Page 229 - Touhpad Ai

Output:

Miles Kilometers
0 15 24.1401
1 20 32.1868
2 25 40.2335

Program 28: To encode categorical variables
import pandas as pd

df = pd.DataFrame({'Response': ['Yes', 'No', 'Yes', 'No']})
# Convert Yes/No to 1 OR 0
df['Response_Encoded'] = df['Response'].map({'Yes': 1, 'No': 0})

print(df)
Output:

Response Response_Encoded
0 Yes 1
1 No 0

2 Yes 1
3 No 0
Program 29: To handle missing values in a DataFrame

import numpy as np
import pandas as pd
df = pd.DataFrame({'Score': [85, np.nan, 90, np.nan, 95]})

# Fill missing values with the mean
df['Score_Filled'] = df['Score'].fillna(df['Score'].mean())
print(df)
Output:
Score Score_Filled

0 85.0 85.0
1 NaN 90.0
2 90.0 90.0
3 NaN 90.0
4 95.0 95.0

Program 30: To aggregate data from monthly sales to yearly sales

import pandas as pd
# Sample data
data = {
'Month': ['Jul', 'Aug', 'Sep', 'Jul', 'Aug', 'Sep'],

'Year': [2023, 2023, 2023, 2024, 2024, 2024],
'Sales': [100, 250, 150, 120, 220, 180]
}

Theoretical and Practical Aspects of Data Processing 227