Chart visualization

Note

The examples below assume that you're using Jupyter.

This section demonstrates visualization through charting. For information on visualization of tabular data please see the section on Table Visualization.

We use the standard convention for referencing the matplotlib API:

.. ipython:: python

   import matplotlib.pyplot as plt

   plt.close("all")

We provide the basics in pandas to easily create decent looking plots. See the :ref:`ecosystem <ecosystem.visualization>` section for visualization libraries that go beyond the basics documented here.

Note

All calls to np.random are seeded with 123456.

Basic plotting: `plot`

We will demonstrate the basics, see the :ref:`cookbook<cookbook.plotting>` for some advanced strategies.

The plot method on Series and DataFrame is just a simple wrapper around :meth:`plt.plot() <matplotlib.axes.Axes.plot>`:

.. ipython:: python
   :suppress:

   np.random.seed(123456)

.. ipython:: python

   ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
   ts = ts.cumsum()

   @savefig series_plot_basic.png
   ts.plot();

If the index consists of dates, it calls :meth:`gcf().autofmt_xdate() <matplotlib.figure.Figure.autofmt_xdate>` to try to format the x-axis nicely as per above.

On DataFrame, :meth:`~DataFrame.plot` is a convenience to plot all of the columns with labels:

.. ipython:: python
   :suppress:

   plt.close("all")
   np.random.seed(123456)

.. ipython:: python

   df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list("ABCD"))
   df = df.cumsum()

   plt.figure();
   @savefig frame_plot_basic.png
   df.plot();

You can plot one column versus another using the x and y keywords in :meth:`~DataFrame.plot`:

.. ipython:: python
   :suppress:

   plt.close("all")
   plt.figure()
   np.random.seed(123456)

.. ipython:: python

   df3 = pd.DataFrame(np.random.randn(1000, 2), columns=["B", "C"]).cumsum()
   df3["A"] = pd.Series(list(range(len(df))))

   @savefig df_plot_xy.png
   df3.plot(x="A", y="B");

Note

For more formatting and styling options, see :ref:`formatting <visualization.formatting>` below.

.. ipython:: python
    :suppress:

    plt.close("all")

Other plots

Plotting methods allow for a handful of plot styles other than the default line plot. These methods can be provided as the kind keyword argument to :meth:`~DataFrame.plot`, and include:

:ref:`'bar' <visualization.barplot>` or :ref:`'barh' <visualization.barplot>` for bar plots
:ref:`'hist' <visualization.hist>` for histogram
:ref:`'box' <visualization.box>` for boxplot
:ref:`'kde' <visualization.kde>` or :ref:`'density' <visualization.kde>` for density plots
:ref:`'area' <visualization.area_plot>` for area plots
:ref:`'scatter' <visualization.scatter>` for scatter plots
:ref:`'hexbin' <visualization.hexbin>` for hexagonal bin plots
:ref:`'pie' <visualization.pie>` for pie plots

For example, a bar plot can be created the following way:

.. ipython:: python

   plt.figure();

   @savefig bar_plot_ex.png
   df.iloc[5].plot(kind="bar");

You can also create these other plots using the methods DataFrame.plot.<kind> instead of providing the kind keyword argument. This makes it easier to discover plot methods and the specific arguments they use:

.. ipython::
    :verbatim:

    In [14]: df = pd.DataFrame()

    In [15]: df.plot.<TAB>  # noqa: E225, E999
    df.plot.area     df.plot.barh     df.plot.density  df.plot.hist     df.plot.line     df.plot.scatter
    df.plot.bar      df.plot.box      df.plot.hexbin   df.plot.kde      df.plot.pie

In addition to these kind s, there are the :ref:`DataFrame.hist() <visualization.hist>`, and :ref:`DataFrame.boxplot() <visualization.box>` methods, which use a separate interface.

Finally, there are several :ref:`plotting functions <visualization.tools>` in pandas.plotting that take a :class:`Series` or :class:`DataFrame` as an argument. These include:

:ref:`Scatter Matrix <visualization.scatter_matrix>`
:ref:`Andrews Curves <visualization.andrews_curves>`
:ref:`Parallel Coordinates <visualization.parallel_coordinates>`
:ref:`Lag Plot <visualization.lag>`
:ref:`Autocorrelation Plot <visualization.autocorrelation>`
:ref:`Bootstrap Plot <visualization.bootstrap>`
:ref:`RadViz <visualization.radviz>`

Plots may also be adorned with :ref:`errorbars <visualization.errorbars>` or :ref:`tables <visualization.table>`.

Bar plots

For labeled, non-time series data, you may wish to produce a bar plot:

.. ipython:: python

   plt.figure();

   @savefig bar_plot_ex.png
   df.iloc[5].plot.bar();
   plt.axhline(0, color="k");

Calling a DataFrame's :meth:`plot.bar() <DataFrame.plot.bar>` method produces a multiple bar plot:

.. ipython:: python
   :suppress:

   plt.close("all")
   plt.figure()
   np.random.seed(123456)

.. ipython:: python

   df2 = pd.DataFrame(np.random.rand(10, 4), columns=["a", "b", "c", "d"])

   @savefig bar_plot_multi_ex.png
   df2.plot.bar();

To produce a stacked bar plot, pass stacked=True:

.. ipython:: python
   :suppress:

   plt.close("all")
   plt.figure()

.. ipython:: python

   @savefig bar_plot_stacked_ex.png
   df2.plot.bar(stacked=True);

To get horizontal bar plots, use the barh method:

.. ipython:: python
   :suppress:

   plt.close("all")
   plt.figure()

.. ipython:: python

   @savefig barh_plot_stacked_ex.png
   df2.plot.barh(stacked=True);

Histograms

Histograms can be drawn by using the :meth:`DataFrame.plot.hist` and :meth:`Series.plot.hist` methods.

