按日期合并数据框使用不等的日期

Question

我的过程就是这样：

1. 导入包含日期，激活和取消的数据的csv
2. 通过激活或取消对数据进行子集化
3. 使用aggfunc＆＃39; sum＆＃39;
来转动数据
4. 转换回数据框

现在，我需要将2个数据帧合并在一起，但是在一个数据帧中存在日期而在另一个数据帧中不存在日期。这两个数据框都从2017年1月1日开始到2017年12月31日结束。最好，索引月份需要填写的任何观察的输出都具有相应的值0.

这里是两个数据框中的.head（）：



作为参考，这里是代码：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import datetime

%matplotlib inline

#import data
directory1 = "C:\python\Contracts"
directory_source = os.path.join(directory1, "Contract_Data.csv")
df_source = pd.read_csv(directory_source)

#format date ranges as times
#df_source["Activation_Month"] = pd.to_datetime(df_source["Activation_Month"])
#df_source["Cancellation_Month"] = pd.to_datetime(df_source["Cancellation_Month"])
df_source["Activation_Day"] = pd.to_datetime(df_source["Activation_Day"])
df_source["Cancellation_Day"] = pd.to_datetime(df_source["Cancellation_Day"])


#subset the data based on status
df_active = df_source[df_source["Order Status"]=="Active"]
df_active = pd.DataFrame(df_active[["Activation_Day", "Event_Value"]].copy())
df_cancelled = df_source[df_source["Order Status"]=="Cancelled"]
df_cancelled = pd.DataFrame(df_cancelled[["Cancellation_Day", "Event_Value"]].copy())

#remove activations outside 2017 and cancellations outside 2017
df_cancelled = df_cancelled[(df_cancelled['Cancellation_Day'] > '2016-12-31') & 
                            (df_cancelled['Cancellation_Day'] <= '2017-12-31')]

df_active = df_active[(df_active['Activation_Day'] > '2016-12-31') & 
                            (df_active['Activation_Day'] <= '2017-12-31')]



#pivot the data to aggregate by day
df_active_aggregated = df_active.pivot_table(index='Activation_Day',
                                             values='Event_Value',
                                             aggfunc='sum')

df_cancelled_aggregated = df_cancelled.pivot_table(index='Cancellation_Day',
                                                   values='Event_Value',
                                                   aggfunc='sum')


#convert pivot tables back to useable dataframes
activations_aggregated = pd.DataFrame(df_active_aggregated.to_records())
cancellations_aggregated = pd.DataFrame(df_cancelled_aggregated.to_records())

#rename the time columns so they can be referenced when merging into one DF
activations_aggregated.columns = ["index_month", "Activations"]
#activations_aggregated = activations_aggregated.set_index(pd.DatetimeIndex(activations_aggregated["index_month"]))

cancellations_aggregated.columns = ["index_month", "Cancellations"]
#cancellations_aggregated = cancellations_aggregated.set_index(pd.DatetimeIndex(cancellations_aggregated["index_month"]))

我知道有很多帖子可以解决与此相似的问题，但我还没有找到任何有帮助的帖子。感谢任何能够帮助我的人！

Answer 1

您可以尝试：

activations_aggregated.merge(cancellations_aggregated, how='outer', on='index_month').fillna(0)