# iamseancheney/python_for_data_analysis_2nd_chinese_version

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8,937 stars · 2,745 forks

## Links

- GitHub: https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version
- awesome-repositories: https://awesome-repositories.com/repository/iamseancheney-python-for-data-analysis-2nd-chinese-version.md

## Topics

`matplotlib` `numpy` `pandas`

## Description

This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data.

The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution plots, and faceted grids using Matplotlib.

The material covers a broad range of capabilities, including numerical computing, tabular data manipulation, and time series analysis. It also addresses data cleaning, statistical modeling, machine learning application, and the use of interactive computing workflows within Jupyter notebooks.

The content is presented as a series of interactive computing examples and educational guides designed to demonstrate practical implementations of data science workflows.

## Tags

### Part of an Awesome List

- [Data Manipulation](https://awesome-repositories.com/f/awesome-lists/data/data-manipulation.md) — Provides comprehensive tools for cleaning, reshaping, and aggregating tabular data using labeled axes. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC01%E7%AB%A0%20%E5%87%86%E5%A4%87%E5%B7%A5%E4%BD%9C.md))
- [XML Parsing](https://awesome-repositories.com/f/awesome-lists/data/html-and-xml-parsing/xml-parsing.md) — Extracts structured data from XML files by navigating tree nodes and converting elements into tables. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC06%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E3%80%81%E5%AD%98%E5%82%A8%E4%B8%8E%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F.md))
- [Tabular File Imports](https://awesome-repositories.com/f/awesome-lists/devtools/file-upload/tabular-file-imports.md) — Imports structured tabular data from CSV, JSON, or text files into programmatic data structures. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC14%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E6%A1%88%E4%BE%8B.md))

### Education & Learning Resources

- [Python Data Analysis Tutorials](https://awesome-repositories.com/f/education-learning-resources/python-data-analysis-tutorials.md) — Provides educational guides and code examples for performing data manipulation and statistical analysis using Python.
- [Data Analysis Guides](https://awesome-repositories.com/f/education-learning-resources/python-programming-guides/data-analysis-guides.md) — Provides comprehensive educational guides and code examples for manipulating and processing structured datasets using Python.
- [Rolling Statistics](https://awesome-repositories.com/f/education-learning-resources/sliding-window-algorithms/rolling-statistics.md) — Provides techniques for applying functions across moving data windows to calculate rolling statistics and trends.
- [Tabular Data Analysis](https://awesome-repositories.com/f/education-learning-resources/tabular-data-analysis.md) — Provides educational methods for cleaning, reshaping, and aggregating structured tabular data.
- [Data Visualization Tutorials](https://awesome-repositories.com/f/education-learning-resources/data-visualization-tutorials.md) — Offers educational tutorials for creating publication-quality charts and faceted grids using Matplotlib.
- [Jupyter Notebook Curricula](https://awesome-repositories.com/f/education-learning-resources/jupyter-notebook-curricula.md) — Provides structured learning materials and code examples delivered via Jupyter notebooks for iterative data exploration.

