|
26 | 26 | }, |
27 | 27 | { |
28 | 28 | "cell_type": "markdown", |
29 | | - "metadata": {}, |
| 29 | + "metadata": { |
| 30 | + "jp-MarkdownHeadingCollapsed": true, |
| 31 | + "tags": [] |
| 32 | + }, |
30 | 33 | "source": [ |
31 | 34 | "# 1.3 Python\n", |
32 | | - "* One of the world’s most popular programming languages\n", |
33 | | - "* Particularly popular for **educational and scientific computing**\n", |
34 | | - "* Recently surpassed R as the **most popular data-science programming language**" |
35 | | - ] |
36 | | - }, |
37 | | - { |
38 | | - "cell_type": "markdown", |
39 | | - "metadata": {}, |
40 | | - "source": [ |
41 | 35 | "### Just a Few Reasons Why Python Is Popular\n", |
42 | | - "* Open source and free \n", |
43 | 36 | "* Massive open-source community\n", |
44 | | - "* Easy to learn\n", |
45 | | - "* Easy to read\n", |
| 37 | + "* One of the world’s most popular programming languages\n", |
| 38 | + "* Particularly popular for **educational and scientific computing**\n", |
| 39 | + "* Surpassed R as the **most popular data-science programming language**\n", |
46 | 40 | "* General purpose\n", |
47 | 41 | "* Procedural, functional-style and object-oriented paradigms\n", |
| 42 | + "* Open source and free \n", |
| 43 | + "* Easy to read/learn\n", |
48 | 44 | "* **Enhances productivity** with extensive standard and third-party open-source libraries\n", |
49 | 45 | "* Wide range of libraries for **data science** and **artificial intelligence** where Python use has exploded over the last few years \n", |
50 | 46 | "* **Extensive job market** for Python programmers across many disciplines" |
|
69 | 65 | "cell_type": "markdown", |
70 | 66 | "metadata": {}, |
71 | 67 | "source": [ |
72 | | - "| Some of the Python Standard Library modules we use in our books and videos\n", |
73 | | - "| :-------------------- \n", |
74 | | - "| **`collections`** — Additional data structures beyond lists, tuples, dictionaries and sets.\n", |
75 | | - "| **`csv`** — Processing comma-separated value files.\n", |
76 | | - "| **`datetime`**, **`time`** — Date and time manipulations. \n", |
77 | | - "| **`decimal`** — Fixed-point and floating-point arithmetic, including monetary calculations.\n", |
78 | | - "| **`doctest`** — Simple unit testing via validation tests and expected results embedded in docstrings. \n", |
79 | | - "| **`json`** — JavaScript Object Notation (JSON) processing for use with web services and NoSQL document databases.\n", |
80 | | - "| **`math`** — Common math constants and operations.\n", |
81 | | - "| **`os`** — Interacting with the operating system.\n", |
82 | | - "| **`queue`** — First-in, first-out data structure.\n", |
83 | | - "| **`random`** — Pseudorandom numbers.\n", |
84 | | - "| **`re`** — Regular expressions for pattern matching.\n", |
85 | | - "| **`sqlite3`** — SQLite relational database access.\n", |
86 | | - "| **`statistics`** — Mathematical statistics functions like `mean`, `median`, `mode` and `variance`.\n", |
87 | | - "| **`string`** — String processing.\n", |
88 | | - "| **`sys`** — Command-line argument processing; standard input, standard output and standard error streams.\n", |
89 | | - "| **`timeit`** — Performance analysis." |
| 68 | + "| Some of the Python Standard Library modules we use in our books and videos |\n", |
| 69 | + "| :-------------------- |\n", |
| 70 | + "| **`collections`** — Additional data structures beyond lists, tuples, dictionaries and sets. |\n", |
| 71 | + "| **`csv`** — Processing comma-separated value files. |\n", |
| 72 | + "| **`datetime`**, **`time`** — Date and time manipulations. |\n", |
| 73 | + "| **`decimal`** — Fixed-point and floating-point arithmetic, including monetary calculations. |\n", |
| 74 | + "| **`doctest`** — Simple unit testing via validation tests and expected results embedded in docstrings. |\n", |
