Pandas – Select Rows by Index position or Number

This tutorial will discuss about different ways to select DataFrame rows by index position or index number in Pandas.

Table Of Contents

Preparing DataSet

Let’s create a DataFrame with some dummy data.

import pandas as pd

data = {'Col_A': [21, 12, 13, 14, 15, 16, 17],
        'Col_B': [21, 22, 23, 24, 25, 26, 27],
        'Col_C': [31, 32, 33, 34, 35, 36, 37]}

index=["D1", "D2", "D3", "D4", "D5", "D6", "D7"]

# Create DataFrame from dictionary
df = pd.DataFrame.from_dict(data)

# Set list index as Index of DataFrame
df .set_index(pd.Index(index), inplace=True)

print (df)

Output

    Col_A  Col_B  Col_C
D1     21     21     31
D2     12     22     32
D3     13     23     33
D4     14     24     34
D5     15     25     35
D6     16     26     36
D7     17     27     37

We will now select rows from this DataFrame by Index numbers or positions.

Select a Row by index Position in Pandas DataFrame

In Pandas, the DataFrame provides an attribute iloc[], which accepts an Index Number as an argument and returns a row at that particular Index in the DataFrame.

To select a single row from a DataFrame by index number, we can just pass its index number in the iloc[] attribute. An important point to note is that the indexing starts from 0. So, if you want to select the 3rd row from the DataFrame, then you need to pass the index number 2.

Like in the below example, we will select a row at index number 2 from the DataFrame. Basically we are going to fetch the 3rd row of the DataFrame.

# Select row at index 2 i.e.
# the 3rd row of DataFrame
subDf = df.iloc[2]

print (subDf)

Output

Col_A    13
Col_B    23
Col_C    33
Name: D3, dtype: int64

Select Multiple Rows by index numbers in Pandas DataFrame

We can also select multiple rows from a DataFrame by index numbers. For that we need to pass a list of index numbers in the iloc[] attribute of the DataFrame. In the below example, we will select rows at index number 2, 4 and 6 from the DataFrame. We will pass a list containing these index numbers in the iloc[] attribute of DataFrame. It will return a DataFrame containing the specified rows only.

# Select rows at index number 2, 4 and 6
subDf = df.iloc[[2, 4, 6]]

print (subDf)

Output

    Col_A  Col_B  Col_C
D3     13     23     33
D5     15     25     35
D7     17     27     37

Select multiple rows by index range in Pandas DataFrame

We can also select multiple rows from a DataFrame by index number range. It means, we can select rows from one index number till another index number. For that, we need to pass the start and end index number in the iloc[] attribute (separated by a colon). Like,

df.iloc[start : end]

In the below example we will select all rows from a DataFrame from index number 2 till index number 5.

# Select rows from index number 2 till 5
subDf = df.iloc[2:5]

print (subDf)

Output

    Col_A  Col_B  Col_C
D3     13     23     33
D4     14     24     34
D5     15     25     35

Summary

We learned about different ways to select rows from a DataFrame by index numbers or positions. Thanks.