Drop Rows by Index in Pandas DataFrame

Here are two ways to drop rows by the index in Pandas DataFrame:

(1) Drop a single row by index. For example, to drop the row that has an index of 2:

df.drop(index=2, inplace=True)

 (2) Drop multiple rows by index. For instance, to drop the rows with the index values of 2, 4 and 6:

df.drop(index=[2, 4, 6], inplace=True)

The Example

To start, create a simple DataFrame with 7 rows:

import pandas as pd

data = {
    "Product": ["Computer", "Printer", "Monitor", "Desk", "Phone", "Tablet", "Scanner"],
    "Price": [900, 200, 300, 450, 150, 250, 150],
}

df = pd.DataFrame(data)

print(df)

As you can see, the index values are located on the left, starting from 0:

    Product  Price
0  Computer    900
1   Printer    200
2   Monitor    300
3      Desk    450
4     Phone    150
5    Tablet    250
6   Scanner    150

Drop a Single Row by Index in Pandas DataFrame

To drop a specific row, you’ll need to specify the associated index value that represents that row.

For example, to drop the row with the index of 2 (for the ‘Monitor’ product):

df.drop(index=2, inplace=True)

The complete code:

import pandas as pd

data = {
    "Product": ["Computer", "Printer", "Monitor", "Desk", "Phone", "Tablet", "Scanner"],
    "Price": [900, 200, 300, 450, 150, 250, 150],
}

df = pd.DataFrame(data)

df.drop(index=2, inplace=True)

print(df)

Once you run the code, you’ll no longer see the row with the index of 2:

    Product  Price
0  Computer    900
1   Printer    200
3      Desk    450
4     Phone    150
5    Tablet    250
6   Scanner    150

Drop Multiple Rows by Index in Pandas DataFrame

What if you want to drop multiple rows?

For instance, to drop the rows with the index values of 2, 4 and 6:

df.drop(index=[2, 4, 6], inplace=True)

Here is the complete Python code:

import pandas as pd

data = {
    "Product": ["Computer", "Printer", "Monitor", "Desk", "Phone", "Tablet", "Scanner"],
    "Price": [900, 200, 300, 450, 150, 250, 150],
}

df = pd.DataFrame(data)

df.drop(index=[2, 4, 6], inplace=True)

print(df)

You’ll now notice that the rows with the index values of 2, 4 and 6 are no longer present:

    Product  Price
0  Computer    900
1   Printer    200
3      Desk    450
5    Tablet    250

Index is a String

So far, you have seen how to drop rows where the index values are numeric.

But what if the index values are strings?

For example:

index=["Item_A", "Item_B", "Item_C", "Item_D", "Item_E", "Item_F", "Item_G"]

So the DataFrame with the new index values would be:

import pandas as pd

data = {
    "Product": ["Computer", "Printer", "Monitor", "Desk", "Phone", "Tablet", "Scanner"],
    "Price": [900, 200, 300, 450, 150, 250, 150],
}

df = pd.DataFrame(
    data, index=["Item_A", "Item_B", "Item_C", "Item_D", "Item_E", "Item_F", "Item_G"]
)

print(df)

As you can see, the new non-numeric index values would appear on the left:

         Product  Price
Item_A  Computer    900
Item_B   Printer    200
Item_C   Monitor    300
Item_D      Desk    450
Item_E     Phone    150
Item_F    Tablet    250
Item_G   Scanner    150

To drop two of those index values (“Item_B” and “Item_D”):

df.drop(index=["Item_B", "Item_D"], inplace=True)

Here is the complete syntax:

import pandas as pd

data = {
    "Product": ["Computer", "Printer", "Monitor", "Desk", "Phone", "Tablet", "Scanner"],
    "Price": [900, 200, 300, 450, 150, 250, 150],
}

df = pd.DataFrame(
    data, index=["Item_A", "Item_B", "Item_C", "Item_D", "Item_E", "Item_F", "Item_G"]
)

df.drop(index=["Item_B", "Item_D"], inplace=True)

print(df)

You will no longer see the rows with the index values of “Item_B” and “Item_D”:

         Product  Price
Item_A  Computer    900
Item_C   Monitor    300
Item_E     Phone    150
Item_F    Tablet    250
Item_G   Scanner    150

You can learn more about df.drop by visiting the Pandas Documentation.