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How to return a subset of a DataFrame's columns based on dtypes

Onyejiaku Theophilus Chidalu

Overview

The select_dtypes() function in pandas returns a subset of a given DataFrame's columns based on the column data types.

Syntax

The select_dtypes() function's syntax is shown below:

DataFrame.select_dtypes(include=None, exclude=None)
Syntax for the select_dtypes() function

Parameter value

The select_dtypes() function takes any of the two parameter values: include and exclude (at least one of them must be supplied). These represent the selection of the data type(s) to be included or excluded.

Return value

The select_dtypes() function returns a subset of the DataFrame that includes the data types in include and excludes the data types in exclude.

Example

# A code to illustrate the select_dtypes() function in Pandas

# importing the pandas library
from pandas import DataFrame

# creating a dataframe
my_data_frame = DataFrame({'Id': [1, 2, 3, 4, 5, 6],
                   'Married': [True, False] * 3,
                   'Score': [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]})

# obtaining the columns by their data types
bool_data = my_data_frame.select_dtypes(include=bool)
int_and_float_data = my_data_frame.select_dtypes(include=[int, float])

# obtaining the columns by excluding data types
data_without_int_values = my_data_frame.select_dtypes(exclude=int)

# printing results
print(bool_data)
print(int_and_float_data)
print(data_without_int_values)
Implement the select_dtypes() function

Explanation

  • Line 4: We import the DataFrame from the pandas library.
  • Lines 7–9: We create a DataFrame object, my_data_frame.
  • Lines 12–16: We obtain the columns of the DataFrame based on their data types using the select_dtypes() function. We assign the results to the bool_data, int_and_float_data and data_without_int_values variables.
  • Lines 19–21: We print the values of bool_data, int_and_float_data and data_without_int_values.

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Onyejiaku Theophilus Chidalu
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