| 75 | + "| **`json`** — JavaScript Object Notation (JSON) processing for use with web services and NoSQL document databases. |\n", |
| 76 | + "| **`math`** — Common math constants and operations. |\n", |
| 77 | + "| **`os`** — Interacting with the operating system. |\n", |
| 78 | + "| **`queue`** — First-in, first-out data structure. |\n", |
| 79 | + "| **`random`** — Pseudorandom numbers. |\n", |
| 80 | + "| **`re`** — Regular expressions for pattern matching. |\n", |
| 81 | + "| **`sqlite3`** — SQLite relational database access. |\n", |
| 82 | + "| **`statistics`** — Mathematical statistics functions like `mean`, `median`, `mode` and `variance`. |\n", |
| 83 | + "| **`string`** — String processing. |\n", |
| 84 | + "| **`sys`** — Command-line argument processing; standard input, standard output and standard error streams. |\n", |
| 85 | + "| **`timeit`** — Performance analysis. |" |
90 | 86 | ] |
91 | 87 | }, |
92 | 88 | { |
|
101 | 97 | "cell_type": "markdown", |
102 | 98 | "metadata": {}, |
103 | 99 | "source": [ |
104 | | - "| Popular Python libraries used in data science\n", |
105 | | - "| :----------------------\n", |
106 | | - "| **_Scientific Computing and Statistics_**\n", |
107 | | - "| **NumPy** (Numerical Python)—Python does not have a built-in array data structure. It uses lists, which are convenient but relatively slow. NumPy provides the high-performance `ndarray` data structure to represent lists and matrices, and it also provides routines for processing such data structures. \n", |
108 | | - "| **SciPy** (Scientific Python)—Built on NumPy, SciPy adds routines for scientific processing, such as integrals, differential equations, additional matrix processing and more. `scipy.org` controls SciPy and NumPy. \n", |
109 | | - "| **StatsModels**—Provides support for estimations of statistical models, statistical tests and statistical data exploration.\n", |
110 | | - "| **Data Manipulation and Analysis**\n", |
111 | | - "| **Pandas**—An extremely popular library for data manipulations. Pandas makes abundant use of NumPy’s `ndarray`. Its two key data structures are `Series` (one dimensional) and `DataFrames` (two dimensional). \n", |
112 | | - "|\n", |
113 | | - "| **_Visualization_**\n", |
114 | | - "| **Matplotlib**—A highly customizable visualization and plotting library. Supported plots include regular, scatter, bar, contour, pie, quiver, grid, polar axis, 3D and text.\n", |
115 | | - "| **Seaborn**—A higher-level visualization library built on Matplotlib. Seaborn adds a nicer look-and-feel, additional visualizations and enables you to create visualizations with less code. \n", |
116 | | - "|\n", |
117 | | - "| **_Machine Learning, Deep Learning and Reinforcement Learning_**\n", |
118 | | - "| **scikit-learn**—Top machine-learning library. Machine learning is a subset of AI. Deep learning is a subset of machine learning that focuses on neural networks. \n", |
119 | | - "| **Keras**—One of the easiest to use deep-learning libraries. Keras runs on top of TensorFlow (Google), CNTK (Microsoft’s cognitive toolkit for deep learning) or Theano (Université de Montréal).\n", |
120 | | - "| **TensorFlow**—From Google, this is the most widely used deep learning library. TensorFlow works with GPUs (graphics processing units) or Google’s custom TPUs (Tensor processing units) for performance. TensorFlow is important in AI and big data analytics—where processing demands are huge. You’ll use the version of Keras that’s built into TensorFlow.\n", |
121 | | - "| **OpenAI Gym**—A library and environment for developing, testing and comparing reinforcement-learning algorithms. \n", |
122 | | - "| \n", |
123 | | - "| **_Natural Language Processing (NLP)_**\n", |
124 | | - "| **NLTK** (Natural Language Toolkit)—Used for natural language processing (NLP) tasks.\n", |
125 | | - "| **TextBlob**—An object-oriented NLP text-processing library built on the NLTK and pattern NLP libraries. TextBlob simplifies many NLP tasks.\n", |
