Pandas Series: str.cat() function
Series-str.cat() function
The str.cat() function is used to concatenate strings in the Series/Index with given separator.
If others is specified, this function concatenates the Series/Index and elements of others element-wise. If others is not passed, then all values in the Series/Index are concatenated into a single string with a given sep.
Syntax:
Series.str.cat(self, others=None, sep=None, na_rep=None, join=None)
Parameters:
Name | Description | Type/Default Value | Required / Optional |
---|---|---|---|
others | Series, Index, DataFrame, np.ndarray (one- or two-dimensional) and other list-likes of strings must have the same length as the calling Series/Index, with the exception of indexed objects (i.e. Series/Index/DataFrame) if join is not None. If others is a list-like that contains a combination of Series, Index or np.ndarray (1-dim), then all elements will be unpacked and must satisfy the above criteria individually. If others is None, the method returns the concatenation of all strings in the calling Series/Index. |
Series, Index, DataFrame, np.ndarray or list-like | Required |
sep | The separator between the different elements/columns. By default the empty string '' is used. | str, default '' | Required |
na_rep | Representation that is inserted for all missing values: |
str or None Default Value: None |
Required |
join | Determines the join-style between the calling Series/Index and any Series/Index/DataFrame in others (objects without an index need to match the length of the calling Series/Index). If None, alignment is disabled, but this option will be removed in a future version of pandas and replaced with a default of 'left'. To disable alignment, use .values on any Series/Index/DataFrame in others. | {'left', 'right', 'outer', 'inner'} Default Value: None |
Required |
Returns: str, Series or Index
If others is None, str is returned, otherwise a Series/Index (same type as caller) of objects is returned.
Example - When not passing others, all values are concatenated into a single string:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', np.nan, 's'])
s.str.cat(sep=' ')
Output:
'p q s'
Example - By default, NA values in the Series are ignored. Using na_rep, they can be given a representation:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', np.nan, 's'])
s.str.cat(sep=' ', na_rep='?')
Output:
'p q ? s'
Example - If others is specified, corresponding values are concatenated with the separator. Result will be a Series of strings:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', np.nan, 's'])
s.str.cat(['P', 'Q', 'R', 'S'], sep=',')
Output:
0 p,P 1 q,Q 2 NaN 3 s,S dtype: object
Example - Missing values will remain missing in the result, but can again be represented using na_rep:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', np.nan, 's'])
s.str.cat(['P', 'Q', 'R', 'S'], sep=',', na_rep='-')
Output:
0 p,P 1 q,Q 2 -,R 3 s,S dtype: object
Example - If sep is not specified, the values are concatenated without separation:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', np.nan, 's'])
s.str.cat(['P', 'Q', 'R', 'S'], na_rep='-')
Output:
0 pP 1 qQ 2 -R 3 sS dtype: object
Example - Series with different indexes can be aligned before concatenation. The join-keyword works as in other methods:
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', np.nan, 's'])
t = pd.Series(['s', 'p', 't', 'r'], index=[2, 0, 4, 3])
s.str.cat(t, join='left', na_rep='-')
Output:
0 pp 1 q- 2 -s 3 sr dtype: object
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', np.nan, 's'])
t = pd.Series(['s', 'p', 't', 'r'], index=[2, 0, 4, 3])
s.str.cat(t, join='outer', na_rep='-')
Output:
0 pp 1 q- 2 -s 3 sr 4 -t dtype: object
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', np.nan, 's'])
t = pd.Series(['s', 'p', 't', 'r'], index=[2, 0, 4, 3])
s.str.cat(t, join='inner', na_rep='-')
Output:
0 pp 2 -s 3 sr dtype: object
Python-Pandas Code:
import numpy as np
import pandas as pd
s = pd.Series(['p', 'q', np.nan, 's'])
t = pd.Series(['s', 'p', 't', 'r'], index=[2, 0, 4, 3])
s.str.cat(t, join='right', na_rep='-')
Output:
2 -s 0 pp 4 -t 3 sr dtype: object
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