从分组的熊猫数据框中绘制堆积图

Question

我有一个如下所示的数据框。首先，我想要每个日期中每个状态的计数。例如，2017-11-02 中“完成”的数量是 2。我想要这样的堆栈图。                   status              start_time                end_time  \0             COMPLETED 2017-11-01 19:58:54.726 2017-11-01 20:01:05.414   1             COMPLETED 2017-11-02 19:43:04.000 2017-11-02 19:47:54.877   2     ABANDONED_BY_USER 2017-11-03 23:36:19.059 2017-11-03 23:36:41.045   3  ABANDONED_BY_TIMEOUT 2017-10-31 17:02:38.689 2017-10-31 17:12:38.844   4             COMPLETED 2017-11-02 19:35:33.192 2017-11-02 19:42:51.074   这是数据框的csv：status,start_time,end_timeCOMPLETED,2017-11-01 19:58:54.726,2017-11-01 20:01:05.414COMPLETED,2017-11-02 19:43:04.000,2017-11-02 19:47:54.877ABANDONED_BY_USER,2017-11-03 23:36:19.059,2017-11-03 23:36:41.045ABANDONED_BY_TIMEOUT,2017-10-31 17:02:38.689,2017-10-31 17:12:38.844COMPLETED,2017-11-02 19:35:33.192,2017-11-02 19:42:51.074ABANDONED_BY_TIMEOUT,2017-11-02 19:35:33.192,2017-11-02 19:42:51.074为达到这个：df_['status'].astype('category')df_ = df_.set_index('start_time')grouped = df_.groupby('status')color = {'COMPLETED':'green','ABANDONED_BY_TIMEOUT':'blue',"MISSED":'red',"ABANDONED_BY_USER":'yellow'}for key_, group in grouped:   print(key_)   df_ = group.groupby(lambda x: x.date).count()   print(df_)   df_['status'].plot(label=key_,kind='bar',stacked=True,\   color=color[key_],rot=90)plt.show()以下输出是：ABANDONED_BY_TIMEOUT            status  end_time  2017-10-31       1         1       ABANDONED_BY_USER            status  end_time  2017-11-03       1         1            COMPLETED            status  end_time  2017-11-01       1         1             2017-11-02       2         2 正如我们所看到的，这里的问题只考虑了最后两个日期“2017-11-01”和“2017-11-02”，而不是所有类别中的所有日期。我该如何解决这个问题？欢迎使用全新的堆叠图方法。提前致谢。

Answer 1

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

df_ = pd.read_csv('sam.csv')

df_['date'] = pd.to_datetime(df_['start_time']).dt.date

df_ = df_.set_index('start_time')

grouped = pd.DataFrame(df_.groupby(['date', 'status']).size().reset_index(name="count")).pivot(columns='status', index='date', values='count')

print(grouped)

sns.set()

grouped.plot(kind='bar', stacked=True)

# g = grouped.plot(x='date', kind='bar', stacked=True)

plt.show()

输出：

Answer 2

尝试转型df_与pandas.crosstab替代：

color = ['blue', 'yellow', 'green', 'red']

df_xtab = pd.crosstab(df_.start_time.dt.date, df_.status)

这DataFrame看起来像：

status      ABANDONED_BY_TIMEOUT  ABANDONED_BY_USER  COMPLETED

start_time                                                    

2017-10-31                     1                  0          0

2017-11-01                     0                  0          1

2017-11-02                     1                  0          2

2017-11-03                     0                  1          0

并且更容易绘制。

df_xtab.plot(kind='bar',stacked=True, color=color, rot=90)

Answer 3

使用barplot带有色调的seaborn 库

代码：

import pandas as pd

import matplotlib.pyplot as plt

import seaborn as sns

df_ = pd.read_csv('sam.csv')

df_['date'] = pd.to_datetime(df_['start_time']).dt.date

df_ = df_.set_index('start_time')

print(df_)

grouped = pd.DataFrame(df_.groupby(['date', 'status']).size().reset_index(name="count"))

print(grouped)

g = sns.barplot(x='date', y='count', hue='status', data=grouped)

plt.show()

输出：

数据：

status,start_time,end_time

COMPLETED,2017-11-01 19:58:54.726,2017-11-01 20:01:05.414

COMPLETED,2017-11-02 19:43:04.000,2017-11-02 19:47:54.877

ABANDONED_BY_USER,2017-11-03 23:36:19.059,2017-11-03 23:36:41.045

ABANDONED_BY_TIMEOUT,2017-10-31 17:02:38.689,2017-10-31 17:12:38.844

COMPLETED,2017-11-02 19:35:33.192,2017-11-02 19:42:51.074

ABANDONED_BY_TIMEOUT,2017-11-02 19:35:33.192,2017-11-02 19:42:51.074