Let’s see the various methods to Remove last n rows of a Pandas Dataframe.
First, let’s make a dataframe:
Python3
# Import Required Librariesimport pandas as pd # Create a dictionary for the dataframedict = { 'Name': ['Sukritin', 'Sumit Tyagi', 'Akriti Goel', 'Sanskriti', 'Abhishek Jain'], 'Age': [22, 20, 45, 21, 22], 'Marks': [90, 84, -33, -87, 82]} # Converting Dictionary to # Pandas Dataframedf = pd.DataFrame(dict) # Print Dataframeprint(df) |
Output:
Method 1: Using Dataframe.drop() .
We can remove the last n rows using the drop() method. drop() method gets an inplace argument which takes a boolean value. If inplace attribute is set to True then the dataframe gets updated with the new value of dataframe (dataframe with last n rows removed).
Example:
Python3
# Import Required Librariesimport pandas as pd # Create a dictionary for the dataframedict = { 'Name': ['Sukritin', 'Sumit Tyagi', 'Akriti Goel', 'Sanskriti', 'Abhishek Jain'], 'Age': [22, 20, 45, 21, 22], 'Marks': [90, 84, -33, -87, 82]} # Converting Dictionary to # Pandas Dataframedf = pd.DataFrame(dict) # Number of rows to dropn = 3 # Dropping last n rows using dropdf.drop(df.tail(n).index, inplace = True) # Printing dataframeprint(df) |
Output:
Method 2: Using Datarame.iloc[ ].
This method is used when the index label of a data frame is something other than numeric series of 0, 1, 2, 3….n or in case the user doesn’t know the index label.
Example:
Python3
# Import Required Librariesimport pandas as pd # Create a dictionary for the dataframedict = { 'Name': ['Sukritin', 'Sumit Tyagi', 'Akriti Goel', 'Sanskriti', 'Abhishek Jain'], 'Age': [22, 20, 45, 21, 22], 'Marks': [90, 84, -33, -87, 82]} # Converting Dictionary to # Pandas Dataframedf = pd.DataFrame(dict) # Number of rows to dropn = 3 # Removing last n rowsdf_dropped_last_n = df.iloc[:-n] # Printing dataframeprint(df_dropped_last_n) |
Output:
Method 3: Using Dataframe.head().
This method is used to return top n (5 by default) rows of a data frame or series.
Example:
Python3
# Import Required Librariesimport pandas as pd # Create a dictionary for the dataframedict = { 'Name': ['Sukritin', 'Sumit Tyagi', 'Akriti Goel', 'Sanskriti', 'Abhishek Jain'], 'Age': [22, 20, 45, 21, 22], 'Marks': [90, 84, -33, -87, 82]} # Converting Dictionary to # Pandas Dataframedf = pd.DataFrame(dict) # Number of rows to dropn = 3 # Using head() to # drop last n rowsdf1 = df.head(-n) # Printing dataframeprint(df1) |
Output:
Method 4: Using Dataframe slicing [ ].
Example:
Python3
# Import Required Librariesimport pandas as pd # Create a dictionary for the dataframedict = { 'Name': ['Sukritin', 'Sumit Tyagi', 'Akriti Goel', 'Sanskriti', 'Abhishek Jain'], 'Age': [22, 20, 45, 21, 22], 'Marks': [90, 84, -33, -87, 82]} # Converting Dictionary to # Pandas Dataframedf = pd.DataFrame(dict) # Number of rows to dropn = 3 # Slicing last n rowsdf1 = df[:-n] # Printing dataframeprint(df1) |
Output:
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