### Data & Databases

- [Custom Column Functions](https://awesome-repositories.com/f/data-databases/custom-column-functions.md) — Provides examples of executing custom functions across every row or column of a tabular dataset. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC05%E7%AB%A0%20pandas%E5%85%A5%E9%97%A8.md))
- [Cross-Tabulations](https://awesome-repositories.com/f/data-databases/data-compression-algorithms/exploratory-frequency-tables/cross-tabulations.md) — Creates specialized pivot tables that count combinations of categorical variables for frequency analysis. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC10%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%81%9A%E5%90%88%E4%B8%8E%E5%88%86%E7%BB%84%E8%BF%90%E7%AE%97.md))
- [Dataset Pivoting](https://awesome-repositories.com/f/data-databases/dataset-pivoting.md) — Provides tools for pivoting datasets to transform long-form data into wide-format for categorical comparison. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC14%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E6%A1%88%E4%BE%8B.md))
- [Group-By Aggregations](https://awesome-repositories.com/f/data-databases/group-by-aggregations.md) — Provides comprehensive examples of organizing datasets by categories to calculate summaries like totals, counts, and averages. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC10%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%81%9A%E5%90%88%E4%B8%8E%E5%88%86%E7%BB%84%E8%BF%90%E7%AE%97.md))
- [Grouping Key Specifications](https://awesome-repositories.com/f/data-databases/grouped-aggregations/grouping-key-specifications.md) — Teaches how to split data into groups using various key types to prepare for aggregate operations. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC10%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%81%9A%E5%90%88%E4%B8%8E%E5%88%86%E7%BB%84%E8%BF%90%E7%AE%97.md))
- [Grouped Function Application](https://awesome-repositories.com/f/data-databases/grouped-function-application.md) — Implements the split-apply-combine pattern by executing user-defined functions on independently grouped data subsets. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC10%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%81%9A%E5%90%88%E4%B8%8E%E5%88%86%E7%BB%84%E8%BF%90%E7%AE%97.md))
- [Grouped Transformations](https://awesome-repositories.com/f/data-databases/grouped-transformations.md) — Demonstrates transformations on grouped data that preserve the original indexing and shape of the dataset. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC12%E7%AB%A0%20pandas%E9%AB%98%E7%BA%A7%E5%BA%94%E7%94%A8.md))
- [Interactive Data Science](https://awesome-repositories.com/f/data-databases/interactive-data-science.md) — Facilitates an interactive data science workflow using executable documents for iterative analysis and debugging.
- [Label-Based Data Selection](https://awesome-repositories.com/f/data-databases/label-based-data-selection.md) — Explains how to use explicit axis labels to match and align data points across different tabular objects.
- [Labeled Series Data Structures](https://awesome-repositories.com/f/data-databases/labeled-series-data-structures.md) — Builds one-dimensional labeled arrays that associate data points with custom index labels. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC05%E7%AB%A0%20pandas%E5%85%A5%E9%97%A8.md))
- [Long-to-Wide Reshaping](https://awesome-repositories.com/f/data-databases/long-to-wide-reshaping.md) — Implements reshaping operations that convert datasets from a long format to a wide matrix-like structure. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC08%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%A7%84%E6%95%B4%EF%BC%9A%E8%81%9A%E5%90%88%E3%80%81%E5%90%88%E5%B9%B6%E5%92%8C%E9%87%8D%E5%A1%91.md))
- [Pivot Table Aggregators](https://awesome-repositories.com/f/data-databases/table-data-processing/pivot-table-aggregators.md) — Generates pivot tables by aggregating data across multiple keys into a rectangular summary grid. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC10%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%81%9A%E5%90%88%E4%B8%8E%E5%88%86%E7%BB%84%E8%BF%90%E7%AE%97.md))
- [Tabular Data Manipulation Guides](https://awesome-repositories.com/f/data-databases/tabular-data-manipulation-guides.md) — Offers a detailed guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas.
- [Tabular Data Sorting](https://awesome-repositories.com/f/data-databases/tabular-data-sorting.md) — Enables ordering of tabular data by index labels or values and assigns numerical ranks. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC05%E7%AB%A0%20pandas%E5%85%A5%E9%97%A8.md))
- [Tabular DataFrames](https://awesome-repositories.com/f/data-databases/tabular-dataframes.md) — Constructs two-dimensional labeled table structures with ordered columns sharing a common index. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC05%E7%AB%A0%20pandas%E5%85%A5%E9%97%A8.md))
- [Time Series Analysis](https://awesome-repositories.com/f/data-databases/time-series-analysis.md) — Implements analytical methods for processing date-based indexing, resampling, and temporal trend analysis. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC14%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E6%A1%88%E4%BE%8B.md))