.. ipython:: python

   df4 = pd.DataFrame(
       {
           "a": np.random.randn(1000) + 1,
           "b": np.random.randn(1000),
           "c": np.random.randn(1000) - 1,
       },
       columns=["a", "b", "c"],
   )

   plt.figure();

   @savefig hist_new.png
   df4.plot.hist(alpha=0.5);

.. ipython:: python
   :suppress:

   plt.close("all")

A histogram can be stacked using stacked=True. Bin size can be changed using the bins keyword.

.. ipython:: python

   plt.figure();

   @savefig hist_new_stacked.png
   df4.plot.hist(stacked=True, bins=20);

.. ipython:: python
   :suppress:

   plt.close("all")

You can pass other keywords supported by matplotlib hist. For example, horizontal and cumulative histograms can be drawn by orientation='horizontal' and cumulative=True.

.. ipython:: python

   plt.figure();

   @savefig hist_new_kwargs.png
   df4["a"].plot.hist(orientation="horizontal", cumulative=True);

.. ipython:: python
   :suppress:

   plt.close("all")

See the :meth:`hist <matplotlib.axes.Axes.hist>` method and the matplotlib hist documentation for more.

The existing interface DataFrame.hist to plot histogram still can be used.

.. ipython:: python

   plt.figure();

   @savefig hist_plot_ex.png
   df["A"].diff().hist();

.. ipython:: python
   :suppress:

   plt.close("all")

:meth:`DataFrame.hist` plots the histograms of the columns on multiple subplots:

.. ipython:: python

   plt.figure();

   @savefig frame_hist_ex.png
   df.diff().hist(color="k", alpha=0.5, bins=50);

The by keyword can be specified to plot grouped histograms:

.. ipython:: python
   :suppress:

   plt.close("all")
   plt.figure()
   np.random.seed(123456)

.. ipython:: python

   data = pd.Series(np.random.randn(1000))

   @savefig grouped_hist.png
   data.hist(by=np.random.randint(0, 4, 1000), figsize=(6, 4));

.. ipython:: python
   :suppress:

   plt.close("all")
   np.random.seed(123456)

In addition, the by keyword can also be specified in :meth:`DataFrame.plot.hist`.

.. versionchanged:: 1.4.0

.. ipython:: python

   data = pd.DataFrame(
       {
           "a": np.random.choice(["x", "y", "z"], 1000),
           "b": np.random.choice(["e", "f", "g"], 1000),
           "c": np.random.randn(1000),
           "d": np.random.randn(1000) - 1,
       },
   )

   @savefig grouped_hist_by.png
   data.plot.hist(by=["a", "b"], figsize=(10, 5));

.. ipython:: python
   :suppress:

   plt.close("all")

Box plots

Boxplot can be drawn calling :meth:`Series.plot.box` and :meth:`DataFrame.plot.box`, or :meth:`DataFrame.boxplot` to visualize the distribution of values within each column.

For instance, here is a boxplot representing five trials of 10 observations of a uniform random variable on [0,1).

.. ipython:: python
   :suppress:

   plt.close("all")
   np.random.seed(123456)

.. ipython:: python

   df = pd.DataFrame(np.random.rand(10, 5), columns=["A", "B", "C", "D", "E"])

   @savefig box_plot_new.png
   df.plot.box();

Boxplot can be colorized by passing color keyword. You can pass a dict whose keys are boxes, whiskers, medians and caps. If some keys are missing in the dict, default colors are used for the corresponding artists. Also, boxplot has sym keyword to specify fliers style.

When you pass other type of arguments via color keyword, it will be directly passed to matplotlib for all the boxes, whiskers, medians and caps colorization.

The colors are applied to every boxes to be drawn. If you want more complicated colorization, you can get each drawn artists by passing :ref:`return_type <visualization.box.return>`.

.. ipython:: python

   color = {
       "boxes": "DarkGreen",
       "whiskers": "DarkOrange",
       "medians": "DarkBlue",
       "caps": "Gray",
   }

   @savefig box_new_colorize.png
   df.plot.box(color=color, sym="r+");

.. ipython:: python
   :suppress:

   plt.close("all")

Also, you can pass other keywords supported by matplotlib boxplot. For example, horizontal and custom-positioned boxplot can be drawn by vert=False and positions keywords.

.. ipython:: python

   @savefig box_new_kwargs.png
   df.plot.box(vert=False, positions=[1, 4, 5, 6, 8]);

See the :meth:`boxplot <matplotlib.axes.Axes.boxplot>` method and the matplotlib boxplot documentation for more.

The existing interface DataFrame.boxplot to plot boxplot still can be used.

.. ipython:: python
   :suppress:

   plt.close("all")
   np.random.seed(123456)

.. ipython:: python
   :okwarning:

   df = pd.DataFrame(np.random.rand(10, 5))
   plt.figure();

   @savefig box_plot_ex.png
   bp = df.boxplot()

You can create a stratified boxplot using the by keyword argument to create groupings. For instance,

.. ipython:: python
   :suppress:

   plt.close("all")
   np.random.seed(123456)

.. ipython:: python
   :okwarning:

   df = pd.DataFrame(np.random.rand(10, 2), columns=["Col1", "Col2"])
   df["X"] = pd.Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])

   plt.figure();

   @savefig box_plot_ex2.png
   bp = df.boxplot(by="X")

You can also pass a subset of columns to plot, as well as group by multiple columns:

.. ipython:: python
   :suppress:

   plt.close("all")
   np.random.seed(123456)

