根据日期，使用来自另一个 dataframe 的值在 pandas dataframe 中创建一个新列
[英]Create a new column in pandas dataframe with values from another dataframe, based on date
问题描述
I have 2 dataframes.
我有 2 个数据框。
First dataframe contains PLN data:首先 dataframe 包含 PLN 数据：
date polarity subjectivity
0 2022-03-25 0.118771 0.517508
1 2022-03-28 0.136532 0.555640
2 2022-04-04 0.447222 0.419444
3 2022-04-11 -0.022222 0.397222
4 2022-04-18 -0.098333 0.526111
5 2022-04-20 -0.077778 0.644444
6 2022-04-22 0.187500 0.437500
7 2022-04-25 0.350000 0.375000
The second dataframe contains stock data:第二个 dataframe 包含股票数据：
open high low close adjclose volume date
0 277.559998 282.500000 272.059998 282.190002 282.190002 42549400 2022-03-28
1 286.950012 289.459991 279.799988 286.559998 286.559998 48898400 2022-03-29
2 283.040009 284.959991 275.029999 276.899994 276.899994 46348700 2022-03-30
3 277.820007 282.480011 272.700012 272.859985 272.859985 52344000 2022-03-31
4 273.750000 274.959991 262.670013 267.119995 267.119995 51653400 2022-04-01
5 267.279999 275.579987 266.130005 273.600006 273.600006 39712000 2022-04-04
6 272.540009 273.190002 258.200012 259.309998 259.309998 43661500 2022-04-05
7 249.339996 253.000000 240.029999 244.070007 244.070007 70383300 2022-04-06
8 244.410004 247.220001 234.779999 242.080002 242.080002 55799200 2022-04-07
9 239.169998 239.229996 230.619995 231.190002 231.190002 52409100 2022-04-08
10 222.139999 223.199997 216.600006 219.169998 219.169998 57520700 2022-04-11
What I want is to create both 'polarity' and 'subjetivity' columns in the second dataframe (the one with stock data) based on the date field the most efficient way .我想要的是以最有效的方式基于日期字段在第二个 dataframe（具有股票数据的那个）中创建“极性”和“主观性”列。
Edit Adding desired output:编辑添加所需的 output：
open high low close adjclose volume date polarity subjetivity
0 277.559998 282.500000 272.059998 282.190002 282.190002 42549400 2022-03-28 0.136532 0.555640
1 286.950012 289.459991 279.799988 286.559998 286.559998 48898400 2022-03-29 NaN NaN
2 283.040009 284.959991 275.029999 276.899994 276.899994 46348700 2022-03-30 NaN NaN
3 277.820007 282.480011 272.700012 272.859985 272.859985 52344000 2022-03-31 NaN NaN
4 273.750000 274.959991 262.670013 267.119995 267.119995 51653400 2022-04-01 NaN NaN
5 267.279999 275.579987 266.130005 273.600006 273.600006 39712000 2022-04-04 0.447222 0.419444
6 272.540009 273.190002 258.200012 259.309998 259.309998 43661500 2022-04-05 NaN NaN
7 249.339996 253.000000 240.029999 244.070007 244.070007 70383300 2022-04-06 NaN NaN
8 244.410004 247.220001 234.779999 242.080002 242.080002 55799200 2022-04-07 NaN NaN
9 239.169998 239.229996 230.619995 231.190002 231.190002 52409100 2022-04-08 NaN NaN
10 222.139999 223.199997 216.600006 219.169998 219.169998 57520700 2022-04-11 -0.022222 0.397222
2 个解决方案
解决方案1
1 已采纳 2022-04-25 18:33:30
df2.merge(df1, how = 'left', on = 'date')
open high low ... date polarity subjectivity
0 277.559998 282.500000 272.059998 ... 2022-03-28 0.136532 0.555640
1 286.950012 289.459991 279.799988 ... 2022-03-29 NaN NaN
2 283.040009 284.959991 275.029999 ... 2022-03-30 NaN NaN
3 277.820007 282.480011 272.700012 ... 2022-03-31 NaN NaN
4 273.750000 274.959991 262.670013 ... 2022-04-01 NaN NaN
5 267.279999 275.579987 266.130005 ... 2022-04-04 0.447222 0.419444
6 272.540009 273.190002 258.200012 ... 2022-04-05 NaN NaN
7 249.339996 253.000000 240.029999 ... 2022-04-06 NaN NaN
8 244.410004 247.220001 234.779999 ... 2022-04-07 NaN NaN
9 239.169998 239.229996 230.619995 ... 2022-04-08 NaN NaN
10 222.139999 223.199997 216.600006 ... 2022-04-11 -0.022222 0.397222
解决方案2
1 2022-04-25 18:42:04
Try this尝试这个
df2.merge(df, on='date', how='left')
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