Before you can use pandas, you’ll need to get it installed.
Pandas is part of the Anaconda distribution and can be installed with Anaconda or Miniconda:
Pandas can be installed via pip from PyPI.
Installing a specific version? Installing from source? Check the advanced installation page.
Learn more
Straight to tutorial…
When working with tabular data, such as data stored in spreadsheets or databases, Pandas is the right tool for you. Pandas will help you to explore, clean and process your data. In Pandas, a data table is called a DataFrame.
DataFrame
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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 these data sources is provided by function with the prefix read_*. Similarly, the to_* methods are used to store data.
read_*
to_*
Selecting or filtering specific rows and/or columns? Filtering the data on a condition? Methods for slicing, selecting, and extracting the data 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 a DataFrame 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 entire data 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 can melt() your data table from wide to long/tidy form or pivot() from long to wide format. With aggregations built-in, a pivot table is created with a sinlge command.
melt()
pivot()
Multiple tables can be concatenated both column wise as row wise and 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 cleaning textual data and extract useful information from it.
Currently working with other software for data manipulation in a tabular format? You’re probably familiar to typical data operations and know what to do with your tabular data, but lacking the syntax to execute these operations. Get to know the pandas syntax by looking for equivalents from the software you already know:
The R programming language provides the data.frame data structure and multiple packages, such as tidyverse use and extend data.frames for convenient data handling functionalities similar to pandas.
data.frame
Already familiar to SELECT, GROUP BY, JOIN,...? Most of these SQL manipulations do have equivalents in pandas.
SELECT
GROUP BY
JOIN
The data set included in the STATA statistical software suite corresponds to the pandas data.frame. Many of the operations known from STATA have an equivalent in pandas.
data set
The SAS statistical software suite also provides the data set corresponding to the pandas data.frame. Also vectorized operations, filtering, string processing operations,... from SAS have similar functions in pandas.
The community produces a wide variety of tutorials available online. Some of the material is enlisted in the community contributed Tutorials.