Access Pandas Series Elements Using iloc Attribute

The pandas.Series.iloc attribute is used to access elements from pandas series object that is based on integer location-based indexing. And It is very similar to pandas.Series “iat” attribute but the difference is, the “iloc” attribute can access a group of elements whereas the “iat” attribute access only a single element.

The “.iloc” attribute is used to allows inputs values like an integer value, a list of integer values, and a slicing object with integers, etc.

Example 1

import pandas as pd
import numpy as np

# create a pandas series
s = pd.Series([1,2,3,4,5,6,7,8,9,10])

print(s)

print("Output: ")
print(s.iloc[2])

Explanation

In this following example, we created a pandas series object “s” using a python list of integers and we haven’t initialized the index labels, so the pandas.Series constructor will provide a range of index values based on the data given to the pandas.Series constructor.

For this example, the integer location-based indexing starts from 0 to 9.

Output

0  1
1  2
2  3
3  4
4  5
5  6
6  7
7  8
8  9
9  10
dtype: int64

Output: 3

We have accessed a single element from pandas.Series object by providing the integer-based index value to the “iloc” attribute.

Example 2

import pandas as pd
import numpy as np

# create a series
s = pd.Series([1,2,3,4,5,6,7,8,9,10])

print(s)

# access number of elements by using a list of integers
print("Output: ")
print(s.iloc[[1,4,5]])

Explanation

Let’s access the group of elements from pandas.Series object by providing the list of integer values that denotes the integer-based index position of a given series.

In this example, we provided the list of integers [1,4,5] to the “iloc” attribute.

Output

0  1
1  2
2  3
3  4
4  5
5  6
6  7
7  8
8  9
9  10
dtype: int64
Output:
1  2
4  5
5  6
dtype: int64

We have successfully accessed the group of pandas.Series elements by using the “iloc” attribute. as a result, it returns another series object which is displayed in the above output block.