Merging Data Frames with Pandas-CodePudding

I'm trying to merge certain data frames into a single data frame and fill in the missing values using pandas. An example case is as follows:

import pandas as pd

data1 = {'SKU' : ['C1', 'D1'],
     'Description' : ['c2', 'd'],
     'Unit Cost' : [0.2, 1.5],
     'Qty1' : [18, 10]}
idx1 = ['RM0001', 'RM0004']

data2 = {'SKU' : ['C1', np.nan],
     'Description' : ['c', 'e'],
     'Qty2' : [15, 8]}
idx2 = ['RM0001', 'RM0010']

data3 = {'SKU' : ['D1', 'E1'],
     'Description' : ['d', 'e'],
     'Qty3' : [7, 9]}
idx3 = ['RM0004', 'RM0010']

df1 = pd.DataFrame(data1, index=idx1)
df2 = pd.DataFrame(data2, index=idx2)
df3 = pd.DataFrame(data3, index=idx3)

And the desired output would be of the form:

       SKU Description  Unit Cost  Qty1  Qty2  Qty3
RM0001  C1           c        0.2  18.0  15.0   NaN
RM0004  D1           d        1.5  10.0   NaN   7.0
RM0010  E1           e        NaN   NaN   8.0   9.0

I've tried various pd.merge and functools.reduce applications, but none are giving me the output I'd like. I'm still learning pandas, so I think I'm missing something, since this doesn't feel like it should be too complicated. A small explanation of the steps (or a link to an excellent source) would be appreciated.

CodePudding user response：

Try combine_first, which you can chain them:

df1.combine_first(df2).combine_first(df3)

Output:

       Description  Qty1  Qty2  Qty3 SKU  Unit Cost
RM0001          c2  18.0  15.0   NaN  C1        0.2
RM0004           d  10.0   NaN   7.0  D1        1.5
RM0010           e   NaN   8.0   9.0  E1        NaN

Or you can use concat with groupby:

pd.concat([df1,df2,df3]).groupby(level=0).first()

Output:

       SKU Description  Unit Cost  Qty1  Qty2  Qty3
RM0001  C1          c2        0.2  18.0  15.0   NaN
RM0004  D1           d        1.5  10.0   NaN   7.0
RM0010  E1           e        NaN   NaN   8.0   9.0