- [Unique Value Counting](https://awesome-repositories.com/f/data-databases/unique-value-counting.md) — Identifies unique elements in a series and computes the frequency of each distinct value. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC05%E7%AB%A0%20pandas%E5%85%A5%E9%97%A8.md))
- [Wide-to-Long Reshaping](https://awesome-repositories.com/f/data-databases/wide-to-long-reshaping.md) — Provides functionality to melt wide-format data into a long format by merging multiple columns. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC08%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%A7%84%E6%95%B4%EF%BC%9A%E8%81%9A%E5%90%88%E3%80%81%E5%90%88%E5%B9%B6%E5%92%8C%E9%87%8D%E5%A1%91.md))
- [Advanced Array Indexing](https://awesome-repositories.com/f/data-databases/advanced-array-indexing.md) — Provides advanced array indexing to retrieve specific data subsets using integer arrays to specify order. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC04%E7%AB%A0%20NumPy%E5%9F%BA%E7%A1%80%EF%BC%9A%E6%95%B0%E7%BB%84%E5%92%8C%E7%9F%A2%E9%87%8F%E8%AE%A1%E7%AE%97.md))
- [Axis Item Management](https://awesome-repositories.com/f/data-databases/axis-item-management.md) — Removes specified labels, rows, or columns from labeled data structures to return a new object. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC05%E7%AB%A0%20pandas%E5%85%A5%E9%97%A8.md))
- [Binary Data Formats](https://awesome-repositories.com/f/data-databases/binary-data-formats.md) — Persists data using compact binary serialization formats to facilitate high-performance read and write access. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC06%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E3%80%81%E5%AD%98%E5%82%A8%E4%B8%8E%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F.md))
- [Boolean Data Filtering](https://awesome-repositories.com/f/data-databases/boolean-data-filtering.md) — Provides capabilities to isolate and filter data points using logical conditions and boolean masks. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC04%E7%AB%A0%20NumPy%E5%9F%BA%E7%A1%80%EF%BC%9A%E6%95%B0%E7%BB%84%E5%92%8C%E7%9F%A2%E9%87%8F%E8%AE%A1%E7%AE%97.md))
- [Categorical Data Optimization](https://awesome-repositories.com/f/data-databases/categorical-data-optimization.md) — Implements memory-efficient representations for categorical data to optimize performance during grouping operations. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC12%E7%AB%A0%20pandas%E9%AB%98%E7%BA%A7%E5%BA%94%E7%94%A8.md))
- [Categorical Encodings](https://awesome-repositories.com/f/data-databases/categorical-encodings.md) — Converts non-numeric categorical labels into indicator columns for use in mathematical and statistical modeling. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC13%E7%AB%A0%20Python%E5%BB%BA%E6%A8%A1%E5%BA%93%E4%BB%8B%E7%BB%8D.md))
- [Column Value Replacements](https://awesome-repositories.com/f/data-databases/column-value-replacements.md) — Swaps specified values across a dataset to standardize markers and labels. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC07%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E6%B8%85%E6%B4%97%E5%92%8C%E5%87%86%E5%A4%87.md))
- [Bar Charts](https://awesome-repositories.com/f/data-databases/data-analysis-visualization/analytical-platforms-engines/data-analysis-tools/bar-charts.md) — Generates vertical and horizontal bar graphs, including stacked variants for comparing categories. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC09%E7%AB%A0%20%E7%BB%98%E5%9B%BE%E5%92%8C%E5%8F%AF%E8%A7%86%E5%8C%96.md))
- [Data Concatenations](https://awesome-repositories.com/f/data-databases/data-concatenations.md) — Supports stacking multiple data objects together along specified axes for structural data combination. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC08%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%A7%84%E6%95%B4%EF%BC%9A%E8%81%9A%E5%90%88%E3%80%81%E5%90%88%E5%B9%B6%E5%92%8C%E9%87%8D%E5%A1%91.md))
- [HDF5](https://awesome-repositories.com/f/data-databases/data-exporters/hdf5.md) — Retrieves specific subsets of large-scale scientific arrays from HDF5 files using conditional query syntax. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC06%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E3%80%81%E5%AD%98%E5%82%A8%E4%B8%8E%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F.md))
- [Modeling Data Converters](https://awesome-repositories.com/f/data-databases/data-format-converters/analytical-data-converters/modeling-data-converters.md) — Transforms structured tabular data into arrays or design matrices compatible with machine learning libraries. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC13%E7%AB%A0%20Python%E5%BB%BA%E6%A8%A1%E5%BA%93%E4%BB%8B%E7%BB%8D.md))
- [Tabular Data Exports](https://awesome-repositories.com/f/data-databases/data-import-and-export/tabular-data-exports.md) — Writes tabular data to delimited text files with customizable separators and missing value handling. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC06%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E3%80%81%E5%AD%98%E5%82%A8%E4%B8%8E%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F.md))
