pandas is part of theAnacondadistribution and can be installed with Anaconda or Miniconda:

pandas can be installed via pip fromPyPI.

Installing a specific version? Installing from source? Check the advancedinstallation page.

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When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help youto explore, clean, and process your data. In pandas, a data table is called aDataFrame.

pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). Importing data from each of thesedata sources is provided by function with the prefixread_*. Similarly, theto_* methods are used to store data.

Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting thedata you need are available in pandas.

pandas provides plotting your data out of the box, using the power of Matplotlib. You can pick the plot type (scatter, bar, boxplot,…)corresponding to your data.

There is no need to loop over all rows of your data table to do calculations. Data manipulations on a column work elementwise.Adding a column to aDataFrame based on existing data in other columns is straightforward.

Basic statistics (mean, median, min, max, counts…) are easily calculable. These or custom aggregations can be applied on the entiredata set, a sliding window of the data, or grouped by categories. The latter is also known as the split-apply-combine approach.

Change the structure of your data table in multiple ways. You canmelt() your data table from wide to long/tidy form orpivot()from long to wide format. With aggregations built-in, a pivot table is created with a single command.

Multiple tables can be concatenated both column wise and row wise as database-like join/merge operations are provided to combine multiple tables of data.

pandas has great support for time series and has an extensive set of tools for working with dates, times, and time-indexed data.

Data sets do not only contain numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it.

TheR programming language provides thedata.frame data structure and multiple packages, such astidyverse use and extenddata.framefor convenient data handling functionalities similar to pandas.

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Already familiar toSELECT,GROUPBY,JOIN, etc.?Most of these SQL manipulations do have equivalents in pandas.

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Thedataset included in theSTATAstatistical software suite corresponds to the pandasDataFrame.Many of the operations known from STATA have an equivalent in pandas.

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Users ofExcelor other spreadsheet programs will find that many of the concepts aretransferrable to pandas.

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TheSAS statistical software suitealso provides thedataset corresponding to the pandasDataFrame.Also SAS vectorized operations, filtering, string processing operations,and more have similar functions in pandas.

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