Data Frame Plots

documentation: http://pandas.pydata.org/pandas-docs/stable/visualization.html



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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('ggplot')

The plot method on Series and DataFrame is just a simple wrapper around plt.plot()

If the index consists of dates, it calls gcf().autofmt_xdate() to try to format the x-axis nicely as show in the plot window.



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ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
ts.plot()
plt.show()

On DataFrame, plot() is a convenience to plot all of the columns, and include a legend within the plot.



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df = pd.DataFrame(np.random.randn(1000, 4), index=pd.date_range('1/1/2016', periods=1000), columns=list('ABCD'))
df = df.cumsum()
plt.figure()
df.plot()
plt.show()

You can plot one column versus another using the x and y keywords in plot():



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df3 = pd.DataFrame(np.random.randn(1000, 2), columns=['B', 'C']).cumsum()
df3['A'] = pd.Series(list(range(len(df))))
df3.plot(x='A', y='B')
plt.show()



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df3.tail()

Plots other than line plots

Plotting methods allow for a handful of plot styles other than the default Line plot. These methods can be provided as the kind keyword argument to plot(). These include:

‘bar’ or ‘barh’ for bar plots
‘hist’ for histogram
‘box’ for boxplot
‘kde’ or 'density' for density plots
‘area’ for area plots
‘scatter’ for scatter plots
‘hexbin’ for hexagonal bin plots
‘pie’ for pie plots

For example, a bar plot can be created the following way:



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plt.figure()
df.ix[5].plot(kind='bar')
plt.axhline(0, color='k')
plt.show()



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df.ix[5]

stack bar chart



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df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
df2.plot.bar(stacked=True)
plt.show()

horizontal bar chart



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df2.plot.barh(stacked=True)
plt.show()

box plot



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df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])
df.plot.box()
plt.show()

area plot



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df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
df.plot.area()
plt.show()

Plotting with Missing Data

Pandas tries to be pragmatic about plotting DataFrames or Series that contain missing data. Missing values are dropped, left out, or filled depending on the plot type.

Plot Type	NaN Handling
Line	Leave gaps at NaNs
Line (stacked)	Fill 0’s
Bar	Fill 0’s
Scatter	Drop NaNs
Histogram	Drop NaNs (column-wise)
Box	Drop NaNs (column-wise)
Area	Fill 0’s
KDE	Drop NaNs (column-wise)
Hexbin	Drop NaNs
Pie	Fill 0’s

If any of these defaults are not what you want, or if you want to be explicit about how missing values are handled, consider using fillna() or dropna() before plotting.

density plot



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ser = pd.Series(np.random.randn(1000))
ser.plot.kde()
plt.show()

lag plot

Lag plots are used to check if a data set or time series is random. Random data should not exhibit any structure in the lag plot. Non-random structure implies that the underlying data are not random.



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from pandas.tools.plotting import lag_plot
plt.figure()
data = pd.Series(0.1 * np.random.rand(1000) + 0.9 * np.sin(np.linspace(-99 * np.pi, 99 * np.pi, num=1000)))
lag_plot(data)
plt.show()

matplotlib gallery

documentation: http://matplotlib.org/gallery.html