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Pandas Series.map()

Pandas Series.map() with What is Python Pandas, Reading Multiple Files, Null values, Multiple index, Application, Application Basics, Resampling, Plotting the data, Moving windows functions, Series, Read the file, Data operations, Filter Data etc.

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Pandas Series.map()

The main task of map() is used to map the values from two series that have a common column. To map the two Series, the last column of the first Series should be the same as the index column of the second series, and the values should be unique.

Syntax

Series.map(arg, na_action=None)

Parameters

  • arg: function, dict, or Series.
    It refers to the mapping correspondence.
  • na_action: {None, 'ignore'}, Default value None. If ignore, it returns null values, without passing it to the mapping correspondence.

Returns

It returns the Pandas Series with the same index as a caller.

Example

import pandas as pd
import numpy as np
a = pd.Series(['Java', 'C', 'C++', np.nan])
a.map({'Java': 'Core'})

Output

0      Core
1      NaN
2      NaN
3      NaN
dtype: object

Example2

import pandas as pd
import numpy as np
a = pd.Series(['Java', 'C', 'C++', np.nan])
a.map({'Java': 'Core'})
a.map('I like {}'.format, na_action='ignore')

Output

0    I like Java
1       I like C
2     I like C++
3     I like nan
dtype: object

Example3

import pandas as pd
import numpy as np
a = pd.Series(['Java', 'C', 'C++', np.nan])
a.map({'Java': 'Core'})
a.map('I like {}'.format)
a.map('I like {}'.format, na_action='ignore')

Output

0    I like Java
1       I like C
2     I like C++
3            NaN
dtype: object
Next TopicPandas Series.std()




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