我有兩個(gè)數(shù)據(jù)框，如下所示：df1：DATE Value1 Value204.01.05 2.754 2.75705.01.05 2.7316 2.750506.01.05 2.7546 2.756807.01.05 2.7465 2.752510.01.05 2.7385 2.741511.01.05 2.7348 2.738812.01.05 2.7348 2.738813.01.05 2.7348 2.738814.01.05 2.7365 2.743517.01.05 2.7365 2.743518.01.05 2.7365 2.743519.01.05 2.7365 2.7435df2:DATE Value1 Value204.01.05 2.701 2.699505.01.05 2.7065 2.70507.01.05 2.6348 2.633310.01.05 2.635 2.631511.01.05 2.6275 2.626512.01.05 2.6268 2.625313.01.05 2.6285 2.62717.01.05 2.6565 2.655518.01.05 2.6275 2.62619.01.05 2.643 2.6415如果我有完全相同的日期，我下面的代碼就可以工作。一旦日期不相等并且我只想計(jì)算相等的日期，它就不起作用。我的 if 語句不知何故沒有過濾掉正確的日期。我想將計(jì)算值添加到 df1 中。我的代碼如下所示：import pandas as pdfile1 = 'File1.csv'file2 = 'File2.csv'df1 = pd.read_csv(file1, sep=';')df1['DATE'] = pd.to_datetime(df1.DATE)df2 = pd.read_csv(file2, sep=';')df2['DATE'] = pd.to_datetime(df2.DATE)for date1 in df1['DATE']: for date2 in df2['DATE']: if date1 == date2: print(date1, date2) df1['sub'] = df1.Value1 - df2.Value1print(df1)預(yù)期輸出如下：DATE Value1 Value2 LEVEL sub04.01.05 2.701 2.6995 1 Year 0.05305.01.05 2.7065 2.705 1 Year 0.025107.01.05 2.6348 2.6333 1 Year 0.111710.01.05 2.635 2.6315 1 Year 0.103511.01.05 2.6275 2.6265 1 Year 0.107312.01.05 2.6268 2.6253 1 Year 0.10813.01.05 2.6285 2.627 1 Year 0.106317.01.05 2.6565 2.6555 1 Year 0.0818.01.05 2.6275 2.626 1 Year 0.10919.01.05 2.643 2.6415 1 Year 0.0935這意味著僅計(jì)算相同日期的差異。
2 回答
紅顏莎娜
TA貢獻(xiàn)1842條經(jīng)驗(yàn) 獲得超13個(gè)贊
# this will result in merged df wtih all intersecting dates
df1 = df1.merge(df2, on = 'DATE', suffixes=('','_df2')
# the column you need
df1['sub'] = df1['Value1'] - df1['Value1_df2']
# next drop unnecessary columns of df2
df1.drop(columns = ['Value1_df2', 'Value2_df2'], inplace=True)
HUH函數(shù)
TA貢獻(xiàn)1836條經(jīng)驗(yàn) 獲得超4個(gè)贊
首先將索引設(shè)置為'DATE'使其對(duì)齊。然后我們減去。由于您似乎希望將輸出添加到df2我們將執(zhí)行-(df2 - df1)的操作與(df1 - df2)
df1 = df1.set_index('DATE')
df2 = df2.set_index('DATE')
df2['sub'] = -df2['Value1'].sub(df1['Value1'])
Value1 Value2 sub
DATE
04.01.05 2.7010 2.6995 0.0530
05.01.05 2.7065 2.7050 0.0251
07.01.05 2.6348 2.6333 0.1117
10.01.05 2.6350 2.6315 0.1035
11.01.05 2.6275 2.6265 0.1073
12.01.05 2.6268 2.6253 0.1080
13.01.05 2.6285 2.6270 0.1063
17.01.05 2.6565 2.6555 0.0800
18.01.05 2.6275 2.6260 0.1090
19.01.05 2.6430 2.6415 0.0935
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