[python] Python: pandas merge multiple dataframes

I have diferent dataframes and need to merge them together based on the date column. If I only had two dataframes, I could use df1.merge(df2, on='date'), to do it with three dataframes, I use df1.merge(df2.merge(df3, on='date'), on='date'), however it becomes really complex and unreadable to do it with multiple dataframes.

All dataframes have one column in common -date, but they don't have the same number of rows nor columns and I only need those rows in which each date is common to every dataframe.

So, I'm trying to write a recursion function that returns a dataframe with all data but it didn't work. How should I merge multiple dataframes then?

I tried diferent ways and got errors like out of range, keyerror 0/1/2/3 and can not merge DataFrame with instance of type <class 'NoneType'>.

This is the script I wrote:

dfs = [df1, df2, df3] # list of dataframes

def mergefiles(dfs, countfiles, i=0):
    if i == (countfiles - 2): # it gets to the second to last and merges it with the last
        return

    dfm = dfs[i].merge(mergefiles(dfs[i+1], countfiles, i=i+1), on='date')
    return dfm

print(mergefiles(dfs, len(dfs)))

An example: df_1:

May 19, 2017;1,200.00;0.1%
May 18, 2017;1,100.00;0.1%
May 17, 2017;1,000.00;0.1%
May 15, 2017;1,901.00;0.1%

df_2:

May 20, 2017;2,200.00;1000000;0.2%
May 18, 2017;2,100.00;1590000;0.2%
May 16, 2017;2,000.00;1230000;0.2%
May 15, 2017;2,902.00;1000000;0.2%

df_3:

May 21, 2017;3,200.00;2000000;0.3%
May 17, 2017;3,100.00;2590000;0.3%
May 16, 2017;3,000.00;2230000;0.3%
May 15, 2017;3,903.00;2000000;0.3%

Expected merge result:

May 15, 2017;  1,901.00;0.1%;  2,902.00;1000000;0.2%;   3,903.00;2000000;0.3%   

@everestial007 's solution worked for me. This is how I improved it for my use case, which is to have the columns of each different df with a different suffix so I can more easily differentiate between the dfs in the final merged dataframe.

from functools import reduce
import pandas as pd
dfs = [df1, df2, df3, df4]
suffixes = [f"_{i}" for i in range(len(dfs))]
# add suffixes to each df
dfs = [dfs[i].add_suffix(suffixes[i]) for i in range(len(dfs))]
# remove suffix from the merging column
dfs = [dfs[i].rename(columns={f"date{suffixes[i]}":"date"}) for i in range(len(dfs))]
# merge
dfs = reduce(lambda left,right: pd.merge(left,right,how='outer', on='date'), dfs)

Look at this pandas three-way joining multiple dataframes on columns

filenames = ['fn1', 'fn2', 'fn3', 'fn4',....]
dfs = [pd.read_csv(filename, index_col=index_col) for filename in filenames)]
dfs[0].join(dfs[1:])

Thank you for your help @jezrael, @zipa and @everestial007, both answers are what I need. If I wanted to make a recursive, this would also work as intended:

def mergefiles(dfs=[], on=''):
    """Merge a list of files based on one column"""
    if len(dfs) == 1:
         return "List only have one element."

    elif len(dfs) == 2:
        df1 = dfs[0]
        df2 = dfs[1]
        df = df1.merge(df2, on=on)
        return df

    # Merge the first and second datafranes into new dataframe
    df1 = dfs[0]
    df2 = dfs[1]
    df = dfs[0].merge(dfs[1], on=on)

    # Create new list with merged dataframe
    dfl = []
    dfl.append(df)

    # Join lists
    dfl = dfl + dfs[2:] 
    dfm = mergefiles(dfl, on)
    return dfm

If you are filtering by common date this will return it:

dfs = [df1, df2, df3]
checker = dfs[-1]
check = set(checker.loc[:, 0])

for df in dfs[:-1]:
    check = check.intersection(set(df.loc[:, 0]))

print(checker[checker.loc[:, 0].isin(check)])

There are 2 solutions for this, but it return all columns separately:

import functools

dfs = [df1, df2, df3]

df_final = functools.reduce(lambda left,right: pd.merge(left,right,on='date'), dfs)
print (df_final)
          date     a_x   b_x       a_y      b_y   c_x         a        b   c_y
0  May 15,2017  900.00  0.2%  1,900.00  1000000  0.2%  2,900.00  2000000  0.2%

k = np.arange(len(dfs)).astype(str)
df = pd.concat([x.set_index('date') for x in dfs], axis=1, join='inner', keys=k)
df.columns = df.columns.map('_'.join)
print (df)
                0_a   0_b       1_a      1_b   1_c       2_a      2_b   2_c
date                                                                       
May 15,2017  900.00  0.2%  1,900.00  1000000  0.2%  2,900.00  2000000  0.2%

Looks like the data has the same columns, so you can:

df1 = pd.DataFrame(data1)
df2 = pd.DataFrame(data2)

merged_df = pd.concat([df1, df2])

functools.reduce and pd.concat are good solutions but in term of execution time pd.concat is the best.

from functools import reduce
import pandas as pd

dfs = [df1, df2, df3, ...]
nan_value = 0

# solution 1 (fast)
result_1 = pd.concat(dfs, join='outer', axis=1).fillna(nan_value)

# solution 2
result_2 = reduce(lambda df_left,df_right: pd.merge(df_left, df_right, 
                                              left_index=True, right_index=True, 
                                              how='outer'), 
                  dfs).fillna(nan_value)

@dannyeuu's answer is correct. pd.concat naturally does a join on index columns, if you set the axis option to 1. The default is an outer join, but you can specify inner join too. Here is an example:

x = pd.DataFrame({'a': [2,4,3,4,5,2,3,4,2,5], 'b':[2,3,4,1,6,6,5,2,4,2], 'val': [1,4,4,3,6,4,3,6,5,7], 'val2': [2,4,1,6,4,2,8,6,3,9]})
x.set_index(['a','b'], inplace=True)
x.sort_index(inplace=True)

y = x.__deepcopy__()
y.loc[(14,14),:] = [3,1]
y['other']=range(0,11)

y.sort_values('val', inplace=True)

z = x.__deepcopy__()
z.loc[(15,15),:] = [3,4]
z['another']=range(0,22,2)
z.sort_values('val2',inplace=True)


pd.concat([x,y,z],axis=1)