.. ipython:: python
   :okwarning:

   df = pd.DataFrame(np.random.rand(10, 3), columns=["Col1", "Col2", "Col3"])
   df["X"] = pd.Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])
   df["Y"] = pd.Series(["A", "B", "A", "B", "A", "B", "A", "B", "A", "B"])

   plt.figure();

   @savefig box_plot_ex3.png
   bp = df.boxplot(column=["Col1", "Col2"], by=["X", "Y"])

.. ipython:: python
   :suppress:

    plt.close("all")

You could also create groupings with :meth:`DataFrame.plot.box`, for instance:

.. versionchanged:: 1.4.0

.. ipython:: python
   :suppress:

   plt.close("all")
   np.random.seed(123456)

.. ipython:: python
   :okwarning:

   df = pd.DataFrame(np.random.rand(10, 3), columns=["Col1", "Col2", "Col3"])
   df["X"] = pd.Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])

   plt.figure();

   @savefig box_plot_ex4.png
   bp = df.plot.box(column=["Col1", "Col2"], by="X")

.. ipython:: python
   :suppress:

    plt.close("all")

In boxplot, the return type can be controlled by the return_type, keyword. The valid choices are {"axes", "dict", "both", None}. Faceting, created by DataFrame.boxplot with the by keyword, will affect the output type as well:

`return_type`	Faceted	Output type
`None`	No	axes
`None`	Yes	2-D ndarray of axes
`'axes'`	No	axes
`'axes'`	Yes	Series of axes
`'dict'`	No	dict of artists
`'dict'`	Yes	Series of dicts of artists
`'both'`	No	namedtuple
`'both'`	Yes	Series of namedtuples

Groupby.boxplot always returns a Series of return_type.

.. ipython:: python
   :okwarning:

   np.random.seed(1234)
   df_box = pd.DataFrame(np.random.randn(50, 2))
   df_box["g"] = np.random.choice(["A", "B"], size=50)
   df_box.loc[df_box["g"] == "B", 1] += 3

   @savefig boxplot_groupby.png
   bp = df_box.boxplot(by="g")

.. ipython:: python
   :suppress:

   plt.close("all")

The subplots above are split by the numeric columns first, then the value of the g column. Below the subplots are first split by the value of g, then by the numeric columns.

.. ipython:: python
   :okwarning:

   @savefig groupby_boxplot_vis.png
   bp = df_box.groupby("g").boxplot()

.. ipython:: python
   :suppress:

   plt.close("all")

Area plot

You can create area plots with :meth:`Series.plot.area` and :meth:`DataFrame.plot.area`. Area plots are stacked by default. To produce stacked area plot, each column must be either all positive or all negative values.

When input data contains NaN, it will be automatically filled by 0. If you want to drop or fill by different values, use :func:`dataframe.dropna` or :func:`dataframe.fillna` before calling plot.

.. ipython:: python
   :suppress:

   np.random.seed(123456)
   plt.figure()

.. ipython:: python

   df = pd.DataFrame(np.random.rand(10, 4), columns=["a", "b", "c", "d"])

   @savefig area_plot_stacked.png
   df.plot.area();

To produce an unstacked plot, pass stacked=False. Alpha value is set to 0.5 unless otherwise specified:

.. ipython:: python
   :suppress:

   plt.close("all")
   plt.figure()

.. ipython:: python

   @savefig area_plot_unstacked.png
   df.plot.area(stacked=False);

Scatter plot

Scatter plot can be drawn by using the :meth:`DataFrame.plot.scatter` method. Scatter plot requires numeric columns for the x and y axes. These can be specified by the x and y keywords.

.. ipython:: python
   :suppress:

   np.random.seed(123456)
   plt.close("all")
   plt.figure()

.. ipython:: python

   df = pd.DataFrame(np.random.rand(50, 4), columns=["a", "b", "c", "d"])
   df["species"] = pd.Categorical(
       ["setosa"] * 20 + ["versicolor"] * 20 + ["virginica"] * 10
   )

   @savefig scatter_plot.png
   df.plot.scatter(x="a", y="b");

To plot multiple column groups in a single axes, repeat plot method specifying target ax. It is recommended to specify color and label keywords to distinguish each groups.

.. ipython:: python
   :okwarning:

   ax = df.plot.scatter(x="a", y="b", color="DarkBlue", label="Group 1")
   @savefig scatter_plot_repeated.png
   df.plot.scatter(x="c", y="d", color="DarkGreen", label="Group 2", ax=ax);

.. ipython:: python
   :suppress:

   plt.close("all")

The keyword c may be given as the name of a column to provide colors for each point:

.. ipython:: python

   @savefig scatter_plot_colored.png
   df.plot.scatter(x="a", y="b", c="c", s=50);

.. ipython:: python
   :suppress:

   plt.close("all")

If a categorical column is passed to c, then a discrete colorbar will be produced:

.. versionadded:: 1.3.0

.. ipython:: python

   @savefig scatter_plot_categorical.png
   df.plot.scatter(x="a", y="b", c="species", cmap="viridis", s=50);

.. ipython:: python
   :suppress:

   plt.close("all")

You can pass other keywords supported by matplotlib :meth:`scatter <matplotlib.axes.Axes.scatter>`. The example below shows a bubble chart using a column of the DataFrame as the bubble size.

.. ipython:: python

   @savefig scatter_plot_bubble.png
   df.plot.scatter(x="a", y="b", s=df["c"] * 200);

.. ipython:: python
   :suppress:

   plt.close("all")

See the :meth:`scatter <matplotlib.axes.Axes.scatter>` method and the matplotlib scatter documentation for more.

Hexagonal bin plot

You can create hexagonal bin plots with :meth:`DataFrame.plot.hexbin`. Hexbin plots can be a useful alternative to scatter plots if your data are too dense to plot each point individually.

