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# pandas.core.groupby.DataFrameGroupBy.plot

property DataFrameGroupBy.plot

  Make plots of Series or DataFrame.

  Uses the backend specified by the option `plotting.backend`. By default, matplotlib is used.

  Parameters:
    dataSeries or DataFrame

      The object for which the method is called.

    xlabel or position, default None

      Only used if data is a DataFrame.

    ylabel, position or list of label, positions, default None

      Allows plotting of one column versus another. Only used if data is a DataFrame.

    kindstr

      The kind of plot to produce:

      - ‘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 (DataFrame only)

      - ‘hexbin’ : hexbin plot (DataFrame only)

    axmatplotlib axes object, default None

      An axes of the current figure.

    subplotsbool or sequence of iterables, default False

      Whether to group columns into subplots:

      - `False` : No subplots will be used

      - `True` : Make separate subplots for each column.

      - sequence of iterables of column labels: Create a subplot for each group of columns. For example [(‘a’, ‘c’), (‘b’, ‘d’)] will create 2 subplots: one with columns ‘a’ and ‘c’, and one with columns ‘b’ and ‘d’. Remaining columns that aren’t specified will be plotted in additional subplots (one per column).

        Added in version 1.5.0.

    sharexbool, 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.

    shareybool, default False

      In case `subplots=True`, share y axis and set some y axis labels to invisible.

    layouttuple, optional

      (rows, columns) for the layout of subplots.

    figsizea tuple (width, height) in inches

      Size of a figure object.

    use_indexbool, default True

      Use index as ticks for x axis.

    titlestr or list

      Title to use for the plot. If a string is passed, print the string at the top of the figure. If a list is passed and subplots is True, print each item in the list above the corresponding subplot.

    gridbool, default None (matlab style default)

      Axis grid lines.

    legendbool or {‘reverse’}

      Place legend on axis subplots.

    stylelist or dict

      The matplotlib line style per column.

    logxbool or ‘sym’, default False

      Use log scaling or symlog scaling on x axis.

    logybool or ‘sym’ default False

      Use log scaling or symlog scaling on y axis.

    loglogbool or ‘sym’, default False

      Use log scaling or symlog scaling on both x and y axes.

    xtickssequence

      Values to use for the xticks.

    ytickssequence

      Values to use for the yticks.

    xlim2-tuple/list

      Set the x limits of the current axes.

    ylim2-tuple/list

      Set the y limits of the current axes.

    xlabellabel, optional

      Name to use for the xlabel on x-axis. Default uses index name as xlabel, or the x-column name for planar plots.

      Changed in version 2.0.0: Now applicable to histograms.

    ylabellabel, optional

      Name to use for the ylabel on y-axis. Default will show no ylabel, or the y-column name for planar plots.

      Changed in version 2.0.0: Now applicable to histograms.

    rotfloat, default None

      Rotation for ticks (xticks for vertical, yticks for horizontal plots).

    fontsizefloat, default None

      Font size for xticks and yticks.

    colormapstr or matplotlib colormap object, default None

      Colormap to select colors from. If string, load colormap with that name from matplotlib.

    colorbarbool, optional

      If True, plot colorbar (only relevant for ‘scatter’ and ‘hexbin’ plots).

    positionfloat

      Specify relative alignments for bar plot layout. From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center).

    tablebool, 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.

    yerrDataFrame, Series, array-like, dict and str

      See Plotting with Error Bars for detail.

    xerrDataFrame, Series, array-like, dict and str

      Equivalent to yerr.

    stackedbool, default False in line and bar plots, and True in area plot

      If True, create stacked plot.

    secondary_ybool 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_rightbool, default True

      When using a secondary_y axis, automatically mark the column labels with “(right)” in the legend.

    include_boolbool, default is False

      If True, boolean values can be plotted.

    backendstr, default None

      Backend to use instead of the backend specified in the option `plotting.backend`. For instance, ‘matplotlib’. Alternatively, to specify the `plotting.backend` for the whole session, set `pd.options.plotting.backend`.

    **kwargs

      Options to pass to matplotlib plotting method.

  Returns:
    `matplotlib.axes.Axes` or numpy.ndarray of them

      If the backend is not the default matplotlib one, the return value will be the object returned by the backend.

  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)

  Examples

  For Series:

      >>> ser = pd.Series([1, 2, 3, 3])
      >>> plot = ser.plot(kind='hist', title="My plot")

  For DataFrame:

      >>> df = pd.DataFrame({'length': [1.5, 0.5, 1.2, 0.9, 3],
      ...                   'width': [0.7, 0.2, 0.15, 0.2, 1.1]},
      ...                   index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])
      >>> plot = df.plot(title="DataFrame Plot")

  For SeriesGroupBy:

      >>> lst = [-1, -2, -3, 1, 2, 3]
      >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst)
      >>> plot = ser.groupby(lambda x: x > 0).plot(title="SeriesGroupBy Plot")

  For DataFrameGroupBy:

      >>> df = pd.DataFrame({"col1" : [1, 2, 3, 4],
      ...                   "col2" : ["A", "B", "A", "B"]})
      >>> plot = df.groupby("col2").plot(kind="bar", title="DataFrameGroupBy Plot")