.. currentmodule:: pandas
.. ipython:: python
:suppress:
import numpy as np
np.random.seed(123456)
from pandas import *
import pandas.util.testing as tm
randn = np.random.randn
np.set_printoptions(precision=4, suppress=True)
import matplotlib.pyplot as plt
plt.close('all')
Note
We intend to build more plotting integration with matplotlib as time goes on.
We use the standard convention for referencing the matplotlib API:
.. ipython:: python import matplotlib.pyplot as plt
The plot method on Series and DataFrame is just a simple wrapper around
plt.plot:
.. ipython:: python
ts = Series(randn(1000), index=date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
@savefig series_plot_basic.png width=4.5in
ts.plot()
If the index consists of dates, it calls gcf().autofmt_xdate() to try to
format the x-axis nicely as per above. The method takes a number of arguments
for controlling the look of the plot:
.. ipython:: python @savefig series_plot_basic2.png width=4.5in plt.figure(); ts.plot(style='k--', label='Series'); plt.legend()
On DataFrame, plot is a convenience to plot all of the columns with labels:
.. ipython:: python
df = DataFrame(randn(1000, 4), index=ts.index,
columns=['A', 'B', 'C', 'D'])
df = df.cumsum()
@savefig frame_plot_basic.png width=4.5in
plt.figure(); df.plot(); plt.legend(loc='best')
You may set the legend argument to False to hide the legend, which is
shown by default.
.. ipython:: python @savefig frame_plot_basic_noleg.png width=4.5in df.plot(legend=False)
Some other options are available, like plotting each Series on a different axis:
.. ipython:: python @savefig frame_plot_subplots.png width=4.5in df.plot(subplots=True, figsize=(8, 8)); plt.legend(loc='best')
You may pass logy to get a log-scale Y axis.
.. ipython:: python
plt.figure();
ts = Series(randn(1000), index=date_range('1/1/2000', periods=1000))
ts = np.exp(ts.cumsum())
@savefig series_plot_logy.png width=4.5in
ts.plot(logy=True)
You can pass an ax argument to Series.plot to plot on a particular axis:
.. ipython:: python
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 5))
df['A'].plot(ax=axes[0,0]); axes[0,0].set_title('A')
df['B'].plot(ax=axes[0,1]); axes[0,1].set_title('B')
df['C'].plot(ax=axes[1,0]); axes[1,0].set_title('C')
@savefig series_plot_multi.png width=4.5in
df['D'].plot(ax=axes[1,1]); axes[1,1].set_title('D')
For labeled, non-time series data, you may wish to produce a bar plot:
.. ipython:: python plt.figure(); @savefig bar_plot_ex.png width=4.5in df.ix[5].plot(kind='bar'); plt.axhline(0, color='k')
Calling a DataFrame's plot method with kind='bar' produces a multiple
bar plot:
.. ipython:: python :suppress: plt.figure();
.. ipython:: python df2 = DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd']) @savefig bar_plot_multi_ex.png width=5in df2.plot(kind='bar');
To produce a stacked bar plot, pass stacked=True:
.. ipython:: python :suppress: plt.figure();
.. ipython:: python @savefig bar_plot_stacked_ex.png width=5in df2.plot(kind='bar', stacked=True);
To get horizontal bar plots, pass kind='barh':
.. ipython:: python :suppress: plt.figure();
.. ipython:: python @savefig barh_plot_stacked_ex.png width=5in df2.plot(kind='barh', stacked=True);
.. ipython:: python plt.figure(); @savefig hist_plot_ex.png width=4.5in df['A'].diff().hist()
For a DataFrame, hist plots the histograms of the columns on multiple
subplots:
.. ipython:: python plt.figure() @savefig frame_hist_ex.png width=4.5in df.diff().hist(color='k', alpha=0.5, bins=50)
DataFrame has a boxplot method which allows you to visualize the
distribution of values within each column.
For instance, here is a boxplot representing five trials of 10 observations of a uniform random variable on [0,1).
.. ipython:: python df = DataFrame(np.random.rand(10,5)) plt.figure(); @savefig box_plot_ex.png width=4.5in bp = df.boxplot()
You can create a stratified boxplot using the by keyword argument to create
groupings. For instance,
.. ipython:: python df = DataFrame(np.random.rand(10,2), columns=['Col1', 'Col2'] ) df['X'] = Series(['A','A','A','A','A','B','B','B','B','B']) plt.figure(); @savefig box_plot_ex2.png width=4.5in bp = df.boxplot(by='X')
You can also pass a subset of columns to plot, as well as group by multiple columns:
.. ipython:: python df = DataFrame(np.random.rand(10,3), columns=['Col1', 'Col2', 'Col3']) df['X'] = Series(['A','A','A','A','A','B','B','B','B','B']) df['Y'] = Series(['A','B','A','B','A','B','A','B','A','B']) plt.figure(); @savefig box_plot_ex3.png width=4.5in bp = df.boxplot(column=['Col1','Col2'], by=['X','Y'])
- New in 0.7.3. You can create a scatter plot matrix using the
scatter_matrixmethod inpandas.tools.plotting:
.. ipython:: python from pandas.tools.plotting import scatter_matrix df = DataFrame(np.random.randn(1000, 4), columns=['a', 'b', 'c', 'd']) @savefig scatter_matrix_kde.png width=6in scatter_matrix(df, alpha=0.2, figsize=(8, 8), diagonal='kde')
New in 0.8.0 You can create density plots using the Series/DataFrame.plot and setting kind='kde':
.. ipython:: python :suppress: plt.figure();
.. ipython:: python ser = Series(np.random.randn(1000)) @savefig kde_plot.png width=6in ser.plot(kind='kde')
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.
.. ipython:: python
from pandas import read_csv
from pandas.tools.plotting import andrews_curves
data = read_csv('data/iris.data')
plt.figure()
@savefig andrews_curves.png width=6in
andrews_curves(data, 'Name')
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.
.. ipython:: python
from pandas.tools.plotting import lag_plot
plt.figure()
data = Series(0.1 * np.random.random(1000) +
0.9 * np.sin(np.linspace(-99 * np.pi, 99 * np.pi, num=1000)))
@savefig lag_plot.png width=6in
lag_plot(data)
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.
.. ipython:: python
from pandas.tools.plotting import autocorrelation_plot
plt.figure()
data = Series(0.7 * np.random.random(1000) +
0.3 * np.sin(np.linspace(-9 * np.pi, 9 * np.pi, num=1000)))
@savefig autocorrelation_plot.png width=6in
autocorrelation_plot(data)
Probability plots are used to check if given data follows some probability distribution. With default parameters it plots against normal distribution. The data are plotted against the theoretical distribution in such a way that if the data follow the distribution it should display a straight line.
.. ipython:: python from pandas.tools.plotting import probability_plot plt.figure() u_data = Series(np.random.random(1000)) n_data = Series(np.random.randn(1000)) @savefig probability_plot_u.png width=6in probability_plot(u_data, dist='norm', marker='+', color='black') plt.figure() @savefig probability_plot_n.png width=6in probability_plot(n_data, dist='norm', marker='+', color='black')