.. ipython:: python
   :suppress:

   plt.figure()
   np.random.seed(123456)

.. ipython:: python

   df = pd.DataFrame(np.random.randn(1000, 2), columns=["a", "b"])
   df["b"] = df["b"] + np.arange(1000)

   @savefig hexbin_plot.png
   df.plot.hexbin(x="a", y="b", gridsize=25);

A useful keyword argument is gridsize; it controls the number of hexagons in the x-direction, and defaults to 100. A larger gridsize means more, smaller bins.

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). In this example the positions are given by columns a and b, while the value is given by column z. The bins are aggregated with NumPy's max function.

.. ipython:: python
   :suppress:

   plt.close("all")
   plt.figure()
   np.random.seed(123456)

.. ipython:: python

   df = pd.DataFrame(np.random.randn(1000, 2), columns=["a", "b"])
   df["b"] = df["b"] + np.arange(1000)
   df["z"] = np.random.uniform(0, 3, 1000)

   @savefig hexbin_plot_agg.png
   df.plot.hexbin(x="a", y="b", C="z", reduce_C_function=np.max, gridsize=25);

.. ipython:: python
   :suppress:

   plt.close("all")

See the :meth:`hexbin <matplotlib.axes.Axes.hexbin>` method and the matplotlib hexbin documentation for more.

Pie plot

You can create a pie plot with :meth:`DataFrame.plot.pie` or :meth:`Series.plot.pie`. If your data includes any NaN, they will be automatically filled with 0. A ValueError will be raised if there are any negative values in your data.

.. ipython:: python
   :suppress:

   np.random.seed(123456)
   plt.figure()

.. ipython:: python
   :okwarning:

   series = pd.Series(3 * np.random.rand(4), index=["a", "b", "c", "d"], name="series")

   @savefig series_pie_plot.png
   series.plot.pie(figsize=(6, 6));

.. ipython:: python
   :suppress:

   plt.close("all")

For pie plots it's best to use square figures, i.e. a figure aspect ratio 1. You can create the figure with equal width and height, or force the aspect ratio to be equal after plotting by calling ax.set_aspect('equal') on the returned axes object.

Note that pie plot with :class:`DataFrame` requires that you either specify a target column by the y argument or subplots=True. When y is specified, pie plot of selected column will be drawn. If subplots=True is specified, pie plots for each column are drawn as subplots. A legend will be drawn in each pie plots by default; specify legend=False to hide it.

.. ipython:: python
   :suppress:

   np.random.seed(123456)
   plt.figure()

.. ipython:: python

   df = pd.DataFrame(
       3 * np.random.rand(4, 2), index=["a", "b", "c", "d"], columns=["x", "y"]
   )

   @savefig df_pie_plot.png
   df.plot.pie(subplots=True, figsize=(8, 4));

.. ipython:: python
   :suppress:

   plt.close("all")

You can use the labels and colors keywords to specify the labels and colors of each wedge.

Warning

Most pandas plots use the label and color arguments (note the lack of "s" on those). To be consistent with :func:`matplotlib.pyplot.pie` you must use labels and colors.

If you want to hide wedge labels, specify labels=None. If fontsize is specified, the value will be applied to wedge labels. Also, other keywords supported by :func:`matplotlib.pyplot.pie` can be used.

.. ipython:: python
   :suppress:

   plt.figure()

.. ipython:: python

   @savefig series_pie_plot_options.png
   series.plot.pie(
       labels=["AA", "BB", "CC", "DD"],
       colors=["r", "g", "b", "c"],
       autopct="%.2f",
       fontsize=20,
       figsize=(6, 6),
   );

If you pass values whose sum total is less than 1.0 they will be rescaled so that they sum to 1.

.. ipython:: python
   :suppress:

   plt.close("all")
   plt.figure()

.. ipython:: python
   :okwarning:

   series = pd.Series([0.1] * 4, index=["a", "b", "c", "d"], name="series2")

   @savefig series_pie_plot_semi.png
   series.plot.pie(figsize=(6, 6));

See the matplotlib pie documentation for more.

.. ipython:: python
    :suppress:

    plt.close("all")

Plotting with missing data

pandas tries to be pragmatic about plotting DataFrames or Series that contain missing data. Missing values are dropped, left out, or filled depending on the plot type.

Plot Type	NaN Handling
Line	Leave gaps at NaNs
Line (stacked)	Fill 0's
Bar	Fill 0's
Scatter	Drop NaNs
Histogram	Drop NaNs (column-wise)
Box	Drop NaNs (column-wise)
Area	Fill 0's
KDE	Drop NaNs (column-wise)
Hexbin	Drop NaNs
Pie	Fill 0's

If any of these defaults are not what you want, or if you want to be explicit about how missing values are handled, consider using :meth:`~pandas.DataFrame.fillna` or :meth:`~pandas.DataFrame.dropna` before plotting.

Plotting tools

These functions can be imported from pandas.plotting and take a :class:`Series` or :class:`DataFrame` as an argument.

Scatter matrix plot

You can create a scatter plot matrix using the scatter_matrix method in pandas.plotting:

.. ipython:: python
   :suppress:

   np.random.seed(123456)

.. ipython:: python

   from pandas.plotting import scatter_matrix

   df = pd.DataFrame(np.random.randn(1000, 4), columns=["a", "b", "c", "d"])

   @savefig scatter_matrix_kde.png
   scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal="kde");

.. ipython:: python
   :suppress:

   plt.close("all")

Density plot

You can create density plots using the :meth:`Series.plot.kde` and :meth:`DataFrame.plot.kde` methods.

