, # For complex - "p_infinite": n_infinite / series_description["n"], - "n_infinite": n_infinite, - } - ) - - chi_squared_threshold = config["vars"]["num"]["chi_squared_threshold"].get( - float - ) - if chi_squared_threshold > 0.0: - histogram, _ = np.histogram(finite_values, bins="auto") - stats["chi_squared"] = chisquare(histogram) - - stats["range"] = stats["max"] - stats["min"] - stats.update( - { - f"{percentile:.0%}": value - for percentile, value in series.quantile(quantiles).to_dict().items() - } - ) - stats["iqr"] = stats["75%"] - stats["25%"] - stats["cv"] = stats["std"] / stats["mean"] if stats["mean"] else np.NaN - stats["p_zeros"] = stats["n_zeros"] / series_description["n"] - - stats["monotonic_increase"] = series.is_monotonic_increasing - stats["monotonic_decrease"] = series.is_monotonic_decreasing - - stats["monotonic_increase_strict"] = ( - stats["monotonic_increase"] and series.is_unique - ) - stats["monotonic_decrease_strict"] = ( - stats["monotonic_decrease"] and series.is_unique - ) - - stats.update(histogram_compute(finite_values, series_description["n_distinct"])) - - return stats - - def describe_date_1d(series: pd.Series, series_description: dict) -> dict: - """Describe a date series. - - Args: - series: The Series to describe. - series_description: The dict containing the series description so far. - - Returns: - A dict containing calculated series description values. - """ - stats = { - "min": pd.Timestamp.to_pydatetime(series.min()), - "max": pd.Timestamp.to_pydatetime(series.max()), - } - - stats["range"] = stats["max"] - stats["min"] - - values = series[series.notnull()].values.astype(np.int64) // 10 ** 9 - - chi_squared_threshold = config["vars"]["num"]["chi_squared_threshold"].get( - float - ) - if chi_squared_threshold > 0.0: - histogram, _ = np.histogram(values, bins="auto") - stats["chi_squared"] = chisquare(histogram) - - stats.update(histogram_compute(values, series_description["n_distinct"])) - return stats - - def describe_categorical_1d(series: pd.Series, series_description: dict) -> dict: - """Describe a categorical series. - - Args: - series: The Series to describe. - series_description: The dict containing the series description so far. - - Returns: - A dict containing calculated series description values. - """ - # Make sure we deal with strings (Issue #100) - series = series.astype(str) - - # Only run if at least 1 non-missing value - value_counts = series_description["value_counts_without_nan"] - - stats = {"top": value_counts.index[0], "freq": value_counts.iloc[0]} - - redact = config["vars"]["cat"]["redact"].get(float) - if not redact: - stats.update({"first_rows": series.head(5)}) - - stats.update( - histogram_compute( - value_counts, len(value_counts), name="histogram_frequencies" - ) - ) - - chi_squared_threshold = config["vars"]["num"]["chi_squared_threshold"].get( - float - ) - if chi_squared_threshold > 0.0: - stats["chi_squared"] = list(chisquare(value_counts.values)) - - check_length = config["vars"]["cat"]["length"].get(bool) - if check_length: - stats.update(length_summary(series)) - stats.update( - histogram_compute( - stats["length"], stats["length"].nunique(), name="histogram_length" - ) - ) - - check_unicode = config["vars"]["cat"]["characters"].get(bool) - if check_unicode: - stats.update(unicode_summary(series)) - stats["n_characters_distinct"] = stats["n_characters"] - stats["n_characters"] = stats["character_counts"].values.sum() - - stats["category_alias_counts"].index = stats[ - "category_alias_counts" - ].index.str.replace("_", " ") - - words = config["vars"]["cat"]["words"] - if words: - stats.update(word_summary(series)) - - coerce_str_to_date = config["vars"]["cat"]["coerce_str_to_date"].get(bool) - if coerce_str_to_date: - stats["date_warning"] = warning_type_date(series) - - return stats - - def describe_url_1d(series: pd.Series, series_description: dict) -> dict: - """Describe a url series. - - Args: - series: The Series to describe. - series_description: The dict containing the series description so far. - - Returns: - A dict containing calculated series description values. - """ - # Make sure we deal with strings (Issue #100) - series = series[~series.isnull()].astype(str) - series = series.apply(urlsplit) - - stats = url_summary(series) - - # Only run if at least 1 non-missing value - value_counts = series_description["value_counts_without_nan"] - - stats["top"] = value_counts.index[0] - stats["freq"] = value_counts.iloc[0] - - return stats - - def describe_file_1d(series: pd.Series, series_description: dict) -> dict: - if "p_series" not in series_description: - series = series[~series.isnull()].astype(str) - series = series.map(Path) - series_description["p_series"] = series - else: - series = series_description["p_series"] - - stats = file_summary(series) - - series_description.update(describe_path_1d(series, series_description)) - stats.update( - histogram_compute( - stats["file_size"], - stats["file_size"].nunique(), - name="histogram_file_size", - ) - ) - - return stats - - def describe_path_1d(series: pd.Series, series_description: dict) -> dict: - """Describe a path series. - - Args: - series: The Series to describe. - series_description: The dict containing the series description so far. - - Returns: - A dict containing calculated series description values. - """ - series_description.update(describe_categorical_1d(series, series_description)) - - # Make sure we deal with strings (Issue #100) - if "p_series" not in series_description: - series = series[~series.isnull()].astype(str) - series = series.map(Path) - else: - series = series_description["p_series"] - del series_description["p_series"] - - stats = path_summary(series) - - # Only run if at least 1 non-missing value - value_counts = series_description["value_counts_without_nan"] - - stats["top"] = value_counts.index[0] - stats["freq"] = value_counts.iloc[0] - - return stats - - def describe_image_1d(series: pd.Series, series_description: dict): - if "p_series" not in series_description: - series = series[~series.isnull()].astype(str) - series = series.map(Path) - series_description["p_series"] = series - else: - series = series_description["p_series"] - - extract_exif = config["vars"]["image"]["exif"].get(bool) - - stats = image_summary(series, extract_exif) - - series_description.update(describe_file_1d(series, series_description)) - - return stats - - def describe_boolean_1d(series: pd.Series, series_description: dict) -> dict: - """Describe a boolean series. - - Args: - series: The Series to describe. - series_description: The dict containing the series description so far. - - Returns: - A dict containing calculated series description values. - """ - value_counts = series_description["value_counts_without_nan"] - - stats = {"top": value_counts.index[0], "freq": value_counts.iloc[0]} - - return stats - # Make sure pd.NA is not in the series series = series.fillna(np.nan) # Infer variable types - # TODO: use visions for type inference - # https://github.com/dylan-profiler/visions - series_description = base.get_var_type(series) + vtype = typeset.infer_type(series) + series = typeset.cast_to_inferred(series) - # Run type specific analysis - if series_description["type"] == Variable.S_TYPE_UNSUPPORTED: - series_description.update(describe_unsupported(series, series_description)) - else: - series_description.update(describe_supported(series, series_description)) - - type_to_func = { - Variable.TYPE_BOOL: describe_boolean_1d, - Variable.TYPE_NUM: describe_numeric_1d, - Variable.TYPE_DATE: describe_date_1d, - Variable.TYPE_CAT: describe_categorical_1d, - Variable.TYPE_URL: describe_url_1d, - Variable.TYPE_PATH: describe_path_1d, - Variable.TYPE_IMAGE: describe_image_1d, - Variable.TYPE_FILE: describe_file_1d, - } - - if series_description["type"] in type_to_func: - series_description.update( - type_to_func[series_description["type"]](series, series_description) - ) - else: - raise ValueError("Unexpected type") - - # light weight of series_description - if "value_counts_with_nan" in series_description.keys(): - del series_description["value_counts_with_nan"] - if "value_counts_without_nan" in series_description.keys(): - del series_description["value_counts_without_nan"] - - # Return the description obtained - return series_description + return summarizer.summarize(series, dtype=vtype) -def get_series_description(series): - return describe_1d(series) +def sort_column_names(dct: Mapping, sort: str): + sort = sort.lower() + if sort.startswith("asc"): + dct = dict(sorted(dct.items(), key=lambda x: x[0].casefold())) + elif sort.startswith("desc"): + dct = dict(reversed(sorted(dct.items(), key=lambda x: x[0].casefold()))) + elif sort != "none": + raise ValueError('"sort" should be "ascending", "descending" or "None".') + return dct -def get_series_descriptions(df, pbar): +def get_series_descriptions(df, summarizer, typeset, pbar): def multiprocess_1d(args) -> Tuple[str, dict]: """Wrapper to process series in parallel. @@ -513,14 +69,16 @@ def multiprocess_1d(args) -> Tuple[str, dict]: A tuple with column and the series description. """ column, series = args - return column, describe_1d(series) + return column, describe_1d(series, summarizer, typeset) - # Multiprocessing of Describe 1D for each column pool_size = config["pool_size"].get(int) + sort = config["sort"].get(str) + + # Multiprocessing of Describe 1D for each column if pool_size <= 0: pool_size = multiprocessing.cpu_count() - args = [(column, series) for column, series in df.iteritems()] + args = [(name, series) for name, series in df.iteritems()] series_description = {} if pool_size == 1: @@ -530,9 +88,6 @@ def multiprocess_1d(args) -> Tuple[str, dict]: series_description[column] = description pbar.update() else: - # Store the original order - original_order = {k: v for v, k in enumerate([column for column, _ in args])} - # TODO: use `Pool` for Linux-based systems with multiprocessing.pool.ThreadPool(pool_size) as executor: for i, (column, description) in enumerate( @@ -543,20 +98,14 @@ def multiprocess_1d(args) -> Tuple[str, dict]: pbar.update() # Restore the original order - series_description = dict( - sorted( - series_description.items(), - key=lambda index: original_order.get(index[0]), - ) - ) + series_description = {k: series_description[k] for k in df.columns} # Mapping from column name to variable type - sort = config["sort"].get(str) series_description = sort_column_names(series_description, sort) return series_description -def get_table_stats(df: pd.DataFrame, variable_stats: pd.DataFrame) -> dict: +def get_table_stats(df: pd.DataFrame, variable_stats: dict) -> dict: """General statistics for the DataFrame. Args: @@ -576,18 +125,25 @@ def get_table_stats(df: pd.DataFrame, variable_stats: pd.DataFrame) -> dict: "n_var": len(df.columns), "memory_size": memory_size, "record_size": record_size, - "n_cells_missing": variable_stats.loc["n_missing"].sum(), - "n_vars_with_missing": sum((variable_stats.loc["n_missing"] > 0).astype(int)), - "n_vars_all_missing": sum((variable_stats.loc["n_missing"] == n).astype(int)), + "n_cells_missing": 0, + "n_vars_with_missing": 0, + "n_vars_all_missing": 0, } + for series_summary in variable_stats.values(): + if "n_missing" in series_summary and series_summary["n_missing"] > 0: + table_stats["n_vars_with_missing"] += 1 + table_stats["n_cells_missing"] += series_summary["n_missing"] + if series_summary["n_missing"] == n: + table_stats["n_vars_all_missing"] += 1 + table_stats["p_cells_missing"] = table_stats["n_cells_missing"] / ( table_stats["n"] * table_stats["n_var"] ) - supported_columns = variable_stats.transpose()[ - variable_stats.transpose().type != Variable.S_TYPE_UNSUPPORTED - ].index.tolist() + supported_columns = [ + k for k, v in variable_stats.items() if v["type"] != Unsupported + ] table_stats["n_duplicates"] = ( sum(df.duplicated(subset=supported_columns)) if len(supported_columns) > 0 @@ -600,13 +156,8 @@ def get_table_stats(df: pd.DataFrame, variable_stats: pd.DataFrame) -> dict: ) # Variable type counts - table_stats.update({k.value: 0 for k in Variable}) table_stats.update( - { - "types": dict( - variable_stats.loc["type"].apply(lambda x: x.value).value_counts() - ) - } + {"types": dict(Counter([v["type"] for v in variable_stats.values()]))} ) return table_stats @@ -665,10 +216,26 @@ def missing_diagram(name) -> Callable: }[name] missing_map = { - "bar": {"min_missing": 0, "name": "Count"}, - "matrix": {"min_missing": 0, "name": "Matrix"}, - "heatmap": {"min_missing": 2, "name": "Heatmap"}, - "dendrogram": {"min_missing": 1, "name": "Dendrogram"}, + "bar": { + "min_missing": 0, + "name": "Count", + "caption": "A simple visualization of nullity by column.", + }, + "matrix": { + "min_missing": 0, + "name": "Matrix", + "caption": "Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.", + }, + "heatmap": { + "min_missing": 2, + "name": "Heatmap", + "caption": "The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.", + }, + "dendrogram": { + "min_missing": 1, + "name": "Dendrogram", + "caption": "The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.", + }, } missing_map = { @@ -689,6 +256,7 @@ def missing_diagram(name) -> Callable: ): missing[name] = { "name": settings["name"], + "caption": settings["caption"], "matrix": missing_diagram(name)(df), } except ValueError as e: @@ -697,12 +265,8 @@ def missing_diagram(name) -> Callable: return missing -def get_scatter_matrix(df, variables): +def get_scatter_matrix(df, continuous_variables): if config["interactions"]["continuous"].get(bool): - continuous_variables = [ - column for column, type in variables.items() if type == Variable.TYPE_NUM - ] - targets = config["interactions"]["targets"].get(list) if len(targets) == 0: targets = continuous_variables @@ -712,6 +276,9 @@ def get_scatter_matrix(df, variables): for x in targets: for y in continuous_variables: if x in continuous_variables: + if y == x: + continue + # check if any na still exists, and remove it before computing scatter matrix df_temp = df[[x, y]].dropna() scatter_matrix[x][y] = scatter_pairwise( diff --git a/src/pandas_profiling/model/summary_algorithms.py b/src/pandas_profiling/model/summary_algorithms.py new file mode 100644 index 000000000..0f7d2c0f8 --- /dev/null +++ b/src/pandas_profiling/model/summary_algorithms.py @@ -0,0 +1,425 @@ +import functools +from typing import Tuple +from urllib.parse import urlsplit + +import numpy as np +import pandas as pd +from pandas.core.arrays.integer import _IntegerDtype +from visions.utils import func_nullable_series_contains + +from pandas_profiling.config import config +from pandas_profiling.model.summary_helpers import ( + chi_square, + file_summary, + histogram_compute, + image_summary, + length_summary, + mad, + path_summary, + unicode_summary, + url_summary, + word_summary, +) + + +def describe_counts(series: pd.Series, summary: dict) -> Tuple[pd.Series, dict]: + """Counts the