126 | | - "| **Gensim**—Similar to NLTK. Commonly used to build an index for a collection of documents, then determine how similar another document is to each of those in the index. " |
| 100 | + "| Popular Python libraries used in data science |\n", |
| 101 | + "| :---------------------- |\n", |
| 102 | + "| **_Scientific Computing and Statistics_** |\n", |
| 103 | + "| **NumPy** (Numerical Python)—Python does not have a built-in array data structure. It uses lists, which are convenient but relatively slow. NumPy provides the high-performance `ndarray` data structure to represent lists and matrices, and it also provides routines for processing such data structures. |\n", |
| 104 | + "| **SciPy** (Scientific Python)—Built on NumPy, SciPy adds routines for scientific processing, such as integrals, differential equations, additional matrix processing and more. `scipy.org` controls SciPy and NumPy. |\n", |
| 105 | + "| **StatsModels**—Provides support for estimations of statistical models, statistical tests and statistical data exploration. |\n", |
| 106 | + "| **Data Manipulation and Analysis** |\n", |
| 107 | + "| **Pandas**—An extremely popular library for data manipulations. Pandas makes abundant use of NumPy’s `ndarray`. Its two key data structures are `Series` (one dimensional) and `DataFrames` (two dimensional). |\n", |
| 108 | + "| |\n", |
| 109 | + "| **_Visualization_** |\n", |
| 110 | + "| **Matplotlib**—A highly customizable visualization and plotting library. Supported plots include regular, scatter, bar, contour, pie, quiver, grid, polar axis, 3D and text. |\n", |
| 111 | + "| **Seaborn**—A higher-level visualization library built on Matplotlib. Seaborn adds a nicer look-and-feel, additional visualizations and enables you to create visualizations with less code. |\n", |
| 112 | + "| |\n", |
| 113 | + "| **_Machine Learning, Deep Learning and Reinforcement Learning_** |\n", |
| 114 | + "| **scikit-learn**—Top machine-learning library. Machine learning is a subset of AI. Deep learning is a subset of machine learning that focuses on neural networks. |\n", |
| 115 | + "| **Keras**—One of the easiest to use deep-learning libraries. Keras runs on top of TensorFlow (Google). |\n", |
| 116 | + "| **TensorFlow**—From Google, this is the most widely used deep learning library. TensorFlow works with GPUs (graphics processing units) or Google’s custom TPUs (Tensor processing units) for performance. TensorFlow is important in AI and big data analytics—where processing demands are huge. You’ll use the version of Keras that’s built into TensorFlow. |\n", |
| 117 | + "| **OpenAI Gym**—A library and environment for developing, testing and comparing reinforcement-learning algorithms. |\n", |
| 118 | + "| |\n", |
| 119 | + "| **_Natural Language Processing (NLP)_** |\n", |
| 120 | + "| **NLTK** (Natural Language Toolkit)—Used for natural language processing (NLP) tasks. |\n", |
| 121 | + "| **TextBlob**—An object-oriented NLP text-processing library built on the NLTK and pattern NLP libraries. TextBlob simplifies many NLP tasks. |\n", |
| 122 | + "| **Gensim**—Similar to NLTK. Commonly used to build an index for a collection of documents, then determine how similar another document is to each of those in the index. |" |
127 | 123 | ] |
128 | 124 | }, |
129 | 125 | { |
|
198 | 194 | ], |
199 | 195 | "metadata": { |
200 | 196 | "kernelspec": { |
201 | | - "display_name": "Python 3", |
| 197 | + "display_name": "Python 3 (ipykernel)", |
202 | 198 | "language": "python", |
203 | 199 | "name": "python3" |
204 | 200 | }, |
|
212 | 208 | "name": "python", |
213 | 209 | "nbconvert_exporter": "python", |
214 | 210 | "pygments_lexer": "ipython3", |
215 | | - "version": "3.9.4" |
| 211 | + "version": "3.9.13" |
216 | 212 | } |
217 | 213 | }, |
218 | 214 | "nbformat": 4, |
|
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