Learn how to create and manipulate pandas Series, versatile data structures for efficient data handling in Python. Explore labeled and unlabeled formats, perform operations, and level up your data analysis skills! 🐼📊 Table of Contents Introduction Step-by-Step Guide to Pandas Series Importing Libraries and Initializing Data Creating Series from Lists and Arrays Using Numpy Arrays to Create Series Indexing and Operating on Series Performing Operations on Different Series Project Purpose and Conclusion GitHub Medium Thank you Introduction Welcome, dear readers! In this blog post, we will explore an essential building block of data analysis in Python - the “pandas.Series” data structure. With “pandas.Series,” you can efficiently store, manipulate, and analyze data, both with labeled and unlabeled formats. We’ll dive into the step-by-step process of creating and using the pandas Series, followed by practical examples. Step-by-Step Guide to Pandas Series Importing Libraries and Initializing Data import numpy as np
import pandas as pd

# Let's start with a dictionary and create a pandas Series from it:
myDictionary = {"Orion": 50, "Insist": 40, "Thinkpad": 30}
pd.Series(myDictionary) Here, we import the necessary libraries, numpy, and pandas. We initialize data as a Python dictionary containing items and their corresponding values. Next, we create a pandas Series using the “pd.Series()” function. Creating Series From Lists and Arrays myAges = [50, 40, 30]
myNames = ["Orion", "Insist", "Thinkpad"]

# We can create a Series from a list directly:
pd.Series(myAges)

# Or, by pairing the list with another list to set custom labels:
pd.Series(myAges, myNames)

# Alternatively, we can set the index explicitly using the "index" parameter:
pd.Series(data=myAges, index=myNames) In this section, we demonstrate how to create the Series from lists and arrays. We initially create a Series directly from the “myAges” list. Then, we create another Series by pairing “myAges” with “myNames” to set custom labels. Finally, we create a Series by explicitly specifying the index using the “index” parameter. Using Numpy Arrays to Create Series numpyArray = np.array([50, 40, 30])

# We can create a Series directly from a Numpy array:
pd.Series(numpyArray)

# Or, by setting custom labels using the "myNames" list:
pd.Series(numpyArray, myNames) In this part, we demonstrate how to create a Series from Numpy arrays. We directly create a Series from the “numpyArray” using the “pd.Series()” function. Additionally, we show how to set custom labels using the “myNames” list. Indexing and Operating on Series # Series can have custom indices assigned to each value:
pd.Series(["Orion", "Insist", "Thinkpad"], [1, 2, 3])

# We can perform operations on Series:
competitionResult1 = pd.Series([10, 5, 1], ["Orion", "Insist", "Thinkpad"])
competitionResult2 = pd.Series([20, 10, 8], ["Orion", "Insist", "Thinkpad"])
competitionResult2["Insist"]
lastResult = competitionResult1 + competitionResult2 In this section, we illustrate how to use custom indices in Series. We create a Series with custom indices assigned to each value. Furthermore, we demonstrate the operations that can be performed on Series. We first create two Series, “competitionResult1” and “competitionResult2,” representing the scores for different competitors. We then access the value associated with the label “Insist” in “competitionResult2” using indexing. Finally, we perform addition on the two Series, “competitionResult1” and “competitionResult2,” to get the “lastResult.” Performing Operations on Different Series differentSeries = pd.Series([20, 30, 40, 50], ["a", "b", "c", "d"])
differentSeries2 = pd.Series([10, 5, 3, 1], ["a", "c", "f", "g"])
differentSeries + differentSeries2 In this section, we showcase how to perform operations on different Series. We create two Series, “differentSeries” and “differentSeries2,” with distinct indices. Then, we perform addition on these Series. Note that the resulting Series will contain NaN (Not a Number) for the non-overlapping indices. Project Purpose and Conclusion The main aim of this project was to understand the concept of the pandas Series and explore how they can be created, manipulated, and combined. We learned that the pandas Series is incredibly versatile and allows for easy data handling with labeled or unlabeled formats. With the ability to perform operations on Series, data analysis becomes more efficient and convenient. In conclusion, the pandas Series play a crucial role in data analysis with Python, providing a powerful data structure to work with various datasets. As you delve deeper into the world of data science and analysis, mastering the pandas Series will prove to be an invaluable skill. GitHub ⭐ GitHub: https://github.com/orioninsist/tensorflow-python-fundamentals ⭐ GitHub: https://github.com/orioninsist/tensorflow-python-fundamentals/blob/main/section6/pandasIntro.ipynb Medium Subscribe on to stay in the loop with our latest articles, tutorials, and projects. Join our community of tech enthusiasts, developers, and learners. Don’t miss out on valuable insights, coding tips, and exciting projects. Medium Click the link to subscribe and embark on a journey of knowledge and growth. 📚🚀 Feel free to use this link description for your “Subscribe” link to entice readers to join your community and stay updated with your valuable content. Best of luck with your platform and building a thriving community! 📝✨ Thank You Thank you for your continued support, and I’m excited to have you on this enriching journey! Sincerely, Founder of orioninsist Follow the white rabbit Thank you for your support! Hello friends! I want to express my gratitude for your support. Your interest and encouragement mean a lot to me. To keep our connection strong and to provide you with more valuable content, I encourage you to stay connected with me on my social media platforms. I am excited to share more content with you through these platforms, and I value your engagement and feedback. Thank you once again for your support. Let’s stay connected and keep the conversation going! Your feedback and engagement mean the world to me. Thank you once again for your unwavering support. Let’s continue to “follow the white rabbit” and discover new horizons together! Best regards, Muhammet Murat Kurkoglu Founder of orioninsist Google Survey Forms Stay connected with me 🔗 🔗 🔗 🔗 🔗 🔗 🔗 🔗 🔗 🔗 🔗 🔗 Patreon Buymeacoffee Instagram Twitter Facebook Pinterest Website GitHub YouTube Medium LinkedIn-Personal LinkedIn-Company

A Guide to Understanding Pandas Series Labeled and Unlabeled Data Structures

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

A Comprehensive Guide to Docker's Basic Commands

10 Ways Stand Out as a Java Developer and Land that Dream Job

10 Fantastic JavaScript Projects for Beginners

11 Great Tips From A Guy Who Leveled Up From Intern To Dev

125 Stories To Learn How To Do X

3 Free Python Courses For Beginners: 2020 Edition

A Comprehensive Guide to Docker's Basic Commands

10 Ways Stand Out as a Java Developer and Land that Dream Job

10 Fantastic JavaScript Projects for Beginners

11 Great Tips From A Guy Who Leveled Up From Intern To Dev

125 Stories To Learn How To Do X

3 Free Python Courses For Beginners: 2020 Edition

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps