It's built-off of matplotlib, but it baked into pandas for easier usage

Imports

import numpy as np

import pandas as pd

%matplotlib inline

StyleSheets

Matplotlib has style sheets you can use to make your plots look a little nicer. These style sheets include plot_bmh,plot_fivethirtyeight,plot_ggplot and more. They basically create a set of style rules that your plots follow. I recommend using them, they make all your plots have the same look and feel more professional. You can even create your own if you want your company's plots to all have the same look (it is a bit tedious to create on though).

Call the style:

import matplotlib.pyplot as plt

Different Styles

• plt.style.use('ggplot')

• plt.style.use('bmh')

• plt.style.use('dark_background')

• plt.style.use('fivethirtyeight')

Plot Types

There are several plot types built-in to pandas, most of them statistical plots by nature:

• • df.plot.area

  • df.plot.barh

  • df.plot.density

  • df.plot.hist

  • df.plot.line

  • df.plot.scatter

  • df.plot.bar

  • df.plot.box

  • df.plot.hexbin

  • df.plot.kde

  • df.plot.pie

You can also just call df.plot(kind='hist') or replace that kind argument with any of the key terms shown in the list above (e.g. 'box','barh', etc..)

Area

df2.plot.area(alpha=0.4)

df2.plot.bar(stacked=True)

Histograms¶

df1['A'].plot.hist(bins=50)

Line Plots

df1.plot.line(x=df1.index,y='B',figsize=(12,3),lw=1)

Scatter Plots

df1.plot.scatter(x='A',y='B')

You can use c to color based off another column value Use cmap to indicate colormap to use. For all the colormaps, check out: http://matplotlib.org/users/colormaps.html

df1.plot.scatter(x='A',y='B',c='C',cmap='cool warm')

Or use s to indicate size based off another column. s parameter needs to be an array, not just the name of a column:

df1.plot.scatter(x='A',y='B',s=df1['C']*200)

BoxPlots

df2.plot.box() # Can also pass a by= argument for groupie

Hexagonal Bin Plot

Useful for Bivariate Data, alternative to scatterplot:

df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])

df.plot.hexbin(x='a',y='b',gridsize=25,cmap='Oranges')

Kernel Density Estimation plot (KDE)

df2['a'].plot.kde()

df2.plot.density()

This method of plotting will be a lot easier to use than full-on matplotlib, it balances ease of use with control over the figure. A lot of the plot calls also accept additional arguments of their parent matplotlib plt. call.