values in a series (with and without NaN, distinct). + + Args: + series: Series for which we want to calculate the values. + + Returns: + A dictionary with the count values (with and without NaN, distinct). + """ + try: + value_counts_with_nan = series.value_counts(dropna=False) + _ = set(value_counts_with_nan.index) + hashable = True + except: + hashable = False + + summary["hashable"] = hashable + + if hashable: + value_counts_with_nan = value_counts_with_nan[value_counts_with_nan > 0] + + null_index = value_counts_with_nan.index.isnull() + if null_index.any(): + n_missing = value_counts_with_nan[null_index].sum() + value_counts_without_nan = value_counts_with_nan[~null_index] + else: + n_missing = 0 + value_counts_without_nan = value_counts_with_nan + + summary.update( + { + "value_counts_without_nan": value_counts_without_nan, + } + ) + else: + n_missing = series.isna().sum() + + summary["n_missing"] = n_missing + + return series, summary + + +def series_hashable(fn): + @functools.wraps(fn) + def inner(series, summary): + if not summary["hashable"]: + return series, summary + return fn(series, summary) + + return inner + + +@series_hashable +def describe_supported( + series: pd.Series, series_description: dict +) -> Tuple[pd.Series, dict]: + """Describe a supported series. + Args: + series: The Series to describe. + series_description: The dict containing the series description so far. + Returns: + A dict containing calculated series description values. + """ + + # number of non-NaN observations in the Series + count = series_description["count"] + + value_counts = series_description["value_counts_without_nan"] + distinct_count = len(value_counts) + unique_count = value_counts.where(value_counts == 1).count() + + stats = { + "n_distinct": distinct_count, + "p_distinct": distinct_count / count if count > 0 else 0, + "is_unique": unique_count == count, + "n_unique": unique_count, + "p_unique": unique_count / count if count > 0 else 0, + } + stats.update(series_description) + + return series, stats + + +def describe_generic(series: pd.Series, summary: dict) -> Tuple[pd.Series, dict]: + """Describe generic series. + Args: + series: The Series to describe. + summary: The dict containing the series description so far. + Returns: + A dict containing calculated series description values. + """ + + # number of observations in the Series + length = len(series) + + summary.update( + { + "n": length, + "p_missing": summary["n_missing"] / length, + "count": length - summary["n_missing"], + "memory_size": series.memory_usage(deep=config["memory_deep"].get(bool)), + } + ) + + return series, summary + + +def numeric_stats_pandas(series: pd.Series): + # summary["min"] = summary["value_counts_without_nan"].index.min() + # vc.index.min() + return { + "mean": series.mean(), + "std": series.std(), + "variance": series.var(), + "min": series.min(), + "max": series.max(), + # Unbiased kurtosis obtained using Fisher's definition (kurtosis of normal == 0.0). Normalized by N-1. + "kurtosis": series.kurt(), + # Unbiased skew normalized by N-1 + "skewness": series.skew(), + "sum": series.sum(), + } + + +def numeric_stats_numpy(present_values, series, series_description): + vc = series_description["value_counts_without_nan"] + index_values = vc.index.values + return { + "mean": np.mean(present_values), + "std": np.std(present_values, ddof=1), + "variance": np.var(present_values, ddof=1), + "min": np.min(index_values), + "max": np.max(index_values), + # Unbiased kurtosis obtained using Fisher's definition (kurtosis of normal == 0.0). Normalized by N-1. + "kurtosis": series.kurt(), + # Unbiased skew normalized by N-1 + "skewness": series.skew(), + "sum": np.sum(present_values), + } + + +@series_hashable +@func_nullable_series_contains +def describe_numeric_1d(series: pd.Series, summary: dict) -> Tuple[pd.Series, dict]: + """Describe a numeric series. + Args: + series: The Series to describe. + summary: The dict containing the series description so far. + Returns: + A dict containing calculated series description values. + """ + + # Config + chi_squared_threshold = config["vars"]["num"]["chi_squared_threshold"].get(float) + quantiles = config["vars"]["num"]["quantiles"].get(list) + + value_counts = summary["value_counts_without_nan"] + + summary["n_zeros"] = 0 + + infinity_values = [np.inf, -np.inf] + infinity_index = value_counts.index.isin(infinity_values) + summary["n_infinite"] = value_counts.loc[infinity_index].sum() + + if 0 in value_counts.index: + summary["n_zeros"] = value_counts.loc[0] + + stats = summary + + if isinstance(series.dtype, _IntegerDtype): + stats.update(numeric_stats_pandas(series)) + present_values = series.astype(str(series.dtype).lower()) + finite_values = present_values + else: + present_values = series.values + finite_values = present_values[np.isfinite(present_values)] + stats.update(numeric_stats_numpy(present_values, series, summary)) + + stats.update( + { + "mad": mad(present_values), + } + ) + + if chi_squared_threshold > 0.0: + stats["chi_squared"] = chi_square(finite_values) + + stats["range"] = stats["max"] - stats["min"] + stats.update( + { + f"{percentile:.0%}": value + for percentile, value in series.quantile(quantiles).to_dict().items() + } + ) + stats["iqr"] = stats["75%"] - stats["25%"] + stats["cv"] = stats["std"] / stats["mean"] if stats["mean"] else np.NaN + stats["p_zeros"] = stats["n_zeros"] / summary["n"] + stats["p_infinite"] = summary["n_infinite"] / summary["n"] + + stats["monotonic_increase"] = series.is_monotonic_increasing + stats["monotonic_decrease"] = series.is_monotonic_decreasing + + stats["monotonic_increase_strict"] = ( + stats["monotonic_increase"] and series.is_unique + ) + stats["monotonic_decrease_strict"] = ( + stats["monotonic_decrease"] and series.is_unique + ) + + stats.update( + histogram_compute( + value_counts[~infinity_index].index.values, + summary["n_distinct"], + weights=value_counts[~infinity_index].values, + ) + ) + + return series, stats + + +@series_hashable +@func_nullable_series_contains +def describe_date_1d(series: pd.Series, summary: dict) -> Tuple[pd.Series, dict]: + """Describe a date series. + + Args: + series: The Series to describe. + summary: The dict containing the series description so far. + + Returns: + A dict containing calculated series description values. + """ + chi_squared_threshold = config["vars"]["num"]["chi_squared_threshold"].get(float) + + summary.update( + { + "min": pd.Timestamp.to_pydatetime(series.min()), + "max": pd.Timestamp.to_pydatetime(series.max()), + } + ) + + summary["range"] = summary["max"] - summary["min"] + + values = series.values.astype(np.int64) // 10 ** 9 + + if chi_squared_threshold > 0.0: + summary["chi_squared"] = chi_square(values) + + summary.update(histogram_compute(values, summary["n_distinct"])) + return values, summary + + +@series_hashable +@func_nullable_series_contains +def describe_categorical_1d(series: pd.Series, summary: dict) -> Tuple[pd.Series, dict]: + """Describe a categorical series. + + Args: + series: The Series to describe. + summary: The dict containing the series description so far. + + Returns: + A dict containing calculated series description values. + """ + + # Make sure we deal with strings (Issue #100) + series = series.astype(str) + + # Only run if at least 1 non-missing value + value_counts = summary["value_counts_without_nan"] + + summary.update( + histogram_compute( + value_counts, summary["n_distinct"], name="histogram_frequencies" + ) + ) + + redact = config["vars"]["cat"]["redact"].get(float) + if not redact: + summary.update({"first_rows": series.head(5)}) + + chi_squared_threshold = config["vars"]["num"]["chi_squared_threshold"].get(float) + if chi_squared_threshold > 0.0: + summary["chi_squared"] = chi_square(histogram=value_counts.values) + + check_length = config["vars"]["cat"]["length"].get(bool) + if check_length: + summary.update(length_summary(series)) + summary.update( + histogram_compute( + summary["length"], summary["length"].nunique(), name="histogram_length" + ) + ) + + check_unicode = config["vars"]["cat"]["characters"].get(bool) + if check_unicode: + summary.update(unicode_summary(series)) + summary["n_characters_distinct"] = summary["n_characters"] + summary["n_characters"] = summary["character_counts"].values.sum() + + try: + summary["category_alias_counts"].index = summary[ + "category_alias_counts" + ].index.str.replace("_", " ") + except AttributeError: + pass + + words = config["vars"]["cat"]["words"] + if words: + summary.update(word_summary(series)) + + return series, summary + + +def describe_url_1d(series: pd.Series, summary: dict) -> Tuple[pd.Series, dict]: + """Describe a url series. + + Args: + series: The Series to describe. + summary: The dict containing the series description so far. + + Returns: + A dict containing calculated series description values. + """ + + # Make sure we deal with strings (Issue #100) + assert not series.hasnans + assert hasattr(series, "str") + + # Transform + series = series.apply(urlsplit) + + # Update + summary.update(url_summary(series)) + + return series, summary + + +def describe_file_1d(series: pd.Series, summary: dict) -> Tuple[pd.Series, dict]: + assert not series.hasnans + assert hasattr(series, "str") + + summary.update(file_summary(series)) + summary.update( + histogram_compute( + summary["file_size"], + summary["file_size"].nunique(), + name="histogram_file_size", + ) + ) + + return series, summary + + +def describe_path_1d(series: pd.Series, summary: dict) -> Tuple[pd.Series, dict]: + """Describe a path series. + + Args: + series: The Series to describe. + summary: The dict containing the series description so far. + + Returns: + A dict containing calculated series description values. + """ + + # Make sure we deal with strings (Issue #100) + assert not series.hasnans + assert hasattr(series, "str") + + summary.update(path_summary(series)) + + return series, summary + + +def describe_image_1d(series: pd.Series, summary: dict) -> Tuple[pd.Series, dict]: + assert not series.hasnans + assert hasattr(series, "str") + + extract_exif = config["vars"]["image"]["exif"].get(bool) + + summary.update(image_summary(series, extract_exif)) + + return series, summary + + +@series_hashable +def describe_boolean_1d(series: pd.Series, summary: dict) -> Tuple[pd.Series, dict]: + """Describe a boolean series. + + Args: + series: The Series to describe. + summary: The dict containing the series description so far. + + Returns: + A dict containing calculated series description values. + """ + + value_counts = summary["value_counts_without_nan"] + summary.update({"top": value_counts.index[0], "freq": value_counts.iloc[0]}) + + return series, summary diff --git a/src/pandas_profiling/model/summary_helpers.py b/src/pandas_profiling/model/summary_helpers.py new file mode 100644 index 000000000..714a947d6 --- /dev/null +++ b/src/pandas_profiling/model/summary_helpers.py @@ -0,0 +1,364 @@ +import os +import string +from collections import Counter +from datetime import datetime +from functools import partial +from pathlib import Path + +import numpy as np +import pandas as pd +from scipy.stats.stats import chisquare +from tangled_up_in_unicode import block, block_abbr, category, category_long, script + +from pandas_profiling.config import config +from pandas_profiling.model.summary_helpers_image import ( + extract_exif, + hash_image, + is_image_truncated, + open_image, +) + + +def mad(arr): + """Median Absolute Deviation: a "Robust" version of standard deviation. + Indices variability of the sample. + https://en.wikipedia.org/wiki/Median_absolute_deviation + """ + return np.median(np.abs(arr - np.median(arr))) + + +def named_aggregate_summary(series: pd.Series, key: str): + summary = { + f"max_{key}": np.max(series), + f"mean_{key}": np.mean(series), + f"median_{key}": np.median(series), + f"min_{key}": np.min(series), + } + + return summary + + +def length_summary(series: pd.Series, summary=None) -> dict: + if summary is None: + summary = {} + + length = series.str.len() + + summary.update({"length": length}) + summary.update(named_aggregate_summary(length, "length")) + + return summary + + +def file_summary(series: pd.Series) -> dict: + """ + + Args: + series: series to summarize + + Returns: + + """ + + # Transform + stats = series.map(lambda x: os.stat(x)) + + def convert_datetime(x): + return datetime.fromtimestamp(x).strftime("%Y-%m-%d %H:%M:%S") + + # Transform some more + summary = { + "file_size": stats.map(lambda x: x.st_size), + "file_created_time": stats.map(lambda x: x.st_ctime).map(convert_datetime), + "file_accessed_time": stats.map(lambda x: x.st_atime).map(convert_datetime), + "file_modified_time": stats.map(lambda x: x.st_mtime).map(convert_datetime), + } + return summary + + +def path_summary(series: pd.Series) -> dict: + """ + + Args: + series: series to summarize + + Returns: + + """ + + # TODO: optimize using value counts + summary = { + "common_prefix": os.path.commonprefix(series.values.tolist()) + or "No common prefix", + "stem_counts": series.map(lambda x: os.path.splitext(x)[0]).value_counts(), + "suffix_counts": series.map(lambda x: os.path.splitext(x)[1]).value_counts(), + "name_counts": series.map(lambda x: os.path.basename(x)).value_counts(), + "parent_counts": series.map(lambda x: os.path.dirname(x)).value_counts(), + "anchor_counts": series.map(lambda x: os.path.splitdrive(x)[0]).value_counts(), + } + + summary["n_stem_unique"] = len(summary["stem_counts"]) + summary["n_suffix_unique"] = len(summary["suffix_counts"]) + summary["n_name_unique"] = len(summary["name_counts"]) + summary["n_parent_unique"] = len(summary["parent_counts"]) + summary["n_anchor_unique"] = len(summary["anchor_counts"]) + + return summary + + +def url_summary(series: pd.Series) -> dict: + """ + + Args: + series: series to summarize + + Returns: + + """ + summary = { + "scheme_counts": series.map(lambda x: x.scheme).value_counts(), + "netloc_counts": series.map(lambda x: x.netloc).value_counts(), + "path_counts": series.map(lambda x: x.path).value_counts(), + "query_counts": series.map(lambda x: x.query).value_counts(), + "fragment_counts": series.map(lambda x: x.fragment).value_counts(), + } + + return summary + + +def count_duplicate_hashes(image_descriptions: dict) -> int: + """ + + Args: + image_descriptions: + + Returns: + + """ + counts = pd.Series( + [x["hash"] for x in image_descriptions if "hash" in x] + ).value_counts() + return counts.sum() - len(counts) + + +def extract_exif_series(image_exifs: list) -> dict: + """ + + Args: + image_exifs: + + Returns: + + """ + exif_keys = [] + exif_values: dict = {} + + for image_exif in image_exifs: + # Extract key + exif_keys.extend(list(image_exif.keys())) + + # Extract values per key + for exif_key, exif_val in image_exif.items(): + if exif_key not in exif_values: + exif_values[exif_key] = [] + + exif_values[exif_key].append(exif_val) + + series = {"exif_keys": pd.Series(exif_keys).value_counts().to_dict()} + + for k, v in exif_values.items(): + series[k] = pd.Series(v).value_counts() + + return series + + +def extract_image_information( + path: Path, exif: bool = False, hash: bool = False +) -> dict: + """Extracts all image information per file, as opening files is slow + + Args: + path: Path to the image + exif: extract exif information + hash: calculate hash (for duplicate detection) + + Returns: + A dict containing image information + """ + information: dict = {} + image = open_image(path) + information["opened"] = image is not None + if image is not None: + information["truncated"] = is_image_truncated(image) + if not information["truncated"]: + information["size"] = image.size + if exif: + information["exif"] = extract_exif(image) + if hash: + information["hash"] = hash_image(image) + + return information + + +def image_summary(series: pd.Series, exif: bool = False, hash: bool = False) -> dict: + """ + + Args: + series: series to summarize + exif: extract exif information + hash: calculate hash (for duplicate detection) + + Returns: + + """ + + image_information = series.apply( + partial(extract_image_information, exif=exif, hash=hash) + ) + summary = { + "n_truncated": sum( + [1 for x in image_information if "truncated" in x and x["truncated"]] + ), + "image_dimensions": pd.Series( + [x["size"] for x in image_information if "size" in x], + name="image_dimensions", + ), + } + + image_widths = summary["image_dimensions"].map(lambda x: x[0]) + summary.update(named_aggregate_summary(image_widths, "width")) + image_heights = summary["image_dimensions"].map(lambda x: x[1]) + summary.update(named_aggregate_summary(image_heights, "height")) + image_areas = image_widths * image_heights + summary.update(named_aggregate_summary(image_areas, "area")) + + if hash: + summary["n_duplicate_hash"] = count_duplicate_hashes(image_information) + + if exif: + exif_series = extract_exif_series( + [x["exif"] for x in image_information if "exif" in x] + ) + summary["exif_keys_counts"] = exif_series["exif_keys"] + summary["exif_data"] = exif_series + + return summary + + +def get_character_counts(series: pd.Series) -> Counter: + """Function to return the character counts + + Args: + series: the Series to process + + Returns: + A dict with character counts + """ + return Counter(series.str.cat()) + + +def counter_to_series(counter: Counter) -> pd.Series: + if not counter: + return pd.Series([], dtype=object) + + counter_as_tuples = counter.most_common() + items, counts = zip(*counter_as_tuples) + return pd.Series(counts, index=items) + + +def unicode_summary(series: pd.Series) -> dict: + # Unicode Character Summaries (category and script name) + character_counts = get_character_counts(series) + + character_counts_series = counter_to_series(character_counts) + + char_to_block = {key: block(key) for key in character_counts.keys()} + char_to_category_short = {key: category(key) for key in character_counts.keys()} + char_to_script = {key: script(key) for key in character_counts.keys()} + + summary = { + "n_characters": len(character_counts_series), + "character_counts": character_counts_series, + "category_alias_values": { + key: category_long(value) for key, value in char_to_category_short.items() + }, + "block_alias_values": { + key: block_abbr(value) for key, value in char_to_block.items() + }, + } + # Retrieve original distribution + block_alias_counts: Counter = Counter() + per_block_char_counts: dict = { + k: Counter() for k in summary["block_alias_values"].values() + } + for char, n_char in character_counts.items(): + block_name = summary["block_alias_values"][char] + block_alias_counts[block_name] += n_char + per_block_char_counts[block_name][char] = n_char + summary["block_alias_counts"] = counter_to_series(block_alias_counts) + summary["block_alias_char_counts"] = { + k: counter_to_series(v) for k, v in per_block_char_counts.items() + } + + script_counts: Counter = Counter() + per_script_char_counts: dict = {k: Counter() for k in char_to_script.values()} + for char, n_char in character_counts.items(): + script_name = char_to_script[char] + script_counts[script_name] += n_char + per_script_char_counts[script_name][char] = n_char + summary["script_counts"] = counter_to_series(script_counts) + summary["script_char_counts"] = { + k: counter_to_series(v) for k, v in per_script_char_counts.items() + } + + category_alias_counts: Counter = Counter() + per_category_alias_char_counts: dict = { + k: Counter() for k in summary["category_alias_values"].values() + } + for char, n_char in character_counts.items(): + category_alias_name = summary["category_alias_values"][char] + category_alias_counts[category_alias_name] += n_char + per_category_alias_char_counts[category_alias_name][char] += n_char + summary["category_alias_counts"] = counter_to_series(category_alias_counts) + summary["category_alias_char_counts"] = { + k: counter_to_series(v) for k, v in per_category_alias_char_counts.items() + } + + # Unique counts + summary["n_category"] = len(summary["category_alias_counts"]) + summary["n_scripts"] = len(summary["script_counts"]) + summary["n_block_alias"] = len(summary["block_alias_counts"]) + if len(summary["category_alias_counts"]) > 0: + summary["category_alias_counts"].index = summary[ + "category_alias_counts" + ].index.str.replace("_", " ") + + return summary + + +def histogram_compute(finite_values, n_unique, name="histogram", weights=None): + stats = {} + bins = config["plot"]["histogram"]["bins"].get(int) + bins = "auto" if bins == 0 else min(bins, n_unique) + stats[name] = np.histogram(finite_values, bins=bins, weights=weights) + + max_bins = config["plot"]["histogram"]["max_bins"].get(int) + if bins == "auto" and len(stats[name][1]) > max_bins: + stats[name] = np.histogram(finite_values, bins=max_bins, weights=None) + + return stats + + +def chi_square(values=None, histogram=None): + if histogram is None: + histogram, _ = np.histogram(values, bins="auto") + return dict(chisquare(histogram)._asdict()) + + +def word_summary(series: pd.Series) -> dict: + # TODO: preprocess (stopwords) + # TODO: configurable lowercase/punctuation etc. + word_lists = series.str.lower().str.split() + words = word_lists.explode() + words = words.str.strip(string.punctuation) + return {"word_counts": words.value_counts()} diff --git a/src/pandas_profiling/model/summary_helpers_image.py b/src/pandas_profiling/model/summary_helpers_image.py new file mode 100644 index 000000000..089f6ce7b --- /dev/null +++ b/src/pandas_profiling/model/summary_helpers_image.py @@ -0,0 +1,114 @@ +import imghdr +from pathlib import Path +from typing import Optional, Tuple, Union + +import imagehash +from PIL import ExifTags, Image + +from pandas_profiling.model.imghdr_patch import * + + +def open_image(path: Path) -> Optional[Image.Image]: + """ + + Args: + path: + + Returns: + + """ + try: + return Image.open(path) + except (OSError, AttributeError): + return None + + +def is_image_truncated(image: Image) -> bool: + """Returns True if the path refers to a truncated image + + Args: + image: + + Returns: + True if the image is truncated + """ + try: + image.load() + return False + except (OSError, AttributeError): + return True + + +def get_image_shape(image: Image) -> Optional[Tuple[int, int]]: + """ + + Args: + image: + + Returns: + + """ + try: + return image.size + except (OSError, AttributeError): + return None + + +def hash_image(image: Image) -> Optional[str]: + """ + + Args: + image: + + Returns: + + """ + try: + return str(imagehash.phash(image)) + except (OSError, AttributeError): + return None + + +def decode_byte_exif(exif_val: Union[str, bytes]) -> str: + """Decode byte encodings + + Args: + exif_val: + + Returns: + + """ + if isinstance(exif_val, str): + return exif_val + else: + return exif_val.decode() + + +def extract_exif(image: Image) -> dict: + """ + + Args: + image: + + Returns: + + """ + try: + exif_data = image._getexif() + if exif_data is not None: + exif = { + ExifTags.TAGS[k]: decode_byte_exif(v) + for k, v in exif_data.items() + if k in ExifTags.TAGS + } + else: + exif = {} + except (AttributeError, OSError): + # Not all file types (e.g. .gif) have exif information. + exif = {} + + return exif + + +def path_is_image(p: Path) -> bool: + return imghdr.what(p) is not None diff --git a/src/pandas_profiling/model/typeset.py b/src/pandas_profiling/model/typeset.py new file mode 100644 index 000000000..00dbcd198 --- /dev/null +++ b/src/pandas_profiling/model/typeset.py @@ -0,0 +1,229 @@ +import imghdr +import os +import warnings +from urllib.parse import urlparse + +import pandas as pd +import visions +from pandas.api import types as pdt +from visions.relations import IdentityRelation, InferenceRelation +from visions.utils import nullable_series_contains +from visions.utils.series_utils import series_not_empty + +from pandas_profiling.config import config +from pandas_profiling.model.typeset_relations import ( + category_is_numeric, + category_to_numeric, + numeric_is_category, + series_is_string, + string_is_bool, + string_to_bool, + to_bool, + to_category, +) + + +class Unsupported(visions.Generic): + pass + + +class Numeric(visions.VisionsBaseType): + @classmethod + def get_relations(cls): + return [ + IdentityRelation(cls, Unsupported), + InferenceRelation( + cls, + Categorical, + relationship=category_is_numeric, + transformer=category_to_numeric, + ), + ] + + @classmethod + @nullable_series_contains + @series_not_empty + def contains_op(cls, series: pd.Series, state: dict) -> bool: + return pdt.is_numeric_dtype(series) and not pdt.is_bool_dtype(series) + + +def is_date(series, state): + try: + _ = pd.to_datetime(series) + return True + except: + return False + + +def to_date(series): + return pd.to_datetime(series) + + +class DateTime(visions.VisionsBaseType): + @classmethod + def get_relations(cls): + return [ + IdentityRelation(cls, Unsupported), + # TODO: optional + # TODO: coerce + # InferenceRelation(cls, Categorical, relationship=is_date, transformer=to_date), + ] + + @classmethod + def contains_op(cls, series: pd.Series, state: dict) -> bool: + return pdt.is_datetime64_any_dtype(series) + + +class Categorical(visions.VisionsBaseType): + @classmethod + def get_relations(cls): + return [ + IdentityRelation(cls, Unsupported), + InferenceRelation( + cls, + Numeric, + relationship=numeric_is_category, + transformer=to_category, + ), + ] + + @classmethod + @series_not_empty + @nullable_series_contains + def contains_op(cls, series: pd.Series, state: dict) -> bool: + is_valid_dtype = pdt.is_categorical_dtype(series) and not pdt.is_bool_dtype( + series + ) + if is_valid_dtype: + return True + + return series_is_string(series) + + +class Boolean(visions.VisionsBaseType): + @classmethod + def get_relations(cls): + # Numeric [0, 1] goes via Categorical with distinct_count_without_nan <= 2 + return [ + IdentityRelation(cls, Unsupported), + InferenceRelation( + cls, + Categorical, + relationship=string_is_bool, + transformer=lambda s, st: to_bool(string_to_bool(s, st)), + ), + ] + + @classmethod + @series_not_empty + @nullable_series_contains + def contains_op(cls, series: pd.Series, state: dict) -> bool: + if pdt.is_object_dtype(series): + try: + return series.isin({True, False}).all() + except: + return False + + return pdt.is_bool_dtype(series) + + +class URL(visions.VisionsBaseType): + @classmethod + def get_relations(cls): + relations = [IdentityRelation(cls, Categorical)] + return relations + + @classmethod + @nullable_series_contains + def contains_op(cls, series: pd.Series, state: dict) -> bool: + # TODO: use coercion utils + try: + url_gen = (urlparse(x) for x in series) + return all(x.netloc and x.scheme for x in url_gen) + except AttributeError: + return False + + +class Path(visions.VisionsBaseType): + @classmethod + def get_relations(cls): + relations = [IdentityRelation(cls, Categorical)] + return relations + + @classmethod + @nullable_series_contains + def contains_op(cls, series: pd.Series, state: dict) -> bool: + # TODO: use coercion utils + try: + return all(os.path.isabs(p) for p in series) + except TypeError: + return False + + +class File(visions.VisionsBaseType): + @classmethod + def get_relations(cls): + relations = [IdentityRelation(cls, Path)] + return relations + + @classmethod + @nullable_series_contains + def contains_op(cls, series: pd.Series, state: dict) -> bool: + return all(os.path.exists(p) for p in series) + + +class Image(visions.VisionsBaseType): + @classmethod + def get_relations(cls): + relations = [IdentityRelation(cls, File)] + return relations + + @classmethod + @nullable_series_contains + def contains_op(cls, series: pd.Series, state: dict) -> bool: + return all(imghdr.what(p) for p in series) + + +class Complex(visions.VisionsBaseType): + @classmethod + def get_relations(cls): + return [IdentityRelation(cls, Numeric)] + + @classmethod + @nullable_series_contains + def contains_op(cls, series: pd.Series, state: dict) -> bool: + return pdt.is_complex_dtype(series) + + +class ProfilingTypeSet(visions.VisionsTypeset): + def __init__(self): + types = { + Unsupported, + Boolean, + Numeric, + Categorical, + DateTime, + } + + if config["vars"]["path"]["active"].get(bool): + types.add(Path) + if config["vars"]["file"]["active"].get(bool): + types.add(File) + if config["vars"]["image"]["active"].get(bool): + types.add(Image) + + if config["vars"]["url"]["active"].get(bool): + types.add(URL) + + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=UserWarning) + super().__init__(types) + + +if __name__ == "__main__": + from matplotlib import pyplot as plt + + config.set_arg_group("explorative") + ts = ProfilingTypeSet() + ts.plot_graph() + plt.show() diff --git a/src/pandas_profiling/model/typeset_relations.py b/src/pandas_profiling/model/typeset_relations.py new file mode 100644 index 000000000..5cb179e97 --- /dev/null +++ b/src/pandas_profiling/model/typeset_relations.py @@ -0,0 +1,122 @@ +import functools + +import pandas as pd +from pandas.api import types as pdt +from visions.utils import func_nullable_series_contains + +from pandas_profiling.config import config + + +def is_nullable(series, state) -> bool: + return series.count() > 0 + + +# def applied_to_nonnull(fn): +# @functools.wraps(fn) +# def inner(series, *args, **kwargs): +# if series.hasnans: +# new_series = series.copy() +# notna = series.notna() +# new_series[notna] = fn(series[notna], *args, **kwargs) +# return new_series +# return fn(series, *args, **kwargs) +# +# return inner + + +def try_func(fn): + @functools.wraps(fn) + def inner(series: pd.Series, *args, **kwargs) -> bool: + try: + return fn(series, *args, **kwargs) + except: + return False + + return inner + + +def get_boolean_map(): + bool_map = {} + for true_value, false_value in config["vars"]["bool"]["mappings"].get(list): + bool_map[true_value] = True + bool_map[false_value] = False + return bool_map + + +PP_bool_map = get_boolean_map() + + +def string_is_bool(series, state) -> bool: + @func_nullable_series_contains + @try_func + def tester(s: pd.Series, state: dict) -> bool: + return s.str.lower().isin(PP_bool_map.keys()).all() + + if pdt.is_categorical_dtype(series): + return False + + return tester(series, state) + + +# @applied_to_nonnull +def string_to_bool(series, state): + return series.str.lower().map(PP_bool_map) + + +def numeric_is_category(series, state): + n_unique = series.nunique() + threshold = config["vars"]["num"]["low_categorical_threshold"].get(int) + # TODO <= threshold OR < threshold? + return 1 <= n_unique <= threshold + + +# @applied_to_nonnull +def to_category(series, state): + # TODO: deal with nans + return series.astype(str) + + +@func_nullable_series_contains +def series_is_string(series: pd.Series, state: dict) -> bool: + return all(isinstance(v, str) for v in series) + + +@func_nullable_series_contains +def category_is_numeric(series, state): + if pdt.is_bool_dtype(series) or object_is_bool(series, state): + return False + + try: + _ = series.astype(float) + r = pd.to_numeric(series, errors="coerce") + if r.hasnans and r.count() == 0: + return False + except: + return False + + return not numeric_is_category(series, state) + + +def category_to_numeric(series, state): + return pd.to_numeric(series, errors="coerce") + + +hasnan_bool_name = "boolean" if int(pd.__version__.split(".")[0]) >= 1 else "Bool" + + +def to_bool(series: pd.Series) -> pd.Series: + dtype = hasnan_bool_name if series.hasnans else bool + return series.astype(dtype) + + +@func_nullable_series_contains +def object_is_bool(series: pd.Series, state) -> bool: + if pdt.is_object_dtype(series): + bool_set = {True, False} + try: + ret = all(item in bool_set for item in series) + except: + ret = False + + return ret + return False diff --git a/src/pandas_profiling/profile_report.py b/src/pandas_profiling/profile_report.py index cfd922580..08984efb4 100644 --- a/src/pandas_profiling/profile_report.py +++ b/src/pandas_profiling/profile_report.py @@ -1,7 +1,7 @@ import json import warnings from pathlib import Path -from typing import Optional, Union +from typing import Any, Optional, Union import numpy as np import pandas as pd @@ -10,6 +10,8 @@ from pandas_profiling.config import config from pandas_profiling.model.describe import describe as describe_df from pandas_profiling.model.messages import MessageType +from pandas_profiling.model.summarizer import PandasProfilingSummarizer, format_summary +from pandas_profiling.model.typeset import ProfilingTypeSet from pandas_profiling.report import get_report_structure from pandas_profiling.report.presentation.flavours.html.templates import ( create_html_assets, @@ -27,13 +29,13 @@ class ProfileReport(SerializeReport): def __init__( self, - df=None, - minimal=False, - explorative=False, - sensitive=False, - dark_mode=False, - orange_mode=False, - sample=None, + df: Optional[pd.DataFrame] = None, + minimal: bool = False, + explorative: bool = False, + sensitive: bool = False, + dark_mode: bool = False, + orange_mode: bool = False, + sample: Optional[dict] = None, config_file: Union[Path, str] = None, lazy: bool = True, **kwargs, @@ -45,6 +47,7 @@ def __init__( minimal: minimal mode is a default configuration with minimal computation config_file: a config file (.yml), mutually exclusive with `minimal` lazy: compute when needed + sample: optional dict(name="Sample title", caption="Caption", data=pd.DataFrame()) **kwargs: other arguments, for valid arguments, check the default configuration file. """ if config_file is not None and minimal: @@ -61,7 +64,6 @@ def __init__( config.set_file(get_config("config_minimal.yaml")) elif not config.is_default: pass - # TODO: logging instead of warning # warnings.warn( # "Currently configuration is not the default, if you want to restore " # "default configuration, please run 'pandas_profiling.clear_config()'" @@ -86,6 +88,8 @@ def __init__( self._html = None self._widgets = None self._json = None + self._typeset = None + self._summarizer = None if df is not None: # preprocess df @@ -95,7 +99,7 @@ def __init__( # Trigger building the report structure _ = self.report - def set_variable(self, key, value): + def set_variable(self, key: str, value: Any): """Change a single configuration variable Args: @@ -108,11 +112,11 @@ def set_variable(self, key, value): >>> ProfileReport(df).set_variables("html.minify_html", False) """ - key = key.split(".") - for e in reversed(key[1:]): + keys = key.split(".") + for e in reversed(keys[1:]): value = {e: value} - self.set_variables(**{key[0]: value}) + self.set_variables(**{keys[0]: value}) def set_variables(self, **vars): """Change configuration variables (invalidates caches where necessary) @@ -148,10 +152,24 @@ def set_variables(self, **vars): else: config.set_kwargs(vars) + @property + def typeset(self): + if self._typeset is None: + self._typeset = ProfilingTypeSet() + return self._typeset + + @property + def summarizer(self): + if self._summarizer is None: + self._summarizer = PandasProfilingSummarizer(self.typeset) + return self._summarizer + @property def description_set(self): if self._description_set is None: - self._description_set = describe_df(self.title, self.df, self._sample) + self._description_set = describe_df( + self.title, self.df, self.summarizer, self.typeset, self._sample + ) return self._description_set @property @@ -322,32 +340,30 @@ def _render_widgets(self): return widgets def _render_json(self): - class CustomEncoder(json.JSONEncoder): - def key_to_json(self, data): - if data is None or isinstance(data, (bool, int, str)): - return data - return str(data) - - def default(self, o): - if isinstance(o, pd.Series): - return self.default(o.to_dict()) - - if isinstance(o, pd.DataFrame): + def encode_it(o): + if isinstance(o, dict): + return {encode_it(k): encode_it(v) for k, v in o.items()} + else: + if isinstance(o, (bool, int, float, str)): + return o + elif isinstance(o, list): + return [encode_it(v) for v in o] + elif isinstance(o, set): + return {encode_it(v) for v in o} + elif isinstance(o, (pd.DataFrame, pd.Series)): return o.to_json() - - if isinstance(o, np.integer): - return o.tolist() - - if isinstance(o, dict): - return {self.key_to_json(key): self.default(o[key]) for key in o} - - return str(o) + elif isinstance(o, np.ndarray): + return encode_it(o.tolist()) + else: + return str(o) description = self.description_set disable_progress_bar = not config["progress_bar"].get(bool) with tqdm(total=1, desc="Render JSON", disable=disable_progress_bar) as pbar: - data = json.dumps(description, indent=4, cls=CustomEncoder) + description = format_summary(description) + description = encode_it(description) + data = json.dumps(description, indent=4) pbar.update() return data diff --git a/src/pandas_profiling/report/structure/report.py b/src/pandas_profiling/report/structure/report.py index 185ed627d..03f2f21b1 100644 --- a/src/pandas_profiling/report/structure/report.py +++ b/src/pandas_profiling/report/structure/report.py @@ -6,46 +6,20 @@ from tqdm.auto import tqdm from pandas_profiling.config import config -from pandas_profiling.model.base import ( - AbsolutePath, - Boolean, - Categorical, - Complex, - Count, - Date, - FilePath, - Generic, - ImagePath, - Real, - Url, -) +from pandas_profiling.model.handler import get_render_map from pandas_profiling.model.messages import MessageType from pandas_profiling.report.presentation.core import ( HTML, Collapse, Container, Duplicate, - Image, - Sample, - ToggleButton, - Variable, ) +from pandas_profiling.report.presentation.core import Image as ImageWidget +from pandas_profiling.report.presentation.core import Sample, ToggleButton, Variable from pandas_profiling.report.presentation.core.renderable import Renderable from pandas_profiling.report.presentation.core.root import Root from pandas_profiling.report.structure.correlations import get_correlation_items from pandas_profiling.report.structure.overview import get_dataset_items -from pandas_profiling.report.structure.variables import ( - render_boolean, - render_categorical, - render_complex, - render_date, - render_generic, - render_image, - render_path, - render_real, - render_url, -) -from pandas_profiling.report.structure.variables.render_file import render_file def get_missing_items(summary) -> list: @@ -61,13 +35,13 @@ def get_missing_items(summary) -> list: items = [] for key, item in summary["missing"].items(): items.append( - # TODO: Add informative caption - Image( + ImageWidget( item["matrix"], image_format=image_format, alt=item["name"], name=item["name"], anchor_id=key, + caption=item["caption"], ) ) @@ -84,22 +58,15 @@ def render_variables_section(dataframe_summary: dict) -> list: Returns: The rendered HTML, where each row represents a variable. """ - type_to_func = { - Boolean: render_boolean, - Real: render_real, - Count: render_real, - Complex: render_complex, - Date: render_date, - Categorical: render_categorical, - Url: render_url, - AbsolutePath: render_path, - FilePath: render_file, - ImagePath: render_image, - Generic: render_generic, - } templs = [] + descriptions = config["variables"]["descriptions"].get(dict) + show_description = config["show_variable_description"].get(bool) + reject_variables = config["reject_variables"].get(bool) + + render_map = get_render_map() + for idx, summary in dataframe_summary["variables"].items(): # Common template variables warnings = [ @@ -121,9 +88,6 @@ def render_variables_section(dataframe_summary: dict) -> list: if warning.column_name == idx } - descriptions = config["variables"]["descriptions"].get(dict) - show_description = config["show_variable_description"].get(bool) - template_variables = { "varname": idx, "varid": hash(idx), @@ -135,10 +99,10 @@ def render_variables_section(dataframe_summary: dict) -> list: template_variables.update(summary) # Per type template variables - template_variables.update(type_to_func[summary["type"]](template_variables)) + template_variables.update(render_map[summary["type"]](template_variables)) # Ignore these - if config["reject_variables"].get(bool): + if reject_variables: ignore = MessageType.REJECTED in warning_types else: ignore = False @@ -212,13 +176,10 @@ def get_sample_items(sample: dict): Returns: List of sample items to show in the interface. """ - items = [] - for obj in sample: - items.append( - Sample( - sample=obj.data, name=obj.name, anchor_id=obj.id, caption=obj.caption - ) - ) + items = [ + Sample(sample=obj.data, name=obj.name, anchor_id=obj.id, caption=obj.caption) + for obj in sample + ] return items @@ -244,7 +205,7 @@ def clean_name(name): items = [] for y_col, splot in y_cols.items(): items.append( - Image( + ImageWidget( splot, image_format=image_format, alt=f"{x_col} x {y_col}", diff --git a/src/pandas_profiling/report/structure/variables/__init__.py b/src/pandas_profiling/report/structure/variables/__init__.py index efcff1d11..b24c2f4ef 100644 --- a/src/pandas_profiling/report/structure/variables/__init__.py +++ b/src/pandas_profiling/report/structure/variables/__init__.py @@ -6,6 +6,7 @@ from pandas_profiling.report.structure.variables.render_complex import render_complex from pandas_profiling.report.structure.variables.render_count import render_count from pandas_profiling.report.structure.variables.render_date import render_date +from pandas_profiling.report.structure.variables.render_file import render_file from pandas_profiling.report.structure.variables.render_generic import render_generic from pandas_profiling.report.structure.variables.render_image import render_image from pandas_profiling.report.structure.variables.render_path import render_path diff --git a/src/pandas_profiling/report/structure/variables/render_boolean.py b/src/pandas_profiling/report/structure/variables/render_boolean.py index 659eed0fe..a2ca9bb87 100644 --- a/src/pandas_profiling/report/structure/variables/render_boolean.py +++ b/src/pandas_profiling/report/structure/variables/render_boolean.py @@ -66,7 +66,7 @@ def render_boolean(summary): fqm = FrequencyTableSmall( freq_table( - freqtable=summary["value_counts"], + freqtable=summary["value_counts_without_nan"], n=summary["n"], max_number_to_print=n_obs_bool, ), @@ -88,7 +88,10 @@ def render_boolean(summary): if max_unique > 0: items.append( Image( - pie_plot(summary["value_counts"], legend_kws={"loc": "upper right"}), + pie_plot( + summary["value_counts_without_nan"], + legend_kws={"loc": "upper right"}, + ), image_format=image_format, alt="Chart", name="Chart", diff --git a/src/pandas_profiling/report/structure/variables/render_categorical.py b/src/pandas_profiling/report/structure/variables/render_categorical.py index a6d431d97..c15fac2a2 100644 --- a/src/pandas_profiling/report/structure/variables/render_categorical.py +++ b/src/pandas_profiling/report/structure/variables/render_categorical.py @@ -347,7 +347,7 @@ def render_categorical(summary): fqm = FrequencyTableSmall( freq_table( - freqtable=summary["value_counts"], + freqtable=summary["value_counts_without_nan"], n=summary["count"], max_number_to_print=n_obs_cat, ), @@ -408,7 +408,10 @@ def render_categorical(summary): if max_unique > 0 and summary["n_distinct"] <= max_unique: string_items.append( Image( - pie_plot(summary["value_counts"], legend_kws={"loc": "upper right"}), + pie_plot( + summary["value_counts_without_nan"], + legend_kws={"loc": "upper right"}, + ), image_format=image_format, alt="Pie chart", name="Pie chart", diff --git a/src/pandas_profiling/report/structure/variables/render_common.py b/src/pandas_profiling/report/structure/variables/render_common.py index 3d201144d..426f258b1 100644 --- a/src/pandas_profiling/report/structure/variables/render_common.py +++ b/src/pandas_profiling/report/structure/variables/render_common.py @@ -10,19 +10,20 @@ def render_common(summary): n_freq_table_max = config["n_freq_table_max"].get(int) template_variables = { + # TODO: with nan "freq_table_rows": freq_table( - freqtable=summary["value_counts"], + freqtable=summary["value_counts_without_nan"], n=summary["n"], max_number_to_print=n_freq_table_max, ), "firstn_expanded": extreme_obs_table( - freqtable=summary["value_counts"], + freqtable=summary["value_counts_without_nan"], number_to_print=n_extreme_obs, n=summary["n"], ascending=True, ), "lastn_expanded": extreme_obs_table( - freqtable=summary["value_counts"], + freqtable=summary["value_counts_without_nan"], number_to_print=n_extreme_obs, n=summary["n"], ascending=False, diff --git a/src/pandas_profiling/report/structure/variables/render_date.py b/src/pandas_profiling/report/structure/variables/render_date.py index 9f9f9c299..b7f905643 100644 --- a/src/pandas_profiling/report/structure/variables/render_date.py +++ b/src/pandas_profiling/report/structure/variables/render_date.py @@ -10,7 +10,6 @@ def render_date(summary): varid = summary["varid"] - # TODO: render common? template_variables = {} image_format = config["plot"]["image_format"].get(str) diff --git a/src/pandas_profiling/report/structure/variables/render_url.py b/src/pandas_profiling/report/structure/variables/render_url.py index 760b78ab9..7e914351f 100644 --- a/src/pandas_profiling/report/structure/variables/render_url.py +++ b/src/pandas_profiling/report/structure/variables/render_url.py @@ -122,7 +122,7 @@ def render_url(summary): fqm = FrequencyTableSmall( freq_table( - freqtable=summary["value_counts"], + freqtable=summary["value_counts_without_nan"], n=summary["n"], max_number_to_print=n_obs_cat, ), diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/conftest.py b/tests/conftest.py index bb32e4bf4..f6c2abd5a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,6 +5,8 @@ import pytest from pandas_profiling import config +from pandas_profiling.model.summarizer import PandasProfilingSummarizer +from pandas_profiling.model.typeset import ProfilingTypeSet from pandas_profiling.utils.cache import cache_file @@ -36,6 +38,16 @@ def test_output_dir(tmpdir_factory): shutil.rmtree(str(test_path)) +@pytest.fixture(scope="module") +def summarizer(typeset): + return PandasProfilingSummarizer(typeset) + + +@pytest.fixture(scope="module") +def typeset(): + return ProfilingTypeSet() + + def pytest_runtest_setup(item): platforms = {"darwin", "linux", "win32"} supported_platforms = platforms.intersection( diff --git a/tests/issues/test_issue120.py b/tests/issues/test_issue120.py index 66240072d..325368033 100644 --- a/tests/issues/test_issue120.py +++ b/tests/issues/test_issue120.py @@ -16,16 +16,9 @@ def test_issue_120(get_data_file): report = ProfileReport( df, - correlations={ - "cramers": {"calculate": False}, - "phi_k": {"calculate": False}, - "kendall": {"calculate": False}, - "spearman": {"calculate": False}, - "pearson": {"calculate": False}, - "recoded": {"calculate": False}, - }, + correlations=None, progress_bar=False, - pool_size=0, + pool_size=1, vars={"cat": {"words": True, "characters": True}}, ) _ = report.report diff --git a/tests/issues/test_issue249.py b/tests/issues/test_issue249.py index 706e95468..259a5fa6b 100644 --- a/tests/issues/test_issue249.py +++ b/tests/issues/test_issue249.py @@ -9,5 +9,6 @@ def test_issue249(): df = pd.DataFrame(data=[[1], [2]], index=["foo", 1], columns=["a"]) - report = df.profile_report() + report = df.profile_report(explorative=True) assert type(report.title) == str + assert len(report.to_html()) > 0 diff --git a/tests/issues/test_issue351.py b/tests/issues/test_issue351.py index 7897136f8..0b4ea2300 100644 --- a/tests/issues/test_issue351.py +++ b/tests/issues/test_issue351.py @@ -1,14 +1,10 @@ import pandas as pd from pandas_profiling import ProfileReport -from pandas_profiling.model.base import Variable +from pandas_profiling.model.typeset import Unsupported def test_issue351(): data = pd.DataFrame(["Jan", 1]).set_index(0) - profile = ProfileReport(data) - assert ( - profile.get_description()["variables"]["0"]["type"] - == Variable.S_TYPE_UNSUPPORTED - ) + assert profile.get_description()["variables"]["0"]["type"] == Unsupported diff --git a/tests/issues/test_issue353.py b/tests/issues/test_issue353.py index 7a07ac61d..b323c6262 100644 --- a/tests/issues/test_issue353.py +++ b/tests/issues/test_issue353.py @@ -16,4 +16,4 @@ def test_issue353(): profile = ProfileReport( df, title="Pandas Profiling Report", html={"style": {"full_width": True}} ) - profile.get_description() + assert len(profile.to_html()) > 0 diff --git a/tests/issues/test_issue377.py b/tests/issues/test_issue377.py index 358d2bdca..2ffa39a92 100644 --- a/tests/issues/test_issue377.py +++ b/tests/issues/test_issue377.py @@ -24,7 +24,7 @@ def test_issue377(): original_order = tuple(df.columns.values) profile = pandas_profiling.ProfileReport( - df, sort="None", pool_size=5, progress_bar=False + df, sort="None", pool_size=1, progress_bar=False ) new_order = tuple(profile.get_description()["variables"].keys()) assert original_order == new_order diff --git a/tests/issues/test_issue397.py b/tests/issues/test_issue397.py index 4c2ab1931..6af98988b 100644 --- a/tests/issues/test_issue397.py +++ b/tests/issues/test_issue397.py @@ -6,9 +6,11 @@ import pandas as pd import pandas_profiling +from pandas_profiling.model.typeset import Categorical, Numeric def test_issue397(): + # Note: warnings are expected with np.inf values df = pd.DataFrame.from_dict( { "float-inf": pd.Series([np.inf, 3.0, 4.0, np.NINF], dtype="float"), @@ -25,7 +27,7 @@ def test_issue397(): description = report.description_set - assert description["table"]["types"] == {"CAT": 1, "NUM": 4} + assert description["table"]["types"] == {Categorical: 1, Numeric: 4} assert description["variables"]["float-inf"]["p_infinite"] == 0.5 assert description["variables"]["float-inf"]["n_infinite"] == 2 diff --git a/tests/issues/test_issue416.py b/tests/issues/test_issue416.py index efaaa3581..52abcaf47 100644 --- a/tests/issues/test_issue416.py +++ b/tests/issues/test_issue416.py @@ -3,13 +3,12 @@ https://github.com/pandas-profiling/pandas-profiling/issues/416 """ import pandas as pd -import pytest import pandas_profiling +from pandas_profiling.model.typeset import URL, Categorical, Path from pandas_profiling.utils.cache import cache_file -@pytest.mark.linux def test_issue416(): file_name = cache_file( "products.tsv", @@ -20,8 +19,14 @@ def test_issue416(): df["path"] = df["url"].str.replace("http://www.acme.com", "") profile = pandas_profiling.ProfileReport( - df, title="Pandas Profiling Report", html={"style": {"full_width": True}} + df, + title="Pandas Profiling Report", + html={"style": {"full_width": True}}, + explorative=True, ) - data = profile.to_json() - assert '"PATH": 1' in data - assert '"common_prefix": "/",' in data + data = profile.get_description() + + assert data["table"]["types"][Categorical] == 1 + assert data["table"]["types"][Path] == 1 + assert data["table"]["types"][URL] == 1 + assert data["variables"]["path"]["common_prefix"] == "/" diff --git a/tests/issues/test_issue437.py b/tests/issues/test_issue437.py index d5c4e88fa..dc2c0728c 100644 --- a/tests/issues/test_issue437.py +++ b/tests/issues/test_issue437.py @@ -6,7 +6,7 @@ import pandas as pd import pandas_profiling -from pandas_profiling.model.base import Variable +from pandas_profiling.model.typeset import Numeric def test_issue437(): @@ -40,6 +40,6 @@ def test_issue437(): report = pandas_profiling.ProfileReport(df) description_set = report.description_set - assert description_set["variables"]["a"]["type"] == Variable.TYPE_NUM - assert description_set["variables"]["b"]["type"] == Variable.TYPE_NUM - assert description_set["variables"]["c"]["type"] == Variable.TYPE_NUM + assert description_set["variables"]["a"]["type"] == Numeric + assert description_set["variables"]["b"]["type"] == Numeric + assert description_set["variables"]["c"]["type"] == Numeric diff --git a/tests/issues/test_issue502.py b/tests/issues/test_issue502.py index 25ec41f1e..54a4ed208 100644 --- a/tests/issues/test_issue502.py +++ b/tests/issues/test_issue502.py @@ -7,17 +7,17 @@ from pandas_profiling.model.summary import describe_1d -def test_issue502(): - series = pd.Series([1, 2, 3, 4, 5], dtype=pd.Int64Dtype()) +def test_issue502(summarizer, typeset): + series = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], dtype=pd.Int64Dtype()) - result = describe_1d(series) + result = describe_1d(series, summarizer, typeset) assert result["min"] == 1 - assert result["max"] == 5 + assert result["max"] == 11 -def test_issue502_missing(): - series = pd.Series([1, 2, 3, 4, 5, None], dtype=pd.Int64Dtype()) +def test_issue502_missing(summarizer, typeset): + series = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, None], dtype=pd.Int64Dtype()) - result = describe_1d(series) + result = describe_1d(series, summarizer, typeset) assert result["min"] == 1 - assert result["max"] == 5 + assert result["max"] == 11 diff --git a/tests/issues/test_issue51.py b/tests/issues/test_issue51.py index 7144be256..eb6043bf3 100644 --- a/tests/issues/test_issue51.py +++ b/tests/issues/test_issue51.py @@ -2,14 +2,14 @@ Test for issue 51: https://github.com/pandas-profiling/pandas-profiling/issues/51 """ -from pathlib import Path - import numpy as np import pandas as pd -import requests import pandas_profiling +# FIXME: correlations can be computed stand alone to speed up tests +from pandas_profiling.config import config + def test_issue51(get_data_file): # Categorical has empty ('') value @@ -20,13 +20,16 @@ def test_issue51(get_data_file): df = pd.read_pickle(str(file_name)) - report = df.profile_report(title="Pandas Profiling Report") + report = df.profile_report( + title="Pandas Profiling Report", progress_bar=False, explorative=True + ) assert ( "Pandas Profiling Report" in report.to_html() ), "Profile report should be generated." def test_issue51_similar(): + config["vars"]["num"]["low_categorical_threshold"] = 0 df = pd.DataFrame( { "test": ["", "hoi", None], @@ -35,35 +38,55 @@ def test_issue51_similar(): } ) - report = df.profile_report(title="Pandas Profiling Report") + report = df.profile_report( + title="Pandas Profiling Report", progress_bar=False, explorative=True + ) + # FIXME: assert correlation values + # print(report.get_description()["correlations"]) assert ( "Pandas Profiling Report" in report.to_html() ), "Profile report should be generated." -# def test_issue51_mixed(): -# df = pd.DataFrame( -# { -# "test": ["", "hoi", None, "friet"], -# "blest": [None, "", "geert", "pizza"], -# "bert": ["snor", "", np.nan, ""], -# "fruit": ["", "ok", np.nan, ""], -# } -# ) -# report = df.profile_report(title="Pandas Profiling Report") -# assert ( -# "data-toggle=tab>Recoded" in report.to_html() -# ), "Recoded should be present" - - def test_issue51_empty(): + config["vars"]["num"]["low_categorical_threshold"] = 0 df = pd.DataFrame( - {"test": ["", "", ""], "blest": ["", "", ""], "bert": ["", "", ""]} + { + "test": ["", "", "", "", ""], + "blest": ["", "", "", "", ""], + "bert": ["", "", "", "", ""], + } ) - report = df.profile_report(title="Pandas Profiling Report") + report = df.profile_report( + title="Pandas Profiling Report", + progress_bar=False, + explorative=True, + ) assert ( - "Pandas Profiling Report" in report.to_html() - ), "Profile report should be generated." + "cramers" not in report.get_description()["correlations"] + or ( + report.get_description()["correlations"]["cramers"].values + == np.ones((3, 3)) + ).all() + ) + + +def test_issue51_identical(): + config["vars"]["num"]["low_categorical_threshold"] = 0 + df = pd.DataFrame( + { + "test": ["v1", "v1", "v1"], + "blest": ["v1", "v1", "v1"], + "bert": ["v1", "v1", "v1"], + } + ) + + report = df.profile_report( + title="Pandas Profiling Report", progress_bar=False, explorative=True + ) + assert ( + report.get_description()["correlations"]["cramers"].values == np.ones((3, 3)) + ).all() diff --git a/tests/issues/test_issue523.py b/tests/issues/test_issue523.py index 3e087e9e9..d111b7ac2 100644 --- a/tests/issues/test_issue523.py +++ b/tests/issues/test_issue523.py @@ -31,4 +31,4 @@ def test_issue523(): df = pd.DataFrame({"col": data}, dtype=pd.Int64Dtype()) profile_report = ProfileReport(df, title="Test Report") - assert len(profile_report.to_html()) > 0 + assert len(profile_report.get_description()) > 0 diff --git a/tests/issues/test_issue537.py b/tests/issues/test_issue537.py index 5e8e49a37..73f2d3955 100644 --- a/tests/issues/test_issue537.py +++ b/tests/issues/test_issue537.py @@ -19,6 +19,7 @@ """ import multiprocessing +from functools import partial from gzip import decompress from typing import Tuple @@ -29,7 +30,7 @@ from pandas_profiling.model.summary import describe_1d -def mock_multiprocess_1d(args) -> Tuple[str, dict]: +def mock_multiprocess_1d(args, summarizer, typeset) -> Tuple[str, dict]: """Wrapper to process series in parallel. copy of multiprocess_1d function in get_series_descriptions, summary.py @@ -41,10 +42,10 @@ def mock_multiprocess_1d(args) -> Tuple[str, dict]: A tuple with column and the series description. """ column, series = args - return column, describe_1d(series) + return column, describe_1d(series, summarizer, typeset) -def test_multiprocessing_describe1d(): +def test_multiprocessing_describe1d(summarizer, typeset): """ this test serves to get a large dataset, and ensure that even across parallelised describe1d operations, there is no ValueError raised. Previously, series.fillna(np.nan,inplace=True) was used instead of @@ -114,7 +115,9 @@ def download_and_process_data(): def run_multiprocess(df): pool = multiprocessing.pool.ThreadPool(10) args = [(column, series) for column, series in df.iteritems()] - results = pool.imap_unordered(mock_multiprocess_1d, args) + results = pool.imap_unordered( + partial(mock_multiprocess_1d, summarizer=summarizer, typeset=typeset), args + ) pool.close() pool.join() list(results) diff --git a/tests/issues/test_issue587.py b/tests/issues/test_issue587.py deleted file mode 100644 index bfc082d53..000000000 --- a/tests/issues/test_issue587.py +++ /dev/null @@ -1,21 +0,0 @@ -""" -Test for issue 587: -https://github.com/pandas-profiling/pandas-profiling/issues/587 -""" -import pandas as pd -import pytest - -from pandas_profiling import config -from pandas_profiling.model.base import get_counts, is_numeric - - -@pytest.mark.skipif( - int(pd.__version__.split(".")[0]) < 1, reason="requires pandas 1 or higher" -) -def test_issue587(): - config["vars"]["num"]["low_categorical_threshold"] = 0 - - # Minimal reproducible code - series = pd.Series([1, None], dtype="Int64") - series_description = get_counts(series) - assert is_numeric(series, series_description) diff --git a/tests/issues/test_issue72.py b/tests/issues/test_issue72.py index 4f75225fa..874e1af14 100644 --- a/tests/issues/test_issue72.py +++ b/tests/issues/test_issue72.py @@ -7,22 +7,21 @@ import pandas_profiling from pandas_profiling.config import config -from pandas_profiling.model.base import Variable +from pandas_profiling.model.typeset import Categorical, Numeric def test_issue72_higher(): # Showcase (and test) different ways of interfacing with config/profiling report config["vars"]["num"]["low_categorical_threshold"].set(2) - config["correlations"]["recoded"]["calculate"].set(False) df = pd.DataFrame({"A": [1, 2, 3, 3]}) df["B"] = df["A"].apply(str) - report = pandas_profiling.ProfileReport(df) + report = pandas_profiling.ProfileReport(df, correlations=None) # 3 > 2, so numerical - assert report.get_description()["variables"]["A"]["type"] == Variable.TYPE_NUM + assert report.get_description()["variables"]["A"]["type"] == Numeric # Strings are always categorical - assert report.get_description()["variables"]["B"]["type"] == Variable.TYPE_CAT + assert report.get_description()["variables"]["B"]["type"] == Numeric def test_issue72_equal(): @@ -31,13 +30,13 @@ def test_issue72_equal(): report = pandas_profiling.ProfileReport( df, vars={"num": {"low_categorical_threshold": 3}}, - correlations={"recoded": {"calculate": False}}, + correlations=None, ) - # 3 == 3, so numerical - assert report.get_description()["variables"]["A"]["type"] == Variable.TYPE_NUM + # 3 == 3, so categorical + assert report.get_description()["variables"]["A"]["type"] == Categorical # Strings are always categorical - assert report.get_description()["variables"]["B"]["type"] == Variable.TYPE_CAT + assert report.get_description()["variables"]["B"]["type"] == Categorical def test_issue72_lower(): @@ -45,9 +44,9 @@ def test_issue72_lower(): df = pd.DataFrame({"A": [1, 2, 3, 3, np.nan]}) df["B"] = df["A"].apply(str) - report = df.profile_report(correlations={"recoded": {"calculate": False}}) + report = df.profile_report(correlations=None) # 3 < 10, so categorical - assert report.get_description()["variables"]["A"]["type"] == Variable.TYPE_CAT + assert report.get_description()["variables"]["A"]["type"] == Categorical # Strings are always categorical - assert report.get_description()["variables"]["B"]["type"] == Variable.TYPE_CAT + assert report.get_description()["variables"]["B"]["type"] == Categorical diff --git a/tests/issues/test_issue85.py b/tests/issues/test_issue85.py deleted file mode 100644 index 057f6b34a..000000000 --- a/tests/issues/test_issue85.