survy

A Python library for survey data — treating multiselect questions as first-class variables, not DataFrame workarounds.

graph TD
    A[📂 Data Sources] --> B[⚙️ survy]
    B --> C[🔄 Transform]
    C --> D[📤 Export]
    C --> E[🔍 Explore]
    E --> F[📊 Analyze]

📋 Table of Contents

Why survy?
Features
Installation
Quick Demo
Usage
Agent Skills
Design Philosophy
Contributing
License
References

📦 Why survy?

Survey data has a construct that no general-purpose Python tool handles correctly: multiselect questions — "choose all that apply" questions where one respondent selects multiple answers. Raw data stores these as either multiple columns (hobby_1, hobby_2, hobby_3) or delimited strings ("Sport;Book"), but pandas treats them as unrelated columns or plain text. Every project, you rewrite the same boilerplate to split, group, count, and export them — and get it subtly wrong (counting responses instead of respondents, losing column groupings, breaking on format changes).

SPSS solved this decades ago with native multiple response sets. R has partial solutions scattered across expss, MRCV, and surveydata. But Python — the language AI coding tools actually generate — had nothing.

survy makes MULTISELECT a first-class variable type. Load your data, and survy auto-detects the format, merges columns into logical variables, and carries that type awareness through frequencies, crosstabs, filtering, and export. The correct code is also the simple code — which means AI assistants can generate it reliably too.

✨ Features

🔹 Multiselect as a first-class concept — both compact and wide formats auto-detected
🔹 Read & write multiple formats: CSV, Excel, JSON, SPSS
🔹 Built-in tools for validation, tracking, and analysis
🔹 Cross-tabulation with significance testing
🔹 AI-ready — pair with survy-agent-skills so LLM coding assistants generate correct survy code

🚀 Installation

pip install survy

⚡ Quick Demo

import survy

# Load a CSV with wide multiselect columns (hobby_1, hobby_2, ...)
survey = survy.read_csv("data.csv")

# Or load a CSV with compact multiselect columns ("Sport;Book")
survey = survy.read_csv("data_compact.csv", auto_detect=True, compact_separator=";")

print(survey)
# Survey (4 variables)
#   Variable(id=gender, base=3, label=gender, value_indices={'Female': 1, 'Male': 2})
#   Variable(id=yob, base=3, label=yob, value_indices={})
#   Variable(id=hobby, base=3, label=hobby, value_indices={'Book': 1, 'Movie': 2, 'Sport': 3})
#   Variable(id=animal, base=3, label=animal, value_indices={'Cat': 1, 'Dog': 2})

# Both formats produce the same result
print(survey.get_df())
# ┌────────┬──────┬────────────────────┬────────────────┐
# │ gender ┆ yob  ┆ hobby              ┆ animal         │
# │ ---    ┆ ---  ┆ ---                ┆ ---            │
# │ str    ┆ i64  ┆ list[str]          ┆ list[str]      │
# ╞════════╪══════╪════════════════════╪════════════════╡
# │ Male   ┆ 2000 ┆ ["Book", "Sport"]  ┆ ["Cat", "Dog"] │
# │ Female ┆ 1999 ┆ ["Movie", "Sport"] ┆ ["Dog"]        │
# │ Male   ┆ 1998 ┆ ["Movie"]          ┆ ["Cat"]        │
# └────────┴──────┴────────────────────┴────────────────┘

# Frequencies
print(survey["gender"].frequencies)
# ┌────────┬───────┬────────────┐
# │ gender ┆ count ┆ proportion │
# │ ---    ┆ ---   ┆ ---        │
# │ str    ┆ u32   ┆ f64        │
# ╞════════╪═══════╪════════════╡
# │ Female ┆ 1     ┆ 0.333333   │
# │ Male   ┆ 2     ┆ 0.666667   │
# └────────┴───────┴────────────┘

# Crosstab with significance testing
print(survy.crosstab(survey["gender"], survey["hobby"]))
# {'Total': shape: (3, 3)
# ┌───────┬──────────┬────────────┐
# │ hobby ┆ Male (A) ┆ Female (B) │
# │ ---   ┆ ---      ┆ ---        │
# │ str   ┆ str      ┆ str        │
# ╞═══════╪══════════╪════════════╡
# │ Book  ┆ 1        ┆ 0          │
# │ Movie ┆ 1        ┆ 1          │
# │ Sport ┆ 1        ┆ 1          │
# └───────┴──────────┴────────────┘}

📥 Usage

Understanding Multiselect Formats

The key challenge with survey data is multiselect questions — questions where a respondent can choose multiple answers. Raw data encodes these in two different layouts, and survy handles both.

