### Navigation

- index
- modules |
- next |
- previous |
- pandas 0.14.1 documentation »
- API Reference »

# 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, bar, or scatter plots of DataFrame series with the index on the x-axis using matplotlib / pylab.

  Parameters : | frame : DataFrame x : label or position, default None y : label or position, default None Allows plotting of one column versus another yerr : DataFrame (with matching labels), Series, list-type (tuple, list, ndarray), or str of column name containing y error values xerr : similar functionality as yerr, but for x error values 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 : False/True/’reverse’ 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’, ‘area’, scatter’, ‘hexbin’} line : line plot bar : vertical bar plot barh : horizontal bar plot kde/density : Kernel Density Estimation plot area : area plot scatter : scatter plot hexbin : hexbin plot logx : boolean, default False Use log scaling on x axis logy : boolean, default False Use log scaling on y axis loglog : boolean, default False Use log scaling on both x and y axes 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 a list/tuple, which columns to plot on secondary y-axis mark_right: boolean, default True When using a secondary_y axis, should the legend label the axis of the various columns automatically colormap : str or matplotlib colormap object, default None Colormap to select colors from. If string, load colormap with that name from matplotlib. position : float Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center) table : boolean, Series or DataFrame, default False If True, draw a table using the data in the DataFrame and the data will be transposed to meet matplotlib’s default layout. If a Series or DataFrame is passed, use passed data to draw a table. kwds : keywords Options to pass to matplotlib plotting method
  Returns : | ax_or_axes : matplotlib.AxesSubplot or list of them

  Notes

  If kind`=’hexbin’, you can control the size of the bins with the `gridsize argument. By default, a histogram of the counts around each (x, y) point is computed. You can specify alternative aggregations by passing values to the C and reduce_C_function arguments. C specifies the value at each (x, y) point and reduce_C_function is a function of one argument that reduces all the values in a bin to a single number (e.g. mean, max, sum, std).

### Navigation

- index
- modules |
- next |
- previous |
- pandas 0.14.1 documentation »
- API Reference »