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -Test for issue 85: -https://github.com/pandas-profiling/pandas-profiling/issues/85 -""" -import numpy as np -import pandas as pd - -from pandas_profiling import ProfileReport -from pandas_profiling.model.base import Variable - - -def test_issue85(): - data = { - "booleans_type": [False, True, True], - "booleans_type_nan": [False, True, np.nan], - "integers": [1, 0, 0], - "integers_nan": [1, 0, np.nan], - "str_yes_no": ["Y", "N", "Y"], - "str_yes_no_mixed": ["Y", "n", "y"], - "str_yes_no_nana": ["Y", "N", np.nan], - "str_true_false": ["True", "False", "False"], - "str_true_false_nan": ["True", "False", np.nan], - } - - df = pd.DataFrame(data) - - report = ProfileReport( - df, - pool_size=1, - title="Dataset with Boolean Variables", - samples={"head": 20}, - ) - for col, variable_stats in report.get_description()["variables"].items(): - assert ( - variable_stats["type"] == Variable.TYPE_BOOL - ), "Variable should be boolean" diff --git a/tests/notebooks/lazy_pipeline.ipynb b/tests/notebooks/lazy_pipeline.ipynb index b1b621985..f56cf78d9 100644 --- a/tests/notebooks/lazy_pipeline.ipynb +++ b/tests/notebooks/lazy_pipeline.ipynb @@ -1,54194 +1,54199 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test notebook lazy pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Installed packages\n", - "import pandas as pd\n", - "\n", - "# Testing\n", - "from IPython.utils.capture import capture_output\n", - "\n", - "# Our package\n", - "from pandas_profiling import ProfileReport\n", - "from pandas_profiling.utils.cache import cache_file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Read the Titanic Dataset\n", - "file_name = cache_file(\n", - " \"titanic.csv\",\n", - " \"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\",\n", - ")\n", - "df = pd.read_csv(file_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate the Profiling Report (with progress bar)\n", - "with capture_output() as out:\n", - " profile = ProfileReport(df, title=\"Titanic Dataset\", progress_bar=True, lazy=False)\n", - "\n", - "assert all(\"FloatProgress\" in s.data[\"text/plain\"] for s in out.outputs)\n", - "assert len(out.outputs) == 2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# Generate the Profiling Report (without progress bar)\n", - "with capture_output() as out:\n", - " profile = df.profile_report(\n", - " title=\"Titanic Dataset\",\n", - " html={\"style\": {\"full_width\": True}},\n", - " progress_bar=True,\n", - " lazy=True,\n", - " )\n", - "\n", - "assert len(out.outputs) == 0\n", - "\n", - "with capture_output() as out:\n", - " _ = profile.to_html()\n", - "\n", - "assert all(\"FloatProgress\" in s.data[\"text/plain\"] for s in out.outputs)\n", - "assert len(out.outputs) == 3\n", - "\n", - "with capture_output() as out:\n", - " _ = profile.to_file(\"/tmp/tmpfile.html\")\n", - "\n", - "assert \"Export report to file\" in out.outputs[0].data[\"text/plain\"]\n", - "assert len(out.outputs) == 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Test caching of the iterative building process\n", - "with capture_output() as out:\n", - " profile = ProfileReport(df, title=\"Titanic Dataset\", progress_bar=True, lazy=True)\n", - "assert len(out.outputs) == 0\n", - "\n", - "with capture_output() as out:\n", - " profile.description_set\n", - "assert len(out.outputs) == 1\n", - "\n", - "with capture_output() as out:\n", - " profile.report\n", - "assert len(out.outputs) == 1\n", - "\n", - "with capture_output() as out:\n", - " profile.html\n", - "assert len(out.outputs) == 1\n", - "\n", - "with capture_output() as out:\n", - " profile.set_variable(\"html\", {\"style\": {\"theme\": \"united\"}})\n", - " profile.to_file(\"/tmp/cache1.html\")\n", - "assert len(out.outputs) == 2\n", - "\n", - "with capture_output() as out:\n", - " profile.set_variables(pool_size=1)\n", - " profile.html\n", - "assert len(out.outputs) == 0\n", - "\n", - "with capture_output() as out:\n", - " profile.set_variables(pool_size=0, samples={\"head\": 5, \"tail\": 15})\n", - 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AgeCabinEmbarkedFareNameParchPassengerIdPclassSexSibSpSurvivedTicket
022.0NaNS7.2500Braund, Mr. Owen Harris013male10A/5 21171
138.0C85C71.2833Cumings, Mrs. John Bradley (Florence Briggs Thayer)021female11PC 17599
226.0NaNS7.9250Heikkinen, Miss. Laina033female01STON/O2. 3101282
335.0C123S53.1000Futrelle, Mrs. Jacques Heath (Lily May Peel)041female11113803
435.0NaNS8.0500Allen, Mr. William Henry053male00373450
5NaNNaNQ8.4583Moran, Mr. James063male00330877
654.0E46S51.8625McCarthy, Mr. Timothy J071male0017463
72.0NaNS21.0750Palsson, Master. Gosta Leonard183male30349909
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pandas as pd\n", + "\n", + "# Testing\n", + "from IPython.utils.capture import capture_output\n", + "\n", + "# Our package\n", + "from pandas_profiling import ProfileReport\n", + "from pandas_profiling.utils.cache import cache_file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the Titanic Dataset\n", + "file_name = cache_file(\n", + " \"titanic.csv\",\n", + " \"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\",\n", + ")\n", + "df = pd.read_csv(file_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate the Profiling Report (with progress bar)\n", + "with capture_output() as out:\n", + " profile = ProfileReport(df, title=\"Titanic Dataset\", progress_bar=True, lazy=False)\n", + "\n", + "assert all(\n", + " any(v in s.data[\"text/plain\"] for v in [\"%|\", \"FloatProgress\"]) for s in out.outputs\n", + 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"metadata": {}, - "source": [ - "# Test notebook Meteorites" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import requests\n", - "from IPython.display import display\n", - "from IPython.utils.capture import capture_output\n", - "\n", - "import pandas_profiling\n", - "from pandas_profiling.utils.cache import cache_file" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "file_name = cache_file(\n", - " \"meteorites.csv\",\n", - " \"https://data.nasa.gov/api/views/gh4g-9sfh/rows.csv?accessType=DOWNLOAD\",\n", - ")\n", - "\n", - "df = pd.read_csv(file_name)\n", - "\n", - "# Note: Pandas does not support dates before 1880, so we ignore these for this analysis\n", - "df[\"year\"] = pd.to_datetime(df[\"year\"], errors=\"coerce\")\n", - "\n", - "# Example: Constant variable\n", - "df[\"source\"] = \"NASA\"\n", - "\n", - "# Example: Boolean variable\n", - "df[\"boolean\"] = np.random.choice([True, False], df.shape[0])\n", - "\n", - "# Example: Mixed with base types\n", - "df[\"mixed\"] = np.random.choice([1, \"A\"], df.shape[0])\n", - "\n", - "# Example: Highly correlated variables\n", - "df[\"reclat_city\"] = df[\"reclat\"] + np.random.normal(scale=5, size=(len(df)))\n", - "\n", - "# Example: Duplicate observations\n", - "duplicates_to_add = pd.DataFrame(df.iloc[0:10])\n", - "duplicates_to_add[\"name\"] = duplicates_to_add[\"name\"] + \" copy\"\n", - "\n", - "df = df.append(duplicates_to_add, ignore_index=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# Inline report without saving\n", - "with capture_output() as out:\n", - " pr = df.profile_report(\n", - " sort=\"None\",\n", - " html={\"style\": {\"full_width\": True}},\n", - " progress_bar=False,\n", - " minimal=True,\n", - " )\n", - " display(pr)\n", - "\n", - "assert len(out.outputs) == 2\n", - "assert out.outputs[0].data[\"text/plain\"] == \"\"\n", - "assert all(\n", - " s in out.outputs[0].data[\"text/html\"]\n", - " for s in [\"\"\n", - "assert all(\n", - " s in out.outputs[0].data[\"text/html\"]\n", - " for s in [\"\"\n", + "assert all(\n", + " s in out.outputs[0].data[\"text/html\"]\n", + " for s in [\"\"\n", + "assert all(\n", + " s in out.outputs[0].data[\"text/html\"]\n", + " for s in [\"'\n", - "# assert 'pandas-profiling' in out.outputs[1].data['text/html']" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Or use the HTML report in an iframe\n", - "with capture_output() as out:\n", - " profile.to_notebook_iframe()\n", - "\n", - "assert len(out.outputs) == 1\n", - "assert out.outputs[0].data[\"text/plain\"] == \"\"\n", - "assert all(\n", - " s in out.outputs[0].data[\"text/html\"]\n", - " for s in [\"\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
AgeCabinEmbarkedFareNameParchPassengerIdPclassSexSibSpSurvivedTicket
88133.0NaNS7.8958Markun, Mr. Johann08823male00349257
88222.0NaNS10.5167Dahlberg, Miss. Gerda Ulrika08833female007552
88328.0NaNS10.5000Banfield, Mr. Frederick James08842male00C.A./SOTON 34068
88425.0NaNS7.0500Sutehall, Mr. Henry Jr08853male00SOTON/OQ 392076
88539.0NaNQ29.1250Rice, Mrs. William (Margaret Norton)58863female00382652
88627.0NaNS13.0000Montvila, Rev. Juozas08872male00211536
88719.0B42S30.0000Graham, Miss. Margaret Edith08881female01112053
888NaNNaNS23.4500Johnston, Miss. Catherine Helen \"Carrie\"28893female10W./C. 6607
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827.0NaNS11.1333Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)293female01347742
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AgeCabinEmbarkedFareNameParchPassengerIdPclassSexSibSpSurvivedTicket
88133.0NaNS7.8958Markun, Mr. Johann08823male00349257
88222.0NaNS10.5167Dahlberg, Miss. Gerda Ulrika08833female007552
88328.0NaNS10.5000Banfield, Mr. Frederick James08842male00C.A./SOTON 34068
88425.0NaNS7.0500Sutehall, Mr. Henry Jr08853male00SOTON/OQ 392076
88539.0NaNQ29.1250Rice, Mrs. William (Margaret Norton)58863female00382652
88627.0NaNS13.0000Montvila, Rev. Juozas08872male00211536
88719.0B42S30.0000Graham, Miss. Margaret Edith08881female01112053
888NaNNaNS23.4500Johnston, Miss. Catherine Helen \"Carrie\"28893female10W./C. 6607
88926.0C148C30.0000Behr, Mr. Karl Howell08901male01111369
89032.0NaNQ7.7500Dooley, Mr. Patrick08913male00370376
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b/tests/unit/test_console.py @@ -1,3 +1,5 @@ +import os + import pytest from pandas_profiling.controller import console @@ -12,6 +14,7 @@ def console_data(get_data_file): ) +@pytest.mark.skipif(os.name == "nt", reason="multiprocessing+pytest broken on Windows") def test_console_multiprocessing(console_data, test_output_dir): report = test_output_dir / "test_samples.html" console.main(["-s", "--pool_size", "0", str(console_data), str(report)]) diff --git a/tests/unit/test_date.py b/tests/unit/test_date.py deleted file mode 100644 index de085861f..000000000 --- a/tests/unit/test_date.py +++ /dev/null @@ -1,14 +0,0 @@ -import pandas as pd - -from pandas_profiling import ProfileReport -from pandas_profiling.model.messages import MessageType - - -def test_check_date_type_warning(): - df = pd.DataFrame(["2018-01-01", "2017-02-01", "2018-04-07"], columns=["date"]) - - report = ProfileReport(df, vars={"cat": {"coerce_str_to_date": True}}) - assert any( - message.message_type == MessageType.TYPE_DATE - for message in report.get_description()["messages"] - ), "Date warning should be present" diff --git a/tests/unit/test_decorator.py b/tests/unit/test_decorator.py index 9695e235d..25d0a58bc 100644 --- a/tests/unit/test_decorator.py +++ b/tests/unit/test_decorator.py @@ -19,7 +19,7 @@ def test_decorator(get_data_file): def test_empty_decorator(): - df = pd.DataFrame().profile_report() + df = pd.DataFrame().profile_report(progress_bar=False) with pytest.raises(ValueError) as e: df.get_description() diff --git a/tests/unit/test_describe.py b/tests/unit/test_describe.py index 8eda1e9a8..4fd2e87ae 100644 --- a/tests/unit/test_describe.py +++ b/tests/unit/test_describe.py @@ -5,40 +5,59 @@ import pytest from pandas_profiling import config -from pandas_profiling.model.base import Variable from pandas_profiling.model.describe import describe from pandas_profiling.model.summary import describe_1d +from pandas_profiling.model.typeset import DateTime, Numeric check_is_NaN = "pandas_profiling.check_is_NaN" testdata = [ # Unique values - (pd.Series([1, 2]), True, 1, 1), + (pd.Series([1, 2]), {"is_unique": True, "p_distinct": 1, "p_unique": 1}), # Unique values including nan - (pd.Series([np.nan]), None, None, None), + (pd.Series([np.nan]), {"is_unique": None, "p_distinct": None, "p_unique": None}), # Unique values all nan - (pd.Series([1, 2, np.nan]), True, 1, 1), + (pd.Series([1, 2, np.nan]), {"is_unique": True, "p_distinct": 1, "p_unique": 1}), # Non unique values - (pd.Series([1, 2, 2]), False, 2 / 3, 1 / 3), + ( + pd.Series([1, 2, 2]), + {"is_unique": False, "p_distinct": 2 / 3, "p_unique": 1 / 3}, + ), # Non unique nan - (pd.Series([1, np.nan, np.nan]), True, 1, 1), + ( + pd.Series([1, np.nan, np.nan]), + {"is_unique": True, "p_distinct": 1, "p_unique": 1}, + ), # Non unique values including nan - (pd.Series([1, 2, 2, np.nan]), False, 2 / 3, 1 / 3), + ( + pd.Series([1, 2, 2, np.nan]), + {"is_unique": False, "p_distinct": 2 / 3, "p_unique": 1 / 3}, + ), # Non unique values including non unique nan - (pd.Series([1, 2, 2, np.nan, np.nan]), False, 2 / 3, 1 / 3), + ( + pd.Series([1, 2, 2, np.nan, np.nan]), + {"is_unique": False, "p_distinct": 2 / 3, "p_unique": 1 / 3}, + ), ] -@pytest.mark.parametrize("data,is_unique,p_distinct,p_unique", testdata) -def test_describe_unique(data, is_unique, p_distinct, p_unique): +@pytest.mark.parametrize("data,expected", testdata) +def test_describe_unique(data, expected, summarizer, typeset): """Test the unique feature of 1D data""" + config["vars"]["num"]["low_categorical_threshold"] = 0 - desc_1d = describe_1d(data) - if is_unique is not None: - assert desc_1d["p_unique"] == p_unique, "Describe 1D p_unique incorrect" - assert desc_1d["p_distinct"] == p_distinct, "Describe 1D p_distinct incorrect" - assert desc_1d["is_unique"] == is_unique, "Describe 1D