The DataFrame.join() method used to join the columns of another Dataframe either on index or on a key column.
DataFrame.join(self, other, on=None, how='left', lsuffix='', rsuffix='', sort=False)
Parameters:
other - DataFrame, Series, or list of DataFrame.
Index should be similar to one of the columns in this one.
on - str, list of str (optional)
how - {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘left’
left: use calling frame’s index (or column if on is specified)
right: use other’s index.
outer: form union of calling frame’s index (or column if on is specified)
with other’s index, and sort it. lexicographically.
inner: form intersection of calling frame’s index (or column if on is specified)
with other’s index, preserving the order of the calling’s one.
lsuffix - Suffix to use from left frame’s overlapping columns.(default ‘’)
rsuffix - Suffix to use from right frame’s overlapping columns.(default ‘’)
sort - (default False)
Example
In [1]:
# Let's define the DataFrames
import pandas as pd
df1 = pd.DataFrame({'name': ['Mark', 'Juli', 'Denial'], 'Place': ['Paris', 'London', 'Moscow']}, index=['K0', 'K1', 'K2'])
df2 = pd.DataFrame({'age': [25, 35, 28], 'Gender': ['M', 'F', 'M']}, index=['K0', 'K2', 'K3'])
Join the DataFrame df1 and df2.
In [2]: df1.join(df2)
Out[2]:
name Place age Gender
K0 Mark Paris 25.0 M
K1 Juli London NaN NaN
K2 Denial Moscow 35.0 F
In [3]: df1.join(df2, how='outer') # how='outer'
Out[3]:
name Place age Gender
K0 Mark Paris 25.0 M
K1 Juli London NaN NaN
K2 Denial Moscow 35.0 F
K3 NaN NaN 28.0 M
In [4]: df1.join(df2, how='inner') # how='inner'
Out[4]:
name Place age Gender
K0 Mark Paris 25 M
K2 Denial Moscow 35 F
Joining key columns on an index
join() takes an optional on argument which may be a column or multiple column names, which specifies that the passed DataFrame is to be aligned on that column in the DataFrame.
In [5]:
df1 = pd.DataFrame({'key':['k0','k1','k0'] , 'name': ['Mark', 'Juli', 'Denial'] ,
'Place': ['Paris', 'London', 'Moscow']})
In [6]:
df2 = pd.DataFrame({'age': [25, 35, 28], 'Gender': ['M', 'F', 'M']},
index=['k0', 'k1', 'k2'])
In [7]: Result = df1.join(df2,on='key')
Joining Multiple DataFrames
In [8]:
df1 = pd.DataFrame({'A': [1, 2, 3]}, index=['K0', 'K1', 'K2'])
df2 = pd.DataFrame({'A': [4, 5, 6]}, index=['K0', 'K0', 'K3'])
df3 = pd.DataFrame({'A': [7, 8, 9]}, index=['K1', 'K1', 'K2'])
In [9]: Result = df1.join([df2, df3])
. . .