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# Getting started

To quickly try pandas in your browser without installing anything, you can use the Try pandas online page.

## Installation

Working with conda?

pandas can be installed via conda from conda-forge.

Prefer pip?

pandas can be installed via pip from PyPI.

In-depth instructions?

Installing a specific version? Installing from source? Check the advanced installation page.

## Intro to pandas

What kind of data does pandas handle?

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`.

To introduction tutorial

To user guide

How do I read and write tabular data?

Straight to tutorial…

pandas supports the integration with many file formats or data sources out of the box (csv, excel, sql, json, parquet,…). The ability to import data from each of these data sources is provided by functions with the prefix, `read_*`. Similarly, the `to_*` methods are used to store data.

To introduction tutorial

To user guide

How do I select a subset of a table?

Straight to tutorial…

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

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To user guide

How to create plots in pandas?

Straight to tutorial…

pandas provides plotting for your data right out of the box with the power of Matplotlib. Simply pick the plot type (scatter, bar, boxplot,…) corresponding to your data.

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To user guide

How to create new columns derived from existing columns?

Straight to tutorial…

There’s no need to loop over all rows of your data table to do calculations. Column data manipulations work elementwise in pandas. Adding a column to a `DataFrame` based on existing data in other columns is straightforward.

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To user guide

How to calculate summary statistics?

Straight to tutorial…

Basic statistics (mean, median, min, max, counts…) are easily calculable across data frames. These, or even 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.

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To user guide

How to reshape the layout of tables?

Straight to tutorial…

Change the structure of your data table in a variety of ways. You can use `melt()` to reshape your data from a wide format to a long and tidy one. Use `pivot()`

  to go from long to wide format. With aggregations built-in, a pivot table can be created with a single command.

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To user guide

How to combine data from multiple tables?

Straight to tutorial…

Multiple tables can be concatenated column wise or row wise with pandas’ database-like join and merge operations.

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To user guide

How to handle time series data?

Straight to tutorial…

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

To introduction tutorial

To user guide

How to manipulate textual data?

Straight to tutorial…

Data sets often contain more than just numerical data. pandas provides a wide range of functions to clean textual data and extract useful information from it.

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To user guide

## Coming from…

Are you familiar with other software for manipulating tabular data? Learn the pandas-equivalent operations compared to software you already know:

The R programming language provides a `data.frame` data structure as well as packages like tidyverse which use and extend `data.frame` for convenient data handling functionalities similar to pandas.

Already familiar with `SELECT`, `GROUP BY`, `JOIN`, etc.? Many SQL manipulations have equivalents in pandas.

The `data set` included in the STATA statistical software suite corresponds to the pandas `DataFrame`. Many of the operations known from STATA have an equivalent in pandas.

Users of Excel or other spreadsheet programs will find that many of the concepts are transferable to pandas.

SAS, the statistical software suite, uses the `data set` structure, which closely corresponds pandas’ `DataFrame`. Also SAS vectorized operations such as filtering or string processing operations have similar functions in pandas.

## Tutorials

For a quick overview of pandas functionality, see 10 Minutes to pandas.

You can also reference the pandas cheat sheet for a succinct guide for manipulating data with pandas.

The community produces a wide variety of tutorials available online. Some of the material is enlisted in the community contributed Tutorials and Books.