Series.plot(*args, **kwargs)[source]#

Make plots of Series or DataFrame.

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

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.


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.


Values to use for the xticks.


Values to use for the yticks.


Set the x limits of the current axes.


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).


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.


Options to pass to matplotlib plotting method.

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.

See also


Plot y versus x as lines and/or markers.


Make a histogram.


Make a box plot.


Make a scatter plot with varying marker point size and color.


Make a hexagonal binning plot of two variables.


Make Kernel Density Estimate plot using Gaussian kernels.


Make a stacked area plot.

Make a bar plot.


Make a horizontal bar plot.


  • 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)


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")