# Visualization

We use the standard convention for referencing the matplotlib API:

    In [1]: import matplotlib.pyplot as plt

    In [2]: plt.close('all')

We provide the basics in pandas to easily create decent looking plots. See the ecosystem 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 cookbook for some advanced strategies.

The `plot` method on Series and DataFrame is just a simple wrapper around `plt.plot()`:

    In [3]: ts = pd.Series(np.random.randn(1000),
       ...:                index=pd.date_range('1/1/2000', periods=1000))
       ...:

    In [4]: ts = ts.cumsum()

    In [5]: ts.plot()
    Out[5]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe540f70c40>

If the index consists of dates, it calls `gcf().autofmt_xdate()` to try to format the x-axis nicely as per above.

On DataFrame, `plot()` is a convenience to plot all of the columns with labels:

    In [6]: df = pd.DataFrame(np.random.randn(1000, 4),
       ...:                   index=ts.index, columns=list('ABCD'))
       ...:

    In [7]: df = df.cumsum()

    In [8]: plt.figure();

    In [9]: df.plot();

You can plot one column versus another using the x and y keywords in `plot()`:

    In [10]: df3 = pd.DataFrame(np.random.randn(1000, 2), columns=['B', 'C']).cumsum()

    In [11]: df3['A'] = pd.Series(list(range(len(df))))

    In [12]: df3.plot(x='A', y='B')
    Out[12]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe540e07f40>

Note

For more formatting and styling options, see formatting below.

## 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 `plot()`, and include:

- ‘bar’ or ‘barh’ for bar plots
- ‘hist’ for histogram
- ‘box’ for boxplot
- ‘kde’ or ‘density’ for density plots
- ‘area’ for area plots
- ‘scatter’ for scatter plots
- ‘hexbin’ for hexagonal bin plots
- ‘pie’ for pie plots

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

    In [13]: plt.figure();

    In [14]: 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:

    In [15]: df = pd.DataFrame()

    In [16]: 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 DataFrame.hist(), and DataFrame.boxplot() methods, which use a separate interface.

Finally, there are several plotting functions in `pandas.plotting` that take a `Series` or `DataFrame` as an argument. These include:

- Scatter Matrix
- Andrews Curves
- Parallel Coordinates
- Lag Plot
- Autocorrelation Plot
- Bootstrap Plot
- RadViz

Plots may also be adorned with errorbars or tables.

### Bar plots

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

    In [17]: plt.figure();

    In [18]: df.iloc[5].plot.bar()
    Out[18]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe540d92400>

    In [19]: plt.axhline(0, color='k');

Calling a DataFrame’s `plot.bar()` method produces a multiple bar plot:

    In [20]: df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])

    In [21]: df2.plot.bar();

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

    In [22]: df2.plot.bar(stacked=True);

To get horizontal bar plots, use the `barh` method:

    In [23]: df2.plot.barh(stacked=True);

### Histograms

Histograms can be drawn by using the `DataFrame.plot.hist()` and `Series.plot.hist()` methods.

    In [24]: df4 = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),
       ....:                     'c': np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
       ....:

    In [25]: plt.figure();

    In [26]: df4.plot.hist(alpha=0.5)
    Out[26]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe540ba3460>

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

    In [27]: plt.figure();

    In [28]: df4.plot.hist(stacked=True, bins=20)
    Out[28]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe540b6bbe0>

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

    In [29]: plt.figure();

    In [30]: df4['a'].plot.hist(orientation='horizontal', cumulative=True)
    Out[30]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5409c8ee0>

See the `hist` method and the matplotlib hist documentation for more.

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

    In [31]: plt.figure();

    In [32]: df['A'].diff().hist()
    Out[32]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe540a36100>

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

    In [33]: plt.figure()
    Out[33]: <Figure size 640x480 with 0 Axes>

    In [34]: df.diff().hist(color='k', alpha=0.5, bins=50)
    Out[34]:
    array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fe5408bbdc0>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe5408eb880>],
           [<matplotlib.axes._subplots.AxesSubplot object at 0x7fe540898d30>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe540851f10>]],
          dtype=object)

The `by` keyword can be specified to plot grouped histograms:

    In [35]: data = pd.Series(np.random.randn(1000))

    In [36]: data.hist(by=np.random.randint(0, 4, 1000), figsize=(6, 4))
    Out[36]:
    array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fe5405eb310>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe5405859d0>],
           [<matplotlib.axes._subplots.AxesSubplot object at 0x7fe540531e80>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe540569340>]],
          dtype=object)

### Box plots

Boxplot can be drawn calling `Series.plot.box()` and `DataFrame.plot.box()`, or `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).

    In [37]: df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])

    In [38]: df.plot.box()
    Out[38]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe54044e2e0>

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 return_type.

    In [39]: color = {'boxes': 'DarkGreen', 'whiskers': 'DarkOrange',
       ....:          'medians': 'DarkBlue', 'caps': 'Gray'}
       ....:

    In [40]: df.plot.box(color=color, sym='r+')
    Out[40]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5403f65e0>

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.

    In [41]: df.plot.box(vert=False, positions=[1, 4, 5, 6, 8])
    Out[41]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5403488e0>

See the `boxplot` method and the matplotlib boxplot documentation for more.

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

    In [42]: df = pd.DataFrame(np.random.rand(10, 5))

    In [43]: plt.figure();

    In [44]: bp = df.boxplot()

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

    In [45]: df = pd.DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2'])

    In [46]: df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])

    In [47]: plt.figure();

    In [48]: bp = df.boxplot(by='X')

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

    In [49]: df = pd.DataFrame(np.random.rand(10, 3), columns=['Col1', 'Col2', 'Col3'])

    In [50]: df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])

    In [51]: df['Y'] = pd.Series(['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B'])

    In [52]: plt.figure();

    In [53]: bp = df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y'])

Warning

The default changed from `'dict'` to `'axes'` in version 0.19.0.

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`.

    In [54]: np.random.seed(1234)

    In [55]: df_box = pd.DataFrame(np.random.randn(50, 2))

    In [56]: df_box['g'] = np.random.choice(['A', 'B'], size=50)

    In [57]: df_box.loc[df_box['g'] == 'B', 1] += 3

    In [58]: bp = df_box.boxplot(by='g')

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.

    In [59]: bp = df_box.groupby('g').boxplot()

### Area plot

You can create area plots with `Series.plot.area()` and `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 `dataframe.dropna()` or `dataframe.fillna()` before calling plot.

    In [60]: df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])

    In [61]: df.plot.area();

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

    In [62]: df.plot.area(stacked=False);

### Scatter plot

Scatter plot can be drawn by using the `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.

    In [63]: df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])

    In [64]: 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.

    In [65]: ax = df.plot.scatter(x='a', y='b', color='DarkBlue', label='Group 1');

    In [66]: df.plot.scatter(x='c', y='d', color='DarkGreen', label='Group 2', ax=ax);

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

    In [67]: df.plot.scatter(x='a', y='b', c='c', s=50);

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

    In [68]: df.plot.scatter(x='a', y='b', s=df['c'] * 200);

See the `scatter` method and the matplotlib scatter documentation for more.

### Hexagonal bin plot

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

    In [69]: df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])

    In [70]: df['b'] = df['b'] + np.arange(1000)

    In [71]: df.plot.hexbin(x='a', y='b', gridsize=25)
    Out[71]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5449ecc10>

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.

    In [72]: df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])

    In [73]: df['b'] = df['b'] = df['b'] + np.arange(1000)

    In [74]: df['z'] = np.random.uniform(0, 3, 1000)

    In [75]: df.plot.hexbin(x='a', y='b', C='z', reduce_C_function=np.max, gridsize=25)
    Out[75]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5459089d0>

See the `hexbin` method and the matplotlib hexbin documentation for more.

### Pie plot

You can create a pie plot with `DataFrame.plot.pie()` or `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.

    In [76]: series = pd.Series(3 * np.random.rand(4),
       ....:                    index=['a', 'b', 'c', 'd'], name='series')
       ....:

    In [77]: series.plot.pie(figsize=(6, 6))
    Out[77]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe545908a00>

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 `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.

    In [78]: df = pd.DataFrame(3 * np.random.rand(4, 2),
       ....:                   index=['a', 'b', 'c', 'd'], columns=['x', 'y'])
       ....:

    In [79]: df.plot.pie(subplots=True, figsize=(8, 4))
    Out[79]:
    array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fe56eaf1970>,
           <matplotlib.axes._subplots.AxesSubplot object at 0x7fe55d71fe80>],
          dtype=object)

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 `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 `matplotlib.pyplot.pie()` can be used.

