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If hvplot and pandas are both installed, then we can use the pandas.options.plotting.backend to control the output of pd.DataFrame.plot
and pd.Series.plot
. This notebook is meant to recreate the pandas visualization docs.
import numpy as np
import pandas as pd
pd.options.plotting.backend = 'holoviews'
Basic Plotting: plot
The plot method on Series and DataFrame is just a simple wrapper around hvplot()
:
ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
ts.plot()
If the index consists of dates, they will be sorted (as long as sort_date=True
) and formatted the x-axis nicely as per above.
On DataFrame, plot()
is a convenience to plot all of the columns with labels:
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))
df = df.cumsum()
df.plot()
You can plot one column versus another using the x and y keywords in plot()
:
df3 = pd.DataFrame(np.random.randn(1000, 2), columns=['B', 'C']).cumsum()
df3['A'] = pd.Series(list(range(len(df))))
df3.plot(x='A', y='B')
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()
. These include:
bar
orbarh
for bar plotshist
for histogrambox
for boxplotkde
ordensity
for density plotsarea
for area plotsscatter
for scatter plotshexbin
for hexagonal bin plotspie
for pie plots
For example, a bar plot can be created the following way:
df.iloc[5].plot(kind='bar')
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:
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 hvplot.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:
import holoviews as hv
df.iloc[5].plot.bar() * hv.HLine(0).opts(color='k')
Calling a DataFrame’s plot.bar()
method produces a multiple bar plot:
df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
df2.plot.bar()
To produce a stacked bar plot, pass stacked=True
:
df2.plot.bar(stacked=True)
To get horizontal bar plots, use the barh
method:
df2.plot.barh(stacked=True)
Histograms
Histogram can be drawn by using the DataFrame.plot.hist()
and Series.plot.hist()
methods.
df4 = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),
'c': np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
df4.plot.hist(alpha=0.5)
Using the matplotlib backend, histograms can be stacked using stacked=True
; stacking is not yet supported in the holoviews backend. Bin size can be changed by bins
keyword.
# Stacked not supported
df4.plot.hist(stacked=True, bins=20)
You can pass other keywords supported by matplotlib hist. For example, horizontal and cumulative histogram can be drawn by invert=True
and cumulative=True
.
df4['a'].plot.hist(invert=True, cumulative=True)
The existing interface DataFrame.hist
to plot histogram still can be used.
df['A'].diff().hist()
DataFrame.hist()
plots the histograms of the columns on multiple subplots:
df.diff().hist(color='k', alpha=0.5, bins=50, subplots=True, width=300).cols(2)
The by keyword can be specified to plot grouped histograms:
# by does not support arrays, instead the array should be added as a column
data = pd.Series(np.random.randn(1000))
data = pd.DataFrame({'data': data, 'by_column': np.random.randint(0, 4, 1000)})
data.hist(by='by_column', width=300, subplots=True).cols(2)
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).
df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])
df.plot.box()
Using the matplotlib backend, boxplot can be colorized by passing color
keyword. You can pass a dict
whose keys are boxes
, whiskers
, medians
and caps
.
This behavior is not supported in the holoviews backend.
You can pass other keywords supported by holoviews boxplot. For example, horizontal and custom-positioned boxplot can be drawn by invert=True
and positions keywords.
# positions not supported
df.plot.box(invert=True, 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.
df = pd.DataFrame(np.random.rand(10, 5))
df.boxplot()
You can create a stratified boxplot using the groupby
keyword argument to create groupings. For instance,
df = pd.DataFrame(np.random.rand(10,2), columns=['Col1', 'Col2'])
df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])
df.boxplot(col='X')
You can also pass a subset of columns to plot, as well as group by multiple columns:
df = pd.DataFrame(np.random.rand(10,3), columns=['Col1', 'Col2', 'Col3'])
df['X'] = pd.Series(['A','A','A','A','A','B','B','B','B','B'])
df['Y'] = pd.Series(['A','B','A','B','A','B','A','B','A','B'])
df.boxplot(y=['Col1','Col2'], col='X', row='Y')
df_box = pd.DataFrame(np.random.randn(50, 2))
df_box['g'] = np.random.choice(['A', 'B'], size=50)
df_box.loc[df_box['g'] == 'B', 1] += 3
df_box.boxplot(row='g')
For more control over the ordering of the levels, we can perform a groupby on the data before plotting.
