Data-is-Cool

A brief guide to learn data-visualisation, by learning how the functions work and intuitions in practice.

This guide uses jupyter notebooks.

Using Pandas

• Manipulating Data
  • Reading, selecting, and filtering data.
• Plotting with Pandas
  • Plotting univariate, bivariate, and multivariate graphs using pandas APIs.

Using Seaborn

• Plotting with Seaborn
  • Plotting univariate and bivariate graphs.
• Faceting with Seaborn
  • Faceting and pairplots in seaborn

Using Matplotlib

• Subplotting
  • Forming subplots in matplotlib

Using Plotnine

• Grammar of Graphics
  • Learing plotting in plotnine with grammar.

About Datasets

- `pokemon.csv`: Data about pokemons with complex (and a lot) fields/features.
- `pokemon_lite.csv`: Data about pokemons (800X13), with features that are easier to read.
- `AppleStore.csv`: Information about applications on apple store.

Note to Reader

Whilst writing jupyter notebooks, I faced several warning statuses during my calls in matplotlib, numpy, and pandas. I tried to suppress it with the function from warnings library as warnings.filterwarnings("always"); sometimes it worked, and sometimes it didn't.

These warnings are for developers, and can be safely ignored.

For Contributions

If you have any doubt regarding the code in these notebooks, feel free to raise an issue, or drop a mail at [ankit03june at gmail dot com].

Any contributions are welcome.