Python Pandas 简明教程

Python Pandas - Visualization

Basic Plotting: plot

Series 和 DataFrame 上的此功能只是对 matplotlib libraries plot() 方法的简单包装。

This functionality on Series and DataFrame is just a simple wrapper around the matplotlib libraries plot() method.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(10,4),index=pd.date_range('1/1/2000',
   periods=10), columns=list('ABCD'))

df.plot()

它的 output 如下所示 −

Its output is as follows −

如果索引包含日期，它将调用 gct().autofmt_xdate() 将 x 轴格式化为如上图所示。

If the index consists of dates, it calls gct().autofmt_xdate() to format the x-axis as shown in the above illustration.

我们可以使用 x 和 y 关键字将一列与另一列作对比。

We can plot one column versus another using the x and y keywords.

绘图方法允许一些绘图样式，这些样式与默认的线图不同。这些方法可以作为 plot() 的 kind 关键字参数提供。它们包括 -

Plotting methods allow 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 or barh for bar plots
hist for histogram
box for boxplot
'area' for area plots
'scatter' for scatter plots

Bar Plot

让我们通过创建一个条形图来看一个条形图是什么。条形图可以通过以下方式创建 -

Let us now see what a Bar Plot is by creating one. A bar plot can be created in the following way −

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d')
df.plot.bar()

它的 output 如下所示 −

Its output is as follows −

若要制作堆叠条形图，使用 pass stacked=True −

To produce a stacked bar plot, pass stacked=True −

import pandas as pd
df = pd.DataFrame(np.random.rand(10,4),columns=['a','b','c','d')
df.plot.bar(stacked=True)

它的 output 如下所示 −

Its output is as follows −

若要获得水平条形图，使用 barh 方法 −

To get horizontal bar plots, use the barh method −

import pandas as pd
import numpy as np

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

df.plot.barh(stacked=True)

它的 output 如下所示 −

Its output is as follows −

Histograms

可以使用 plot.hist() 方法绘制直方图。我们可以指定柱的数量。

Histograms can be plotted using the plot.hist() method. We can specify number of bins.

import pandas as pd
import numpy as np

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

df.plot.hist(bins=20)

它的 output 如下所示 −

Its output is as follows −

若要针对每一列绘制不同的直方图，使用以下代码 −

To plot different histograms for each column, use the following code −

import pandas as pd
import numpy as np

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

df.diff.hist(bins=20)

它的 output 如下所示 −

Its output is as follows −

Box Plots

可以通过调用 Series.box.plot() 和 DataFrame.box.plot() 或 DataFrame.boxplot() 绘制箱形图，以可视化每列中的值分布。

Boxplot can be drawn calling Series.box.plot() and DataFrame.box.plot(), or DataFrame.boxplot() to visualize the distribution of values within each column.

例如，这里是一个箱线图，表示在 [0,1) 上的均匀随机变量的 10 次观测的五次试验。

For instance, here is a boxplot representing five trials of 10 observations of a uniform random variable on [0,1).

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])
df.plot.box()

它的 output 如下所示 −

Its output is as follows −

Area Plot

可以使用 Series.plot.area() 或 DataFrame.plot.area() 方法创建面积图。

Area plot can be created using the Series.plot.area() or the DataFrame.plot.area() methods.

import pandas as pd
import numpy as np

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

它的 output 如下所示 −

Its output is as follows −

Scatter Plot

可以使用 DataFrame.plot.scatter() 方法创建散点图。

Scatter plot can be created using the DataFrame.plot.scatter() methods.

import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
df.plot.scatter(x='a', y='b')

它的 output 如下所示 −

Its output is as follows −

Pie Chart

可以使用 DataFrame.plot.pie() 方法创建饼状图。

Pie chart can be created using the DataFrame.plot.pie() method.

import pandas as pd
import numpy as np

df = pd.DataFrame(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], columns=['x'])
df.plot.pie(subplots=True)

它的 output 如下所示 −

Its output is as follows −