Pandas Data Series: Change the data type of given a column or a Series

Pandas: Data Series Exercise-7 with Solution

Write a Pandas program to change the data type of given a column or a Series.

Sample Series:
Original Data Series:
0 100
1 200
2 python
3 300.12
4 400
dtype: object
Change the said data type to numeric:
0 100.00
1 200.00
2 NaN
3 300.12
4 400.00
dtype: float64

Sample Solution :

Python Code :

import pandas as pd
s1 = pd.Series(['100', '200', 'python', '300.12', '400'])
print("Original Data Series:")
print(s1)
print("Change the said data type to numeric:")
s2 = pd.to_numeric(s1, errors='coerce')
print(s2)

Sample Output:

Original Data Series:
0       100
1       200
2    python
3    300.12
4       400
dtype: object
Change the said data type to numeric:
0    100.00
1    200.00
2       NaN
3    300.12
4    400.00
dtype: float64                              

Explanation:

s1 = pd.Series(['100', '200', 'python', '300.12', '400']): This line creates a Pandas Series object 's1' containing a sequence of five string values: ['100', '200', 'python', '300.12', '400'].

s2 = pd.to_numeric(s1, errors='coerce'): This line applies the pd.to_numeric() function to the Series object 's1' with the 'errors' parameter set to 'coerce'. This function attempts to convert each value in the Series object to a numeric type (e.g., integer or float). If a value cannot be converted, it will be replaced with a NaN (not a number) value.

value in the Series object to a numeric type (e.g., integer or float). If a value cannot be converted, it will be replaced with a NaN (not a number) value.

The resulting Series object 's2' will have the same index as the original Series object 's1' but with numeric values where possible and NaN values where not possible.

Python-Pandas Code Editor:

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