Wide format spreads each answer across its own column, using a shared prefix plus a separator and numeric suffix (e.g. _1, _2, ...):

gender	yob	hobby_1	hobby_2	hobby_3	animal_1	animal_2
Male	2000	Book		Sport	Cat	Dog
Female	1999		Movie			Dog
Male	1998		Movie		Cat

survy groups columns by parsing the name with a name_pattern template (default "id(_multi)?"). The tokens id and multi are named placeholders, and _, ., : are recognized separators. So hobby_1 is parsed as id="hobby", multi="1" — all columns sharing the same id are merged into one multiselect variable. This happens automatically — no extra parameters needed.

Compact format stores all selected answers in a single cell, joined by a separator (typically ;):

gender	yob	hobby	animal
Male	2000	Book;Sport	Cat;Dog
Female	1999	Movie;Sport	Dog
Male	1998	Movie	Cat

survy splits these cells on the separator to recover individual choices. Because a semicolon could be regular text, compact format is not auto-detected by default — you must either list the compact columns with compact_ids or enable auto_detect=True.

After reading, both formats produce the exact same internal representation — a MULTISELECT variable with a sorted list of chosen values per respondent.

Load Data

# Available read functions
survy.read_csv       # CSV files
survy.read_excel     # Excel files (.xlsx)
survy.read_json      # survy-format JSON
survy.read_polars    # Polars DataFrame already in memory
survy.read_spss      # SPSS data (.sav)

CSV / Excel

# Wide format — auto-detected, no special parameters needed
survey = survy.read_csv("data.csv")

# Compact format — explicitly specify which columns are compact
survey = survy.read_csv(
    "data_compact.csv",
    compact_ids=["hobby", "animal"],  # columns that use compact encoding
    compact_separator=";",            # delimiter inside cells
)

# Compact format — let survy scan for the separator automatically
survey = survy.read_csv(
    "data_compact.csv",
    auto_detect=True,          # scans all columns for the separator
    compact_separator=";",
)

# Mixed: some columns are wide, some are compact
# Wide is always auto-detected; just specify the compact ones
survey = survy.read_csv("data_mixed.csv", compact_ids=["Q5"], compact_separator=";")

# Custom name_pattern for wide detection if columns use a different naming convention
# Tokens: "id" (base name), "multi" (suffix). Separators: _ . :
# Example: "id.multi" would match "Q1.1", "Q1.2", etc.
survey = survy.read_csv("data.csv", name_pattern="id.multi")

# Excel — identical API
survey = survy.read_excel("data.xlsx", auto_detect=True, compact_separator=";")

Important: Do not combine auto_detect=True with compact_ids in the same call. Use one approach or the other.

JSON

The JSON file must follow survy's format — a "variables" array where each entry has "id", "data", "label", and "value_indices":

# Expected format: data.json
# {
#     "variables": [
#         {
#             "id": "gender",
#             "data": ["Male", "Female", "Male"],
#             "label": "",
#             "value_indices": {"Female": 1, "Male": 2}
#         },
#         {
#             "id": "yob",
#             "data": [2000, 1999, 1998],
#             "label": "",
#             "value_indices": {}
#         },
#         {
#             "id": "hobby",
#             "data": [["Book", "Sport"], ["Movie", "Sport"], ["Movie"]],
#             "label": "",
#             "value_indices": {"Book": 1, "Movie": 2, "Sport": 3}
#         }
#     ]
# }

survey = survy.read_json("data.json")

The "data" field varies by type: a flat list of strings for SELECT, a flat list of numbers for NUMBER, and a list of lists for MULTISELECT. The "value_indices" field should be {} for NUMBER variables.

