Pandas for Data Science

 

Introduction

In modern data science, handling and analysing tabular (structured) data is one of the most common tasks — whether it’s survey data, business data, time-series data, logs, or CSV/Excel/SQL exports. The Python library pandas has become the de-facto standard for this work. “Pandas for Data Science” is a course designed to teach you how to leverage pandas effectively: from reading data, cleaning it, manipulating, analyzing, and preparing it for further data science or machine learning tasks.

If you want to build a solid foundation in data handling and manipulation — this course offers a well-structured path.

---

Why This Course Matters

1. Structured Learning of a Core Data Tool

   • Pandas is foundational in the Python data science ecosystem: with its data structures (Series, DataFrame) you can handle almost any tabular data. 

   • Knowing pandas well lets you move beyond spreadsheets (Excel) into programmable, reproducible data workflows — an essential skill for data scientists, analysts, and ML engineers.

2. Focus on Real-World Data Challenges

   • In practice, data is messy: missing values, inconsistent types, duplicate rows, mixed sources. This course teaches how to read different data formats, clean and standardize data, deal with anomalies and missing data. 

   • It emphasizes best practices — loading data correctly, cleaning it, managing data types — critical steps before any analysis or modeling. 

3. End-to-End Skills—From Raw Data to Analysis-Ready Tables

   • You learn not just data loading and cleaning, but also data manipulation: filtering, merging/joining tables, combining data from multiple sources, querying, aggregating. These are everyday tasks in real data workflows.

   • As a result, you get the confidence to handle datasets of varying complexity — useful whether you do exploratory data analysis (EDA), report generation, or feed data into ML pipelines.

4. Accessibility for Beginners

   • The course is marked beginner-level. If you know basic Python (variables, lists/dicts, functions), you can follow along and build solid pandas skills. 

   • This makes it a great bridge for developers, analysts, or students who want to move into data science but don’t yet have deep ML or statistics background.

---

What You Learn — Course Contents & Core Skills

The course is organized into four main modules. Here’s what each module covers and what you’ll learn:

1. Intro to pandas + Strings and I/O

• Reading data from files (CSV, Excel, maybe text files) into pandas.

• Writing data back to files after manipulation.

• Handling string data: cleaning, parsing, converting.

• Basic file operations, data import/export, and understanding data I/O workflows. 

2. Tabular Data with pandas

• Introduction to pandas core data structures: DataFrame, Series. 

• Recognizing the characteristics and challenges of tabular data.

• Basic data manipulation: indexing/filtering rows and columns, selecting subsets, etc. 

3. Loading & Cleaning Data

• Reading from various common data formats used in data science.

• Data cleaning: dealing with missing values, inconsistent types or formats, malformed data.

• Best practices to make raw data ready for analysis or modeling. 

4. Data Manipulation & Combining Datasets

• Techniques to merge, join, concatenate data from different sources or tables. Important for multi-table datasets (e.g. relational-style data). 

• Efficient querying and subsetting of data — selecting/filtering based on conditions.

• Aggregation, grouping, summarization (though this course may focus mostly on manipulation — but pandas supports all these.) 

Skills You Gain

• Data import/export, cleaning, and preprocessing using Python & pandas. 

• Data manipulation and integration — combining data, transforming it, shaping it. 

• Preparation of data for further tasks: analysis, visualization, machine learning, reporting, etc.

---

Who Should Take This Course

This course is particularly useful for:

• Aspiring data scientists / analysts who want a strong foundation in data handling.

• Software developers or engineers who are new to data science, but already know Python and want to learn data workflows.

• Students or researchers working with CSV/Excel/tabular data who want to automate cleaning and analysis.

• Business analysts or domain experts who frequently handle datasets and want to move beyond spreadsheets to programmatic data manipulation.

• Anyone preparing for machine learning or data-driven projects — mastering pandas is often the first step before building statistical models, ML pipelines, or visualization dashboards.

---

How to Make the Most of the Course

• Code along in a notebook (Jupyter / Colab) — Don’t just watch: write code alongside lessons to internalize syntax, workflows, data operations.

• Practice on real datasets — Use publicly available datasets (CSV, Excel, JSON) — maybe from open data portals — and try cleaning, merging, filtering, summarizing them.

• Try combining multiple data sources — E.g. separate CSV files that together form a relational dataset: merge, join, or concatenate to build a unified table.

• Explore edge cases — Missing data, inconsistent types, duplicated records: clean and handle them as taught, since real datasets often have such issues.