- [Data Layout Transposition](https://awesome-repositories.com/f/data-databases/data-layout-transposition.md) — Demonstrates how to rotate data by converting columns into rows or vice versa to change the table layout. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC08%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%A7%84%E6%95%B4%EF%BC%9A%E8%81%9A%E5%90%88%E3%80%81%E5%90%88%E5%B9%B6%E5%92%8C%E9%87%8D%E5%A1%91.md))
- [Transformation Chains](https://awesome-repositories.com/f/data-databases/data-transformation-functions/transformation-chains.md) — Shows how to sequence multiple data operations into a single pipeline to eliminate temporary intermediate variables.
- [Scatter Charts](https://awesome-repositories.com/f/data-databases/data-visualization-charts/scatter-charts.md) — Generates scatter plots and regression lines to analyze correlations between multiple variables. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC09%E7%AB%A0%20%E7%BB%98%E5%9B%BE%E5%92%8C%E5%8F%AF%E8%A7%86%E5%8C%96.md))
- [Dataset Joins](https://awesome-repositories.com/f/data-databases/dataset-joins.md) — Combines rows from different dataframes based on common identifiers using relational-style join types. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC08%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%A7%84%E6%95%B4%EF%BC%9A%E8%81%9A%E5%90%88%E3%80%81%E5%90%88%E5%B9%B6%E5%92%8C%E9%87%8D%E5%A1%91.md))
- [Date and Time Libraries](https://awesome-repositories.com/f/data-databases/date-and-time-libraries.md) — Manages date, time, and duration objects and converts them between strings and structured formats. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC02%E7%AB%A0%20Python%E8%AF%AD%E6%B3%95%E5%9F%BA%E7%A1%80%EF%BC%8CIPython%E5%92%8CJupyter%20Notebooks.md))
- [Time Period Managements](https://awesome-repositories.com/f/data-databases/date-and-time-libraries/time-period-managements.md) — Manages fixed time intervals such as months or quarters and handles frequency conversions. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC11%E7%AB%A0%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97.md))
- [Delimited Data Parsers](https://awesome-repositories.com/f/data-databases/delimited-data-parsers.md) — Parses delimited text files into data structures using custom delimiters and automatic type inference. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC06%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E3%80%81%E5%AD%98%E5%82%A8%E4%B8%8E%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F.md))
- [Excel Automation](https://awesome-repositories.com/f/data-databases/excel-automation.md) — Loads tabular data from XLS and XLSX files, allowing for the selection of specific sheets. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC06%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E3%80%81%E5%AD%98%E5%82%A8%E4%B8%8E%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F.md))
- [Excel File Parsers](https://awesome-repositories.com/f/data-databases/excel-file-parsers.md) — Exports tabular data to Excel workbooks with support for specifying sheet names and managing file writers. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC06%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E3%80%81%E5%AD%98%E5%82%A8%E4%B8%8E%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F.md))
- [Facet Grids](https://awesome-repositories.com/f/data-databases/facet-grids.md) — Demonstrates how to split visualizations into grids of subplots based on categorical variables to compare data dimensions.
- [Hierarchical Index Creation](https://awesome-repositories.com/f/data-databases/hierarchical-index-creation.md) — Constructs multi-dimensional indices to organize high-dimensional data on a single axis. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC08%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%A7%84%E6%95%B4%EF%BC%9A%E8%81%9A%E5%90%88%E3%80%81%E5%90%88%E5%B9%B6%E5%92%8C%E9%87%8D%E5%A1%91.md))
- [Index-Based Data Alignment](https://awesome-repositories.com/f/data-databases/index-based-data-alignment.md) — Synchronizes and aligns data points from different objects based on shared index labels during arithmetic operations. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC05%E7%AB%A0%20pandas%E5%85%A5%E9%97%A8.md))
- [Interactive Visualization Rendering](https://awesome-repositories.com/f/data-databases/interactive-visualization-rendering.md) — Configures interactive environments to render dynamic charts and plots directly within the session. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC02%E7%AB%A0%20Python%E8%AF%AD%E6%B3%95%E5%9F%BA%E7%A1%80%EF%BC%8CIPython%E5%92%8CJupyter%20Notebooks.md))
- [Series Value Mappings](https://awesome-repositories.com/f/data-databases/key-value-stores/structured-object-mappings/object-linked-key-value-mappings/map-value-transformers/series-value-mappings.md) — Applies functions or dictionaries to transform individual elements within a data series. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC07%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E6%B8%85%E6%B4%97%E5%92%8C%E5%87%86%E5%A4%87.md))
