pandas.DataFrame.plot

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

Make plots of DataFrame using matplotlib / pylab.

New in version 0.17.0: Each plot kind has a corresponding method on the DataFrame.plot accessor: df.plot(kind='line') is equivalent to df.plot.line().

Parameters:

data : DataFrame x : label or position, default None y : label or position, default None Allows plotting of one column versus another kind : str ‘line’ : line plot (default) ‘bar’ : vertical bar plot ‘barh’ : horizontal bar plot ‘hist’ : histogram ‘box’ : boxplot ‘kde’ : Kernel Density Estimation plot ‘density’ : same as ‘kde’ ‘area’ : area plot ‘pie’ : pie plot ‘scatter’ : scatter plot ‘hexbin’ : hexbin plot ax : matplotlib axes object, default None subplots : boolean, default False Make separate subplots for each column sharex : boolean, default True if ax is None else False In case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in; Be aware, that passing in both an ax and sharex=True will alter all x axis labels for all axis in a figure! sharey : boolean, default False In case subplots=True, share y axis and set some y axis labels to invisible layout : tuple (optional) (rows, columns) for the layout of subplots figsize : a tuple (width, height) in inches use_index : boolean, default True Use index as ticks for x axis 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 style : list or dict matplotlib line style per column 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 (xticks for vertical, yticks for horizontal plots) fontsize : int, default None Font size for xticks and yticks colormap : str or matplotlib colormap object, default None Colormap to select colors from. If string, load colormap with that name from matplotlib. colorbar : boolean, optional If True, plot colorbar (only relevant for ‘scatter’ and ‘hexbin’ plots) position : float Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center) layout : tuple (optional) (rows, columns) for the layout of the plot 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. yerr : DataFrame, Series, array-like, dict and str See Plotting with Error Bars for detail. xerr : same types as yerr. stacked : boolean, default False in line and bar plots, and True in area plot. If True, create stacked plot. sort_columns : boolean, default False Sort column names to determine plot ordering 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, automatically mark the column labels with “(right)” in the legend kwds : keywords Options to pass to matplotlib plotting method

Returns:

axes : matplotlib.AxesSubplot or np.array of them

Notes

• See matplotlib documentation online for more on this subject
• If kind = ‘bar’ or ‘barh’, you can specify relative alignments for bar plot layout by position keyword. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)
• If kind = ‘scatter’ and the argument c is the name of a dataframe column, the values of that column are used to color each point.
• 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).