• After pandas, move forward to visualization or ML — Once your data is clean and structured, you can plug it into plotting libraries, statistical analysis, or ML pipelines.

---

What You’ll Walk Away With

• Strong command over pandas library — confident in reading, cleaning, manipulating, and preparing data.

• Ability to handle messy real-world datasets: cleaning inconsistencies, combining sources, restructuring data.

• Ready-to-use data science workflow: from raw data to clean, analysis-ready tables.

• The foundation to proceed further: data visualization, statistical analysis, machine learning, data pipelines, etc.

• Confidence to work on data projects independently — not relying on manual tools like spreadsheets but programmable, reproducible workflows.

---

Join Now: Pandas for Data Science

Conclusion

“Pandas for Data Science” gives you critical, practical skills — the kind that form the backbone of almost every data-driven application or analysis. If you want to build data science or machine learning projects, or even simple data-driven scripts, pandas mastery is non-negotiable.

This course offers a clear, structured, beginner-friendly yet deep introduction. If you put in the effort, code along, and practice on real datasets, you’ll come out ready to handle data like a pro.

Read More

Python for Data Learners: A Beginner's Guide to Exploring Real-World Data with Python, Pandas, and Jupyter

 

Python for Data Learners: A Beginner's Guide to Exploring Real-World Data with Python, Pandas, and Jupyter

Introduction: Why Learn Python for Data?

Data is everywhere — from business reports and social media to customer feedback and financial dashboards. For beginners, the challenge isn’t finding data, but knowing how to explore and make sense of it. Python, combined with pandas and Jupyter Notebook, offers a simple yet powerful way to work with real-world data. You don’t need to be a programmer — if you can use Excel, you can start learning Python for data.

Getting Started with the Right Tools

To begin your journey, you need a basic setup: Python, pandas, and Jupyter Notebook. Together, they form a beginner-friendly environment where you can experiment with data step by step. Jupyter Notebook acts like your interactive lab, pandas handles the heavy lifting with datasets, and Python ties everything together.

First Steps with Data

The first thing you’ll do with Python is load and explore a dataset. Unlike scrolling through Excel, you’ll be able to instantly check the shape of your data, see the first few rows, and identify any missing values. This gives you a quick understanding of what you’re working with before doing any analysis.

Cleaning Real-World Data

Real-world data is rarely perfect. You’ll face missing values, incorrect data types, and formatting issues. Python makes it easy to clean and prepare your data with simple commands. This ensures that your analysis is always reliable and based on accurate information.

Exploring and Analyzing Data

Once your data is clean, you can start exploring. With pandas, you can filter, group, and summarize information in seconds. Whether you want to see sales by region, average scores by student, or customer counts by category, Python gives you precise control — and saves you from manual calculations.

Visualizing Insights

Data becomes much more powerful when it’s visual. With Python, you can create clear charts and graphs inside Jupyter Notebook. Visuals like bar charts, line graphs, and histograms help you spot trends and patterns that raw numbers might hide.

Why Jupyter Notebook Helps Beginners

Jupyter Notebook is like an interactive diary for your data journey. You can write notes in plain language, run code in chunks, and see results immediately. This makes it an excellent learning tool, as you can experiment freely and document your process along the way.

What You’ll Learn

By following this beginner’s guide, you will learn how to:

• Load and explore real-world datasets
• Clean messy data and handle missing values
• Summarize and analyze information with pandas
• Automate repetitive data tasks
• Visualize trends and insights with charts
• Use Jupyter Notebook as an interactive workspace

Hard Copy: Python for Data Learners: A Beginner's Guide to Exploring Real-World Data with Python, Pandas, and Jupyter

Kindle: Python for Data Learners: A Beginner's Guide to Exploring Real-World Data with Python, Pandas, and Jupyter

Conclusion: Start Your Data Journey

Python is not about replacing Excel — it’s about expanding your possibilities. With pandas and Jupyter Notebook, you can quickly go from raw data to meaningful insights, all while building skills that grow with you. For learners, the first step is the most important: open Jupyter, load your first dataset, and begin exploring. The more you practice, the more confident you’ll become as a data explorer.

Read More

Python Quiz on NumPy and Pandas

 

Read More

Python Quiz on Pandas Read CSV

 

Read More

Python Quiz on Pandas Series

 

Read More

Python Quiz on Pandas DataFrames

 

Read More

Python Quiz on Pandas Basics

 

Read More

Python Pandas Quiz

 

Read More

Pandas in Action

 

Take the next steps in your data science career! This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software.

