Accessing elements of a Pandas Series
Last Updated :
11 Jul, 2025
Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). Labels need not be unique but must be a hashable type. An element in the series can be accessed similarly to that in an ndarray. Elements of a series can be accessed in two ways:
- Accessing Element from Series with Position
- Accessing Element Using Label (index)
In this article, we are using “nba.csv” file, to download the CSV, click here.
Accessing Element from Series with Position
In order to access the series element refers to the index number. Use the index operator [ ] to access an element in a series. The index must be an integer.
In order to access multiple elements from a series, we use Slice operation. Slice operation is performed on Series with the use of the colon(:). To print elements from beginning to a range use [:Index], to print elements from end-use [:-Index], to print elements from specific Index till the end use [Index:], to print elements within a range, use [Start Index:End Index] and to print whole Series with the use of slicing operation, use [:]. Further, to print the whole Series in reverse order, use [::-1].
Accessing the First Element of Series
In this example, a Pandas Series named 'ser' is created from a NumPy array 'data' containing the elements 'g', 'e', 'e', 'k', 's', 'f', 'o', 'r', 'g', 'e', 'e', 'k', 's'. The first element of the series is accessed and printed using `print(ser[0])`
Python
# import pandas and numpy
import pandas as pd
import numpy as np
# creating simple array
data = np.array(['g', 'e', 'e', 'k', 's', 'f',
'o', 'r', 'g', 'e', 'e', 'k', 's'])
ser = pd.Series(data)
# retrieve the first element
print(ser[0])
Output:
g
Accessing First 5 Elements of Series
In this example, a Pandas Series named 'ser' is created from a NumPy array 'data' containing the elements 'g', 'e', 'e', 'k', 's', 'f', 'o', 'r', 'g', 'e', 'e', 'k', 's'. The first five elements of the series are accessed and printed using print(ser[:5]).
Python
# import pandas and numpy
import pandas as pd
import numpy as np
# creating simple array
data = np.array(['g', 'e', 'e', 'k', 's', 'f',
'o', 'r', 'g', 'e', 'e', 'k', 's'])
ser = pd.Series(data)
# retrieve the first element
print(ser[:5])
Output:
0 g
1 e
2 e
3 k
4 s
dtype: object
Accessing Last 10 Elements of Series
In this example, a Pandas Series named 'ser' is created from a NumPy array 'data' containing the elements 'g', 'e', 'e', 'k', 's', 'f', 'o', 'r', 'g', 'e', 'e', 'k', 's'. The last 10 elements of the series are accessed and printed using `print(ser[-10:])`.
Python
# import pandas and numpy
import pandas as pd
import numpy as np
# creating simple array
data = np.array(['g', 'e', 'e', 'k', 's', 'f',
'o', 'r', 'g', 'e', 'e', 'k', 's'])
ser = pd.Series(data)
# retrieve the first element
print(ser[-10:])
Output:
3 k
4 s
5 f
6 o
7 r
8 g
9 e
10 e
11 k
12 s
dtype: object
Accessing First 5 Elements of Series in nba.csv File
In this example, the Pandas module is imported, and a DataFrame 'df' is created by reading data from a CSV file named "nba.csv" using `pd.read_csv`. A Pandas Series 'ser' is then created by selecting the 'Name' column from the DataFrame. Finally, the first 10 elements of the series are accessed and displayed using ser.head(10).
Python
# importing pandas module
import pandas as pd
# making data frame
df = pd.read_csv("nba.csv")
ser = pd.Series(df['Name'])
ser.head(10)
Now we access first 5 elements of series.
Python
# get first five names
ser[:5]
Output:
Access an Element in Pandas Using Label
In order to access an element from series, we have to set values by index label. A Series is like a fixed-size dictionary in that you can get and set values by index label. Here, we will access an element in Pandas using label.
Accessing a Single Element Using index Label
In this example, a Pandas Series 'ser' is created from a NumPy array 'data' with custom indices provided. The element at index 16 is accessed and printed using `print(ser[16])`.
Python
# import pandas and numpy
import pandas as pd
import numpy as np
# creating simple array
data = np.array(['g', 'e', 'e', 'k', 's', 'f',
'o', 'r', 'g', 'e', 'e', 'k', 's'])
ser = pd.Series(data, index=[10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22])
# accessing a element using index element
print(ser[16])
Output:
o
Accessing a Multiple Element Using index Label
In this example, a Pandas Series 'ser' is created from a NumPy array 'data' with custom indices provided. Multiple elements at indices 10, 11, 12, 13, and 14 are accessed and printed using `print(ser[[10, 11, 12, 13, 14]])`.
Python
# import pandas and numpy
import pandas as pd
import numpy as np
# creating simple array
data = np.array(['g', 'e', 'e', 'k', 's', 'f',
'o', 'r', 'g', 'e', 'e', 'k', 's'])
ser = pd.Series(data, index=[10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22])
# accessing a multiple element using
# index element
print(ser[[10, 11, 12, 13, 14]])
Output:
10 g
11 e
12 e
13 k
14 s
dtype: object
Access Multiple Elements by Providing Label of Index
In this example, a Pandas Series 'ser' is created using NumPy's arange() function with values from 3 to 8 and custom indices. Elements at indices 'a', 'd' are accessed and printed using `print(ser[['a', 'd']])`.
Python
# importing pandas and numpy
import pandas as pd
import numpy as np
ser = pd.Series(np.arange(3, 9), index=['a', 'b', 'c', 'd', 'e', 'f'])
print(ser[['a', 'd']])
Output:
a 3.0
d 6.0
dtype: float64
Accessing a Multiple Element Using Index Label in nba.csv File
In this example, the Pandas module is imported, and a DataFrame 'df' is created by reading data from a CSV file named "nba.csv" using `pd.read_csv`. A Pandas Series 'ser' is then created by selecting the 'Name' column from the DataFrame. Finally, the first 10 elements of the series are accessed and displayed using `ser.head(10)`.
Python
# importing pandas module
import pandas as pd
# making data frame
df = pd.read_csv("nba.csv")
ser = pd.Series(df['Name'])
ser.head(10)
Now we access an multiple element using index label.
Python
Output:
Similar Reads
Access the elements of a Series in Pandas Pandas Series is a one-dimensional labeled array capable of holding data of any type (integer, string, float, python objects, etc.). Labels need not be unique but must be a hashable type. Let's discuss different ways to access the elements of given Pandas Series. First create a Pandas Series. Python
2 min read
How to access the last element in a Pandas series? Prerequisite: Pandas Pandas series is useful in handling various analytical operations independently or as being a part of pandas data frame. So it is important for us to know how various operations are performed in pandas series. The following article discusses various ways in which last element o
3 min read
Creating a Pandas Series A Pandas Series is like a single column of data in a spreadsheet. It is a one-dimensional array that can hold many types of data such as numbers, words or even other Python objects. Each value in a Series is associated with an index, which makes data retrieval and manipulation easy. This article exp
3 min read
Creating a Pandas Series A Pandas Series is like a single column of data in a spreadsheet. It is a one-dimensional array that can hold many types of data such as numbers, words or even other Python objects. Each value in a Series is associated with an index, which makes data retrieval and manipulation easy. This article exp
3 min read
Creating a Pandas Series from Lists A Pandas Series is a one-dimensional labeled array capable of holding various data types such as integers, strings, floating-point numbers and Python objects. Unlike Python lists a Series ensures that all elements have the same data type. It is widely used in data manipulation and analysis.In this a
3 min read
Creating a Pandas Series from Lists A Pandas Series is a one-dimensional labeled array capable of holding various data types such as integers, strings, floating-point numbers and Python objects. Unlike Python lists a Series ensures that all elements have the same data type. It is widely used in data manipulation and analysis.In this a
3 min read