根据来自另一个数据帧的值更新数据帧

Question

I have two data frames that look like this:

            A   B
date        
2017-10-5   2   3
2017-10-6   5   5
2017-11-5   7   8
2017-11-6   11  13


             W1     W2
date        
2017-09-30  -0.2    0.01
2017-10-31  -0.003  0.04

I would like to create a new data frame that contains the following:

            W1 * A       W2 * B
date        
2017-10-5   -0.2 * 2     0.01 * 3
2017-10-6   -0.2 * 5     0.01 * 5
2017-11-5   -0.003 * 7   0.04 * 8
2017-11-6   -0.003 * 11  0.04 * 13

Answer 1

Use np.repeat on df2 and multiply. It looks like the index plays no part here.

df1 = df1.mul(np.repeat(df2.values, 2, axis=0))

Or, more generally,

df1 = df1.mul(np.repeat(df2.values, len(df1) // len(df2), axis=0))
print(df1)
               A     B
date                  
2017-10-5 -0.400  0.03
2017-10-6 -1.000  0.05
2017-11-5 -0.021  0.32
2017-11-6 -0.033  0.52

Where len(df1) // len(df2) computes the ratio of their sizes.

Answer 2

In case the index does mean something, ie you have a value that changes on a certain date and you want to keep using it until it changes the next time. You can then use the reindex command with the argument method='ffill' to create a dataframe that is aligned to the original dataframe. Here's how it looks like:

import pandas as pd
import dateutil

df = pd.DataFrame([['2017-10-5',2,3],
                  ['2017-10-6',5,5],
                  ['2017-11-5',7,8],
                  ['2017-11-6',11,13]],
                  columns = ['date','A','B'])
df['date'] = df['date'].apply(dateutil.parser.parse)
df = df.set_index('date')

wdf = pd.DataFrame([['2017-09-30',-0.2,0.01],
                    ['2017-10-31',-0.03,0.04]],
                     columns=['date','W1','W2'])
wdf['date'] = wdf['date'].apply(dateutil.parser.parse)
wdf = wdf.set_index('date')
wdf_r = wdf.reindex(df.index,
                    method='ffill')

res = df.drop(['A','B'],axis=1).assign(W1_x_A = wdf_r.W1 * df.A,
                                       W2_x_B = wdf_r.W2 * df.B)
print(res)

which outputs

            W1_x_A  W2_x_B
date                      
2017-10-05   -0.40    0.03
2017-10-06   -1.00    0.05
2017-11-05   -0.21    0.32
2017-11-06   -0.33    0.52