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1.1 Installation

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

conda install pandas

pandas can be installed via pip from PyPI.

pip install pandas

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1.2 Intro to pandas

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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 you to explore, clean and process your data. In pandas, a data table is called a 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.

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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.

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pandas: powerful Python data analysis toolkit, Release 1.1.5

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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.

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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.

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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.

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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 single command.

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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.

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

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