notes_estom/Python/pandas/user_guide/visualization.md

# Visualization

We use the standard convention for referencing the matplotlib API:

``` python
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](https://pandas.pydata.org/pandas-docs/stable/ecosystem.html#ecosystem-visualization) section for visualization
libraries that go beyond the basics documented here.

::: tip Note

All calls to ``np.random`` are seeded with 123456.

:::

## Basic plotting: ``plot``

We will demonstrate the basics, see the [cookbook](cookbook.html#cookbook-plotting) for
some advanced strategies.

The ``plot`` method on Series and DataFrame is just a simple wrapper around
[``plt.plot()``](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot):

``` python
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 0x7f65d8c0ac50>
```

![series_plot_basic](https://static.pypandas.cn/public/static/images/series_plot_basic.png)

If the index consists of dates, it calls [``gcf().autofmt_xdate()``](https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.autofmt_xdate)
to try to format the x-axis nicely as per above.

On DataFrame, [``plot()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html#pandas.DataFrame.plot) is a convenience to plot all of the columns with labels:

``` python
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();
```

![frame_plot_basic](https://static.pypandas.cn/public/static/images/frame_plot_basic.png)

You can plot one column versus another using the *x* and *y* keywords in
[``plot()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html#pandas.DataFrame.plot):

``` python
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 0x7f65d97c1668>
```

![df_plot_xy](https://static.pypandas.cn/public/static/images/df_plot_xy.png)

::: tip Note

For more formatting and styling options, see
[formatting](#visualization-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()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html#pandas.DataFrame.plot), and include:

- [‘bar’](#visualization-barplot) or [‘barh’](#visualization-barplot) for bar plots
- [‘hist’](#visualization-hist) for histogram
- [‘box’](#visualization-box) for boxplot
- [‘kde’](#visualization-kde) or [‘density’](#visualization-kde) for density plots
- [‘area’](#visualization-area-plot) for area plots
- [‘scatter’](#visualization-scatter) for scatter plots
- [‘hexbin’](#visualization-hexbin) for hexagonal bin plots
- [‘pie’](#visualization-pie) for pie plots

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

``` python
In [13]: plt.figure();

In [14]: df.iloc[5].plot(kind='bar');
```

![bar_plot_ex](https://static.pypandas.cn/public/static/images/bar_plot_ex.png)

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

``` python
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()](#visualization-hist),
and [DataFrame.boxplot()](#visualization-box) methods, which use a separate interface.

Finally, there are several [plotting functions](#visualization-tools) in ``pandas.plotting``
that take a [``Series``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series) or [``DataFrame``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) as an argument. These
include:

- [Scatter Matrix](#visualization-scatter-matrix)
- [Andrews Curves](#visualization-andrews-curves)
- [Parallel Coordinates](#visualization-parallel-coordinates)
- [Lag Plot](#visualization-lag)
- [Autocorrelation Plot](#visualization-autocorrelation)
- [Bootstrap Plot](#visualization-bootstrap)
- [RadViz](#visualization-radviz)

Plots may also be adorned with [errorbars](#visualization-errorbars)
or [tables](#visualization-table).

### Bar plots

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

``` python
In [17]: plt.figure();

In [18]: df.iloc[5].plot.bar()
Out[18]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65da446a90>

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

![bar_plot_ex](https://static.pypandas.cn/public/static/images/bar_plot_ex.png)

Calling a DataFrame’s [``plot.bar()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.bar.html#pandas.DataFrame.plot.bar) method produces a multiple
bar plot:

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

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

![bar_plot_multi_ex](https://static.pypandas.cn/public/static/images/bar_plot_multi_ex.png)

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

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

![bar_plot_stacked_ex](https://static.pypandas.cn/public/static/images/bar_plot_stacked_ex.png)

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

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

![barh_plot_stacked_ex](https://static.pypandas.cn/public/static/images/barh_plot_stacked_ex.png)

### Histograms

Histograms can be drawn by using the [``DataFrame.plot.hist()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.hist.html#pandas.DataFrame.plot.hist) and [``Series.plot.hist()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.hist.html#pandas.Series.plot.hist) methods.