df_box.groupby('g').boxplot()
Area Plot
You can create area plots with Series.plot.area()
and DataFrame.plot.area()
. Area plots are not 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.
df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
df.plot.area(alpha=0.5)
To produce an stacked plot, pass stacked=True
.
df.plot.area(stacked=True)
Scatter Plot
Scatter plot can be drawn by using the DataFrame.plot.scatter()
method. Scatter plot require that x and y be specified using the x
and y
keywords.
df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
df.plot.scatter(x='a', y='b')
To plot multiple column groups in a single axes, repeat plot
method and then use the overlay operator (*
) to combine the plots. It is recommended to specify color and label keywords to distinguish each groups.
plot_1 = df.plot.scatter(x='a', y='b', color='DarkBlue', label='Group 1')
plot_2 = df.plot.scatter(x='c', y='d', color='DarkGreen', label='Group 2')
plot_1 * plot_2
The keyword c
may be given as the name of a column to provide colors for each point:
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.
df.plot.scatter(x='a', y='b', s=df['c']*500)
The same effect can be accomplished using the scale
option.
df.plot.scatter(x='a', y='b', s='c', scale=25)
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.
df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])
df['b'] = df['b'] + np.arange(1000)
df.plot.hexbin(x='a', y='b', gridsize=25, width=500, height=400)
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.
df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])
df['b'] = df['b'] = df['b'] + np.arange(1000)
df['z'] = np.random.uniform(0, 3, 1000)
df.plot.hexbin(x='a', y='b', C='z', reduce_function=np.max, gridsize=25, width=500, height=400)
Density Plot
You can create density plots using the Series.plot.kde()
and DataFrame.plot.kde()
methods.
ser = pd.Series(np.random.randn(1000))
ser.plot.kde()
Pie plot
NOT SUPPORTED
Plotting Tools
These functions can be imported from hvplot.plotting
and take a Series
or DataFrame
as an argument.
Scatter Matrix Plot
You can create a scatter plot matrix using the scatter_matrix
function:
import pandas as pd, numpy as np
from hvplot import scatter_matrix
df = pd.DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd'])
scatter_matrix(df, alpha=0.2, diagonal='kde')
Since scatter matrix plots may generate a large number of points (the one above renders 120,000 points!), you may want to take advantage of the power provided by Datashader to rasterize the off-diagonal plots into a fixed-resolution representation:
df = pd.DataFrame(np.random.randn(10000, 4), columns=['a', 'b', 'c', 'd'])
scatter_matrix(df, rasterize=True, dynspread=True)
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. 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.
from bokeh.sampledata import iris
from hvplot import andrews_curves
data = iris.flowers
andrews_curves(data, 'species')
Parallel Coordinates
Parallel coordinates is a plotting technique for plotting multivariate data. It 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.
from hvplot import parallel_coordinates
parallel_coordinates(data, 'species')
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.
from hvplot import lag_plot
data = pd.Series(0.1 * np.random.rand(1000) +
0.9 * np.sin(np.linspace(-99 * np.pi, 99 * np.pi, num=1000)))
lag_plot(data, width=400, height=400)
Autocorrelation Plot
NOT SUPPORTED
Bootstrap Plot
NOT SUPPORTED
RadViz
NOT SUPPORTED
Plot Formatting
General plot style arguments
Most plotting methods have a set of keyword arguments that control the layout and formatting of the returned plot:
ts.plot(c='k', line_dash='dashed', label='Series')
For each kind of plot (e.g. line, bar, scatter) any additional arguments keywords are passed along to the corresponding holoviews object (hv.Curve
, hv.Bar
, hv.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. You can also control the placement of the legend using the same legend
argument set to one of: 'top_right', 'top_left', 'bottom_left', 'bottom_right', 'right', 'left', 'top', or 'bottom'.
