Pandas Series Series.map() Function

Minahil Noor Jan 30, 2023 Pandas Pandas Series
  1. Syntax of pandas.Series.map()
  2. Example Codes: Series.map()
  3. Example Codes: Series.map() to Pass a Dictionary as arg Parameter
  4. Example Codes: Series.map() to Pass a Function as arg Parameter
  5. Example Codes: Series.map() to Apply It on a DataFrame
Pandas Series Series.map() Function

Python Pandas Series.map() function substitutes the values of a Series. The substituted values may be derived from a Series, dictionary, or a function. This function works only for a Series. If we apply this function to a DataFrame then it will generate an AttributeError.

Syntax of pandas.Series.map()

Series.map(arg, na_action=None)

Parameters
arg It is a function, dictionary, or a Series. The values to be substituted are derived from this function, dictionary, or Series.
na_action This parameter accepts two values: None and ignore. Its default value is None. If its value is ignore then it does not map the derived values to NaN values. It ignores NaN values.

Return

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

Example Codes: Series.map()

We will generate a Series that contains the NaN values to check the output after passing the na_action parameter.

import pandas as pd
import numpy as np

series = pd.Series(['Rose', 
                    'Lili', 
                    'Tulip', 
                    np.NaN, 
                    'Orchid', 
                    'Hibiscus', 
                    'Jasmine', 
                    'Daffodil',
                    np.NaN , 
                    'SunFlower', 
                    'Daisy'])

print(series)

The example Series is,

0          Rose
1          Lili
2         Tulip
3           NaN
4        Orchid
5      Hibiscus
6       Jasmine
7      Daffodil
8           NaN
9     SunFlower
10        Daisy
dtype: object

We use NumPy to generate NaN values.

The parameter arg is a mandatory parameter. If it is not passed then the function generates a TypeError. We will first pass a Series as arg parameter.

To map two Series the last column of the first Series should be the same as the index of the second Series.

import pandas as pd
import numpy as np

first_series = pd.Series(
    [
        "Rose",
        "Lili",
        "Tulip",
        np.NaN,
        "Orchid",
        "Hibiscus",
        "Jasmine",
        "Daffodil",
        np.NaN,
        "SunFlower",
        "Daisy",
    ]
)

second_series = pd.Series(
    [23, 34, 67, 90, 21, 45, 29, 70, 56],
    index=[
        "Rose",
        "Lili",
        "Tulip",
        "Orchid",
        "Hibiscus",
        "Jasmine",
        "Daffodil",
        "SunFlower",
        "Daisy",
    ],
)

series1 = first_series.map(second_series)
print(series1)

Output:

0     23.0
1     34.0
2     67.0
3      NaN
4     90.0
5     21.0
6     45.0
7     29.0
8      NaN
9     70.0
10    56.0
dtype: float64

Note that the function has substituted the values after comparing the two Series.

Example Codes: Series.map() to Pass a Dictionary as arg Parameter

import pandas as pd
import numpy as np

series = pd.Series(
    [
        "Rose",
        "Lili",
        "Tulip",
        np.NaN,
        "Orchid",
        "Hibiscus",
        "Jasmine",
        "Daffodil",
        np.NaN,
        "SunFlower",
        "Daisy",
    ]
)

dictionary = {
    "Rose": "One",
    "Lili": "Two",
    "Orchid": "Three",
    "Jasmine": "Four",
    "Daisy": "Five",
}

series1 = series.map(dictionary)
print(series1)

Output:

0       One
1       Two
2       NaN
3       NaN
4     Three
5       NaN
6      Four
7       NaN
8       NaN
9       NaN
10     Five
dtype: object

The values in the Series that are not in the dictionary are replaced with a NaN value.

Example Codes: Series.map() to Pass a Function as arg Parameter

Now we will pass a function as a parameter.

import pandas as pd
import numpy as np

series = pd.Series(
    [
        "Rose",
        "Lili",
        "Tulip",
        np.NaN,
        "Orchid",
        "Hibiscus",
        "Jasmine",
        "Daffodil",
        np.NaN,
        "SunFlower",
        "Daisy",
    ]
)

series1 = series.map("The name of the flower is {}.".format)
print(series1)

Output:

0          The name of the flower is Rose.
1          The name of the flower is Lili.
2         The name of the flower is Tulip.
3           The name of the flower is nan.
4        The name of the flower is Orchid.
5      The name of the flower is Hibiscus.
6       The name of the flower is Jasmine.
7      The name of the flower is Daffodil.
8           The name of the flower is nan.
9     The name of the flower is SunFlower.
10        The name of the flower is Daisy.
dtype: object

Here, we have passed string.format() function as a parameter. Note that the function has been applied to the NaN values as well. If we do not want to apply this function to the NaN values then we will pass ignore value to the na_action parameter.

import pandas as pd
import numpy as np

series = pd.Series(
    [
        "Rose",
        "Lili",
        "Tulip",
        np.NaN,
        "Orchid",
        "Hibiscus",
        "Jasmine",
        "Daffodil",
        np.NaN,
        "SunFlower",
        "Daisy",
    ]
)

series1 = series.map("The name of the flower is {}.".format, na_action="ignore")
print(series1)

Output:

0          The name of the flower is Rose.
1          The name of the flower is Lili.
2         The name of the flower is Tulip.
3                                      NaN
4        The name of the flower is Orchid.
5      The name of the flower is Hibiscus.
6       The name of the flower is Jasmine.
7      The name of the flower is Daffodil.
8                                      NaN
9     The name of the flower is SunFlower.
10        The name of the flower is Daisy.
dtype: object

The above example code has ignored the NaN values.

Example Codes: Series.map() to Apply It on a DataFrame

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Attendance": {0: 60, 1: 100, 2: 80, 3: 75, 4: 95},
        "Name": {0: "Olivia", 1: "John", 2: "Laura", 3: "Ben", 4: "Kevin"},
        "Obtained Marks": {0: 56, 1: 75, 2: 82, 3: 64, 4: 67},
    }
)

dataframe1 = dataframe.map("The flower name is {}.".format)
print(dataframe1)

Output:

AttributeError: 'DataFrame' object has no attribute 'map'

The function has generated the AttributeError.

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