.. ipython:: python
   :suppress:

   plt.figure()
   np.random.seed(123456)

.. ipython:: python

   ser = pd.Series(np.random.randn(1000))

   @savefig kde_plot.png
   ser.plot.kde();

.. ipython:: python
   :suppress:

   plt.close("all")

Andrews curves

Andrews curves allow one to plot multivariate data as a large number of curves that are created using the attributes of samples as coefficients for Fourier series, see the Wikipedia entry for more information. By coloring these curves differently for each class it is possible to visualize data clustering. Curves belonging to samples of the same class will usually be closer together and form larger structures.

Note: The "Iris" dataset is available here.

.. ipython:: python

   from pandas.plotting import andrews_curves

   data = pd.read_csv("data/iris.data")

   plt.figure();

   @savefig andrews_curves.png
   andrews_curves(data, "Name");

Parallel coordinates

Parallel coordinates is a plotting technique for plotting multivariate data, see the Wikipedia entry for an introduction. Parallel coordinates allows one to see clusters in data and to estimate other statistics visually. Using parallel coordinates points are represented as connected line segments. Each vertical line represents one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together.

.. ipython:: python

   from pandas.plotting import parallel_coordinates

   data = pd.read_csv("data/iris.data")

   plt.figure();

   @savefig parallel_coordinates.png
   parallel_coordinates(data, "Name");

.. ipython:: python
   :suppress:

   plt.close("all")

Lag plot

Lag plots are used to check if a data set or time series is random. Random data should not exhibit any structure in the lag plot. Non-random structure implies that the underlying data are not random. The lag argument may be passed, and when lag=1 the plot is essentially data[:-1] vs. data[1:].

.. ipython:: python
   :suppress:

   np.random.seed(123456)

.. ipython:: python

   from pandas.plotting import lag_plot

   plt.figure();

   spacing = np.linspace(-99 * np.pi, 99 * np.pi, num=1000)
   data = pd.Series(0.1 * np.random.rand(1000) + 0.9 * np.sin(spacing))

   @savefig lag_plot.png
   lag_plot(data);

.. ipython:: python
   :suppress:

   plt.close("all")

Autocorrelation plot

Autocorrelation plots are often used for checking randomness in time series. This is done by computing autocorrelations for data values at varying time lags. If time series is random, such autocorrelations should be near zero for any and all time-lag separations. If time series is non-random then one or more of the autocorrelations will be significantly non-zero. The horizontal lines displayed in the plot correspond to 95% and 99% confidence bands. The dashed line is 99% confidence band. See the Wikipedia entry for more about autocorrelation plots.

.. ipython:: python
   :suppress:

   np.random.seed(123456)

.. ipython:: python

   from pandas.plotting import autocorrelation_plot

   plt.figure();

   spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
   data = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))

   @savefig autocorrelation_plot.png
   autocorrelation_plot(data);

.. ipython:: python
   :suppress:

   plt.close("all")

Bootstrap plot

Bootstrap plots are used to visually assess the uncertainty of a statistic, such as mean, median, midrange, etc. A random subset of a specified size is selected from a data set, the statistic in question is computed for this subset and the process is repeated a specified number of times. Resulting plots and histograms are what constitutes the bootstrap plot.

.. ipython:: python
   :suppress:

   np.random.seed(123456)

.. ipython:: python

   from pandas.plotting import bootstrap_plot

   data = pd.Series(np.random.rand(1000))

   @savefig bootstrap_plot.png
   bootstrap_plot(data, size=50, samples=500, color="grey");

.. ipython:: python
   :suppress:

    plt.close("all")

RadViz

RadViz is a way of visualizing multi-variate data. It is based on a simple spring tension minimization algorithm. Basically you set up a bunch of points in a plane. In our case they are equally spaced on a unit circle. Each point represents a single attribute. You then pretend that each sample in the data set is attached to each of these points by a spring, the stiffness of which is proportional to the numerical value of that attribute (they are normalized to unit interval). The point in the plane, where our sample settles to (where the forces acting on our sample are at an equilibrium) is where a dot representing our sample will be drawn. Depending on which class that sample belongs it will be colored differently. See the R package Radviz for more information.

Note: The "Iris" dataset is available here.

.. ipython:: python

   from pandas.plotting import radviz

   data = pd.read_csv("data/iris.data")

   plt.figure();

   @savefig radviz.png
   radviz(data, "Name");

.. ipython:: python
   :suppress:

   plt.close("all")

Plot formatting

Setting the plot style

From version 1.5 and up, matplotlib offers a range of pre-configured plotting styles. Setting the style can be used to easily give plots the general look that you want. Setting the style is as easy as calling matplotlib.style.use(my_plot_style) before creating your plot. For example you could write matplotlib.style.use('ggplot') for ggplot-style plots.

You can see the various available style names at matplotlib.style.available and it's very easy to try them out.

General plot style arguments

Most plotting methods have a set of keyword arguments that control the layout and formatting of the returned plot:

.. ipython:: python

   plt.figure();
   @savefig series_plot_basic2.png
   ts.plot(style="k--", label="Series");

.. ipython:: python
   :suppress:

   plt.close("all")

For each kind of plot (e.g. line, bar, scatter) any additional arguments keywords are passed along to the corresponding matplotlib function (:meth:`ax.plot() <matplotlib.axes.Axes.plot>`, :meth:`ax.bar() <matplotlib.axes.Axes.bar>`, :meth:`ax.scatter() <matplotlib.axes.Axes.scatter>`). These can be used to control additional styling, beyond what pandas provides.

Controlling the legend

You may set the legend argument to False to hide the legend, which is shown by default.

