[1064] Change values in a DataFrame based on different values

To change values in a DataFrame based on different values, you can use several methods in Pandas. Here are a few common approaches:

Using loc for Conditional Replacement

You can use the loc method to replace values based on a condition:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Category': ['A', 'B', 'C', 'A', 'B'],
    'Value': [10, 20, 30, 40, 50]
})

# Replace values based on condition
df.loc[df['Category'] == 'A', 'Value'] = 100

print(df)

Using replace Method

The replace method allows you to specify a dictionary for replacing values:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Category': ['A', 'B', 'C', 'A', 'B'],
    'Value': [10, 20, 30, 40, 50]
})

# Replace values using a dictionary
df['Category'] = df['Category'].replace({'A': 'X', 'B': 'Y'})

print(df)

Using np.where for Conditional Replacement

You can also use NumPy’s where function for more complex conditions:

import pandas as pd
import numpy as np

# Sample DataFrame
df = pd.DataFrame({
    'Category': ['A', 'B', 'C', 'A', 'B'],
    'Value': [10, 20, 30, 40, 50]
})

# Replace values using np.where
df['Value'] = np.where(df['Category'] == 'A', 100, df['Value'])

print(df)

Using apply with a Lambda Function

For more complex logic, you can use the apply method with a lambda function:

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'Category': ['A', 'B', 'C', 'A', 'B'],
    'Value': [10, 20, 30, 40, 50]
})

# Replace values using apply and lambda
df['Value'] = df.apply(lambda row: 100 if row['Category'] == 'A' else row['Value'], axis=1)

print(df)

These methods should help you replace values in a DataFrame based on different conditions. If you have a specific scenario or need further assistance, feel free to ask!