Pandas: Convert continuous values of a column in a given DataFrame to categorical
Pandas: DataFrame Exercise-70 with Solution
Write a Pandas program to convert continuous values of a column in a given DataFrame to categorical.
Input:
{ 'Name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Syed Wharton'],
'Age': [18, 22, 40, 50, 80, 5] }
Output:
Age group:
0 kids
1 adult
2 elderly
3 adult
4 elderly
5 kids
Name: age_groups, dtype: category
Categories (3, object): [kids < adult < elderly]
Sample Solution :
Python Code :
import pandas as pd
df = pd.DataFrame({
'name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Syed Wharton', 'Kierra Gentry'],
'age': [18, 22, 85, 50, 80, 5]
})
print("Original DataFrame:")
print(df)
print('\nAge group:')
df["age_groups"] = pd.cut(df["age"], bins = [0, 18, 65, 99], labels = ["kids", "adult", "elderly"])
print(df["age_groups"])
Sample Output:
Original DataFrame: name age 0 Alberto Franco 18 1 Gino Mcneill 22 2 Ryan Parkes 85 3 Eesha Hinton 50 4 Syed Wharton 80 5 Kierra Gentry 5 Age group: 0 kids 1 adult 2 elderly 3 adult 4 elderly 5 kids Name: age_groups, dtype: category Categories (3, object): [kids < adult < elderly]
Python-Pandas Code Editor:
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