pandas.DataFrame.plot

DataFrame.plot(frame=None, x=None, y=None, subplots=False, sharex=True, sharey=False, use_index=True, figsize=None, grid=None, legend=True, rot=None, ax=None, style=None, title=None, xlim=None, ylim=None, logx=False, logy=False, xticks=None, yticks=None, kind='line', sort_columns=False, fontsize=None, secondary_y=False, **kwds)

Make line or bar plot of DataFrame’s series with the index on the x-axis using matplotlib / pylab.

x : label or position, default None y : label or position, default None

Allows plotting of one column versus another
subplots : boolean, default False
Make separate subplots for each time series
sharex : boolean, default True
In case subplots=True, share x axis
sharey : boolean, default False
In case subplots=True, share y axis
use_index : boolean, default True
Use index as ticks for x axis
stacked : boolean, default False
If True, create stacked bar plot. Only valid for DataFrame input
sort_columns: boolean, default False
Sort column names to determine plot ordering
title : string
Title to use for the plot
grid : boolean, default None (matlab style default)
Axis grid lines
legend : boolean, default True
Place legend on axis subplots

ax : matplotlib axis object, default None style : list or dict

matplotlib line style per column
kind : {‘line’, ‘bar’, ‘barh’, ‘kde’, ‘density’}
bar : vertical bar plot barh : horizontal bar plot kde/density : Kernel Density Estimation plot
logx : boolean, default False
For line plots, use log scaling on x axis
logy : boolean, default False
For line plots, use log scaling on y axis
xticks : sequence
Values to use for the xticks
yticks : sequence
Values to use for the yticks

xlim : 2-tuple/list ylim : 2-tuple/list rot : int, default None

Rotation for ticks
secondary_y : boolean or sequence, default False
Whether to plot on the secondary y-axis If dict then can select which columns to plot on secondary y-axis
kwds : keywords
Options to pass to matplotlib plotting method

ax_or_axes : matplotlib.AxesSubplot or list of them