我有两个数据帧，即 df1 和 df2。我想对 df2 中的 New_Amount_Dollar 列执行操作。基本上在 df1 中，我有历史货币数据，我想执行从 df2 到 Currency 和 Amount_Dollar 的日期操作计算 df2 中 New_Amount_Dollar 列的值。
例如，在 df2 中，我的第一种货币是 AUD，Date = '01-01-2019'，所以我想计算 New_Amount_Dollar 值使得
New_Amount_Dollar = Amount_Dollar/AUD 来自 df1 的值
即 New_Amount_Dollar = 19298/98 = 196.91
另一个示例，在 df2 中，我将第三种货币作为 COP 用于 Date = '03-01-2019，所以我想计算New_Amount_Dollar 值使得
New_Amount_Dollar = Amount_Dollar/COP 来自 df1 的值
即 New_Amount_Dollar = 5000/0.043 = 116279.06
import pandas as pd
data1 = {'Date':['01-01-2019', '02-01-2019', '03-01-2019', '04-01-2019','05-01-2019'],
'AUD':[98, 98.5, 99, 99.5, 97],
'BWP':[30,31,33,32,31],
'CAD':[0.02,0.0192,0.0196,0.0196,0.0192],
'BND':[0.99,0.952,0.970,0.980,0.970],
'COP':[0.05,0.047,0.043,0.047,0.045]}
df1 = pd.DataFrame(data1)
data2 = {'Date':['01-01-2019', '02-01-2019', '03-01-2019', '04-01-2019','05-01-2019'],
'Currency':['AUD','AUD','COP','CAD','BND'],
'Amount_Dollar':[19298, 19210, 5000, 200, 2300],
'New_Amount_Dollar':[0,0,0,0,0]
}
df2 = pd.DataFrame(data2)
df1
Date AUD BWP CAD BND COP
0 01-01-2019 98.0 30 0.0200 0.990 0.050
1 02-01-2019 98.5 31 0.0192 0.952 0.047
2 03-01-2019 99.0 33 0.0196 0.970 0.043
3 04-01-2019 99.5 32 0.0196 0.980 0.047
4 05-01-2019 97.0 31 0.0192 0.970 0.045
df2
Date Currency Amount_Dollar New_Amount_Dollar
0 01-01-2019 AUD 19298 0
1 02-01-2019 AUD 19210 0
2 03-01-2019 COP 5000 0
3 04-01-2019 CAD 200 0
4 05-01-2019 BND 2300 0
预期结果
Date Currency Amount_Dollar New_Amount_Dollar
0 01-01-2019 AUD 19298 196.91
1 02-01-2019 AUD 19210 195.02
2 03-01-2019 COP 5000 116279.06
3 04-01-2019 CAD 200 10204.08
4 05-01-2019 BND 2300 2371.13
最佳答案
使用DataFrame.lookup与 DataFrame.set_index对于数组并划分 Amount_Dollar 列:
arr = df1.set_index('Date').lookup(df2['Date'], df2['Currency'])
df2['New_Amount_Dollar'] = df2['Amount_Dollar'] / arr
print (df2)
Date Currency Amount_Dollar New_Amount_Dollar
0 01-01-2019 AUD 19298 196.918367
1 02-01-2019 AUD 19210 195.025381
2 03-01-2019 COP 5000 116279.069767
3 04-01-2019 CAD 200 10204.081633
4 05-01-2019 BND 2300 2371.134021
但如果日期时间不匹配，则使用 DataFrame.asfreq :
import pandas as pd
data1 = {'Date':['01-01-2019', '02-01-2019', '03-01-2019',
'04-01-2019','05-01-2019','08-01-2019'],
'AUD':[98, 98.5, 99, 99.5, 97,100],
'BWP':[30,31,33,32,31,20],
'CAD':[0.02,0.0192,0.0196,0.0196,0.0192,0.2],
'BND':[0.99,0.952,0.970,0.980,0.970,.23],
'COP':[0.05,0.047,0.043,0.047,0.045,0.023]}
df1 = pd.DataFrame(data1)
data2 = {'Date':['01-01-2019', '02-01-2019', '03-01-2019', '04-01-2019','07-01-2019'],
'Currency':['AUD','AUD','COP','CAD','BND'],
'Amount_Dollar':[19298, 19210, 5000, 200, 2300],
'New_Amount_Dollar':[0,0,0,0,0]
}
df2 = pd.DataFrame(data2)
print (df1)
Date AUD BWP CAD BND COP
0 01-01-2019 98.0 30 0.0200 0.990 0.050
1 02-01-2019 98.5 31 0.0192 0.952 0.047
2 03-01-2019 99.0 33 0.0196 0.970 0.043
3 04-01-2019 99.5 32 0.0196 0.980 0.047
4 05-01-2019 97.0 31 0.0192 0.970 0.045
5 08-01-2019 100.0 20 0.2000 0.230 0.023
print (df2)
Date Currency Amount_Dollar New_Amount_Dollar
0 01-01-2019 AUD 19298 0
1 02-01-2019 AUD 19210 0
2 03-01-2019 COP 5000 0
3 04-01-2019 CAD 200 0
4 07-01-2019 BND 2300 0
df1['Date'] = pd.to_datetime(df1['Date'], dayfirst=True)
df2['Date'] = pd.to_datetime(df2['Date'], dayfirst=True)
print (df1.set_index('Date').asfreq('D', method='ffill'))
AUD BWP CAD BND COP
Date
2019-01-01 98.0 30 0.0200 0.990 0.050
2019-01-02 98.5 31 0.0192 0.952 0.047
2019-01-03 99.0 33 0.0196 0.970 0.043
2019-01-04 99.5 32 0.0196 0.980 0.047
2019-01-05 97.0 31 0.0192 0.970 0.045
2019-01-06 97.0 31 0.0192 0.970 0.045
2019-01-07 97.0 31 0.0192 0.970 0.045
2019-01-08 100.0 20 0.2000 0.230 0.023
arr = df1.set_index('Date').asfreq('D', method='ffill').lookup(df2['Date'], df2['Currency'])
df2['New_Amount_Dollar'] = df2['Amount_Dollar'] / arr
print (df2)
Date Currency Amount_Dollar New_Amount_Dollar
0 2019-01-01 AUD 19298 196.918367
1 2019-01-02 AUD 19210 195.025381
2 2019-01-03 COP 5000 116279.069767
3 2019-01-04 CAD 200 10204.081633
4 2019-01-07 BND 2300 2371.134021
关于python - 使用 Pandas 对数据框进行列式映射和操作，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/58692313/