Introduction to Pandas

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# Introduction to Pandas

Inria, May 18, 2017

Joris Van den Bossche

https://jorisvandenbossche.github.io/talks/2017_INRIA_pandas

https://github.com/jorisvandenbossche/talks/

.affiliations[
  ![:scale 65%](img/logoUPSayPlusCDS_990.png)
  ![:scale 25%](img/inria-logo.png)
]

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# Why Python for data analysis?

### High level language
### General purpose
### Excellent interactive use

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### + a rich ecosystem of tools for scientific computing and data analysis.

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# Python's scientific ecosystem

#### ## Thanks to Jake VanderPlas for the figure

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![](img/pandas_logo.svg)

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## Pandas: data analysis in python

For data-intensive work in Python the [Pandas](http://pandas.pydata.org) library has become essential.

What is `pandas`?

* Pandas can be thought of as *NumPy arrays with labels* for rows and columns, and better support for heterogeneous data types, but it's also much, much more than that.
* Pandas can also be thought of as `R`'s `data.frame` in Python.
* Powerful for working with missing data, working with time series data, for reading and writing your data, for reshaping, grouping, merging your data, ...

It's documentation: http://pandas.pydata.org/pandas-docs/stable/

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# When do you need pandas?

When working with **tabular or structured data** (like R dataframe, SQL table, Excel spreadsheet, ...):

- Import data
- Clean up messy data
- Explore data, gain insight into data
- Process and prepare your data for analysis
- Analyse your data (together with scikit-learn, statsmodels, ...)

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# Key features

* Fast, easy and flexible input/output for a lot of different data formats
* Working with missing data (`.dropna()`, `pd.isnull()`)
* Merging and joining (`concat`, `join`)
* Grouping: `groupby` functionality
* Reshaping (`stack`, `pivot`)
* Powerful time series manipulation (resampling, timezones, ..)
* Easy plotting

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# Showcase notebook

* Interact with your data
* Import / export for variety of formats
* Easy plotting
* Set of powerful methods to maniputlate data

https://github.com/jorisvandenbossche/talks/blob/master/2017_INRIA_pandas/pandas_introduction.ipynb (solved version is available in same repo)

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# Further reading

* Pandas documentation: http://pandas.pydata.org/pandas-docs/stable/

* Books
    * "Python for Data Analysis" by Wes McKinney
    * "Python Data Science Handbook" by Jake VanderPlas

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# Further reading

* Tutorials
  * https://github.com/jorisvandenbossche/pandas-tutorial
  * https://github.com/brandon-rhodes/pycon-pandas-tutorial

* Tom Augspurger's blog
  * https://tomaugspurger.github.io/modern-1.html

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# Thanks for listening!

## Those slides:

- [jorisvandenbossche.github.io/talks/2016_INRIA_pandas](
    http://jorisvandenbossche.github.io/talks/2017_INRIA_pandas)