.. ipython:: python
   :suppress:

   np.random.seed(123456)

.. ipython:: python

   df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list("ABCD"))
   df = df.cumsum()

   @savefig frame_plot_basic_noleg.png
   df.plot(legend=False);

.. ipython:: python
   :suppress:

   plt.close("all")

Controlling the labels

.. versionadded:: 1.1.0

You may set the xlabel and ylabel arguments to give the plot custom labels for x and y axis. By default, pandas will pick up index name as xlabel, while leaving it empty for ylabel.

.. ipython:: python
   :suppress:

   plt.figure();

.. ipython:: python

   df.plot();

   @savefig plot_xlabel_ylabel.png
   df.plot(xlabel="new x", ylabel="new y");

.. ipython:: python
   :suppress:

   plt.close("all")

Scales

You may pass logy to get a log-scale Y axis.

.. ipython:: python
   :suppress:

   plt.figure()
   np.random.seed(123456)

.. ipython:: python

   ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
   ts = np.exp(ts.cumsum())

   @savefig series_plot_logy.png
   ts.plot(logy=True);

.. ipython:: python
   :suppress:

   plt.close("all")

See also the logx and loglog keyword arguments.

Plotting on a secondary y-axis

To plot data on a secondary y-axis, use the secondary_y keyword:

.. ipython:: python
   :suppress:

   plt.figure()

.. ipython:: python

   df["A"].plot();

   @savefig series_plot_secondary_y.png
   df["B"].plot(secondary_y=True, style="g");

.. ipython:: python
   :suppress:

   plt.close("all")

To plot some columns in a DataFrame, give the column names to the secondary_y keyword:

.. ipython:: python

   plt.figure();
   ax = df.plot(secondary_y=["A", "B"])
   ax.set_ylabel("CD scale");
   @savefig frame_plot_secondary_y.png
   ax.right_ax.set_ylabel("AB scale");

.. ipython:: python
   :suppress:

   plt.close("all")

Note that the columns plotted on the secondary y-axis is automatically marked with "(right)" in the legend. To turn off the automatic marking, use the mark_right=False keyword:

.. ipython:: python

   plt.figure();

   @savefig frame_plot_secondary_y_no_right.png
   df.plot(secondary_y=["A", "B"], mark_right=False);

.. ipython:: python
   :suppress:

   plt.close("all")

Custom formatters for timeseries plots

pandas provides custom formatters for timeseries plots. These change the formatting of the axis labels for dates and times. By default, the custom formatters are applied only to plots created by pandas with :meth:`DataFrame.plot` or :meth:`Series.plot`. To have them apply to all plots, including those made by matplotlib, set the option pd.options.plotting.matplotlib.register_converters = True or use :meth:`pandas.plotting.register_matplotlib_converters`.

Suppressing tick resolution adjustment

pandas includes automatic tick resolution adjustment for regular frequency time-series data. For limited cases where pandas cannot infer the frequency information (e.g., in an externally created twinx), you can choose to suppress this behavior for alignment purposes.

Here is the default behavior, notice how the x-axis tick labeling is performed:

.. ipython:: python

   plt.figure();

   @savefig ser_plot_suppress.png
   df["A"].plot();

.. ipython:: python
   :suppress:

   plt.close("all")

Using the x_compat parameter, you can suppress this behavior:

.. ipython:: python

   plt.figure();

   @savefig ser_plot_suppress_parm.png
   df["A"].plot(x_compat=True);

.. ipython:: python
   :suppress:

   plt.close("all")

If you have more than one plot that needs to be suppressed, the use method in pandas.plotting.plot_params can be used in a with statement:

.. ipython:: python

   plt.figure();

   @savefig ser_plot_suppress_context.png
   with pd.plotting.plot_params.use("x_compat", True):
       df["A"].plot(color="r")
       df["B"].plot(color="g")
       df["C"].plot(color="b")

.. ipython:: python
   :suppress:

   plt.close("all")

Automatic date tick adjustment

TimedeltaIndex now uses the native matplotlib tick locator methods, it is useful to call the automatic date tick adjustment from matplotlib for figures whose ticklabels overlap.

See the :meth:`autofmt_xdate <matplotlib.figure.autofmt_xdate>` method and the matplotlib documentation for more.

Subplots

Each Series in a DataFrame can be plotted on a different axis with the subplots keyword:

.. ipython:: python

   @savefig frame_plot_subplots.png
   df.plot(subplots=True, figsize=(6, 6));

.. ipython:: python
   :suppress:

   plt.close("all")

Using layout and targeting multiple axes

The layout of subplots can be specified by the layout keyword. It can accept (rows, columns). The layout keyword can be used in hist and boxplot also. If the input is invalid, a ValueError will be raised.

The number of axes which can be contained by rows x columns specified by layout must be larger than the number of required subplots. If layout can contain more axes than required, blank axes are not drawn. Similar to a NumPy array's reshape method, you can use -1 for one dimension to automatically calculate the number of rows or columns needed, given the other.

.. ipython:: python

   @savefig frame_plot_subplots_layout.png
   df.plot(subplots=True, layout=(2, 3), figsize=(6, 6), sharex=False);

.. ipython:: python
   :suppress:

   plt.close("all")

The above example is identical to using:

.. ipython:: python

   df.plot(subplots=True, layout=(2, -1), figsize=(6, 6), sharex=False);

.. ipython:: python
   :suppress:

   plt.close("all")

The required number of columns (3) is inferred from the number of series to plot and the given number of rows (2).

You can pass multiple axes created beforehand as list-like via ax keyword. This allows more complicated layouts. The passed axes must be the same number as the subplots being drawn.

When multiple axes are passed via the ax keyword, layout, sharex and sharey keywords don't affect to the output. You should explicitly pass sharex=False and sharey=False, otherwise you will see a warning.

