Geralmente, em um DataFrame do Pandas, a condição if pode ser aplicada em colunas, linhas ou em células individuais. O outro documento ilustra cada um deles com exemplos.

Em primeiro lugar, devemos criar o seguinte DataFrame:

# importing pandas as pd
import pandas as pd
  
# create the DataFrame
df = pd.DataFrame({
    'Product': ['Umbrella', 'Matress', 'Badminton', 
                'Shuttle', 'Sofa', 'Football'],
    'MRP': [1200, 1500, 1600, 352, 5000, 500],
    'Discount': [0, 10, 0, 10, 20, 40]
})
  
# display the DataFrame
print(df)
# if condition with column conditions given
# the condition is if MRP of the product <= 2000 
# and discount > 0 show me those items
df[(df['MRP'] <= 2000) & (df['Discount'] > 0)]
# if condition with row tuple given
df[(df['Product'] == 'Sofa') & (df['MRP'] == 5000) & (df['Discount']== 20)]
# importing pandas as pd 
import pandas as pd 
  
# Create the dataframe 
df = pd.DataFrame({
    'Product': ['Umbrella', 'Matress', 'Badminton', 
                'Shuttle', 'Sofa', 'Football'],
    'MRP': [1200, 1500, 1600, 352, 5000, 500],
    'Discount': [0, 10, 0, 10, 20, 40]
})
  
# Print the dataframe 
print(df) 
  
# If condition on column values using Lambda function 
df['Discount'] = df['Discount'].apply(lambda x : 20 if x > 20 else x)
print(df)
# If condition on a cell value using iloc() or loc() functions
# iloc() is based on index search and loc() based on label search
  
# using iloc()
if df.iloc[2, 1] > 1500:
  print("Badminton Price > 1500")
else:
  print("Badminton Price < 1500")
  
# using loc()
print(df.loc[2, 'MRP'])
if df.iloc[2, 'MRP'] > 1500:         
  print("Badminton Price > 1500")
else:
  print("Badminton Price < 1500")
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