df = pd.DataFrame(np.random.randn(1000, 4),
index=ts.index, columns=list('ABCD'))
df = df.cumsum()
df.plot(legend=False)
df.plot(legend='top_left')
Scales
You may pass logy
to get a log-scale Y axis, similarly use logx
to get a log-scale on the X axis or logz
to get a log-scale on the color axis.
ts = pd.Series(np.random.randn(1000),
index=pd.date_range('1/1/2000', periods=1000))
ts = np.exp(ts.cumsum())
ts.plot(logy=True)
df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])
df['b'] = df['b'] + np.arange(1000)
df.plot.hexbin(x='a', y='b', gridsize=25, width=500, height=400, logz=True)
Plotting on a Secondary Y-axis
NOT SUPPORTED
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:
df = pd.DataFrame(np.random.randn(1000, 4),
index=ts.index, columns=list('ABCD'))
df = df.cumsum()
df.A.plot()
Formatters can be set using format strings, by declaring bokeh TickFormatters, or using custom functions. See HoloViews Tick Docs for more information.
from bokeh.models.formatters import DatetimeTickFormatter
formatter = DatetimeTickFormatter(months='%b %Y')
df.A.plot(yformatter='$%.2f', xformatter=formatter)
Subplots
Each Series
in a DataFrame
can be plotted on a different axis with the subplots
keyword:
df = pd.DataFrame(np.random.randn(1000, 4),
index=ts.index, columns=list('ABCD'))
df = df.cumsum()
df.plot(subplots=True, height=150).cols(1)
Controlling layout and targeting multiple axes
The layout of subplots can be specified using .cols(n)
.
df.plot(subplots=True, shared_axes=False, width=150).cols(3)
Plotting with error bars
Plotting with error bars is not supported in DataFrame.plot()
and Series.plot()
. To add errorbars, users should fall back to using hvplot directly.
import hvplot.pandas # noqa
df3 = pd.DataFrame({'data1': [3, 2, 4, 3, 2, 4, 3, 2],
'data2': [6, 5, 7, 5, 4, 5, 6, 5]})
mean_std = pd.DataFrame({'mean': df3.mean(), 'std': df3.std()})
mean_std
(mean_std.plot.bar(y='mean', alpha=0.7) * \
mean_std.hvplot.errorbars(x='index', y='mean', yerr1='std')
).opts(show_legend=False).redim.range(mean=(0, 6.5))
Plotting tables
The matplotlib backend includes support for the table
keyword in DataFrame.plot()
and Series.plot()
. This same effect can be accomplished with holoviews by using hvplot.table
and creating a layout of the resulting plots.
df = pd.DataFrame(np.random.rand(5, 3), columns=['a', 'b', 'c'])
(df.plot(xaxis=False, legend='top_right') + \
df.T.hvplot.table()).cols(1)
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 colormap or a string that is a name of a colormap.
To use the cubehelix colormap, we can pass colormap='cubehelix'
.
df = pd.DataFrame(np.random.randn(1000, 10), index=ts.index)
df = df.cumsum()
df.plot(colormap='cubehelix')
Colormaps can also be used with other plot types, like bar charts:
dd = pd.DataFrame(np.random.randn(10, 10)).applymap(abs)
dd = dd.cumsum()
dd.plot.bar(colormap='Greens')
Parallel coordinates charts:
from bokeh.sampledata import iris
from hvplot import parallel_coordinates, andrews_curves
data = iris.flowers
parallel_coordinates(data, 'species', colormap='gist_rainbow')
Andrews curves charts:
andrews_curves(data, 'species', colormap='winter')
Plotting directly with holoviews
In some situations it may still be preferable or necessary to prepare plots directly with hvplot or holoviews, 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 holoviews functions without explicit casts.
import holoviews as hv
price = pd.Series(np.random.randn(150).cumsum(),
index=pd.date_range('2000-1-1', periods=150, freq='B'), name='price')
ma = price.rolling(20).mean()
mstd = price.rolling(20).std()
price.plot(c='k') * ma.plot(c='b', label='mean') * \
hv.Area((mstd.index, ma - 2 * mstd, ma + 2 * mstd),
vdims=['y', 'y2']).opts(color='b', alpha=0.2)