Polars DataFrame

import polars

df = polars.DataFrame({
    "gender": ["Male", "Female", "Male"],
    "yob": [2000, 1999, 1998],
    "hobby": ["Sport;Book", "Sport;Movie", "Movie"],
    "animal_1": ["Cat", "", "Cat"],
    "animal_2": ["Dog", "Dog", ""],
})

# Supports the same parameters as read_csv: compact_ids, auto_detect, name_pattern
survey = survy.read_polars(df, auto_detect=True, compact_separator=";")

Work with Survey

print(survey)
# Survey (4 variables)
#   Variable(id=gender, base=3, label=gender, value_indices={'Female': 1, 'Male': 2})
#   Variable(id=yob, base=3, label=yob, value_indices={})
#   Variable(id=hobby, base=3, label=hobby, value_indices={'Book': 1, 'Movie': 2, 'Sport': 3})
#   Variable(id=animal, base=3, label=animal, value_indices={'Cat': 1, 'Dog': 2})

Methods & Properties

Method / Property	Description
`get_df()`	Return survey data as a `polars.DataFrame`
`update()`	Update metadata (labels, value indices) of variables
`add()`	Add a variable to the survey
`drop()`	Remove a variable from the survey
`filter()`	Filter respondents by variable values (returns a new Survey)
`sort()`	Sort variables by given logic
`to_csv()`	Export to CSV (3 files: data + variable info + value mappings)
`to_excel()`	Export to Excel (same structure as CSV)
`to_json()`	Export to JSON
`to_spss()`	Export to SPSS format (.sav + .sps)
`to_database()`	Export to a relational database (4 tables)
`.variables`	Collection of all variables
`.sps`	Render SPSS syntax string
`survey[i]` / `survey["id"]`	Access variable by index or ID
`len(survey)`	Number of variables in the survey
`for var in survey:`	Iterate over all variables

DataFrame Output Formats

The get_df() method supports flexible output through select_dtype and multiselect_dtype parameters:

# Compact multiselect (list columns) — default
print(survey.get_df(select_dtype="text", multiselect_dtype="compact"))
# ┌────────┬──────┬────────────────────┬────────────────┐
# │ gender ┆ yob  ┆ hobby              ┆ animal         │
# │ ---    ┆ ---  ┆ ---                ┆ ---            │
# │ str    ┆ i64  ┆ list[str]          ┆ list[str]      │
# ╞════════╪══════╪════════════════════╪════════════════╡
# │ Male   ┆ 2000 ┆ ["Book", "Sport"]  ┆ ["Cat", "Dog"] │
# │ Female ┆ 1999 ┆ ["Movie", "Sport"] ┆ ["Dog"]        │
# │ Male   ┆ 1998 ┆ ["Movie"]          ┆ ["Cat"]        │
# └────────┴──────┴────────────────────┴────────────────┘

# Wide text format (split columns, numeric category codes)
print(survey.get_df(select_dtype="number", multiselect_dtype="text"))
# ┌────────┬──────┬─────────┬─────────┬─────────┬──────────┬──────────┐
# │ gender ┆ yob  ┆ hobby_1 ┆ hobby_2 ┆ hobby_3 ┆ animal_1 ┆ animal_2 │
# │ ---    ┆ ---  ┆ ---     ┆ ---     ┆ ---     ┆ ---      ┆ ---      │
# │ i64    ┆ i64  ┆ str     ┆ str     ┆ str     ┆ str      ┆ str      │
# ╞════════╪══════╪═════════╪═════════╪═════════╪══════════╪══════════╡
# │ 2      ┆ 2000 ┆ Book    ┆ null    ┆ Sport   ┆ Cat      ┆ Dog      │
# │ 1      ┆ 1999 ┆ null    ┆ Movie   ┆ Sport   ┆ null     ┆ Dog      │
# │ 2      ┆ 1998 ┆ null    ┆ Movie   ┆ null    ┆ Cat      ┆ null     │
# └────────┴──────┴─────────┴─────────┴─────────┴──────────┴──────────┘

# Fully numeric (binary-encoded multiselect)
print(survey.get_df(select_dtype="number", multiselect_dtype="number"))
# ┌────────┬──────┬─────────┬─────────┬─────────┬──────────┬──────────┐
# │ gender ┆ yob  ┆ hobby_1 ┆ hobby_2 ┆ hobby_3 ┆ animal_1 ┆ animal_2 │
# │ ---    ┆ ---  ┆ ---     ┆ ---     ┆ ---     ┆ ---      ┆ ---      │
# │ i64    ┆ i64  ┆ i8      ┆ i8      ┆ i8      ┆ i8       ┆ i8       │
# ╞════════╪══════╪═════════╪═════════╪═════════╪══════════╪══════════╡
# │ 2      ┆ 2000 ┆ 1       ┆ 0       ┆ 1       ┆ 1        ┆ 1        │
# │ 1      ┆ 1999 ┆ 0       ┆ 1       ┆ 1       ┆ 0        ┆ 1        │
# │ 2      ┆ 1998 ┆ 0       ┆ 1       ┆ 0       ┆ 1        ┆ 0        │
# └────────┴──────┴─────────┴─────────┴─────────┴──────────┴──────────┘

Updating Survey Metadata

survey.update(
    [
        {"id": "gender", "label": "Please indicate your gender."},
        {"id": "hobby", "value_indices": {"Sport": 1, "Book": 2, "Movie": 3}},
    ]
)
print(survey)
# Survey (4 variables)
#   Variable(id=gender, base=3, label=Please indicate your gender., value_indices={'Female': 1, 'Male': 2})
#   Variable(id=yob, base=3, label=yob, value_indices={})
#   Variable(id=hobby, base=3, label=hobby, value_indices={'Sport': 1, 'Book': 2, 'Movie': 3})
#   Variable(id=animal, base=3, label=animal, value_indices={'Cat': 1, 'Dog': 2})

Adding, Dropping, Sorting, and Filtering

import polars

# Add a variable (auto-wrapped from polars.Series)
survey.add(polars.Series("region", ["North", "South", "North"]))

# Drop a variable (silently ignored if not found)
survey.drop("region")

# Sort variables in-place
survey.sort()                                      # alphabetical by id
survey.sort(key=lambda v: v.base, reverse=True)    # by response count

# Filter respondents — returns a new Survey, original is not mutated
filtered = survey.filter("hobby", ["Sport", "Book"])
filtered = survey.filter("gender", "Male")         # single value also works

For multiselect variables, filter() keeps a row if any of its selected values appears in the filter list.

Work with Variables

hobby = survey["hobby"]
print(hobby)
# Variable(id=hobby, base=3, label=hobby, value_indices={'Book': 1, 'Movie': 2, 'Sport': 3})

Methods & Properties

Method / Property	Description
`get_df()`	Return variable data as a `polars.DataFrame`
`to_dict()`	Serialize variable to a dictionary
`replace()`	Remap values using a given mapping
`.series`	Variable data as a `polars.Series`
`.id`	Variable identifier (read/write)
`.label`	Variable label string (read/write)
`.value_indices`	Mapping of response values to numeric codes (read/write)
`.vtype`	Variable type: `select`, `multiselect`, or `number`
`.base`	Count of valid (non-null) responses
`.len`	Total number of responses
`.dtype`	Underlying Polars data type
`.frequencies`	DataFrame of counts and proportions per value
`.sps`	SPSS syntax string for this variable
`variable[i]` / `variable[s]`	Access value(s) by index or slice
`len(variable)`	Total number of responses (same as `.len`)
`for val in variable:`	Iterate over raw values

Variable DataFrame Formats

hobby = survey["hobby"]

# Compact (list column)
hobby.get_df("compact")
# shape: (3, 1)
# ┌────────────────────┐
# │ hobby              │
# │ ---                │
# │ list[str]          │
# ╞════════════════════╡
# │ ["Book", "Sport"]  │
# │ ["Movie", "Sport"] │
# │ ["Movie"]          │
# └────────────────────┘

# Wide text (split columns)
hobby.get_df("text")
# shape: (3, 3)
# ┌─────────┬─────────┬─────────┐
# │ hobby_1 ┆ hobby_2 ┆ hobby_3 │
# │ ---     ┆ ---     ┆ ---     │
# │ str     ┆ str     ┆ str     │
# ╞═════════╪═════════╪═════════╡
# │ Book    ┆ null    ┆ Sport   │
# │ null    ┆ Movie   ┆ Sport   │
# │ null    ┆ Movie   ┆ null    │
# └─────────┴─────────┴─────────┘

# Binary-encoded (split columns, 0/1)
hobby.get_df("number")
# shape: (3, 3)
# ┌─────────┬─────────┬─────────┐
# │ hobby_1 ┆ hobby_2 ┆ hobby_3 │
# │ ---     ┆ ---     ┆ ---     │
# │ i8      ┆ i8      ┆ i8      │
# ╞═════════╪═════════╪═════════╡
# │ 1       ┆ 0       ┆ 1       │
# │ 0       ┆ 1       ┆ 1       │
# │ 0       ┆ 1       ┆ 0       │
# └─────────┴─────────┴─────────┘

Updating Variables

hobby.value_indices = {"Sport": 1, "Book": 2, "Movie": 3}
hobby.label = "Please tell us your hobbies."
print(hobby)
# Variable(id=hobby, base=3, label=Please tell us your hobbies., value_indices={'Sport': 1, 'Book': 2, 'Movie': 3})

# Remap values — works for both SELECT and MULTISELECT
hobby.replace({"Book": "Reading"})
print(hobby)
# Variable(id=hobby, base=3, label=Please tell us your hobbies., value_indices={'Movie': 1, 'Reading': 2, 'Sport': 3})

Note: The value_indices setter validates that your mapping covers every value present in the data. If any value is missing, it raises a DataStructureError.

Analyze

Frequency Table

print(survey["gender"].frequencies)
# ┌────────┬───────┬────────────┐
# │ gender ┆ count ┆ proportion │
# │ ---    ┆ ---   ┆ ---        │
# │ str    ┆ u32   ┆ f64        │
# ╞════════╪═══════╪════════════╡
# │ Female ┆ 1     ┆ 0.333333   │
# │ Male   ┆ 2     ┆ 0.666667   │
# └────────┴───────┴────────────┘

Cross-tabulation

The survy.crosstab() function supports count, percent, and numeric aggregations, with optional significance testing and filtering.

Signature:

survy.crosstab(
    column,           # Column variable — the grouping dimension (e.g., gender)
    row,              # Row variable — the analyzed dimension (e.g., hobby)
    filter=None,      # Optional variable to segment into multiple tables
    aggfunc="count",  # "count", "percent", "mean", "median", or "sum"
    alpha=0.05,       # Significance level for statistical tests
)
# Returns: dict[str, polars.DataFrame]
# Key is "Total" when no filter, or each filter-value when filter is provided

Count:

print(survy.crosstab(survey["gender"], survey["hobby"], aggfunc="count"))
# {'Total': shape: (3, 3)
# ┌───────┬──────────┬────────────┐
# │ hobby ┆ Male (A) ┆ Female (B) │
# │ ---   ┆ ---      ┆ ---        │
# │ str   ┆ str      ┆ str        │
# ╞═══════╪══════════╪════════════╡
# │ Book  ┆ 1        ┆ 0          │
# │ Movie ┆ 1        ┆ 1          │
# │ Sport ┆ 1        ┆ 1          │
# └───────┴──────────┴────────────┘}

Percent:

print(survy.crosstab(survey["gender"], survey["hobby"], aggfunc="percent"))
# {'Total': shape: (3, 3)
# ┌───────┬──────────┬────────────┐
# │ hobby ┆ Male (A) ┆ Female (B) │
# │ ---   ┆ ---      ┆ ---        │
# │ str   ┆ str      ┆ str        │
# ╞═══════╪══════════╪════════════╡
# │ Book  ┆ 0.5      ┆ 0.0        │
# │ Movie ┆ 0.5      ┆ 1.0        │
# │ Sport ┆ 0.5      ┆ 1.0        │
# └───────┴──────────┴────────────┘}

Mean (numeric variable):

print(survy.crosstab(survey["gender"], survey["yob"], aggfunc="mean"))
# {'Total': shape: (1, 3)
# ┌─────┬─────────┬─────────┐
# │ yob ┆ Female  ┆ Male    │
# │ --- ┆ ---     ┆ ---     │
# │ str ┆ str     ┆ str     │
# ╞═════╪═════════╪═════════╡
# │ yob ┆ 1999.0  ┆ 1999.0  │
# └─────┴─────────┴─────────┘}

With filter variable (produces one table per filter category):

print(
    survy.crosstab(
        survey["gender"],
        survey["hobby"],
        filter=survey["animal"],
        aggfunc="count",
    )
)
# {'Cat': shape: (3, 2)
# ┌───────┬──────────┐
# │ hobby ┆ Male (A) │
# ...
# 'Dog': shape: (3, 3)
# ┌───────┬──────────┬────────────┐
# │ hobby ┆ Male (A) ┆ Female (B) │
# ...
# └───────┴──────────┴────────────┘}

Significance testing uses a two-proportion z-test for "count"/"percent" and Welch's t-test for numeric aggregations. Significant differences are indicated by column letter labels (e.g. "A", "B").

