pandas 熊猫在图表上显示多个条形图

pandas show multiple bar graphs on a chart

提问by jxn

my data looks like this:

我的数据是这样的：

term    date    change1 change2
aaa  2010-03-01   23.00   24.31
bbb  2010-03-01   25.00   0.00
ccc  2012-05-01   100.00  100.00

The date column can have repeated dates. I want to plot for each term, what the change1 and change2 is. I was thinking to have the term as the x-axis and change1 and change2 share the same y axis but be plotted as bar charts side by side. I know how to do the y axis part but dont know how to set the x axis. I would also like each term to somehow show the date as well if possible otherwise this is not a priority.

日期列可以有重复的日期。我想为每个术语绘制，change1 和 change2 是什么。我想将术语作为 x 轴，change1 和 change2 共享相同的 y 轴，但并排绘制为条形图。我知道如何做 y 轴部分，但不知道如何设置 x 轴。如果可能的话，我还希望每个学期都以某种方式显示日期，否则这不是优先事项。

Heres what i have:

这是我所拥有的：

fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twinx()
df.change1.plot(kind = 'bar', color = 'red', ax = ax , position = 1)
df.change2.plot(kind = 'bar', color = 'blue', ax = ax2, position = 2)
ax.set_ylabel= ('change1')
ax2.set_ylabel=('change2')
plt.show()

Thanks,

谢谢，

回答by unutbu

One way to make the labels along the x-axisto be terms is to set termas the index:

使标签x-axis成为terms 的一种方法是设置term为索引：

df = df.set_index(['term'])

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For example,

例如，

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'change1': [23.0, 25.0, 100.0],
 'change2': [24.309999999999999, 0.0, 100.0],
 'date': ['2010-03-01', '2010-03-01', '2012-05-01'],
 'term': ['aaa', 'bbb', 'ccc']})
df = df.set_index(['term'])
fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twinx()

df['change1'].plot(kind='bar', color='red', ax=ax, position=0, width=0.25)
df['change2'].plot(kind='bar', color='blue', ax=ax2, position=1, width=0.25)
ax.set_ylabel = ('change1')
ax2.set_ylabel = ('change2')
plt.show()

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Or, instead of setting the index, you could set the xticklabels explicitly:

或者，您可以显式设置 xticklabels，而不是设置索引：

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'change1': [23.0, 25.0, 100.0],
 'change2': [24.309999999999999, 0.0, 100.0],
 'date': ['2010-03-01', '2010-03-01', '2012-05-01'],
 'term': ['aaa', 'bbb', 'ccc']})

fig = plt.figure()
ax = fig.add_subplot(111)
ax2 = ax.twinx()


df['change1'].plot(kind='bar', color='red', ax=ax, position=0, width=0.25)
df['change2'].plot(kind='bar', color='blue', ax=ax2, position=1, width=0.25)
ax.set_ylabel = 'change1'
ax2.set_ylabel = 'change2'
labels = ['{}\n{}'.format(date, term) for date, term in zip(df['date'], df['term'])]
ax.set_xticklabels(labels, minor=False)
fig.autofmt_xdate()

plt.show()

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Per the question in the comments, to create a new plot for each date, you could iterate over the groups in df.groupby(['date']):

根据评论中的问题，要为 each 创建一个新图date，您可以遍历 中的组 df.groupby(['date'])：

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({'change1': [23.0, 25.0, 100.0],
 'change2': [24.309999999999999, 0.0, 100.0],
 'date': ['2010-03-01', '2010-03-01', '2012-05-01'],
 'term': ['aaa', 'bbb', 'ccc']})

groups = df.groupby(['date'])
fig, axs = plt.subplots(nrows=groups.ngroups)
for groupi, ax in zip(groups,axs):
    index, grp = groupi
    ax2 = ax.twinx()
    grp['change1'].plot(kind='bar', color='red', ax=ax, position=0, width=0.25)
    grp['change2'].plot(kind='bar', color='blue', ax=ax2, position=1, width=0.25)
    ax.set_ylabel = 'change1'
    ax2.set_ylabel = 'change2'
    ax.set_title(index)
    ax.set_xticklabels(grp['term'].tolist(), minor=False, rotation=0)
fig.tight_layout()
plt.show()