``` python
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 0x7f65da345e48>
```

![hist_new](https://static.pypandas.cn/public/static/images/hist_new.png)

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

``` python
In [27]: plt.figure();

In [28]: df4.plot.hist(stacked=True, bins=20)
Out[28]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65da30b9b0>
```

![hist_new_stacked](https://static.pypandas.cn/public/static/images/hist_new_stacked.png)

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

``` python
In [29]: plt.figure();

In [30]: df4['a'].plot.hist(orientation='horizontal', cumulative=True)
Out[30]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65da69fd68>
```

![hist_new_kwargs](https://static.pypandas.cn/public/static/images/hist_new_kwargs.png)

See the [``hist``](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.hist.html#matplotlib.axes.Axes.hist) method and the
[matplotlib hist documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist) for more.

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

``` python
In [31]: plt.figure();

In [32]: df['A'].diff().hist()
Out[32]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65dac9d240>
```

![hist_plot_ex](https://static.pypandas.cn/public/static/images/hist_plot_ex.png)

[``DataFrame.hist()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.hist.html#pandas.DataFrame.hist) plots the histograms of the columns on multiple
subplots:

``` python
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 0x7f6601550cc0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f66079a9400>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f65f87ac828>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f6604bd6b70>]],
      dtype=object)
```

![frame_hist_ex](https://static.pypandas.cn/public/static/images/frame_hist_ex.png)

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

``` python
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 0x7f6601550ef0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65d9b82438>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f65dc30ba58>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65f63c2320>]],
      dtype=object)
```

![grouped_hist](https://static.pypandas.cn/public/static/images/grouped_hist.png)

### Box plots

Boxplot can be drawn calling [``Series.plot.box()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.box.html#pandas.Series.plot.box) and [``DataFrame.plot.box()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.box.html#pandas.DataFrame.plot.box),
or [``DataFrame.boxplot()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.boxplot.html#pandas.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).

``` python
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 0x7f65da17f898>
```

![box_plot_new](https://static.pypandas.cn/public/static/images/box_plot_new.png)

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](#visualization-box-return).

``` python
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 0x7f65da880b00>
```

![box_new_colorize](https://static.pypandas.cn/public/static/images/box_new_colorize.png)

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.

``` python
In [41]: df.plot.box(vert=False, positions=[1, 4, 5, 6, 8])
Out[41]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65db18ffd0>
```

![box_new_kwargs](https://static.pypandas.cn/public/static/images/box_new_kwargs.png)

See the [``boxplot``](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.boxplot.html#matplotlib.axes.Axes.boxplot) method and the
[matplotlib boxplot documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot) for more.

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

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

In [43]: plt.figure();

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

![box_plot_ex](https://static.pypandas.cn/public/static/images/box_plot_ex.png)

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

``` python
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')
```

![box_plot_ex2](https://static.pypandas.cn/public/static/images/box_plot_ex2.png)

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

``` python
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'])
```

![box_plot_ex3](https://static.pypandas.cn/public/static/images/box_plot_ex3.png)

::: danger 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``.

``` python
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')
```

![boxplot_groupby](https://static.pypandas.cn/public/static/images/boxplot_groupby.png)

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.

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

![groupby_boxplot_vis](https://static.pypandas.cn/public/static/images/groupby_boxplot_vis.png)

### Area plot

You can create area plots with [``Series.plot.area()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.area.html#pandas.Series.plot.area) and [``DataFrame.plot.area()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.area.html#pandas.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*.

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

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

![area_plot_stacked](https://static.pypandas.cn/public/static/images/area_plot_stacked.png)

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

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

![area_plot_unstacked](https://static.pypandas.cn/public/static/images/area_plot_unstacked.png)

### Scatter plot

Scatter plot can be drawn by using the [``DataFrame.plot.scatter()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.scatter.html#pandas.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.

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

In [64]: df.plot.scatter(x='a', y='b');
```

![scatter_plot](https://static.pypandas.cn/public/static/images/scatter_plot.png)

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.

``` python
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);
```

![scatter_plot_repeated](https://static.pypandas.cn/public/static/images/scatter_plot_repeated.png)

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

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

![scatter_plot_colored](https://static.pypandas.cn/public/static/images/scatter_plot_colored.png)

You can pass other keywords supported by matplotlib
[``scatter``](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter). The example  below shows a
bubble chart using a column of the ``DataFrame`` as the bubble size.

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

![scatter_plot_bubble](https://static.pypandas.cn/public/static/images/scatter_plot_bubble.png)

See the [``scatter``](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.scatter.html#matplotlib.axes.Axes.scatter) method and the
[matplotlib scatter documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.scatter) for more.

### Hexagonal bin plot

You can create hexagonal bin plots with [``DataFrame.plot.hexbin()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.hexbin.html#pandas.DataFrame.plot.hexbin).
Hexbin plots can be a useful alternative to scatter plots if your data are
too dense to plot each point individually.

``` python
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 0x7f65d96e4fd0>
```

![hexbin_plot](https://static.pypandas.cn/public/static/images/hexbin_plot.png)

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.

``` python
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 0x7f65d98ea390>
```

![hexbin_plot_agg](https://static.pypandas.cn/public/static/images/hexbin_plot_agg.png)

See the [``hexbin``](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.hexbin.html#matplotlib.axes.Axes.hexbin) method and the
[matplotlib hexbin documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hexbin) for more.

### Pie plot

You can create a pie plot with [``DataFrame.plot.pie()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.pie.html#pandas.DataFrame.plot.pie) or [``Series.plot.pie()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.pie.html#pandas.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.

``` python
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 0x7f65da5ff278>
```

![series_pie_plot](https://static.pypandas.cn/public/static/images/series_pie_plot.png)

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``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.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.

``` python
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 0x7f65d915b0b8>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f65d9493a90>],
      dtype=object)
```

![df_pie_plot](https://static.pypandas.cn/public/static/images/df_pie_plot.png)

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

::: danger Warning

Most pandas plots use the ``label`` and ``color`` arguments (note the lack of “s” on those).
To be consistent with [``matplotlib.pyplot.pie()``](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.pie.html#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()``](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.pie.html#matplotlib.pyplot.pie) can be used.

``` python
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 0x7f65da0be4a8>
```

![series_pie_plot_options](https://static.pypandas.cn/public/static/images/series_pie_plot_options.png)

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

``` python
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 0x7f65d92543c8>
```

![series_pie_plot_semi](https://static.pypandas.cn/public/static/images/series_pie_plot_semi.png)

See the [matplotlib pie documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.pie) 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()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.fillna.html#pandas.DataFrame.fillna) or [``dropna()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html#pandas.DataFrame.dropna)
before plotting.

## Plotting Tools

These functions can be imported from ``pandas.plotting``
and take a [``Series``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series) or [``DataFrame``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) as an argument.

### Scatter matrix plot

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

``` python
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 0x7f65dc209da0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65d9df9588>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65d8fb4b38>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65d9834128>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f65db04d6d8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65d8cdcc88>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65d94e8278>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65d9a67860>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f65d9a67898>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65da9f43c8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65dacb7978>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65daddaf28>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f65dbe47518>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65d9016ac8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65d99540b8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x7f65d9f84668>]],
      dtype=object)
```

![scatter_matrix_kde](https://static.pypandas.cn/public/static/images/scatter_matrix_kde.png)

### Density plot

You can create density plots using the [``Series.plot.kde()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.kde.html#pandas.Series.plot.kde) and [``DataFrame.plot.kde()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.kde.html#pandas.DataFrame.plot.kde) methods.

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

In [87]: ser.plot.kde()
Out[87]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65d909c828>
```

![kde_plot](https://static.pypandas.cn/public/static/images/kde_plot.png)

### 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](https://en.wikipedia.org/wiki/Andrews_plot)
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](https://raw.github.com/pandas-dev/pandas/master/pandas/tests/data/iris.csv).

``` python
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 0x7f65da6e5518>
```

![andrews_curves](https://static.pypandas.cn/public/static/images/andrews_curves.png)

### Parallel coordinates

Parallel coordinates is a plotting technique for plotting multivariate data,
see the [Wikipedia entry](https://en.wikipedia.org/wiki/Parallel_coordinates)
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.

``` python
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 0x7f65d96fbc88>
```

![parallel_coordinates](https://static.pypandas.cn/public/static/images/parallel_coordinates.png)

### 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:]``.

