Beginner's Guide to Pandas Series with Real-World Examples

Pandas is a powerful open-source library built on top of the Python programming language. It's widely used for data analysis and manipulation tasks and integrates seamlessly with NumPy.

In this blog, we’ll focus on Pandas Series. A core component of Pandas.

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What is a Pandas Series?

A Pandas Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floats, Python objects, etc.). Think of it like a single column in a table.

import pandas as pd
import numpy as np

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Creating Series

1. From a List (Default Index)

country = ['Pakistan', 'India', 'USA', 'Korea', 'Japan']
pd.Series(country)

2. From a List (Custom Index)

marks = [99, 97, 98, 95, 97]
courses = ['Financial Accounting', 'Linear Algebra', 'Data Structures & Algorithms', 'App of ICT', 'OOP LAB']
pd.Series(marks, index=courses)

3. From a Dictionary

marks_dict = {'Eng': 87, 'Maths': 97, 'Science': 76}
pd.Series(marks_dict, name='Shehraz Marks')

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Series Attributes

marks_series.size       # Number of elements
marks_series.dtype      # Data type
marks_series.name       # Series name
marks_series.index      # Index labels
marks_series.values     # Series values
marks_series.is_unique  # True if all values are unique

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Series from CSV

subs = pd.read_csv('subs.csv').squeeze("columns")
virat_runs = pd.read_csv('kohli_ipl.csv', index_col='match_no').squeeze("columns")
movies = pd.read_csv('bollywood.csv', index_col='movie').squeeze("columns")

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Series Methods

Head, Tail, Sample

subs.head()           # First 5 rows
virat_runs.tail(10)   # Last 10 rows
movies.sample(5)      # 5 random values

Value Counts

movies.value_counts(ascending=True)

Sorting

virat_runs.sort_values(ascending=False).head(1)  # Highest score
movies.sort_index(ascending=False)               # Sort by index

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Mathematical Methods

subs.sum(), subs.mean(), subs.median(), subs.std(), subs.var()
virat_runs.describe()  # Statistical summary

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Indexing & Slicing

x = pd.Series([1, 23, 231, 54, 4, 10])
x[3]  # Positive index

movies.iloc[0]         # Integer location
movies['Uri: The Surgical Strike']  # Label index

virat_runs[5:16]       # Slicing
movies[-5:]            # Negative slicing

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Editing a Series

x[3] = 55
marks_series.iloc[2] = 84
marks_series['FAP'] = 99  # Append new entry

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Series with Python Functionalities

len(subs), type(subs), sorted(marks_series)
list(marks_series), dict(marks_series)
'Salman Khan' in movies.values

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Arithmetic & Relational Operators

100 - marks_series         # Broadcasting
virat_runs >= 50           # Boolean indexing

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Boolean Indexing (Use Cases)

virat_runs[virat_runs >= 50].size     # 50s and 100s
virat_runs[virat_runs == 0].size      # Ducks
subs[subs > 200].size                 # High-subscriber days
movies.value_counts()[movies.value_counts() > 20]  # Actors with >20 movies

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Plotting Series

subs.plot()
movies.value_counts().head(20).plot(kind='pie')

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Conclusion

A Pandas Series is more than just a column of data. it’s a powerful container with methods and attributes that make analysis seamless. Whether you're dealing with sports stats, YouTube growth, or Bollywood data, mastering Series is your first step into Pandas.

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Bonus Resources

• 📘 Explore My Practical Notebooks: My GitHub Repository on Python & Pandas . This repo includes detailed Jupyter notebooks covering each method and attribute of Pandas with real-world datasets and hands-on explanations.

Keep learning, and happy coding with Pandas!