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!
标签:Category,different,based,df,DataFrame,values,Value,pd From: https://www.cnblogs.com/alex-bn-lee/p/18420358