pandas
- Category
- Frameworks and libraries
- Docs outline
- 17 sections
Official docs outline
pandas.pydata.org- 10 Minutes to pandas
Quick tour of Series, DataFrame, viewing data, selection, missing data, grouping, merging, and plotting.
- Intro to Data Structures
Series and DataFrame construction, index alignment, attributes, and dict-like behavior.
- Essential Basic Functionality
Head/tail, descriptive statistics, binary operations and broadcasting, reindexing, sorting, dtypes, and apply.
- IO Tools
read_csv, read_excel, read_json, read_parquet, read_sql, HDF5, writing data, and parsing options.
- Indexing and Selecting Data
loc, iloc, boolean indexing, query, setting values, views versus copies, and SettingWithCopy.
- MultiIndex and Advanced Indexing
Hierarchical indexes, slicing with MultiIndex, xs, reindexing, and index types.
- Working with Missing Data
NaN, NA and NaT, isna, fillna, dropna, interpolation, and nullable dtypes.
- Merge, Join, Concatenate
concat, merge, join, SQL-style joins, merge keys, validation, and comparing frames.
- Reshaping and Pivot Tables
pivot, pivot_table, melt, stack and unstack, crosstab, and wide versus long formats.
- Group By: Split-Apply-Combine
groupby, aggregation, transform, filter, apply, named aggregation, and grouping by multiple keys.
- Windowing Operations
rolling, expanding, and exponentially weighted windows, and custom window functions.
- Time Series and Date Functionality
DatetimeIndex, date_range, resampling, time zones, shifting, offsets, and periods.
- Working with Text Data
The .str accessor, string methods, regular expressions, extract, split, and string dtypes.
- Categorical Data
Categorical dtype, ordered categories, memory savings, and categorical operations.
- Nullable and Arrow Data Types
Nullable integer and boolean dtypes, pd.NA, and PyArrow-backed dtypes.
- Visualization
DataFrame.plot, plot kinds, subplots, customizing charts, and styling tables.
- Performance and Scaling
Cython, Numba, eval and query, vectorization over apply, chunking, and scaling to large datasets.
Each section links to the official page. Titles and summaries by Extra Credit; the docs belong to their authors.
