Merge multiple dataframes based on a common column [duplicate]

Question

I have Three dataframes. All of them have a common column and I need to merge them based on the common column without missing any data

Input

>>>df1
0 Col1  Col2  Col3
1 data1  3      4
2 data2  4      3
3 data3  2      3
4 data4  2      4
5 data5  1      4

>>>df2
0 Col1  Col4  Col5
1 data1  7      4
2 data2  6      9
3 data3  1      4

>>>df3
0 Col1  Col6  Col7
1 data2  5      8
2 data3  2      7
3 data5  5      3

Expected Output

>>>df
0 Col1  Col2  Col3  Col4 Col5  Col6  Col7
1 data1  3      4    7    4
2 data2  4      3    6    9     5     8
3 data3  2      3    1    4     2     7
4 data4  2      4
5 data5  1      4               5     3

Answer 1

Use merge and reduce

In [86]: from functools import reduce

In [87]: reduce(lambda x,y: pd.merge(x,y, on='Col1', how='outer'), [df1, df2, df3])
Out[87]:
    Col1  Col2  Col3  Col4  Col5  Col6  Col7
0  data1     3     4   7.0   4.0   NaN   NaN
1  data2     4     3   6.0   9.0   5.0   8.0
2  data3     2     3   1.0   4.0   2.0   7.0
3  data4     2     4   NaN   NaN   NaN   NaN
4  data5     1     4   NaN   NaN   5.0   3.0

---

Details

In [88]: df1
Out[88]:
    Col1  Col2  Col3
0  data1     3     4
1  data2     4     3
2  data3     2     3
3  data4     2     4
4  data5     1     4

In [89]: df2
Out[89]:
    Col1  Col4  Col5
0  data1     7     4
1  data2     6     9
2  data3     1     4

In [90]: df3
Out[90]:
    Col1  Col6  Col7
0  data2     5     8
1  data3     2     7
2  data5     5     3

Answer 2

Using pd.concat:

df1.set_index('Col1',inplace=True)
df2.set_index('Col1',inplace=True)
df3.set_index('Col1',inplace=True)
df = pd.concat([df1,df2,df3],axis=1,sort=False).reset_index()
df.rename(columns = {'index':'Col1'})

    Col1    Col2    Col3    Col4    Col5    Col6    Col7
0   data1   3       4       7.0     4.0     NaN     NaN
1   data2   4       3       6.0     9.0     5.0     8.0
2   data3   2       3       1.0     4.0     2.0     7.0
3   data4   2       4       NaN     NaN     NaN     NaN
4   data5   1       4       NaN     NaN     5.0     3.0