Union in more than 2 pandas dataframe

Question

I am trying to convert a sql query to python. The sql statement is as follows:

select * from table 1 
union
select * from table 2
union 
select * from table 3
union
select * from table 4

Now I have those tables in 4 dataframe df1, df2, df3, df4 and I would like to union 4 pandas dataframe which would match the result as the same as sql query. I am confused of what operation to be used which is equivalent to sql union? Thanks in advance!!

Note: The column name for all the dataframes are the same.

Solution 1

If I understand well the issue, you are looking for the concat function.

pandas.concat([df1, df2, df3, df4]) should work correctly if the column names are the same for both dataframes.

Solution 2

IIUC you can use merge and join by columns matching_col of all dataframes:

import pandas as pd

# Merge multiple dataframes
df1 = pd.DataFrame({"matching_col": pd.Series({1: 4, 2: 5, 3: 7}), 
                    "a": pd.Series({1: 52, 2: 42, 3:7})}, columns=['matching_col','a'])
print df1
   matching_col   a
1             4  52
2             5  42
3             7   7

df2 = pd.DataFrame({"matching_col": pd.Series({1: 2, 2: 7, 3: 8}), 
                    "a": pd.Series({1: 62, 2: 28, 3:9})}, columns=['matching_col','a'])
print df2
   matching_col   a
1             2  62
2             7  28
3             8   9

df3 = pd.DataFrame({"matching_col": pd.Series({1: 1, 2: 0, 3: 7}), 
                    "a": pd.Series({1: 28, 2: 52, 3:3})}, columns=['matching_col','a'])
print df3
   matching_col   a
1             1  28
2             0  52
3             7   3

df4 = pd.DataFrame({"matching_col": pd.Series({1: 4, 2: 9, 3: 7}), 
                    "a": pd.Series({1: 27, 2: 24, 3:7})}, columns=['matching_col','a'])
print df4
   matching_col   a
1             4  27
2             9  24
3             7   7

Solution1:

df = pd.merge(pd.merge(pd.merge(df1,df2,on='matching_col'),df3,on='matching_col'), df4, on='matching_col')
set columns names
df.columns = ['matching_col','a1','a2','a3','a4']
print df

   matching_col  a1  a2  a3  a4
0             7   7  28   3   7

Solution2:

dfs = [df1, df2, df3, df4]
#use built-in python reduce
df = reduce(lambda left,right: pd.merge(left,right,on='matching_col'), dfs)
#set columns names
df.columns = ['matching_col','a1','a2','a3','a4']
print df

   matching_col  a1  a2  a3  a4
0             7   7  28   3   7

But if you need only concat dataframes, use concat with reseting index by parameter ignore_index=True:

print pd.concat([df1, df2, df3, df4], ignore_index=True)

    matching_col   a
0              4  52
1              5  42
2              7   7
3              2  62
4              7  28
5              8   9
6              1  28
7              0  52
8              7   3
9              4  27
10             9  24
11             7   7

Solution 3

This should be a comment on Jezrael's answer (+1'd for merge over concat) but I haven't sufficient reputation.

The OP asked how to union the dfs, but merge returns intersection by default: http://pandas.pydata.org/pandas-docs/stable/generated/pandas.merge.html#pandas.merge

To get unions, add how='outer' to the merge calls.