Export

All export methods take a directory path (not a file path) and an optional name parameter for the base filename. Do not pass a full file path like "output/results.csv" — pass the directory and use name=.

CSV / Excel

Writes three files:

{name}_data.csv — survey responses (format depends on the compact parameter)
{name}_variables_info.csv — variable metadata: id, vtype (SINGLE / MULTISELECT / NUMBER), label
{name}_values_info.csv — value-to-index mappings: id, text, index

The compact parameter (default False) controls how multiselect columns appear in the data file. When False, multiselect variables are expanded to wide columns (hobby_1, hobby_2, ...). When True, values are joined into a single cell ("Book;Sport").

# Default (compact=False) — multiselect expanded to wide columns
survey.to_csv("output/", name="results")

# Compact mode — multiselect joined into single cells
survey.to_csv("output/", name="results", compact=True, compact_separator=";")

# Excel — identical API, writes .xlsx files instead
survey.to_excel("output/", name="results")

SPSS

Writes {name}.sav (data) and {name}.sps (syntax). Requires pyreadstat.

survey.to_spss("output/", name="results")

# You can also get the SPSS syntax string directly
print(survey.sps)
# VARIABLE LABELS gender 'gender'.
# VALUE LABELS gender 1 'Female'
# 2 'Male'.
# VARIABLE LEVEL gender (NOMINAL).
# ...

Database

Writes four tables to a relational database using an SQLAlchemy-compatible connection string:

Table	Contents
`fact_response`	Unpivoted responses: `id_variable`, `variable_id` (root question ID), `response`
`dim_respondent`	Respondent-level attributes (selected columns)
`dim_variable`	Variable metadata: `id`, `label`, `base`
`dim_option`	Option metadata: `id`, `variable_id`, `label`, `index`

survey.to_database(
    id_variable="respondent_id",          # column to use as respondent key
    dim_respondent_variables=["respondent_id", "gender", "yob"],  # columns for dim_respondent
    connection="sqlite:///results.db",    # SQLAlchemy connection string
    if_table_exists="replace",            # "replace", "append", or "fail"
)

JSON

Writes {name}.json in the same structure that read_json expects. The output includes an extra "vtype" field per variable that read_json ignores on re-read (the type is re-inferred from the data).

survey.to_json("output/", name="results")

🤖 Agent Skills

Structured agent skills for survy live in a separate repository: survy-agent-skills.

These are reference documents designed for LLM-based coding assistants like Claude, Copilot, and similar tools. When an AI agent reads them, it can generate correct survy code without hallucinating parameters, confusing compact and wide formats, or inventing methods that don't exist.

See the survy-agent-skills README for installation instructions.

🧠 Design Philosophy

Keep survey logic explicit — variables, labels, and value mappings are first-class objects
Treat multiselect questions as a native data type, not a post-processing concern
Provide a clean abstraction over high-performance data processing (powered by Polars)

🤝 Contributing

Contributions are welcome! Feel free to open issues or submit pull requests on GitHub.

📄 License

MIT License — see LICENSE for details.

🔗 References

Powered by Polars for fast data processing

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assets		assets
src/survy		src/survy
tests		tests
.gitignore		.gitignore
.markdownlint.json		.markdownlint.json
.mise.toml		.mise.toml
.python-version		.python-version
CHANGELOG.md		CHANGELOG.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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survy

📋 Table of Contents

📦 Why survy?

✨ Features

🚀 Installation

⚡ Quick Demo

📥 Usage

Understanding Multiselect Formats

Load Data

CSV / Excel

JSON

Polars DataFrame

Work with Survey

Methods & Properties

DataFrame Output Formats

Updating Survey Metadata

Adding, Dropping, Sorting, and Filtering

Work with Variables

Methods & Properties

Variable DataFrame Formats

Updating Variables

Analyze

Frequency Table

Cross-tabulation

Export

CSV / Excel

SPSS

Database

JSON

🤖 Agent Skills

🧠 Design Philosophy

🤝 Contributing

📄 License

🔗 References

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