.. ipython:: python

   fig, axes = plt.subplots(4, 4, figsize=(9, 9))
   plt.subplots_adjust(wspace=0.5, hspace=0.5)
   target1 = [axes[0][0], axes[1][1], axes[2][2], axes[3][3]]
   target2 = [axes[3][0], axes[2][1], axes[1][2], axes[0][3]]

   df.plot(subplots=True, ax=target1, legend=False, sharex=False, sharey=False);
   @savefig frame_plot_subplots_multi_ax.png
   (-df).plot(subplots=True, ax=target2, legend=False, sharex=False, sharey=False);

.. ipython:: python
   :suppress:

   plt.close("all")

Another option is passing an ax argument to :meth:`Series.plot` to plot on a particular axis:

.. ipython:: python
   :suppress:

   np.random.seed(123456)
   ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
   ts = ts.cumsum()

   df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list("ABCD"))
   df = df.cumsum()

.. ipython:: python
   :suppress:

   plt.close("all")

.. ipython:: python

   fig, axes = plt.subplots(nrows=2, ncols=2)
   plt.subplots_adjust(wspace=0.2, hspace=0.5)
   df["A"].plot(ax=axes[0, 0]);
   axes[0, 0].set_title("A");
   df["B"].plot(ax=axes[0, 1]);
   axes[0, 1].set_title("B");
   df["C"].plot(ax=axes[1, 0]);
   axes[1, 0].set_title("C");
   df["D"].plot(ax=axes[1, 1]);
   @savefig series_plot_multi.png
   axes[1, 1].set_title("D");

.. ipython:: python
   :suppress:

    plt.close("all")

Plotting with error bars

Plotting with error bars is supported in :meth:`DataFrame.plot` and :meth:`Series.plot`.

Horizontal and vertical error bars can be supplied to the xerr and yerr keyword arguments to :meth:`~DataFrame.plot()`. The error values can be specified using a variety of formats:

As a :class:`DataFrame` or dict of errors with column names matching the columns attribute of the plotting :class:`DataFrame` or matching the name attribute of the :class:`Series`.
As a str indicating which of the columns of plotting :class:`DataFrame` contain the error values.
As raw values (list, tuple, or np.ndarray). Must be the same length as the plotting :class:`DataFrame`/:class:`Series`.

Here is an example of one way to easily plot group means with standard deviations from the raw data.

.. ipython:: python

   # Generate the data
   ix3 = pd.MultiIndex.from_arrays(
       [
           ["a", "a", "a", "a", "a", "b", "b", "b", "b", "b"],
           ["foo", "foo", "foo", "bar", "bar", "foo", "foo", "bar", "bar", "bar"],
       ],
       names=["letter", "word"],
   )

   df3 = pd.DataFrame(
       {
           "data1": [9, 3, 2, 4, 3, 2, 4, 6, 3, 2],
           "data2": [9, 6, 5, 7, 5, 4, 5, 6, 5, 1],
       },
       index=ix3,
   )

   # Group by index labels and take the means and standard deviations
   # for each group
   gp3 = df3.groupby(level=("letter", "word"))
   means = gp3.mean()
   errors = gp3.std()
   means
   errors

   # Plot
   fig, ax = plt.subplots()
   @savefig errorbar_example.png
   means.plot.bar(yerr=errors, ax=ax, capsize=4, rot=0);

.. ipython:: python
   :suppress:

   plt.close("all")

Asymmetrical error bars are also supported, however raw error values must be provided in this case. For a N length :class:`Series`, a 2xN array should be provided indicating lower and upper (or left and right) errors. For a MxN :class:`DataFrame`, asymmetrical errors should be in a Mx2xN array.

Here is an example of one way to plot the min/max range using asymmetrical error bars.

.. ipython:: python

   mins = gp3.min()
   maxs = gp3.max()

   # errors should be positive, and defined in the order of lower, upper
   errors = [[means[c] - mins[c], maxs[c] - means[c]] for c in df3.columns]

   # Plot
   fig, ax = plt.subplots()
   @savefig errorbar_asymmetrical_example.png
   means.plot.bar(yerr=errors, ax=ax, capsize=4, rot=0);

.. ipython:: python
   :suppress:

   plt.close("all")

Plotting tables

Plotting with matplotlib table is now supported in :meth:`DataFrame.plot` and :meth:`Series.plot` with a table keyword. The table keyword can accept bool, :class:`DataFrame` or :class:`Series`. The simple way to draw a table is to specify table=True. Data will be transposed to meet matplotlib's default layout.

.. ipython:: python
   :suppress:

   np.random.seed(123456)

.. ipython:: python

   fig, ax = plt.subplots(1, 1, figsize=(7, 6.5))
   df = pd.DataFrame(np.random.rand(5, 3), columns=["a", "b", "c"])
   ax.xaxis.tick_top()  # Display x-axis ticks on top.

   @savefig line_plot_table_true.png
   df.plot(table=True, ax=ax);

.. ipython:: python
   :suppress:

   plt.close("all")

Also, you can pass a different :class:`DataFrame` or :class:`Series` to the table keyword. The data will be drawn as displayed in print method (not transposed automatically). If required, it should be transposed manually as seen in the example below.

.. ipython:: python

   fig, ax = plt.subplots(1, 1, figsize=(7, 6.75))
   ax.xaxis.tick_top()  # Display x-axis ticks on top.

   @savefig line_plot_table_data.png
   df.plot(table=np.round(df.T, 2), ax=ax);

.. ipython:: python
   :suppress:

   plt.close("all")

There also exists a helper function pandas.plotting.table, which creates a table from :class:`DataFrame` or :class:`Series`, and adds it to an matplotlib.Axes instance. This function can accept keywords which the matplotlib table has.

