Pandas Series: str.partition() function

Series-str.partition() function

The str.partition() function is used to split the string at the first occurrence of sep.

This method splits the string at the first occurrence of sep, and returns 3 elements containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return 3 elements containing the string itself, followed by two empty strings.

Syntax:

Series.str.partition(self, sep=' ', expand=True)

Parameters:

Returns: DataFrame/MultiIndex or Series/Index of objects

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(['Albert Junior Labuschagne', 'Jason Pitt-Rivers'])
s

Output:

0    Albert Junior Labuschagne
1            Jason Pitt-Rivers
dtype: object

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(['Albert Junior Labuschagne', 'Jason Pitt-Rivers'])
s.str.partition()

Output:

        0	1	                 2
0	Albert		Junior Labuschagne
1	Jason		Pitt-Rivers

Example - To partition by the last space instead of the first one:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(['Albert Junior Labuschagne', 'Jason Pitt-Rivers'])
s.str.rpartition()

Output:

              0	1	         2
0	Albert Junior		Labuschagne
1	Jason		        Pitt-Rivers

Example - To partition by something different than a space:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(['Albert Junior Labuschagne', 'Jason Pitt-Rivers'])
s.str.partition('-')

Output:

                         0	   1	         2
0	Albert Junior Labuschagne		
1	              Jason Pitt	   -	        Rivers

Example - To return a Series containing tuples instead of a DataFrame:

Python-Pandas Code:

import numpy as np
import pandas as pd
s = pd.Series(['Albert Junior Labuschagne', 'Jason Pitt-Rivers'])
s.str.partition('-', expand=False)

Output:

0    (Albert Junior Labuschagne, , )
1            (Jason Pitt, -, Rivers)
dtype: object

Example - Also available on indices:

Python-Pandas Code:

import numpy as np
import pandas as pd
idx = pd.Index(['X 234', 'Y 888'])
idx

Output:

Index(['X 234', 'Y 888'], dtype='object')

Example - Which will create a MultiIndex:

Python-Pandas Code:

import numpy as np
import pandas as pd
idx = pd.Index(['X 234', 'Y 888'])
idx.str.partition()

Output:

MultiIndex(levels=[['X', 'Y'], [' '], ['234', '888']],
           codes=[[0, 1], [0, 0], [0, 1]])

Example - Or an index with tuples with expand=False:

Python-Pandas Code:

import numpy as np
import pandas as pd
idx = pd.Index(['X 234', 'Y 888'])
idx.str.partition(expand=False)

Output:

Index([('X', ' ', '234'), ('Y', ' ', '888')], dtype='object')

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