- [Memory-Mapped File Access](https://awesome-repositories.com/f/data-databases/large-scale-dataset-management/memory-mapped-file-access.md) — Processes massive binary files on disk as in-memory arrays to avoid loading entire datasets into RAM. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E9%99%84%E5%BD%95A%20NumPy%E9%AB%98%E7%BA%A7%E5%BA%94%E7%94%A8.md))
- [Missing Data Removal](https://awesome-repositories.com/f/data-databases/missing-data-removal.md) — Filters out rows or columns containing null values based on non-null entry thresholds. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC07%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E6%B8%85%E6%B4%97%E5%92%8C%E5%87%86%E5%A4%87.md))
- [Missing Value Imputation](https://awesome-repositories.com/f/data-databases/missing-value-imputation.md) — Replaces null values with constants, column-specific dictionaries, or calculated statistics. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC07%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E6%B8%85%E6%B4%97%E5%92%8C%E5%87%86%E5%A4%87.md))
- [Grouped Imputation](https://awesome-repositories.com/f/data-databases/missing-value-imputation/grouped-imputation.md) — Fills missing values using statistics derived from specific data groups for more accurate imputation. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC10%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%81%9A%E5%90%88%E4%B8%8E%E5%88%86%E7%BB%84%E8%BF%90%E7%AE%97.md))
- [Numerical Array Persistence](https://awesome-repositories.com/f/data-databases/multi-format-data-persistence/numerical-array-persistence.md) — Enables saving and loading multidimensional numerical arrays to disk in raw binary formats with compression support. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC04%E7%AB%A0%20NumPy%E5%9F%BA%E7%A1%80%EF%BC%9A%E6%95%B0%E7%BB%84%E5%92%8C%E7%9F%A2%E9%87%8F%E8%AE%A1%E7%AE%97.md))
- [Outlier Filtering](https://awesome-repositories.com/f/data-databases/outlier-filtering.md) — Identifies values exceeding specific thresholds and caps them to reduce the impact of anomalies. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC07%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E6%B8%85%E6%B4%97%E5%92%8C%E5%87%86%E5%A4%87.md))
- [Sliding Window Reductions](https://awesome-repositories.com/f/data-databases/sliding-window-reductions.md) — Provides capabilities for calculating rolling statistics and trends using sliding window reductions on tabular data. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC11%E7%AB%A0%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97.md))
- [SQL to Pandas Ingestion](https://awesome-repositories.com/f/data-databases/sql-to-pandas-ingestion.md) — Executes SQL queries against relational databases and loads the results directly into Pandas DataFrames. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC06%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E3%80%81%E5%AD%98%E5%82%A8%E4%B8%8E%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F.md))
- [Text File Chunking](https://awesome-repositories.com/f/data-databases/storage-abstraction/file-storage-services/chunked-file-storages/text-file-chunking.md) — Reads massive text files in small chunks to avoid memory exhaustion during data import. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC06%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E3%80%81%E5%AD%98%E5%82%A8%E4%B8%8E%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F.md))
- [Subplot Layouts](https://awesome-repositories.com/f/data-databases/subplot-layouts.md) — Organizes multiple individual plots within a single figure layout for comparative analysis. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC09%E7%AB%A0%20%E7%BB%98%E5%9B%BE%E5%92%8C%E5%8F%AF%E8%A7%86%E5%8C%96.md))
- [Time Series Shifting](https://awesome-repositories.com/f/data-databases/time-series-manipulation/time-series-updates/time-series-shifting.md) — Moves data points forward or backward along the time axis while adjusting timestamps. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC11%E7%AB%A0%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97.md))
- [Time Series Resampling](https://awesome-repositories.com/f/data-databases/time-series-resampling.md) — Transforms time series data from one frequency to another via upsampling and downsampling. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC11%E7%AB%A0%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97.md))
- [Time Series Slicing](https://awesome-repositories.com/f/data-databases/time-series-slicing.md) — Extracts specific subsequences from temporal datasets using dates, years, and custom time ranges. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC11%E7%AB%A0%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97.md))
- [Time Zone Conversions](https://awesome-repositories.com/f/data-databases/time-zone-management/time-zone-conversions.md) — Transforms naive timestamps into zone-aware data and converts them between different geographic regions. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC11%E7%AB%A0%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97.md))