.. ipython:: python

   from pandas.plotting import table

   fig, ax = plt.subplots(1, 1)

   table(ax, np.round(df.describe(), 2), loc="upper right", colWidths=[0.2, 0.2, 0.2]);

   @savefig line_plot_table_describe.png
   df.plot(ax=ax, ylim=(0, 2), legend=None);

.. ipython:: python
   :suppress:

   plt.close("all")

Note: You can get table instances on the axes using axes.tables property for further decorations. See the matplotlib table documentation for more.

Colormaps

A potential issue when plotting a large number of columns is that it can be difficult to distinguish some series due to repetition in the default colors. To remedy this, DataFrame plotting supports the use of the colormap argument, which accepts either a Matplotlib colormap or a string that is a name of a colormap registered with Matplotlib. A visualization of the default matplotlib colormaps is available here.

As matplotlib does not directly support colormaps for line-based plots, the colors are selected based on an even spacing determined by the number of columns in the DataFrame. There is no consideration made for background color, so some colormaps will produce lines that are not easily visible.

To use the cubehelix colormap, we can pass colormap='cubehelix'.

.. ipython:: python
   :suppress:

   np.random.seed(123456)

.. ipython:: python

   df = pd.DataFrame(np.random.randn(1000, 10), index=ts.index)
   df = df.cumsum()

   plt.figure();

   @savefig cubehelix.png
   df.plot(colormap="cubehelix");

.. ipython:: python
   :suppress:

   plt.close("all")

Alternatively, we can pass the colormap itself:

.. ipython:: python

   from matplotlib import cm

   plt.figure();

   @savefig cubehelix_cm.png
   df.plot(colormap=cm.cubehelix);

.. ipython:: python
   :suppress:

   plt.close("all")

Colormaps can also be used other plot types, like bar charts:

.. ipython:: python
   :suppress:

   np.random.seed(123456)

.. ipython:: python

   dd = pd.DataFrame(np.random.randn(10, 10)).map(abs)
   dd = dd.cumsum()

   plt.figure();

   @savefig greens.png
   dd.plot.bar(colormap="Greens");

.. ipython:: python
   :suppress:

   plt.close("all")

Parallel coordinates charts:

.. ipython:: python

   plt.figure();

   @savefig parallel_gist_rainbow.png
   parallel_coordinates(data, "Name", colormap="gist_rainbow");

.. ipython:: python
   :suppress:

   plt.close("all")

Andrews curves charts:

.. ipython:: python

   plt.figure();

   @savefig andrews_curve_winter.png
   andrews_curves(data, "Name", colormap="winter");

.. ipython:: python
   :suppress:

   plt.close("all")

Plotting directly with Matplotlib

In some situations it may still be preferable or necessary to prepare plots directly with matplotlib, for instance when a certain type of plot or customization is not (yet) supported by pandas. Series and DataFrame objects behave like arrays and can therefore be passed directly to matplotlib functions without explicit casts.

pandas also automatically registers formatters and locators that recognize date indices, thereby extending date and time support to practically all plot types available in matplotlib. Although this formatting does not provide the same level of refinement you would get when plotting via pandas, it can be faster when plotting a large number of points.

.. ipython:: python
   :suppress:

   np.random.seed(123456)

.. ipython:: python

   price = pd.Series(
       np.random.randn(150).cumsum(),
       index=pd.date_range("2000-1-1", periods=150, freq="B"),
   )
   ma = price.rolling(20).mean()
   mstd = price.rolling(20).std()

   plt.figure();

   plt.plot(price.index, price, "k");
   plt.plot(ma.index, ma, "b");
   @savefig bollinger.png
   plt.fill_between(mstd.index, ma - 2 * mstd, ma + 2 * mstd, color="b", alpha=0.2);

.. ipython:: python
   :suppress:

    plt.close("all")

Plotting backends

Starting in version 0.25, pandas can be extended with third-party plotting backends. The main idea is letting users select a plotting backend different than the provided one based on Matplotlib.

This can be done by passing 'backend.module' as the argument backend in plot function. For example:

>>> Series([1, 2, 3]).plot(backend="backend.module")

Alternatively, you can also set this option globally, do you don't need to specify the keyword in each plot call. For example:

>>> pd.set_option("plotting.backend", "backend.module")
>>> pd.Series([1, 2, 3]).plot()

Or:

>>> pd.options.plotting.backend = "backend.module"
>>> pd.Series([1, 2, 3]).plot()

This would be more or less equivalent to:

>>> import backend.module
>>> backend.module.plot(pd.Series([1, 2, 3]))

The backend module can then use other visualization tools (Bokeh, Altair, hvplot,...) to generate the plots. Some libraries implementing a backend for pandas are listed on the ecosystem :ref:`ecosystem.visualization` page.

Developers guide can be found at https://pandas.pydata.org/docs/dev/development/extending.html#plotting-backends

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visualization.rst

File metadata and controls

Chart visualization

Basic plotting: plot

Other plots

Bar plots

Histograms

Box plots

Area plot

Scatter plot

Hexagonal bin plot

Pie plot

Plotting with missing data

Plotting tools

Scatter matrix plot

Density plot

Andrews curves

Parallel coordinates

Lag plot

Autocorrelation plot

Bootstrap plot

RadViz

Plot formatting

Setting the plot style

General plot style arguments

Controlling the legend

Controlling the labels

Scales

Plotting on a secondary y-axis

Custom formatters for timeseries plots

Suppressing tick resolution adjustment

Automatic date tick adjustment

Subplots

Using layout and targeting multiple axes

Plotting with error bars

Plotting tables

Colormaps

Plotting directly with Matplotlib

Plotting backends

Basic plotting: `plot`