``` python
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 0x7f65da8b5e10>
```

![lag_plot](https://static.pypandas.cn/public/static/images/lag_plot.png)

### 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](https://en.wikipedia.org/wiki/Correlogram) for more about
autocorrelation plots.

``` python
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 0x7f65d19556d8>
```

![autocorrelation_plot](https://static.pypandas.cn/public/static/images/autocorrelation_plot.png)

### 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.

``` python
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>
```

![bootstrap_plot](https://static.pypandas.cn/public/static/images/bootstrap_plot.png)

### 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](https://cran.r-project.org/package=Radviz/)
for more information.

**Note**: The “Iris” dataset is available [here](https://raw.github.com/pandas-dev/pandas/master/pandas/tests/data/iris.csv).

``` python
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 0x7f65f66bd630>
```

![radviz](https://static.pypandas.cn/public/static/images/radviz.png)

## 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:

``` python
In [113]: plt.figure();

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

![series_plot_basic2](https://static.pypandas.cn/public/static/images/series_plot_basic2.png)

For each kind of plot (e.g. *line*, *bar*, *scatter*) any additional arguments
keywords are passed along to the corresponding matplotlib function
([``ax.plot()``](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot),
[``ax.bar()``](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.bar.html#matplotlib.axes.Axes.bar),
[``ax.scatter()``](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.scatter.html#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.

``` python
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 0x7f65dbdbc0f0>
```

![frame_plot_basic_noleg](https://static.pypandas.cn/public/static/images/frame_plot_basic_noleg.png)

### Scales

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

``` python
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 0x7f65dbefdf98>
```

![series_plot_logy](https://static.pypandas.cn/public/static/images/series_plot_logy.png)

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:

``` python
In [121]: df.A.plot()
Out[121]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65f8ef6b00>

In [122]: df.B.plot(secondary_y=True, style='g')
Out[122]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65f6297780>
```

![series_plot_secondary_y](https://static.pypandas.cn/public/static/images/series_plot_secondary_y.png)

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

``` python
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')
```

![frame_plot_secondary_y](https://static.pypandas.cn/public/static/images/frame_plot_secondary_y.png)

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:

``` python
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 0x7f65f6102390>
```

![frame_plot_secondary_y_no_right](https://static.pypandas.cn/public/static/images/frame_plot_secondary_y_no_right.png)

### 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:

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

In [130]: df.A.plot()
Out[130]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65dc39f978>
```

![ser_plot_suppress](https://static.pypandas.cn/public/static/images/ser_plot_suppress.png)

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

``` python
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 0x7f65dc39f1d0>
```

![ser_plot_suppress_parm](https://static.pypandas.cn/public/static/images/ser_plot_suppress_parm.png)

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*:

``` python
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')
   .....:
```

![ser_plot_suppress_context](https://static.pypandas.cn/public/static/images/ser_plot_suppress_context.png)

### 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](http://matplotlib.org/users/recipes.html#fixing-common-date-annoyances) for more.

### Subplots

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

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

![frame_plot_subplots](https://static.pypandas.cn/public/static/images/frame_plot_subplots.png)

### 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.

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

![frame_plot_subplots_layout](https://static.pypandas.cn/public/static/images/frame_plot_subplots_layout.png)

The above example is identical to using:

``` python
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.

``` python
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);
   .....:
```

![frame_plot_subplots_multi_ax](https://static.pypandas.cn/public/static/images/frame_plot_subplots_multi_ax.png)

Another option is passing an ``ax`` argument to [``Series.plot()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.html#pandas.Series.plot) to plot on a particular axis:

``` python
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');
```

![series_plot_multi](https://static.pypandas.cn/public/static/images/series_plot_multi.png)

### Plotting with error bars

Plotting with error bars is supported in [``DataFrame.plot()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html#pandas.DataFrame.plot) and [``Series.plot()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.html#pandas.Series.plot).