    In [80]: series.plot.pie(labels=['AA', 'BB', 'CC', 'DD'], colors=['r', 'g', 'b', 'c'],
       ....:                 autopct='%.2f', fontsize=20, figsize=(6, 6))
       ....:
    Out[80]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5449285e0>

If you pass values whose sum total is less than 1.0, matplotlib draws a semicircle.

    In [81]: series = pd.Series([0.1] * 4, index=['a', 'b', 'c', 'd'], name='series2')

    In [82]: series.plot.pie(figsize=(6, 6))
    Out[82]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe544b30310>

See the matplotlib pie documentation for more.

## 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 `fillna()` or `dropna()` before plotting.

## Plotting Tools

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

### Scatter matrix plot

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

    In [83]: from pandas.plotting import scatter_matrix

    In [84]: df = pd.DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])

    In [85]: scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal='kde')
    Out[85]:
    array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fe545b3c820>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe545b75940>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe5588a8d00>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe55eac5940>],
           [<matplotlib.axes._subplots.AxesSubplot object at 0x7fe5445d7520>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe5446fbdf0>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe5446fbca0>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe5449bf880>],
           [<matplotlib.axes._subplots.AxesSubplot object at 0x7fe54523c4f0>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe544f01eb0>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe544f59f40>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe545018430>],
           [<matplotlib.axes._subplots.AxesSubplot object at 0x7fe5450c67f0>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe545114070>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe5452464c0>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7fe5453655b0>]],
          dtype=object)

### Density plot

You can create density plots using the `Series.plot.kde()` and `DataFrame.plot.kde()` methods.

    In [86]: ser = pd.Series(np.random.randn(1000))

    In [87]: ser.plot.kde()
    Out[87]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5458d1f10>

### 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.

    In [88]: from pandas.plotting import andrews_curves

    In [89]: data = pd.read_csv('data/iris.data')

    In [90]: plt.figure()
    Out[90]: <Figure size 640x480 with 0 Axes>

    In [91]: andrews_curves(data, 'Name')
    Out[91]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe558663a30>

### 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.

    In [92]: from pandas.plotting import parallel_coordinates

    In [93]: data = pd.read_csv('data/iris.data')

    In [94]: plt.figure()
    Out[94]: <Figure size 640x480 with 0 Axes>

    In [95]: parallel_coordinates(data, 'Name')
    Out[95]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe55827bf70>

### 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:]`.

    In [96]: from pandas.plotting import lag_plot

    In [97]: plt.figure()
    Out[97]: <Figure size 640x480 with 0 Axes>

    In [98]: spacing = np.linspace(-99 * np.pi, 99 * np.pi, num=1000)

    In [99]: data = pd.Series(0.1 * np.random.rand(1000) + 0.9 * np.sin(spacing))

    In [100]: lag_plot(data)
    Out[100]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe55d507400>

### 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.

    In [101]: from pandas.plotting import autocorrelation_plot

    In [102]: plt.figure()
    Out[102]: <Figure size 640x480 with 0 Axes>

    In [103]: spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)

    In [104]: data = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))

    In [105]: autocorrelation_plot(data)
    Out[105]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe55d553d00>

### 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.

    In [106]: from pandas.plotting import bootstrap_plot

    In [107]: data = pd.Series(np.random.rand(1000))

    In [108]: bootstrap_plot(data, size=50, samples=500, color='grey')
    Out[108]: <Figure size 640x480 with 6 Axes>

### 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.

    In [109]: from pandas.plotting import radviz

    In [110]: data = pd.read_csv('data/iris.data')

    In [111]: plt.figure()
    Out[111]: <Figure size 640x480 with 0 Axes>

    In [112]: radviz(data, 'Name')
    Out[112]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe544f563a0>

## 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:

    In [113]: plt.figure();

    In [114]: ts.plot(style='k--', label='Series');

For each kind of plot (e.g. line, bar, scatter) any additional arguments keywords are passed along to the corresponding matplotlib function (`ax.plot()`, `ax.bar()`, `ax.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.

