Python Pandas 匹配 2 个文件中的 2 列以填充文件中的值

Question

请帮助填写“file1.csv”（每日数据）和“file2.csv”（每周数据）的值。

下面是“file1.csv”（每日数据）::

date,value,week_num,year,fill_col_1,fill_col_2
01-01-2018,1763.95,1,2018,,
02-01-2018,1736.2,1,2018,,
03-01-2018,1741.1,1,2018,,
04-01-2018,1779.95,1,2018,,
05-01-2018,1801.1,1,2018,,
08-01-2018,1816,2,2018,,
09-01-2018,1823,2,2018,,
10-01-2018,1812.05,2,2018,,
11-01-2018,1825,2,2018,,
12-01-2018,1805,2,2018,,

下面是“file2.csv”（每周数据）::

date,value,week_num,year,fill_col_1,fill_col_2
07-01-2018,1764.46,1,2018,1768.953333,1756.542153
14-01-2018,1816.21,2,2018,1811.966667,1801.030007

"file1.csv"需要填写的2栏是"fill_col_1"和"fill_col_2" ，通过匹配 "week_num" 和 "year" from "file2.csv".

是否有某种方法可以比较 2 个文件中的 2 列，考虑或不考虑两个文件中明显不同的索引（“日期”）。

（如果您看到，“file2.csv”（每周）是使用 pandas 中的 'resample' 函数派生的，基于（每日值）“file1.csv".. 但现在我无法 join/concatentate 基于多个 columns/conditions 匹配的 2 个文件。）

预期的output/result，需要如下：

date,value,match_col_1,match_col_2,fill_col_1,fill_col_2
01-01-2018,1763.95,1,2018,1768.953333,1756.542153
02-01-2018,1736.2,1,2018,1768.953333,1756.542153
03-01-2018,1741.1,1,2018,1768.953333,1756.542153
04-01-2018,1779.95,1,2018,1768.953333,1756.542153
05-01-2018,1801.1,1,2018,1768.953333,1756.542153
08-01-2018,1816,2,2018,1811.966667,1801.030007
09-01-2018,1823,2,2018,1811.966667,1801.030007
10-01-2018,1812.05,2,2018,1811.966667,1801.030007
11-01-2018,1825,2,2018,1811.966667,1801.030007
12-01-2018,1805,2,2018,1811.966667,1801.030007

我试过的代码如下：（这绝对是我现在看到的菜鸟思维过程）

df_daily = pd.read_csv("file1.csv")
df_weekly = pd.read_csv("file2.csv")
df_weekly.loc[df_weekly["year"]]==df_daily.loc[df_daily["year"]] & df_weekly.loc[df_weekly["week_num"]]==df_daily.loc[df_daily["week_num"]]

出现错误：KeyError: "None of [Int64Index([2018, 2018, 2018, 2018, 2018, 2018, 2018, ],\n dtype='int64', length=8)] 在 [index]"

Answer 1

尝试 pandas.DataFrame.merge 功能：

import pandas as pd

df_daily = pd.read_csv('1.csv').iloc[:,:4]
df_weekly = pd.read_csv('2.csv').iloc[:,2:]
print(df_daily.merge(df_weekly,on=['week_num','year'],how='left'))

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