should return unique" + desc_1d = describe_1d(data, summarizer, typeset) + if expected["is_unique"] is not None: + assert ( + desc_1d["p_unique"] == expected["p_unique"] + ), "Describe 1D p_unique incorrect" + assert ( + desc_1d["p_distinct"] == expected["p_distinct"] + ), "Describe 1D p_distinct incorrect" + assert ( + desc_1d["is_unique"] == expected["is_unique"] + ), "Describe 1D should return unique" @pytest.fixture @@ -165,7 +184,6 @@ def expected_results(): "count": 9, "cv": check_is_NaN, "n_distinct": 8, - "freq": 2, "histogram": check_is_NaN, "iqr": check_is_NaN, "is_unique": False, @@ -183,8 +201,6 @@ def expected_results(): "skewness": check_is_NaN, "std": check_is_NaN, "sum": check_is_NaN, - "top": "d", - "type": Variable.TYPE_CAT, "variance": check_is_NaN, }, "x": { @@ -198,7 +214,6 @@ def expected_results(): "p_infinite": 0, "cv": 1.771071190261633, "n_distinct": 6, - "freq": check_is_NaN, "iqr": 24.5, "is_unique": False, "kurtosis": -0.50292858929003803, @@ -216,8 +231,6 @@ def expected_results(): "skewness": 1.0851622393567653, "std": 23.688077169749342, "sum": 107.0, - "top": check_is_NaN, - "type": Variable.TYPE_NUM, "variance": 561.125, }, "y": { @@ -231,7 +244,6 @@ def expected_results(): "p_infinite": 0, "cv": 2.2112992878833846, "n_distinct": 8, - "freq": check_is_NaN, "iqr": 236.66299975000001, "is_unique": True, "kurtosis": 6.974137018717359, @@ -248,8 +260,6 @@ def expected_results(): "skewness": 2.6156591135729266, "std": 1086.1335236468506, "sum": 3929.3949203464999, - "top": check_is_NaN, - "type": Variable.TYPE_NUM, "variance": 1179686.0311895239, }, "cat": { @@ -261,7 +271,6 @@ def expected_results(): "count": 8, "cv": check_is_NaN, "n_distinct": 6, - "freq": 3, "histogram": check_is_NaN, "iqr": check_is_NaN, "is_unique": False, @@ -279,40 +288,37 @@ def expected_results(): "skewness": check_is_NaN, "std": check_is_NaN, "sum": check_is_NaN, - "top": "c", - "type": Variable.TYPE_CAT, "variance": check_is_NaN, }, "s1": { - "25%": check_is_NaN, - "5%": check_is_NaN, - "50%": check_is_NaN, - "75%": check_is_NaN, - "95%": check_is_NaN, + "25%": 1.0, + "5%": 1.0, + "50%": 1.0, + "75%": 1.0, + "95%": 1.0, "count": 9, - "cv": check_is_NaN, - "n_distinct": 1, - "freq": 9, - "histogram": check_is_NaN, - "iqr": check_is_NaN, + "cv": 0, + "iqr": 0, "is_unique": False, - "kurtosis": check_is_NaN, - "mad": check_is_NaN, - "max": check_is_NaN, - "mean": check_is_NaN, - "min": check_is_NaN, - "mini_histogram": check_is_NaN, + "kurtosis": 0, + "mad": 0.0, + "max": 1.0, + "mean": 1.0, + "min": 1.0, "n_missing": 0, "p_missing": 0.0, + "n_infinite": 0, + "n_distinct": 1, "p_distinct": 0.1111111111111111, - "p_zeros": check_is_NaN, - "range": check_is_NaN, - "skewness": check_is_NaN, - "std": check_is_NaN, - "sum": check_is_NaN, - "top": 1.0, - "type": Variable.TYPE_BOOL, - "variance": check_is_NaN, + "p_zeros": 0, + "range": 0, + "skewness": 0, + "std": 0, + "sum": 9, + "variance": 0.0, + "mode": 1.0, + "monotonic_increase": True, + "monotonic_increase_strict": False, }, "s2": { "25%": check_is_NaN, @@ -323,7 +329,6 @@ def expected_results(): "count": 9, "cv": check_is_NaN, "n_distinct": 1, - "freq": 9, "histogram": check_is_NaN, "iqr": check_is_NaN, "is_unique": False, @@ -341,8 +346,6 @@ def expected_results(): "skewness": check_is_NaN, "std": check_is_NaN, "sum": check_is_NaN, - "top": "some constant text $ % value {obj} ", - "type": Variable.TYPE_CAT, "variance": check_is_NaN, }, "somedate": { @@ -354,7 +357,6 @@ def expected_results(): "count": 8, "cv": check_is_NaN, "n_distinct": 5, - "freq": check_is_NaN, "iqr": check_is_NaN, "is_unique": False, "kurtosis": check_is_NaN, @@ -370,8 +372,6 @@ def expected_results(): "skewness": check_is_NaN, "std": check_is_NaN, "sum": check_is_NaN, - "top": check_is_NaN, - "type": Variable.TYPE_DATE, }, "bool_tf": { "25%": check_is_NaN, @@ -382,7 +382,6 @@ def expected_results(): "count": 9, "cv": check_is_NaN, "n_distinct": 2, - "freq": 6, "histogram": check_is_NaN, "iqr": check_is_NaN, "is_unique": False, @@ -399,8 +398,6 @@ def expected_results(): "skewness": check_is_NaN, "std": check_is_NaN, "sum": check_is_NaN, - "top": True, - "type": Variable.TYPE_BOOL, "variance": check_is_NaN, }, "bool_tf_with_nan": { @@ -409,10 +406,13 @@ def expected_results(): "50%": check_is_NaN, "75%": check_is_NaN, "95%": check_is_NaN, + "n": 9, "count": 8, "cv": check_is_NaN, "n_distinct": 2, - "freq": 5, + "p_distinct": 2 / 8, + "n_missing": 1, + "p_missing": 1 / 9, "histogram": check_is_NaN, "iqr": check_is_NaN, "is_unique": False, @@ -421,109 +421,117 @@ def expected_results(): "max": check_is_NaN, "min": check_is_NaN, "mini_histogram": check_is_NaN, - "n_missing": 1, - "p_missing": 0.11111111111111116, - "p_distinct": 2 / 8, "p_zeros": check_is_NaN, "range": check_is_NaN, "skewness": check_is_NaN, "std": check_is_NaN, "sum": check_is_NaN, - "top": False, - "type": Variable.TYPE_BOOL, "variance": check_is_NaN, }, "bool_01": { - "25%": check_is_NaN, - "5%": check_is_NaN, - "50%": check_is_NaN, - "75%": check_is_NaN, - "95%": check_is_NaN, + "5%": 0, + "25%": 0, + "50%": 1.0, + "75%": 1, + "95%": 1, + "n": 9, "count": 9, - "cv": check_is_NaN, + "cv": 0.9486832980505138, "n_distinct": 2, - "freq": 5, - "histogram": check_is_NaN, - "iqr": check_is_NaN, + "iqr": 1.0, "is_unique": False, - "kurtosis": check_is_NaN, - "mad": check_is_NaN, - "max": check_is_NaN, - "min": check_is_NaN, - "mini_histogram": check_is_NaN, + "mad": 0, + "max": 1, + "min": 0, "n_missing": 0, "p_missing": 0, "p_distinct": 2 / 9, - "p_zeros": check_is_NaN, - "range": check_is_NaN, - "skewness": check_is_NaN, - "std": check_is_NaN, - "sum": check_is_NaN, - "top": 1, - "type": Variable.TYPE_BOOL, - "variance": check_is_NaN, + "p_zeros": 4 / 9, + "sum": 5, }, "bool_01_with_nan": { - "25%": check_is_NaN, - "5%": check_is_NaN, - "50%": check_is_NaN, - "75%": check_is_NaN, - "95%": check_is_NaN, + "5%": 0, + "25%": 0, + "50%": 0.5, + "75%": 1, + "95%": 1, + "n": 9, "count": 8, - "cv": check_is_NaN, + "cv": 1.0690449676496976, "n_distinct": 2, - "freq": 4, - "iqr": check_is_NaN, + "iqr": 1.0, "is_unique": False, - "kurtosis": check_is_NaN, - "mad": check_is_NaN, - "max": check_is_NaN, - "min": check_is_NaN, + "kurtosis": -2.8000000000000003, + "mad": 0.5, + "max": 1, + "min": 0, "n_missing": 1, "p_missing": 0.11111111111111116, "p_distinct": 2 / 8, - "p_zeros": check_is_NaN, - "range": check_is_NaN, - "skewness": check_is_NaN, - "std": check_is_NaN, - "sum": check_is_NaN, - "top": 0, - "type": Variable.TYPE_BOOL, - "variance": check_is_NaN, + "n_zeros": 4, + "p_zeros": 4 / 9, + "range": 1.0, + "skewness": 0.0, + "std": 0.5345224838248488, + "sum": 4.0, + "variance": 0.2857142857142857, }, "list": { + "n": 9, "count": 9, "n_missing": 0, "p_missing": 0, - "type": Variable.S_TYPE_UNSUPPORTED, }, "mixed": { + "n": 9, "count": 9, "n_missing": 0, "p_missing": 0, - "type": Variable.S_TYPE_UNSUPPORTED, }, "dict": { + "n": 9, "count": 9, "n_missing": 0, "p_missing": 0, - "type": Variable.S_TYPE_UNSUPPORTED, }, "tuple": { + "n": 9, "count": 9, "n_missing": 0, "p_missing": 0, - "type": Variable.S_TYPE_UNSUPPORTED, }, } -def test_describe_df(describe_data, expected_results): +@pytest.mark.parametrize( + "column", + [ + "id", + "x", + "y", + "cat", + "s1", + "s2", + "somedate", + "bool_tf", + "bool_tf_with_nan", + "bool_01", + "bool_01_with_nan", + "list", + "mixed", + "dict", + "tuple", + ], +) +def test_describe_df(column, describe_data, expected_results, summarizer, typeset): config["vars"]["num"]["low_categorical_threshold"].set(0) - describe_data_frame = pd.DataFrame(describe_data) - describe_data_frame["somedate"] = pd.to_datetime(describe_data_frame["somedate"]) + describe_data_frame = pd.DataFrame({column: describe_data[column]}) + if column == "somedate": + describe_data_frame["somedate"] = pd.to_datetime( + describe_data_frame["somedate"] + ) - results = describe("title", describe_data_frame) + results = describe("title", describe_data_frame, summarizer, typeset) assert { "analysis", @@ -538,45 +546,32 @@ def test_describe_df(describe_data, expected_results): "duplicates", } == set(results.keys()), "Not in results" - assert {"BOOL": 5, "CAT": 3, "UNSUPPORTED": 4, "NUM": 2, "DATE": 1} == results[ - "table" - ]["types"], "Variable analysis failed" - # Loop over variables - for col in describe_data.keys(): - for k, v in expected_results[col].items(): - if v == check_is_NaN: - assert ( - k not in results["variables"][col] - ) == True, "Value `{}` for key `{}` in column `{}` is not NaN".format( - results["variables"][col][k], k, col - ) - elif isinstance(v, float): - assert ( - pytest.approx(v) == results["variables"][col][k] - ), "Value `{}` for key `{}` in column `{}` is not NaN".format( - results["variables"][col][k], k, col - ) - else: - assert ( - v == results["variables"][col][k] - ), "Value `{}` for key `{}` in column `{}` is not NaN".format( - results["variables"][col][k], k, col - ) + for k, v in expected_results[column].items(): + if v == check_is_NaN: + test_condition = k not in results["variables"][column] + elif isinstance(v, float): + test_condition = pytest.approx(v) == results["variables"][column][k] + else: + test_condition = v == results["variables"][column][k] + + assert ( + test_condition + ), f"Value `{results['variables'][column][k]}` for key `{k}` in column `{column}` is not NaN" - if results["variables"][col]["type"].value in ["NUM", "DATE"]: - assert ( - "histogram" in results["variables"][col] - ), f"Histogram missing for column {col} " + if results["variables"][column]["type"] in [Numeric, DateTime]: + assert ( + "histogram" in results["variables"][column] + ), f"Histogram missing for column {column}" -def test_describe_empty(): +def test_describe_empty(summarizer, typeset): empty_frame = pd.DataFrame() with pytest.raises(ValueError): - describe("", empty_frame) + describe("", empty_frame, summarizer, typeset) -def test_describe_list(): +def test_describe_list(summarizer, typeset): with pytest.raises(AttributeError): with pytest.warns(UserWarning): - describe("", [1, 2, 3]) + describe("", [1, 2, 3], summarizer, typeset) diff --git a/tests/unit/test_modular.py b/tests/unit/test_modular.py index 9c4f7cce4..24dd3c8f5 100644 --- a/tests/unit/test_modular.py +++ b/tests/unit/test_modular.py @@ -48,10 +48,11 @@ def test_modular_description_set(tdf): "dendrogram": False, "heatmap": False, }, + pool_size=1, ) html = profile.get_description() - print(html) + assert len(html) > 0 def test_modular_absent(tdf): @@ -60,14 +61,7 @@ def test_modular_absent(tdf): duplicates={"head": 0}, samples={"head": 0, "tail": 0}, interactions=None, - correlations={ - "pearson": {"calculate": False}, - "spearman": {"calculate": False}, - "kendall": {"calculate": False}, - "phi_k": {"calculate": False}, - "cramers": {"calculate": False}, - "recoded": {"calculate": False}, - }, + correlations=None, missing_diagrams=None, ) @@ -89,7 +83,6 @@ def test_modular_present(tdf): "spearman": {"calculate": True}, "kendall": {"calculate": True}, "phi_k": {"calculate": True}, - "recoded": {"calculate": True}, "cramers": {"calculate": True}, }, missing_diagrams={ @@ -98,6 +91,7 @@ def test_modular_present(tdf): "dendrogram": True, "heatmap": True, }, + pool_size=1, ) html = profile.to_html() diff --git a/tests/unit/test_summarizer.py b/tests/unit/test_summarizer.py new file mode 100644 index 000000000..1a3a8c751 --- /dev/null +++ b/tests/unit/test_summarizer.py @@ -0,0 +1,69 @@ +import os + +import pandas as pd + +from pandas_profiling.model.summarizer import PandasProfilingSummarizer, format_summary +from pandas_profiling.model.typeset import ( + URL, + Boolean, + Categorical, + DateTime, + File, + Image, + Numeric, + Path, + ProfilingTypeSet, + Unsupported, +) + +base_path = os.path.abspath(os.path.dirname(__file__)) + + +def test_summarizer(): + pps = PandasProfilingSummarizer(typeset=ProfilingTypeSet()) + + _ = format_summary(pps.summarize(pd.Series([1, 2, 3, 4, 5]), Unsupported)) + _ = format_summary(pps.summarize(pd.Series([1, 2, 3, 4, 5]), Numeric)) + _ = format_summary( + pps.summarize( + pd.Series(pd.date_range(start="1/1/2018", end="1/08/2018")), DateTime + ) + ) + _ = format_summary(pps.summarize(pd.Series(["abc", "abc", "abba"]), Categorical)) + _ = format_summary(pps.summarize(pd.Series(["https://www.example.com"]), URL)) + _ = format_summary( + pps.summarize( + pd.Series( + [ + os.path.abspath( + base_path + + r"../../../src/pandas_profiling/model/typeset_does_not_exist.py" + ) + ] + ), + Path, + ) + ) + _ = format_summary( + pps.summarize( + pd.Series( + [ + os.path.abspath( + base_path + r"../../../src/pandas_profiling/model/typeset.py" + ) + ] + ), + File, + ) + ) + _ = format_summary( + pps.summarize( + pd.Series( + [os.path.abspath(base_path + r"../../../docsrc/assets/lambda-labs.png")] + ), + Image, + ) + ) + _ = format_summary( + pps.summarize(pd.Series([True, False, True, False, False]), Boolean) + ) diff --git a/tests/unit/test_summary_algos.py b/tests/unit/test_summary_algos.py new file mode 100644 index 000000000..98460bec6 --- /dev/null +++ b/tests/unit/test_summary_algos.py @@ -0,0 +1,26 @@ +import numpy as np +import pandas as pd + +from pandas_profiling.model.summary_algorithms import describe_counts + + +def test_count_summary_sorted(): + s = pd.Series([1] + [2] * 1000) + sn, r = describe_counts(s, {}) + assert r["value_counts_without_nan"].index[0] == 2 + assert r["value_counts_without_nan"].index[1] == 1 + + +def test_count_summary_nat(): + s = pd.to_datetime(pd.Series([1, 2] + [np.nan, pd.NaT])) + sn, r = describe_counts(s, {}) + assert len(r["value_counts_without_nan"].index) == 2 + + +def test_count_summary_category(): + s = pd.Categorical( + ["Poor", "Neutral"] + [np.nan] * 100, + categories=["Poor", "Neutral", "Excellent"], + ) + sn, r = describe_counts(s, {}) + assert len(r["value_counts_without_nan"].index) == 2 diff --git a/tests/unit/test_type_inference.py b/tests/unit/test_type_inference.py deleted file mode 100644 index 5103318c2..000000000 --- a/tests/unit/test_type_inference.py +++ /dev/null @@ -1,9 +0,0 @@ -import numpy as np -import pandas as pd - -from pandas_profiling.model.base import Variable, get_var_type - - -def test_numeric_with_inf(): - s = pd.Series([1, 2, 3, 6, np.inf]) - assert get_var_type(s)["type"] == Variable.TYPE_NUM diff --git a/tests/unit/test_typeset_custom.py b/tests/unit/test_typeset_custom.py new file mode 100644 index 000000000..c022fcc20 --- /dev/null +++ b/tests/unit/test_typeset_custom.py @@ -0,0 +1,301 @@ +import datetime + +import numpy as np +import pandas as pd +import pytest +from visions.test.utils import ( + contains, + convert, + get_contains_cases, + get_convert_cases, + get_inference_cases, + infers, +) + +from pandas_profiling.config import config +from pandas_profiling.model.typeset import ( + Boolean, + Categorical, + DateTime, + Numeric, + ProfilingTypeSet, + Unsupported, +) + + +def get_profiling_series(): + data = { + "empty": pd.Series([], dtype=object), + # Boolean + "booleans_type": [False, True, True], + "booleans_type_nan": [False, True, np.nan], + "str_yes_no": ["Y", "N", "Y"], + "str_yes_no_mixed": ["Y", "n", "y"], + "str_yes_no_nan": ["Y", "N", np.nan], + "str_true_false": ["True", "False", "False"], + "str_true_false_nan": ["True", "False", np.nan], + "str_true_false_none": ["True", "False", None], + # Numeric + "num_with_inf": [1, 2, 3, 6, np.inf], + "integers": [1, 0, 0, 0], + "inf_only": [np.inf], + "integers_nan": [1, 0, 1, 0, np.nan], + # test Describe + "id": [chr(97 + c) for c in range(1, 9)] + ["d"], + "catnum": [str(c) for c in range(1, 100)], + "x": [50, 50, -10, 0, 0, 5, 15, -3, np.nan], + "y": [ + 0.000001, + 654.152, + np.nan, + 15.984512, + 3122, + -3.1415926535, + 111, + 15.9, + 13.5, + ], + "cat": [ + "a", + "long text value", + "Élysée", + "", + None, + "some B.s HTML stuff", + "c", + "c", + "c", + ], + "s1": np.ones(9), + "s2": ["some constant text $ % value {obj} " for _ in range(1, 10)], + "somedate": [ + datetime.date(2011, 7, 4), + datetime.datetime(2022, 1, 1, 13, 57), + datetime.datetime(1990, 12, 9), + np.nan, + datetime.datetime(1990, 12, 9), + datetime.datetime(1950, 12, 9), + datetime.datetime(1898, 1, 2), + datetime.datetime(1950, 12, 9), + datetime.datetime(1950, 12, 9), + ], + "bool_tf": [True, True, False, True, False, True, True, False, True], + "bool_tf_with_nan": [ + True, + False, + False, + False, + False, + True, + True, + False, + np.nan, + ], + "bool_01": [1, 1, 0, 1, 1, 0, 0, 0, 1], + "bool_01_with_nan": [1, 0, 1, 0, 0, 1, 1, 0, np.nan], + "list": [ + [1, 2], + [1, 2], + [1, 2], + [1, 2], + [1, 2], + [1, 2], + [1, 2], + [1, 2], + [1, 2], + ], + "mixed": [1, 2, "a", 4, 5, 6, 7, 8, 9], + "dict": [ + {"a": "a"}, + {"b": "b"}, + {"c": "c"}, + {"d": "d"}, + {"e": "e"}, + {"f": "f"}, + {"g": "g"}, + {"h": "h"}, + {"i": "i"}, + ], + "tuple": [ + (1, 2), + (3, 4), + (5, 6), + (7, 8), + (9, 10), + (11, 12), + (13, 14), + (15, 16), + (17, 18), + ], + "date_str": ["2018-01-01", "2017-02-01", "2018-04-07"], + "nullable_int": pd.Series([1, None], dtype="Int64"), + } + + return [pd.Series(values, name=key) for key, values in data.items()] + + +series = get_profiling_series() + +typeset = ProfilingTypeSet() + + +class DataTest: + def __init__(self, name, contains_type, infer_type, cast_result=None): + self.name = name + self.contains_type = contains_type + self.infer_type = infer_type + self.cast_result = cast_result + + +cases = [ + DataTest("x", Numeric, Numeric), +] + +contains_map = { + Numeric: { + "x", + "y", + "s1", + "num_with_inf", + "integers", + "integers_nan", + "bool_01", + "bool_01_with_nan", + "inf_only", + "nullable_int", + }, + Categorical: { + "id", + "cat", + "s2", + "date_str", + "str_yes_no", + "str_yes_no_mixed", + "str_yes_no_nan", + "str_true_false", + "str_true_false_none", + "str_true_false_nan", + "catnum", + }, + Boolean: { + "bool_tf", + "bool_tf_with_nan", + "booleans_type", + "booleans_type_nan", + }, + DateTime: { + "somedate", + }, + Unsupported: {"empty", "list", "mixed", "dict", "tuple"}, +} + + +@pytest.mark.parametrize(**get_contains_cases(series, contains_map, typeset)) +def test_contains(series, type, member): + """Test the generated combinations for "series in type" + + Args: + series: the series to test + type: the type to test against + member: the result + """ + config["vars"]["num"]["low_categorical_threshold"].set(0) + result, message = contains(series, type, member) + assert result, message + + +inference_map = { + "x": Numeric, + "y": Numeric, + "s1": Numeric, + "num_with_inf": Numeric, + "integers": Numeric, + "integers_nan": Numeric, + "bool_01": Numeric, + "bool_01_with_nan": Numeric, + "id": Categorical, + "cat": Categorical, + "s2": Categorical, + "date_str": Categorical, + "bool_tf": Boolean, + "bool_tf_with_nan": Boolean, + "booleans_type": Boolean, + "booleans_type_nan": Boolean, + "str_yes_no": Boolean, + "str_yes_no_mixed": Boolean, + "str_yes_no_nan": Boolean, + "str_true_false": Boolean, + "str_true_false_none": Boolean, + "str_true_false_nan": Boolean, + "somedate": DateTime, + "empty": Unsupported, + "list": Unsupported, + "mixed": Unsupported, + "dict": Unsupported, + "tuple": Unsupported, + "inf_only": Numeric, + "nullable_int": Numeric, + "catnum": Numeric, +} + + +@pytest.mark.parametrize(**get_inference_cases(series, inference_map, typeset)) +def test_inference(series, type, typeset, difference): + """Test the generated combinations for "inference(series) == type" + + Args: + series: the series to test + type: the type to test against + """ + config["vars"]["num"]["low_categorical_threshold"].set(0) + result, message = infers(series, type, typeset, difference) + assert result, message + + +# Conversions in one single step +convert_map = [ + # Model type, Relation type + ( + Categorical, + Numeric, + { + "integers", + "inf_only", + "integers_nan", + "s1", + "bool_01", + "bool_01_with_nan", + "nullable_int", + }, + ), + ( + Numeric, + Categorical, + {"catnum"}, + ), + ( + Boolean, + Categorical, + { + "str_true_false", + "str_yes_no", + "str_yes_no_mixed", + "str_yes_no_nan", + "str_true_false_nan", + "str_true_false_none", + }, + ), +] + + +@pytest.mark.parametrize(**get_convert_cases(series, convert_map, typeset)) +def test_conversion(source_type, relation_type, series, member): + """Test the generated combinations for "convert(series) == type" and "infer(series) = source_type" + + Args: + series: the series to test + source_type: the type to test against + """ + config["vars"]["num"]["low_categorical_threshold"].set(2) + result, message = convert(source_type, relation_type, series, member) + assert result, message diff --git a/tests/unit/test_typeset_default.py b/tests/unit/test_typeset_default.py new file mode 100644 index 000000000..f97759655 --- /dev/null +++ b/tests/unit/test_typeset_default.py @@ -0,0 +1,332 @@ +import pandas as pd +import pytest +from visions.test.series import get_series +from visions.test.utils import ( + contains, + convert, + get_contains_cases, + get_convert_cases, + get_inference_cases, + infers, +) + +from pandas_profiling.config import config +from pandas_profiling.model.typeset import ( + Boolean, + Categorical, + DateTime, + Numeric, + ProfilingTypeSet, + Unsupported, +) + +series = get_series() + +typeset = ProfilingTypeSet() + +contains_map = { + Numeric: { + "int_series", + "Int64_int_series", + "int_range", + "Int64_int_nan_series", + "int_series_boolean", + "np_uint32", + "pd_uint32", + "float_series", + "float_series2", + "float_series3", + "float_series4", + "inf_series", + "float_nan_series", + "float_series5", + "int_nan_series", + "float_with_inf", + "float_series6", + "complex_series", + "complex_series_py", + "complex_series_nan", + "complex_series_py_nan", + "complex_series_nan_2", + "complex_series_float", + }, + Categorical: { + "categorical_float_series", + "categorical_int_series", + "categorical_string_series", + "categorical_complex_series", + "categorical_char", + "ordinal", + "timestamp_string_series", + "string_with_sep_num_nan", + "string_series", + "string_unicode_series", + "string_np_unicode_series", + "path_series_linux_str", + "path_series_windows_str", + "int_str_range", + "string_date", + "textual_float", + "textual_float_nan", + "ip_str", + "string_flt", + "string_num", + "str_url", + "string_str_nan", + "string_num_nan", + "string_bool_nan", + "string_flt_nan", + "str_complex", + "uuid_series_str", + "str_int_leading_zeros", + "email_address_str", + "str_float_non_leading_zeros", + "str_int_zeros", + "str_complex_nan", + }, + Boolean: { + "bool_series", + "bool_series2", + "bool_series3", + "nullable_bool_series", + "mixed", + "bool_nan_series", + }, + DateTime: { + "timestamp_series", + "timestamp_aware_series", + "datetime", + "timestamp_series_nat", + "date_series_nat", + }, +} + +if int(pd.__version__[0]) >= 1: + contains_map[Categorical].add("string_dtype_series") + +contains_map[Unsupported] = { + "module", + "nan_series", + "nan_series_2", + "timedelta_series", + "timedelta_series_nat", + "timedelta_negative", + "path_series_linux", + "path_series_linux_missing", + "path_series_windows", + "url_series", + "url_nan_series", + "url_none_series", + "file_test_py", + "file_mixed_ext", + "file_test_py_missing", + "image_png", + "image_png_missing", + "image_png", + "image_png_missing", + "uuid_series", + "uuid_series_missing", + "mixed_list[str,int]", + "mixed_dict", + "callable", + "mixed_integer", + "mixed_list", + "date", + "time", + "empty", + "empty_bool", + "empty_float", + "empty_object", + "empty_int64", + "ip", + "ip_missing", + "ip_mixed_v4andv6", + "email_address_missing", + "email_address", +} + + +@pytest.mark.parametrize(**get_contains_cases(series, contains_map, typeset)) +def test_contains(series, type, member): + """Test the generated combinations for "series in type" + + Args: + series: the series to test + type: the type to test against + member: the result + """ + config["vars"]["num"]["low_categorical_threshold"].set(0) + result, message = contains(series, type, member) + assert result, message + + +inference_map = { + "int_series": Numeric, + "categorical_int_series": Numeric, + "int_nan_series": Numeric, + "Int64_int_series": Numeric, + "Int64_int_nan_series": Numeric, + "np_uint32": Numeric, + "pd_uint32": Numeric, + "int_range": Numeric, + "float_series": Numeric, + "float_nan_series": Numeric, + "int_series_boolean": Numeric, + "float_series2": Numeric, + "float_series3": Numeric, + "float_series4": Numeric, + "float_series5": Numeric, + "float_series6": Numeric, + "complex_series_float": Numeric, + "categorical_float_series": Numeric, + "float_with_inf": Numeric, + "inf_series": Numeric, + "nan_series": Unsupported, + "nan_series_2": Unsupported, + "string_series": Categorical, + "categorical_string_series": Categorical, + "timestamp_string_series": Categorical, + "string_with_sep_num_nan": Categorical, # TODO: Introduce thousands separator + "string_unicode_series": Categorical, + "string_np_unicode_series": Categorical, + "string_num_nan": Numeric, + "string_num": Numeric, + "string_flt_nan": Numeric, + "string_flt": Numeric, + "string_str_nan": Categorical, + "string_bool_nan": Boolean, + "int_str_range": Numeric, + "string_date": Categorical, + "str_url": Categorical, + "bool_series": Boolean, + "bool_nan_series": Boolean, + "nullable_bool_series": Boolean, + "bool_series2": Boolean, + "bool_series3": Boolean, + "complex_series": Numeric, + "complex_series_nan": Numeric, + "complex_series_nan_2": Numeric, + "complex_series_py_nan": Numeric, + "complex_series_py": Numeric, + "categorical_complex_series": Numeric, + "timestamp_series": DateTime, + "timestamp_series_nat": DateTime, + "timestamp_aware_series": DateTime, + "datetime": DateTime, + "timedelta_series": Unsupported, + "timedelta_series_nat": Unsupported, + "timedelta_negative": Unsupported, + "geometry_series_missing": Unsupported, + "geometry_series": Unsupported, + "path_series_linux": Unsupported, + "path_series_linux_missing": Unsupported, + "path_series_linux_str": Categorical, + "path_series_windows": Unsupported, + "path_series_windows_str": Categorical, + "url_series": Unsupported, + "url_nan_series": Unsupported, + "url_none_series": Unsupported, + "mixed_list[str,int]": Unsupported, + "mixed_dict": Unsupported, + "mixed_integer": Unsupported, + "mixed_list": Unsupported, + "mixed": Boolean, + "callable": Unsupported, + "module": Unsupported, + "textual_float": Numeric, + "textual_float_nan": Numeric, + "empty": Unsupported, + "empty_float": Unsupported, + "empty_bool": Unsupported, + "empty_int64": Unsupported, + "empty_object": Unsupported, + "ip": Unsupported, + "ip_str": Categorical, + "ip_missing": Unsupported, + "date_series_nat": DateTime, + "date": Unsupported, + "time": Unsupported, + "categorical_char": Categorical, + "ordinal": Categorical, + "str_complex": Categorical, + "uuid_series": Unsupported, + "uuid_series_str": Categorical, + "uuid_series_missing": Unsupported, + "ip_mixed_v4andv6": Unsupported, + "file_test_py": Unsupported, + "file_test_py_missing": Unsupported, + "file_mixed_ext": Unsupported, + "image_png": Unsupported, + "image_png_missing": Unsupported, + "str_int_leading_zeros": Numeric, + "str_float_non_leading_zeros": Numeric, + "str_int_zeros": Numeric, + "email_address_str": Categorical, + "str_complex_nan": Categorical, + "email_address": Unsupported, + "email_address_missing": Unsupported, +} +if int(pd.__version__[0]) >= 1: + inference_map["string_dtype_series"] = Categorical + + +@pytest.mark.parametrize(**get_inference_cases(series, inference_map, typeset)) +def test_inference(series, type, typeset, difference): + """Test the generated combinations for "inference(series) == type" + + Args: + series: the series to test + type: the type to test against + """ + config["vars"]["num"]["low_categorical_threshold"].set(0) + result, message = infers(series, type, typeset, difference) + assert result, message + + +# Conversions in one single step +convert_map = [ + # Model type, Relation type + (Categorical, Numeric, {"mixed"}), + ( + Numeric, + Categorical, + { + "string_flt", + "string_num_nan", + "string_num", + "string_flt_nan", + "textual_float", + "textual_float_nan", + "int_str_range", + "str_float_non_leading_zeros", + "str_int_zeros", + "str_int_leading_zeros", + "mixed", + "categorical_complex_series", + "int_series", + "categorical_int_series", + "categorical_float_series", + }, + ), + ( + Boolean, + Categorical, + { + "string_bool_nan", + "nullable_bool_series", + }, + ), +] + + +@pytest.mark.parametrize(**get_convert_cases(series, convert_map, typeset)) +def test_conversion(source_type, relation_type, series, member): + """Test the generated combinations for "convert(series) == type" and "infer(series) = source_type" + + Args: + series: the series to test + type: the type to test against + """ + config["vars"]["num"]["low_categorical_threshold"].set(0) + result, message = convert(source_type, relation_type, series, member) + assert result, message diff --git a/tests/unit/test_url.py b/tests/unit/test_url.py index 5861a5a25..b7cdaeadc 100644 --- a/tests/unit/test_url.py +++ b/tests/unit/test_url.py @@ -18,7 +18,9 @@ def test_urls(get_data_file): df = df.mask(np.random.random(df.shape) < 0.1) profile = df.profile_report( - title="DataFrame with URL column", samples={"head": 0, "tail": 0} + title="DataFrame with URL column", + samples={"head": 0, "tail": 0}, + explorative=True, ) assert "URL" in profile.to_html(), "URL not detected" diff --git a/tests/unit/test_utils.py b/tests/unit/test_utils.py index b5a64cbca..e4eb38d58 100644 --- a/tests/unit/test_utils.py +++ b/tests/unit/test_utils.py @@ -16,6 +16,7 @@ def test_read_pandas_parquet(): "Passed non-file path: dataframe.parquet", # fastparquet "[Errno 2] No such file or directory: 'dataframe.parquet'", + str(FileNotFoundError("dataframe.parquet")), ]