    In [115]: df = pd.DataFrame(np.random.randn(1000, 4),
       .....:                   index=ts.index, columns=list('ABCD'))
       .....:

    In [116]: df = df.cumsum()

    In [117]: df.plot(legend=False)
    Out[117]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5452a37c0>

### Scales

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

    In [118]: ts = pd.Series(np.random.randn(1000),
       .....:                index=pd.date_range('1/1/2000', periods=1000))
       .....:

    In [119]: ts = np.exp(ts.cumsum())

    In [120]: ts.plot(logy=True)
    Out[120]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe558344df0>

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:

    In [121]: df.A.plot()
    Out[121]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe544908f10>

    In [122]: df.B.plot(secondary_y=True, style='g')
    Out[122]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe545a29ac0>

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

    In [123]: plt.figure()
    Out[123]: <Figure size 640x480 with 0 Axes>

    In [124]: ax = df.plot(secondary_y=['A', 'B'])

    In [125]: ax.set_ylabel('CD scale')
    Out[125]: Text(0, 0.5, 'CD scale')

    In [126]: ax.right_ax.set_ylabel('AB scale')
    Out[126]: Text(0, 0.5, 'AB scale')

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:

    In [127]: plt.figure()
    Out[127]: <Figure size 640x480 with 0 Axes>

    In [128]: df.plot(secondary_y=['A', 'B'], mark_right=False)
    Out[128]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5460c0f10>

### 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:

    In [129]: plt.figure()
    Out[129]: <Figure size 640x480 with 0 Axes>

    In [130]: df.A.plot()
    Out[130]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5446027c0>

Using the `x_compat` parameter, you can suppress this behavior:

    In [131]: plt.figure()
    Out[131]: <Figure size 640x480 with 0 Axes>

    In [132]: df.A.plot(x_compat=True)
    Out[132]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe558336280>

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:

    In [133]: plt.figure()
    Out[133]: <Figure size 640x480 with 0 Axes>

    In [134]: with pd.plotting.plot_params.use('x_compat', True):
       .....:     df.A.plot(color='r')
       .....:     df.B.plot(color='g')
       .....:     df.C.plot(color='b')
       .....:

### Automatic date tick adjustment

New in version 0.20.0.

`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 `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:

    In [135]: df.plot(subplots=True, figsize=(6, 6));

### 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.

    In [136]: df.plot(subplots=True, layout=(2, 3), figsize=(6, 6), sharex=False);

The above example is identical to using:

    In [137]: df.plot(subplots=True, layout=(2, -1), figsize=(6, 6), sharex=False);

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.

    In [138]: fig, axes = plt.subplots(4, 4, figsize=(6, 6))

    In [139]: plt.subplots_adjust(wspace=0.5, hspace=0.5)

    In [140]: target1 = [axes[0][0], axes[1][1], axes[2][2], axes[3][3]]

    In [141]: target2 = [axes[3][0], axes[2][1], axes[1][2], axes[0][3]]

    In [142]: df.plot(subplots=True, ax=target1, legend=False, sharex=False, sharey=False);

    In [143]: (-df).plot(subplots=True, ax=target2, legend=False,
       .....:            sharex=False, sharey=False);
       .....:

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

    In [144]: fig, axes = plt.subplots(nrows=2, ncols=2)

    In [145]: df['A'].plot(ax=axes[0, 0]);

    In [146]: axes[0, 0].set_title('A');

    In [147]: df['B'].plot(ax=axes[0, 1]);

    In [148]: axes[0, 1].set_title('B');

    In [149]: df['C'].plot(ax=axes[1, 0]);

    In [150]: axes[1, 0].set_title('C');

    In [151]: df['D'].plot(ax=axes[1, 1]);

    In [152]: axes[1, 1].set_title('D');

### Plotting with error bars

Plotting with error bars is supported in `DataFrame.plot()` and `Series.plot()`.

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

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

Asymmetrical error bars are also supported, however raw error values must be provided in this case. For a `M` length `Series`, a `Mx2` array should be provided indicating lower and upper (or left and right) errors. For a `MxN` `DataFrame`, asymmetrical errors should be in a `Mx2xN` array.

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

    # Generate the data
    In [153]: ix3 = pd.MultiIndex.from_arrays([
       .....:     ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'],
       .....:     ['foo', 'foo', 'bar', 'bar', 'foo', 'foo', 'bar', 'bar']],
       .....:     names=['letter', 'word'])
       .....:

    In [154]: df3 = pd.DataFrame({'data1': [3, 2, 4, 3, 2, 4, 3, 2],
       .....:                     'data2': [6, 5, 7, 5, 4, 5, 6, 5]}, index=ix3)
       .....:

    # Group by index labels and take the means and standard deviations
    # for each group
    In [155]: gp3 = df3.groupby(level=('letter', 'word'))

    In [156]: means = gp3.mean()

    In [157]: errors = gp3.std()

