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

Last update on April 03 2025 13:11:57 (UTC/GMT +8 hours)

7. Change Series DataType

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.

For more Practice: Solve these Related Problems:

• Write a Pandas program to change the data type of a Series from string to numeric and handle conversion errors gracefully.
• Write a Pandas program to convert a Series of mixed types to datetime and filter out invalid dates.
• Write a Pandas program to convert a Series containing numeric strings with commas to floats.
• Write a Pandas program to change the data type of a Series to categorical and then display the category codes.

Python-Pandas Code Editor:

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