Horizontal and vertical error bars can be supplied to the ``xerr`` and ``yerr`` keyword arguments to [``plot()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html#pandas.DataFrame.plot). The error values can be specified using a variety of formats:

- As a [``DataFrame``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) or ``dict`` of errors with column names matching the ``columns`` attribute of the plotting [``DataFrame``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) or matching the ``name`` attribute of the [``Series``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series).
- As a ``str`` indicating which of the columns of plotting [``DataFrame``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) contain the error values.
- As raw values (``list``, ``tuple``, or ``np.ndarray``). Must be the same length as the plotting [``DataFrame``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame)/[``Series``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series).

Asymmetrical error bars are also supported, however raw error values must be provided in this case. For a ``M`` length [``Series``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series), a ``Mx2`` array should be provided indicating lower and upper (or left and right) errors. For a ``MxN`` [``DataFrame``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.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.

``` python
# 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 0x7f65d1048400>
```

![errorbar_example](https://static.pypandas.cn/public/static/images/errorbar_example.png)

### Plotting tables

Plotting with matplotlib table is now supported in  [``DataFrame.plot()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html#pandas.DataFrame.plot) and [``Series.plot()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.plot.html#pandas.Series.plot) with a ``table`` keyword. The ``table`` keyword can accept ``bool``, [``DataFrame``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) or [``Series``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series). The simple way to draw a table is to specify ``table=True``. Data will be transposed to meet matplotlib’s default layout.

``` python
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 0x7f65d0ff7550>
```

![line_plot_table_true](https://static.pypandas.cn/public/static/images/line_plot_table_true.png)

Also, you can pass a different [``DataFrame``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) or [``Series``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.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.

``` python
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 0x7f65d0efdcc0>
```

![line_plot_table_data](https://static.pypandas.cn/public/static/images/line_plot_table_data.png)

There also exists a helper function ``pandas.plotting.table``, which creates a
table from [``DataFrame``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame) or [``Series``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series), and adds it to an
``matplotlib.Axes`` instance. This function can accept keywords which the
matplotlib [table](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.table) has.

``` python
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 0x7f65d0e61b38>

In [172]: df.plot(ax=ax, ylim=(0, 2), legend=None)
Out[172]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65d0eab358>
```

![line_plot_table_describe](https://static.pypandas.cn/public/static/images/line_plot_table_describe.png)

**Note**: You can get table instances on the axes using ``axes.tables`` property for further decorations. See the [matplotlib table documentation](http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.table) 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](http://matplotlib.org/api/cm_api.html)
or a string that is a name of a colormap registered with Matplotlib. A
visualization of the default matplotlib colormaps is available [here](https://matplotlib.org/examples/color/colormaps_reference.html).

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'``.

``` python
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 0x7f65d0defdd8>
```

![cubehelix](https://static.pypandas.cn/public/static/images/cubehelix.png)

Alternatively, we can pass the colormap itself:

``` python
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 0x7f65d0c22a90>
```

![cubehelix_cm](https://static.pypandas.cn/public/static/images/cubehelix_cm.png)

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

``` python
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 0x7f65d0a5de10>
```

![greens](https://static.pypandas.cn/public/static/images/greens.png)

Parallel coordinates charts:

``` python
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 0x7f65d08e5eb8>
```

![parallel_gist_rainbow](https://static.pypandas.cn/public/static/images/parallel_gist_rainbow.png)

Andrews curves charts:

``` python
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 0x7f65d06b6518>
```

![andrews_curve_winter](https://static.pypandas.cn/public/static/images/andrews_curve_winter.png)

## 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.

``` python
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 0x7f65da5f8710>]

In [193]: plt.plot(ma.index, ma, 'b')
Out[193]: [<matplotlib.lines.Line2D at 0x7f65d9ab9518>]

In [194]: plt.fill_between(mstd.index, ma - 2 * mstd, ma + 2 * mstd,
   .....:                  color='b', alpha=0.2)
   .....: 
Out[194]: <matplotlib.collections.PolyCollection at 0x7f65d9ab9128>
```

![bollinger](https://static.pypandas.cn/public/static/images/bollinger.png)

## Trellis plotting interface

::: danger Warning

The ``rplot`` trellis plotting interface has been **removed**. Please use
external packages like [seaborn](https://github.com/mwaskom/seaborn) for
similar but more refined functionality and refer to our 0.18.1 documentation
[here](http://pandas.pydata.org/pandas-docs/version/0.18.1/visualization.html)
for how to convert to using it.

:::