    In [158]: means
    Out[158]:
                 data1  data2
    letter word
    a      bar     3.5    6.0
           foo     2.5    5.5
    b      bar     2.5    5.5
           foo     3.0    4.5

    In [159]: errors
    Out[159]:
                    data1     data2
    letter word
    a      bar   0.707107  1.414214
           foo   0.707107  0.707107
    b      bar   0.707107  0.707107
           foo   1.414214  0.707107

    # Plot
    In [160]: fig, ax = plt.subplots()

    In [161]: means.plot.bar(yerr=errors, ax=ax, capsize=4)
    Out[161]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe545308430>

### Plotting tables

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

    In [162]: fig, ax = plt.subplots(1, 1)

    In [163]: df = pd.DataFrame(np.random.rand(5, 3), columns=['a', 'b', 'c'])

    In [164]: ax.get_xaxis().set_visible(False)   # Hide Ticks

    In [165]: df.plot(table=True, ax=ax)
    Out[165]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5581590d0>

Also, you can pass a different `DataFrame` or `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.

    In [166]: fig, ax = plt.subplots(1, 1)

    In [167]: ax.get_xaxis().set_visible(False)   # Hide Ticks

    In [168]: df.plot(table=np.round(df.T, 2), ax=ax)
    Out[168]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe5401eda30>

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

    In [169]: from pandas.plotting import table

    In [170]: fig, ax = plt.subplots(1, 1)

    In [171]: table(ax, np.round(df.describe(), 2),
       .....:       loc='upper right', colWidths=[0.2, 0.2, 0.2])
       .....:
    Out[171]: <matplotlib.table.Table at 0x7fe5453ca820>

    In [172]: df.plot(ax=ax, ylim=(0, 2), legend=None)
    Out[172]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe546400b80>

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'`.

    In [173]: df = pd.DataFrame(np.random.randn(1000, 10), index=ts.index)

    In [174]: df = df.cumsum()

    In [175]: plt.figure()
    Out[175]: <Figure size 640x480 with 0 Axes>

    In [176]: df.plot(colormap='cubehelix')
    Out[176]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe54542dd00>

Alternatively, we can pass the colormap itself:

    In [177]: from matplotlib import cm

    In [178]: plt.figure()
    Out[178]: <Figure size 640x480 with 0 Axes>

    In [179]: df.plot(colormap=cm.cubehelix)
    Out[179]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe546430970>

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

    In [180]: dd = pd.DataFrame(np.random.randn(10, 10)).applymap(abs)

    In [181]: dd = dd.cumsum()

    In [182]: plt.figure()
    Out[182]: <Figure size 640x480 with 0 Axes>

    In [183]: dd.plot.bar(colormap='Greens')
    Out[183]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe545fe9e80>

Parallel coordinates charts:

    In [184]: plt.figure()
    Out[184]: <Figure size 640x480 with 0 Axes>

    In [185]: parallel_coordinates(data, 'Name', colormap='gist_rainbow')
    Out[185]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe544723760>

Andrews curves charts:

    In [186]: plt.figure()
    Out[186]: <Figure size 640x480 with 0 Axes>

    In [187]: andrews_curves(data, 'Name', colormap='winter')
    Out[187]: <matplotlib.axes._subplots.AxesSubplot at 0x7fe55880d610>

## 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.

    In [188]: price = pd.Series(np.random.randn(150).cumsum(),
       .....:                   index=pd.date_range('2000-1-1', periods=150, freq='B'))
       .....:

    In [189]: ma = price.rolling(20).mean()

    In [190]: mstd = price.rolling(20).std()

    In [191]: plt.figure()
    Out[191]: <Figure size 640x480 with 0 Axes>

    In [192]: plt.plot(price.index, price, 'k')
    Out[192]: [<matplotlib.lines.Line2D at 0x7fe544b54280>]

    In [193]: plt.plot(ma.index, ma, 'b')
    Out[193]: [<matplotlib.lines.Line2D at 0x7fe5455435b0>]

    In [194]: plt.fill_between(mstd.index, ma - 2 * mstd, ma + 2 * mstd,
       .....:                  color='b', alpha=0.2)
       .....:
    Out[194]: <matplotlib.collections.PolyCollection at 0x7fe5583f4ee0>

## Trellis plotting interface

Warning

The `rplot` trellis plotting interface has been removed. Please use external packages like seaborn for similar but more refined functionality and refer to our 0.18.1 documentation here for how to convert to using it.

Scroll To Top