### Development Tools & Productivity

- [Notebook Execution Environments](https://awesome-repositories.com/f/development-tools-productivity/code-execution-environments/notebook-execution-environments.md) — Guides the use of notebook environments to combine executable code, rich text, and visualizations in an iterative workflow.
- [Computational Notebooks](https://awesome-repositories.com/f/development-tools-productivity/computational-notebooks.md) — Implements an execute-explore workflow using computational notebooks to test and debug code incrementally. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC01%E7%AB%A0%20%E5%87%86%E5%A4%87%E5%B7%A5%E4%BD%9C.md))
- [Date String Parsers](https://awesome-repositories.com/f/development-tools-productivity/date-and-time-utilities/relative-date-formatting/date-string-parsers.md) — Provides utilities for converting human-readable date strings into native date objects using automatic or pattern-based parsing. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC11%E7%AB%A0%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97.md))
- [Interactive Execution Environments](https://awesome-repositories.com/f/development-tools-productivity/interactive-execution-interfaces/interactive-execution-environments.md) — Demonstrates how to run statements incrementally in shell and notebook environments for immediate output. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC02%E7%AB%A0%20Python%E8%AF%AD%E6%B3%95%E5%9F%BA%E7%A1%80%EF%BC%8CIPython%E5%92%8CJupyter%20Notebooks.md))
- [Interactive Notebooks](https://awesome-repositories.com/f/development-tools-productivity/interactive-notebooks.md) — Creates shareable documents that combine executable code, rich text, and visualizations in notebook format. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC02%E7%AB%A0%20Python%E8%AF%AD%E6%B3%95%E5%9F%BA%E7%A1%80%EF%BC%8CIPython%E5%92%8CJupyter%20Notebooks.md))

