change_values.py

# Extracted from : https://medium.com/analytics-vidhya/pandas-how-to-change-value-based-on-condition-fc8ee38ba529
import pandas as pd
import numpy as np

data = {'Stock': ['AAPL', 'IBM', 'MSFT', 'WMT'],
       'Price': [144.8, 141.61, 304.21, 139.5],
       'PE': [25, 21, 39, 16],  
       'TradingExchange': ['NASDAQ', 'NYSE', 'NASDAQ', 'NYSE']}

df = pd.DataFrame(data)

#print(df)

# Method 1. Using df.loc
df["Price_Category"] = "Over 150"
df.loc[df["Price"] < 150, "Price_Category"] = "Under 150"

# Method 2. Using np.where
df["Price_Category"] = "Over 150"
df['Price_Category'] = np.where(df["Price"] < 150, "Under 150", df['Price_Category'])

# Method 3: Using Numpy.Select to Set Values Using Multiple Conditions
PE_Conditions = [
    (df['PE'] < 20),
    (df['PE'] >= 20) & (df['PE'] < 30),
    (df['PE'] >= 30)
]
PE_Categories = ['Less than 20', '20-30', '30+']
df['PE_Category'] = np.select(PE_Conditions, PE_Categories)
print(df)

print(df)