Given a Dataframe, return all those index labels for which some condition is satisfied over a specific column.
Solution #1: We can use simple indexing operation to select all those values in the column which satisfies the given condition.
# importing pandas as pd import pandas as pd # Create the dataframe df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Product':['Umbrella', 'Matress', 'Badminton', 'Shuttle'], 'Last_Price':[1200, 1500, 1600, 352], 'Updated_Price':[1250, 1450, 1550, 400], 'Discount':[10, 10, 10, 10]}) # Create the indexes df.index =['Item 1', 'Item 2', 'Item 3', 'Item 4'] # Print the dataframe print(df) |
Output :
Now, we want to find out the index labels of all items whose ‘Updated_Price’ is greater than 1000.
# Select all the rows which satisfies the criteria # convert the collection of index labels to list. Index_label = df[df['Updated Price']>1000].index.tolist() # Print all the labels print(Index_label) |
Output :
As we can see in the output, the above operation has successfully evaluated all the values and has returned a list containing the index labels.
Solution #2: We can use Pandas Dataframe.query() function to select all the rows which satisfies some condition over a given column.
# importing pandas as pd import pandas as pd # Create the dataframe df = pd.DataFrame({'Date':['10/2/2011', '11/2/2011', '12/2/2011', '13/2/2011'], 'Product':['Umbrella', 'Matress', 'Badminton', 'Shuttle'], 'Last_Price':[1200, 1500, 1600, 352], 'Updated_Price':[1250, 1450, 1550, 400], 'Discount':[10, 10, 10, 10]}) # Create the indexes df.index =['Item 1', 'Item 2', 'Item 3', 'Item 4'] # Print the dataframe print(df) |
Output :
Now, we want to find out the index labels of all items whose ‘Updated_Price’ is greater than 1000.
# Select all the rows which satisfies the criteria # convert the collection of index labels to list. Index_label = df.query('Updated_Price > 1000').index.tolist() # Print all the labels print(Index_label) |
Output :
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