日期时间堆积条形图与来自数据帧的 Matplotlib [英] Datetime Stacked Bar Chart with Matplotlib from dataframe

问题描述

背景:

我被困住了.或者只是有一个金发碧眼的时刻.我就像一只看着车灯的鹿.

我从MySQL表中查询了以下数据框.

日期操作2020-05-07 A2020-05-08 B2020-05-08 A2020-05-12 A2020-05-12 A2020-05-12 乙2020-05-13 C2020-05-13 A2020-05-13 乙2020-05-14 A2020-05-19 B2020-05-21 A2020-05-25 A2020-05-26 B2020-05-26 C2020-05-26 A2020-05-26 A2020-05-29 A

我不知道如何使用 matplotlib 使其成为堆叠条形图.

研究:

1. 如果需要每月，则可以使用 dt.to_period('M')获取每月日期和 crosstab :

   pd.crosstab(df.date.dt.to_period('M'),df.operation).plot.bar(stacked=True)

   输出(对于样本数据):

   <小时>

   更新以在 x 轴上显示所有日期:

     df ['date'] = pd.to_datetime(df.date)注意，maxdate = df ['date'].agg(['min'，'max'])all_dates = pd.date_range(mindate，maxdate，freq ='D').date(pd.crosstab(df.date，df.operation).reindex(all_dates).plot.bar(stacked=True))

   输出:

   Background:

   I am stuck. Or just having a blonde moment. I'm like a deer looking at headlights.

   I've queried the following dataframe from MySQL table.

   date          operation
   2020-05-07        A
   2020-05-08        B
   2020-05-08        A
   2020-05-12        A
   2020-05-12        A
   2020-05-12        B
   2020-05-13        C
   2020-05-13        A
   2020-05-13        B
   2020-05-14        A
   2020-05-19        B
   2020-05-21        A
   2020-05-25        A
   2020-05-26        B
   2020-05-26        C
   2020-05-26        A
   2020-05-26        A
   2020-05-29        A
   

   I have no idea how to make it a stacked bar chart with matplotlib.

   Research:

   1. Grouped Bar-Chart with customized DateTime Index using pandas and Matplotlib
   2. Stacked bar plot using Matplotlib

   Question:

   How can I generate a stack-bar-chart with matplot lib with the above sample data?

   Code snippet:

   import datetime as dt
   import mysql.connector
   import os
   import pandas as pd
   # import numpy as np
   import matplotlib.pyplot as plt
   import datetime
   
   def generate_monthly_graph():
       query = "SELECT [...]`"
       mycursor.execute(query)
       mycursor.execute(query)
       data = mycursor.fetchall()
   
       df = pd.DataFrame(data, columns=['date', 'operation'])
   
       df = df.set_index('date')
   
       df.index = pd.to_datetime(df.index)
   

   解决方案

   You can use crosstab then plot.bar:

   # convert to date, no need for set_index
   df.date = pd.to_datetime(df.date)
   
   pd.crosstab(df.date,df.operation).plot.bar(stacked=True)
   

   Output:

   If you want monthly, you can use dt.to_period('M') to get monthly dates and crosstab:

   pd.crosstab(df.date.dt.to_period('M'),df.operation).plot.bar(stacked=True)
   

   Output (for the sample data):

   ---

   Update to have all the dates on the x-axis:

   df['date'] = pd.to_datetime(df.date)
   
   mindate, maxdate = df['date'].agg(['min','max'])
   
   all_dates = pd.date_range(mindate, maxdate, freq='D').date
   
   (pd.crosstab(df.date,df.operation)
      .reindex(all_dates)
      .plot.bar(stacked=True)
   )
   

   Output:

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