pandas.DataFrame.plot

DataFrame.plot(frame=None, subplots=False, sharex=True, sharey=False, use_index=True, figsize=None, grid=False, legend=True, rot=None, ax=None, title=None, xlim=None, ylim=None, 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.

Parameters :

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 True

Axis grid lines

legend : boolean, default True

Place legend on axis subplots

ax : matplotlib axis object, default None

kind : {‘line’, ‘bar’, ‘barh’}

bar : vertical bar plot barh : horizontal bar plot

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

Returns :

ax_or_axes : matplotlib.AxesSubplot or list of them