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 page for visualization
libraries that go beyond the basics documented here.
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]: np.random.seed(123456)
In [4]: ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
In [5]: ts = ts.cumsum()
In [6]: ts.plot();
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 [7]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list("ABCD"))
In [8]: df = df.cumsum()
In [9]: plt.figure();
In [10]: df.plot();
You can plot one column versus another using the x and y keywords in
plot():
In [11]: df3 = pd.DataFrame(np.random.randn(1000, 2), columns=["B", "C"]).cumsum()
In [12]: df3["A"] = pd.Series(list(range(len(df))))
In [13]: df3.plot(x="A", y="B");
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 [14]: plt.figure();
In [15]: 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 [16]: df = pd.DataFrame()
In [17]: 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 [18]: plt.figure();
In [19]: df.iloc[5].plot.bar();
In [20]: plt.axhline(0, color="k");
Calling a DataFrame’s plot.bar() method produces a multiple
bar plot:
In [21]: df2 = pd.DataFrame(np.random.rand(10, 4), columns=["a", "b", "c", "d"])
In [22]: df2.plot.bar();
To produce a stacked bar plot, pass stacked=True:
In [23]: df2.plot.bar(stacked=True);
To get horizontal bar plots, use the barh method:
In [24]: df2.plot.barh(stacked=True);
Histograms
Histograms can be drawn by using the DataFrame.plot.hist() and Series.plot.hist() methods.
In [25]: 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 [26]: plt.figure();
In [27]: df4.plot.hist(alpha=0.5);
A histogram can be stacked using stacked=True. Bin size can be changed
using the bins keyword.
In [28]: plt.figure();
In [29]: df4.plot.hist(stacked=True, bins=20);
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 [30]: plt.figure();
In [31]: df4["a"].plot.hist(orientation="horizontal", cumulative=True);
See the hist method and the
matplotlib hist documentation for more.
The existing interface DataFrame.hist to plot histogram still can be used.
In [32]: plt.figure();
In [33]: df["A"].diff().hist();
DataFrame.hist() plots the histograms of the columns on multiple
subplots:
In [34]: plt.figure();
In [35]: df.diff().hist(color="k", alpha=0.5, bins=50);
The by keyword can be specified to plot grouped histograms:
In [36]: data = pd.Series(np.random.randn(1000))
In [37]: data.hist(by=np.random.randint(0, 4, 1000), figsize=(6, 4));
In addition, the by keyword can also be specified in DataFrame.plot.hist().
Changed in version 1.4.0.
In [38]: 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,
....: },
....: )
....:
In [39]: data.plot.hist(by=["a", "b"], figsize=(10, 5));
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 [40]: df = pd.DataFrame(np.random.rand(10, 5), columns=["A", "B", "C", "D", "E"])
In [41]: 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
return_type.
In [42]: color = {
....: "boxes": "DarkGreen",
....: "whiskers": "DarkOrange",
....: "medians": "DarkBlue",
....: "caps": "Gray",
....: }
....:
In [43]: df.plot.box(color=color, sym="r+");
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 [44]: df.plot.box(vert=False, positions=[1, 4, 5, 6, 8]);
See the boxplot method and the
matplotlib boxplot documentation for more.
The existing interface DataFrame.boxplot to plot boxplot still can be used.
In [45]: df = pd.DataFrame(np.random.rand(10, 5))
In [46]: plt.figure();
In [47]: bp = df.boxplot()
You can create a stratified boxplot using the by keyword argument to create
groupings. For instance,
In [48]: df = pd.DataFrame(np.random.rand(10, 2), columns=["Col1", "Col2"])
In [49]: df["X"] = pd.Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])
In [50]: plt.figure();
In [51]: bp = df.boxplot(by="X")
You can also pass a subset of columns to plot, as well as group by multiple
columns:
In [52]: df = pd.DataFrame(np.random.rand(10, 3), columns=["Col1", "Col2", "Col3"])
In [53]: df["X"] = pd.Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])
In [54]: df["Y"] = pd.Series(["A", "B", "A", "B", "A", "B", "A", "B", "A", "B"])
In [55]: plt.figure();
In [56]: bp = df.boxplot(column=["Col1", "Col2"], by=["X", "Y"])
You could also create groupings with DataFrame.plot.box(), for instance:
Changed in version 1.4.0.
In [57]: df = pd.DataFrame(np.random.rand(10, 3), columns=["Col1", "Col2", "Col3"])
In [58]: df["X"] = pd.Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])
In [59]: plt.figure();
In [60]: bp = df.plot.box(column=["Col1", "Col2"], by="X")
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
2-D ndarray of axes
'axes'
'axes'
Series of axes
'dict'
dict of artists
'dict'
Series of dicts of artists
'both'
namedtuple
'both'
Series of namedtuples
Groupby.boxplot always returns a Series of return_type.
In [61]: np.random.seed(1234)
In [62]: df_box = pd.DataFrame(np.random.randn(50, 2))
In [63]: df_box["g"] = np.random.choice(["A", "B"], size=50)
In [64]: df_box.loc[df_box["g"] == "B", 1] += 3
In [65]: 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 [66]: 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 [67]: df = pd.DataFrame(np.random.rand(10, 4), columns=["a", "b", "c", "d"])
In [68]: df.plot.area();
To produce an unstacked plot, pass stacked=False. Alpha value is set to 0.5 unless otherwise specified:
In [69]: 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 [70]: df = pd.DataFrame(np.random.rand(50, 4), columns=["a", "b", "c", "d"])
In [71]: df["species"] = pd.Categorical(
....: ["setosa"] * 20 + ["versicolor"] * 20 + ["virginica"] * 10
....: )
....:
In [72]: 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 [73]: ax = df.plot.scatter(x="a", y="b", color="DarkBlue", label="Group 1")
In [74]: 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 [75]: df.plot.scatter(x="a", y="b", c="c",
s=50);
If a categorical column is passed to c, then a discrete colorbar will be produced:
New in version 1.3.0.
In [76]: df.plot.scatter(x="a", y="b", c="species", cmap="viridis", 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 [77]: 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 [78]: df = pd.DataFrame(np.random.randn(1000, 2), columns=["a", "b"])
In [79]: df["b"] = df["b"] + np.arange(1000)
In [80]: 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.
In [81]: df = pd.DataFrame(np.random.randn(1000, 2), columns=["a", "b"])
In [82]: df["b"] = df["b"] + np.arange(1000)
In [83]: df["z"] = np.random.uniform(0, 3, 1000)
In [84]: df.plot.hexbin(x="a", y="b", C="z", reduce_C_function=np.max, gridsize=25);
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 [85]: series = pd.Series(3 * np.random.rand(4), index=["a", "b", "c", "d"], name="series")
In [86]: series.plot.pie(figsize=(6, 6));
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 [87]: df = pd.DataFrame(
....: 3 * np.random.rand(4, 2), index=["a", "b", "c", "d"], columns=["x", "y"]
....: )
....:
In [88]: df.plot.pie(subplots=True, figsize=(8, 4));
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 [89]: 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.
In [90]: series = pd.Series([0.1] * 4, index=["a", "b", "c"
, "d"], name="series2")
In [91]: series.plot.pie(figsize=(6, 6));
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
Leave gaps at NaNs
Line (stacked)
Fill 0’s
Fill 0’s
Scatter
Drop NaNs
Histogram
Drop NaNs (column-wise)
Drop NaNs (column-wise)
Fill 0’s
Drop NaNs (column-wise)
Hexbin
Drop NaNs
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 [92]: from pandas.plotting import scatter_matrix
In [93]: df = pd.DataFrame(np.random.randn(1000, 4), columns=["a", "b", "c", "d"])
In [94]: scatter_matrix(df, alpha=0.2, figsize=(6, 6), diagonal="kde");
Density plot
You can create density plots using the Series.plot.kde() and DataFrame.plot.kde() methods.
In [95]: ser = pd.Series(np.random.randn(1000))
In [96]: ser.plot.kde();
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 [97]: from pandas.plotting import andrews_curves
In [98]: data = pd.read_csv("data/iris.data")
In [99]: plt.figure();
In [100]: 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.
In [101]: from pandas.plotting import parallel_coordinates
In [102]: data = pd.read_csv("data/iris.data")
In [103]: plt.figure();
In [104]: parallel_coordinates(data, "Name");
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 [105]: from pandas.plotting import lag_plot
In [106]: plt.figure();
In [107]: spacing = np.linspace(-99 * np.pi, 99 * np.pi, num=1000)
In [108]: data = pd.Series(0.1 * np.random.rand(1000) + 0.9 * np.sin(spacing))
In [109]: lag_plot(data);
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 [110]: from pandas.plotting import autocorrelation_plot
In [111]: plt.figure();
In [112]: spacing = np.linspace(-9 * np.pi, 9 * np.pi, num=1000)
In [113]: data = pd.Series(0.7 * np.random.rand(1000) + 0.3 * np.sin(spacing))
In [114]: autocorrelation_plot(data);
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 [115]: from pandas.plotting import bootstrap_plot
In [116]: data = pd.Series(np.random.rand(1000))
In [117]: bootstrap_plot(data, size=50, samples=500, color="grey");
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 [118]: from pandas.plotting import radviz
In [119]: data = pd.read_csv("data/iris.data")
In [120]: plt.figure();
In [121]: radviz(data, "Name");
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 [122]: plt.figure();
In [123]: 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 [124]: df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list("ABCD"))
In [125]: df = df.cumsum()
In [126]: df.plot(legend=False);
Controlling the labels
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.
In [127]: df.plot();
In [128]: df.plot(xlabel="new x", ylabel="new y");
Scales
You may pass logy to get a log-scale Y axis.
In [129]: ts = pd.Series(np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000))
In [130]: ts = np.exp(ts.cumsum())
In [131]: ts.plot(logy=True);
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 [132]: df["A"].plot();
In [133]: df["B"].plot(secondary_y=True, style="g");
To plot some columns in a DataFrame, give the column names to the secondary_y
keyword:
In [134]: plt.figure();
In [135]: ax = df.plot(secondary_y=["A", "B"])
In [136]: ax.set_ylabel("CD scale");
In [137]: ax.right_ax.set_ylabel("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 [138]: plt.figure();
In [139]: df.plot(secondary_y=["A", "B"], mark_right=False);
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
DataFrame.plot() or 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
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:
In [140]: plt.figure();
In [141]: df["A"].plot();
Using the x_compat parameter, you can suppress this behavior:
In [142]: plt.figure();
In [143]: df["A"].plot(x_compat=True);
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:
