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Table of contents
1.
Introduction
2.
Labels in Pandas Series
3.
Several motives of Labels in the Pandas Series
4.
Creating Labels 
4.1.
Manual Label Assignment
4.1.1.
Explanation:
4.2.
Using Default Numeric Index
4.2.1.
Explanation:
4.3.
Date-based Labels
4.3.1.
Explanation:
4.4.
Using Python Range
4.4.1.
Explanation:
4.5.
Converting a Dictionary to a Series
4.5.1.
Explanation:
5.
Accessing Labels Using Label
5.1.
Using Square Brackets ([ ])
5.1.1.
Explanation:
5.2.
Using .loc[ ] Indexer
5.2.1.
Explanation:
5.3.
Label-based Slicing
5.3.1.
Explanation:
5.4.
Conditional Selection with Labels
5.4.1.
Explanation:
6.
Key/Value Objects as Series
6.1.
Explanation:
7.
Frequently Asked Questions
7.1.
How can we reset the index of a Pandas Series?
7.2.
Can labels in a Pandas Series be non-unique?
7.3.
What are some common methods to handle missing labels in Pandas Series?
8.
Conclusion
Last Updated: Mar 27, 2024
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Labels in Pandas Series

Introduction

Pandas is a Python library containing high-level data structures and tools created to help Python programmers perform powerful data analysis. The ultimate purpose of pandas is to help us quickly discover information in data. The development of pandas was begun in 2008 by Wes McKinney; it was open-sourced in 2009. 

Labels in Pandas Series

Pandas is currently supported and actively developed by various organizations and contributors. Pandas was initially designed with finance in mind, specifically with its ability around time series data manipulation and processing historical stock information. In this article, we will learn about Labels in Pandas Series.

Labels in Pandas Series

Each data point in the Pandas Series has a unique identifier or index the labels provide. These labels enable effective data retrieval, manipulation, and alignment when performing operations between multiple Series objects. Integers, strings, and other hashable objects can all be used as labels.

A fundamental data structure called a Pandas Series resembles a one-dimensional labeled array. It is a cornerstone of Python's data analysis architecture. A Series element each has a label that serves as an index. Series are adaptable for tasks involving data manipulation because they can hold various data types.

Several motives of Labels in the Pandas Series

The Importance of labels is as follows:

  • Efficient Data Retrieval: Labels allow the smooth retrieval of specific records factors inside a Series.
     
  • Data Manipulation: They facilitate modifying information by referencing factors and using labels.
     
  • Alignment: During operations related to multiple Series, facts alignment primarily based on labels ensures consistency and forestalls misalignment errors.
     
  • Clarity: Labels offer context, making it less complicated for information analysts and scientists to understand and paint with the information.

Creating Labels 

We can create labels in a Pandas Series using various methods given below:

Manual Label Assignment

import pandas as pd
# Create a Pandas Series with data and labels
data = [12, 17, 22, 75]
labels = ['Aditya', 'Pradeep', 'Tanish', 'Bhavesh']
series = pd.Series(data, index=labels)
# Print the Pandas Series
print(series)

Output:

Output

Explanation:

We create a Pandas Series with custom labels and data associated with them.

Using Default Numeric Index

import pandas as pd
# Create a Pandas Series with data
data = [12, 17, 22, 75]
# Create a Series using the data
series = pd.Series(data)
# Print the Pandas Series
print(series)

Output:

Output2

Explanation:

A Series is created with default numeric labels primarily based on the position of the data within the listing.

Date-based Labels

import pandas as pd
import datetime

# Create data and dates lists
data = [12, 17, 22, 75]
dates = [datetime.date(2023, 9, 1), datetime.date(2023, 9, 2), datetime.date(2023, 9, 3), datetime.date(2023, 9, 4)]

# Create a Pandas Series with data and custom date indices
series = pd.Series(data, index=dates)

# Print the Pandas Series
print(series)

Output:

Output3

Explanation:

This Series uses dates as labels associated with the provided data.

Using Python Range

import pandas as pd

# Create data and labels lists
data = [12, 17, 22, 75]
labels = list(range(1, 5))

# Create a Pandas Series with data and custom numeric indices
series = pd.Series(data, index=labels)

# Print the Pandas Series
print(series)

Output:

Output4

Explanation:

Labels are created using a Python range, and those labels are connected to the data.

Converting a Dictionary to a Series

import pandas as pd

# Create a dictionary with names as keys and corresponding values
data_dict = {'Aditya': 12, 'Pradeep': 17, 'Tanish': 22, 'Bhavesh': 75}

# Create a Pandas Series using the dictionary
series = pd.Series(data_dict)

# Print the Pandas Series
print(series)

Output:

Output5

Explanation:

A Series is created by changing a dictionary, in which dictionary keys grow to be labels linked to their respective values.

Accessing Labels Using Label

Using Square Brackets ([ ])

import pandas as pd

# Create a dictionary with names as keys and corresponding values
data_dict = {'Aditya': 12, 'Pradeep': 17, 'Tanish': 22, 'Bhavesh': 75}

# Create a Pandas Series using the dictionary
series = pd.Series(data_dict)

# Access and print the value at the 'Aditya' index
value_at_Aditya = series['Aditya']
print(value_at_Aditya)

Output:

Output6

Explanation:

We access data in a Series by specifying a label inside square brackets, in this case, the records associated with 'Aditya.'

Using .loc[ ] Indexer

import pandas as pd

# Create a dictionary with names as keys and corresponding values
data_dict = {'Aditya': 12, 'Pradeep': 17, 'Tanish': 22, 'Bhavesh': 75}

# Create a Pandas Series using the dictionary
series = pd.Series(data_dict)

# Access and print the value at the 'Pradeep' label using .loc
value_at_Pradeep = series.loc['Pradeep']
print(value_at_Pradeep)

Output:

Output7

Explanation:

The .Loc[] indexer is used to get the right of entry to statistics by way of label, mainly retrieving the cost associated with the label 'Pradeep.'

Label-based Slicing

import pandas as pd

# Create a dictionary with names as keys and corresponding values
data_dict = {'Aditya': 12, 'Pradeep': 17, 'Tanish': 22, 'Bhavesh': 75}

# Create a Pandas Series using the dictionary
series = pd.Series(data_dict)

# Select a subset of the Series from 'Pradeep' to 'Bhavesh' using label-based slicing
subset = series['Pradeep':'Bhavesh']

# Print the subset of the Series
print(subset)

Output:

Output8

Explanation:

We slice the Series by using specifying various labels (''Pradeep'' to ''Bhavesh') and get a subset of the facts.

Conditional Selection with Labels

import pandas as pd

# Create a dictionary with names as keys and corresponding values
data_dict = {'Aditya': 12, 'Pradeep': 17, 'Tanish': 22, 'Bhavesh': 75}

# Create a Pandas Series using the dictionary
series = pd.Series(data_dict)

# Select values from the Series that are greater than 25
result = series[series > 25]

# Print the result
print(result)

Output:

Output9

Explanation:

Data is selected primarily based on a condition (values more than 25) the use of boolean indexing with labels.

Key/Value Objects as Series

import pandas as pd

# Create a dictionary with names as keys and corresponding values
data_dict = {'Aditya': 12, 'Pradeep': 17, 'Tanish': 22, 'Bhavesh': 75}

# Create a Pandas Series using the dictionary
series = pd.Series(data_dict)

# Print the Pandas Series
print(series)

Output:

Output10

Explanation:

A Pandas Series is created using a dictionary, in which keys come to be labels, and values end up the data within the Series.

Frequently Asked Questions

How can we reset the index of a Pandas Series?

The .reset_index() method allows the reset of a Series index. The old index will become a new column, and a new Series will be created with a default numeric index.

Can labels in a Pandas Series be non-unique?

Since they act as indices, labels in a Pandas Series should ideally be unique. Non-unique labels may cause unexpected behavior when accessing and manipulating data.

What are some common methods to handle missing labels in Pandas Series?

You can remove rows with missing labels using .dropna() and use .fillna() to replace missing values with specific ones.

Conclusion

In this article, we learn about Labels in Pandas Series. We also learn about Several motives of Labels in the Pandas Series. We concluded the article by Creating Labels, Accessing Labels Using Label and Key/Value Objects as Series.

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