### Scientific & Mathematical Computing

- [Array Broadcasting](https://awesome-repositories.com/f/scientific-mathematical-computing/array-broadcasting.md) — Provides comprehensive guides on expanding smaller arrays to match larger ones for efficient element-wise mathematical operations.
- [Correlation Coefficient Calculators](https://awesome-repositories.com/f/scientific-mathematical-computing/correlation-coefficient-calculators.md) — Calculates statistical correlation coefficients and covariance matrices between different data series. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC05%E7%AB%A0%20pandas%E5%85%A5%E9%97%A8.md))
- [Data Visualizations](https://awesome-repositories.com/f/scientific-mathematical-computing/data-visualizations.md) — Produces publication-quality charts and distribution plots to explore relationships and trends within datasets.
- [Descriptive Statistics Summaries](https://awesome-repositories.com/f/scientific-mathematical-computing/descriptive-statistics-summaries.md) — Computes descriptive statistics such as means and sums to provide a numerical overview of datasets. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC05%E7%AB%A0%20pandas%E5%85%A5%E9%97%A8.md))
- [High-Performance Scientific Computing](https://awesome-repositories.com/f/scientific-mathematical-computing/high-performance-execution-environments/scientific-computing-platforms/high-performance-scientific-computing.md) — Implements high-performance numerical computation using multi-dimensional arrays and optimized primitives. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC01%E7%AB%A0%20%E5%87%86%E5%A4%87%E5%B7%A5%E4%BD%9C.md))
- [Vectorized Array Operations](https://awesome-repositories.com/f/scientific-mathematical-computing/high-performance-execution-environments/scientific-computing-platforms/scientific-computing/vectorized-array-operations.md) — Teaches high-performance element-wise mathematical operations across multi-dimensional arrays to eliminate explicit Python loops.
- [Functional Vectorization](https://awesome-repositories.com/f/scientific-mathematical-computing/high-performance-execution-environments/scientific-computing-platforms/scientific-computing/vectorized-array-operations/vectorized-memory-primitives/functional-vectorization.md) — Provides mechanisms to transform standard functions into vectorized operations for array processing. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E9%99%84%E5%BD%95A%20NumPy%E9%AB%98%E7%BA%A7%E5%BA%94%E7%94%A8.md))
- [Linear Algebra Routines](https://awesome-repositories.com/f/scientific-mathematical-computing/linear-algebra-routines.md) — Performs fundamental linear algebra operations including matrix multiplication, dot products, and decompositions. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC04%E7%AB%A0%20NumPy%E5%9F%BA%E7%A1%80%EF%BC%9A%E6%95%B0%E7%BB%84%E5%92%8C%E7%9F%A2%E9%87%8F%E8%AE%A1%E7%AE%97.md))
- [Multidimensional Array Containers](https://awesome-repositories.com/f/scientific-mathematical-computing/multidimensional-array-containers.md) — Generates n-dimensional array containers from sequences or predefined shapes for efficient storage. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC04%E7%AB%A0%20NumPy%E5%9F%BA%E7%A1%80%EF%BC%9A%E6%95%B0%E7%BB%84%E5%92%8C%E7%9F%A2%E9%87%8F%E8%AE%A1%E7%AE%97.md))
- [Numerical Array Operations](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-array-operations.md) — Performs high-performance mathematical operations and linear algebra on multi-dimensional arrays.
- [Numerical Computing References](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-computing-references.md) — Provides a comprehensive reference for high-performance vectorized operations and linear algebra using NumPy.
- [Quantile Analysis](https://awesome-repositories.com/f/scientific-mathematical-computing/research-analysis-workflows/research-and-data-analysis-tools/statistical-analysis-libraries/quantile-analysis.md) — Segments data into quantiles or bins to perform statistical summaries across specific value ranges. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC10%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E8%81%9A%E5%90%88%E4%B8%8E%E5%88%86%E7%BB%84%E8%BF%90%E7%AE%97.md))
- [Scientific Data Visualizations](https://awesome-repositories.com/f/scientific-mathematical-computing/scientific-data-visualizations.md) — Provides a comprehensive suite of tools for creating publication-quality plots for data exploration. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC01%E7%AB%A0%20%E5%87%86%E5%A4%87%E5%B7%A5%E4%BD%9C.md))
- [Universal Functions](https://awesome-repositories.com/f/scientific-mathematical-computing/universal-functions.md) — Executes high-performance, element-wise mathematical functions across arrays for scientific computing. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC04%E7%AB%A0%20NumPy%E5%9F%BA%E7%A1%80%EF%BC%9A%E6%95%B0%E7%BB%84%E5%92%8C%E7%9F%A2%E9%87%8F%E8%AE%A1%E7%AE%97.md))
- [Vectorized Operations](https://awesome-repositories.com/f/scientific-mathematical-computing/vectorized-operations.md) — Implements high-performance vectorized operations to perform mathematical reductions and accumulations without explicit loops. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E9%99%84%E5%BD%95A%20NumPy%E9%AB%98%E7%BA%A7%E5%BA%94%E7%94%A8.md))
- [Array Slicing](https://awesome-repositories.com/f/scientific-mathematical-computing/array-manipulations/array-slicing.md) — Extracts subsets of data from arrays using integer indices, slices, or recursive access. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC04%E7%AB%A0%20NumPy%E5%9F%BA%E7%A1%80%EF%BC%9A%E6%95%B0%E7%BB%84%E5%92%8C%E7%9F%A2%E9%87%8F%E8%AE%A1%E7%AE%97.md))
- [Windowed Correlations](https://awesome-repositories.com/f/scientific-mathematical-computing/correlation-coefficient-calculators/windowed-correlations.md) — Calculates rolling correlations and covariances between two different time series over a sliding window. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC11%E7%AB%A0%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97.md))
- [Data Discretization](https://awesome-repositories.com/f/scientific-mathematical-computing/data-discretization.md) — Bins continuous numeric data into discrete intervals using fixed boundaries or sample quantiles. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC07%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E6%B8%85%E6%B4%97%E5%92%8C%E5%87%86%E5%A4%87.md))
- [Numerical Binning](https://awesome-repositories.com/f/scientific-mathematical-computing/data-discretization/temporal-discretization/numerical-binning.md) — Converts continuous numerical features into discrete bins or quartiles for distribution analysis. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC14%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%88%86%E6%9E%90%E6%A1%88%E4%BE%8B.md))
- [Design Matrices](https://awesome-repositories.com/f/scientific-mathematical-computing/design-matrices.md) — Converts formula strings into design matrices to organize data for linear model analysis. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC13%E7%AB%A0%20Python%E5%BB%BA%E6%A8%A1%E5%BA%93%E4%BB%8B%E7%BB%8D.md))
- [Distribution Plots](https://awesome-repositories.com/f/scientific-mathematical-computing/distribution-plots.md) — Produces histograms and kernel density estimate plots to visualize the probability distribution of values. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC09%E7%AB%A0%20%E7%BB%98%E5%9B%BE%E5%92%8C%E5%8F%AF%E8%A7%86%E5%8C%96.md))
- [Exponentially Weighted Statistics](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-mathematical-foundations/statistics-probability/statistical-analysis-libraries/statistical-metric-calculators/exponentially-weighted-statistics.md) — Calculates moving statistics using a decay factor to prioritize recent observations over older data. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC11%E7%AB%A0%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97.md))
- [Rolling Window Functions](https://awesome-repositories.com/f/scientific-mathematical-computing/rolling-window-functions.md) — Implements user-defined reduction functions that operate over sliding windows of data. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC11%E7%AB%A0%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97.md))
- [Scientific Computing Applications](https://awesome-repositories.com/f/scientific-mathematical-computing/scientific-computing-applications.md) — Demonstrates the use of numerical integration, optimization, and sparse matrix operations for scientific problem solving. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC01%E7%AB%A0%20%E5%87%86%E5%A4%87%E5%B7%A5%E4%BD%9C.md))
- [Pandas Vectorized Operations](https://awesome-repositories.com/f/scientific-mathematical-computing/vector-operations/pandas-vectorized-operations.md) — Applies string manipulation and regular expression searches across entire series using vectorized operations. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC07%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E6%B8%85%E6%B4%97%E5%92%8C%E5%87%86%E5%A4%87.md))
- [Vectorized Conditional Logic](https://awesome-repositories.com/f/scientific-mathematical-computing/vector-operations/pandas-vectorized-operations/vectorized-conditional-logic.md) — Implements vectorized ternary expressions to select values from multiple arrays based on conditions. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC04%E7%AB%A0%20NumPy%E5%9F%BA%E7%A1%80%EF%BC%9A%E6%95%B0%E7%BB%84%E5%92%8C%E7%9F%A2%E9%87%8F%E8%AE%A1%E7%AE%97.md))

### Operating Systems & Systems Programming

- [File I/O Management](https://awesome-repositories.com/f/operating-systems-systems-programming/file-i-o-management.md) — Provides low-level primitives for reading and writing text or binary data from the operating system. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC03%E7%AB%A0%20Python%E7%9A%84%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E3%80%81%E5%87%BD%E6%95%B0%E5%92%8C%E6%96%87%E4%BB%B6.md))

### Programming Languages & Runtimes

- [Date Range Iterators](https://awesome-repositories.com/f/programming-languages-runtimes/date-range-iterators.md) — Generates programmatic sequences of timestamps based on specified start and end periods. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC11%E7%AB%A0%20%E6%97%B6%E9%97%B4%E5%BA%8F%E5%88%97.md))
- [Functional Programming Patterns](https://awesome-repositories.com/f/programming-languages-runtimes/functional-programming-patterns.md) — Applies transformation pipelines using functional programming paradigms such as lambda functions and partial application. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC03%E7%AB%A0%20Python%E7%9A%84%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E3%80%81%E5%87%BD%E6%95%B0%E5%92%8C%E6%96%87%E4%BB%B6.md))
- [JSON Parsing](https://awesome-repositories.com/f/programming-languages-runtimes/json-parsing.md) — Converts JSON strings or files into analysis-ready tabular structures by mapping nested objects. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC06%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E5%8A%A0%E8%BD%BD%E3%80%81%E5%AD%98%E5%82%A8%E4%B8%8E%E6%96%87%E4%BB%B6%E6%A0%BC%E5%BC%8F.md))
- [Parallel Sequence Iteration](https://awesome-repositories.com/f/programming-languages-runtimes/parallel-sequence-iteration.md) — Implements pairing of multiple sequences using zip and indexed iteration with enumerate for simultaneous processing. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC03%E7%AB%A0%20Python%E7%9A%84%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E3%80%81%E5%87%BD%E6%95%B0%E5%92%8C%E6%96%87%E4%BB%B6.md))
- [Sequence Data Management](https://awesome-repositories.com/f/programming-languages-runtimes/sequence-data-management.md) — Demonstrates how to organize and manipulate data using base Python sequence types like tuples, lists, and sets. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC03%E7%AB%A0%20Python%E7%9A%84%E6%95%B0%E6%8D%AE%E7%BB%93%E6%9E%84%E3%80%81%E5%87%BD%E6%95%B0%E5%92%8C%E6%96%87%E4%BB%B6.md))
- [Unicode Text Handling](https://awesome-repositories.com/f/programming-languages-runtimes/unicode-text-handling.md) — Supports encoding and decoding text between Unicode strings and byte representations to ensure consistent character handling. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC02%E7%AB%A0%20Python%E8%AF%AD%E6%B3%95%E5%9F%BA%E7%A1%80%EF%BC%8CIPython%E5%92%8CJupyter%20Notebooks.md))

### Software Engineering & Architecture

- [Tabular Duplicate Removers](https://awesome-repositories.com/f/software-engineering-architecture/in-place-array-manipulations/duplicate-removal/tabular-duplicate-removers.md) — Identifies and removes duplicate records from tabular datasets based on specified columns. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC07%E7%AB%A0%20%E6%95%B0%E6%8D%AE%E6%B8%85%E6%B4%97%E5%92%8C%E5%87%86%E5%A4%87.md))

### System Administration & Monitoring

- [Line Plots](https://awesome-repositories.com/f/system-administration-monitoring/numerical-data-visualizers/plotting-components/line-plots.md) — Creates linear visualizations by mapping data indices to the X-axis. ([source](https://github.com/iamseancheney/python_for_data_analysis_2nd_chinese_version/blob/master/%E7%AC%AC09%E7%AB%A0%20%E7%BB%98%E5%9B%BE%E5%92%8C%E5%8F%AF%E8%A7%86%E5%8C%96.md))