In Pandas in Action you will learn how to:

    Import datasets, identify issues with their data structures, and optimize them for efficiency

    Sort, filter, pivot, and draw conclusions from a dataset and its subsets

    Identify trends from text-based and time-based data

    Organize, group, merge, and join separate datasets

    Use a GroupBy object to store multiple DataFrames

Pandas has rapidly become one of Python's most popular data analysis libraries. In Pandas in Action, a friendly and example-rich introduction, author Boris Paskhaver shows you how to master this versatile tool and take the next steps in your data science career. You'll learn how easy Pandas makes it to efficiently sort, analyze, filter and munge almost any type of data.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

Data analysis with Python doesn't have to be hard. If you can use a spreadsheet, you can learn pandas! While its grid-style layouts may remind you of Excel, pandas is far more flexible and powerful. This Python library quickly performs operations on millions of rows, and it interfaces easily with other tools in the Python data ecosystem. It's a perfect way to up your data game.

About the book

Pandas in Action introduces Python-based data analysis using the amazing pandas library. You'll learn to automate repetitive operations and gain deeper insights into your data that would be impractical—or impossible—in Excel. Each chapter is a self-contained tutorial. Realistic downloadable datasets help you learn from the kind of messy data you'll find in the real world.

What's inside

    Organize, group, merge, split, and join datasets

    Find trends in text-based and time-based data

    Sort, filter, pivot, optimize, and draw conclusions

    Apply aggregate operations

About the reader

For readers experienced with spreadsheets and basic Python programming.

About the author

Boris Paskhaver is a software engineer, Agile consultant, and online educator. His programming courses have been taken by 300,000 students across 190 countries.

Table of Contents

PART 1 CORE PANDAS

1 Introducing pandas

2 The Series object

3 Series methods

4 The DataFrame object

5 Filtering a DataFrame

PART 2 APPLIED PANDAS

6 Working with text data

7 MultiIndex DataFrames

8 Reshaping and pivoting

9 The GroupBy object

10 Merging, joining, and concatenating

11 Working with dates and times

12 Imports and exports

13 Configuring pandas

14 Visualization

Hard Copy: Pandas in Action

Read More

Data Analytics Course Handwritten Notes

Introduction:

In today's digital age, where typing on keyboards and tapping on screens has become the norm, there is something truly magical about the simplicity and authenticity of handwritten notes. Handwritten notes have a unique charm that digital text cannot replicate. They are a reflection of our personality, creativity, and individuality. In this blog, I want to share my newfound love for handwritten notes and the joy they bring.

The Art of Handwriting:

Handwriting is an art form that allows us to express ourselves in a personal and intimate way. Each stroke of the pen carries a piece of our emotions, thoughts, and ideas. Whether it's elegant cursive, playful doodles, or colorful illustrations, our handwriting reveals a glimpse of our character. Handwritten notes offer a tangible connection between the writer and the reader, creating a more intimate and meaningful experience.

Unleashing Creativity:

Writing by hand stimulates our creativity and imagination. As we put pen to paper, ideas flow more freely, and we are more likely to explore new perspectives and insights. The act of writing itself becomes a therapeutic process, allowing us to slow down, focus, and fully engage with our thoughts. Handwritten notes offer a canvas for our creativity to flourish, enabling us to experiment with different styles, fonts, and embellishments.

A Personal Touch:

When we receive a handwritten note, it feels like a precious gift. The time and effort invested in crafting the note make it a unique and personal gesture. Whether it's a heartfelt letter, a thoughtful thank-you card, or a quick reminder, handwritten notes show that we care. They create a deeper connection and leave a lasting impression on the recipient, unlike impersonal digital messages that can easily be forgotten.

Preserving Memories:

Handwritten notes have an enduring quality that transcends time. They become treasured keepsakes, reminding us of special moments, important milestones, and cherished relationships. Stumbling upon a box of old handwritten letters can evoke a wave of nostalgia and bring back vivid memories. In a world where digital files can be lost or corrupted, handwritten notes stand as tangible and irreplaceable mementos of our lives.

Sharing Handwritten Notes:

In the spirit of celebrating the beauty of handwritten notes, I am excited to share my own collection of handwritten notes with you all. Through my blog and social media platforms, I will be posting images and stories behind my notes, discussing different techniques and styles, and even providing tips on improving handwriting skills. I hope to inspire others to rediscover the joy of writing by hand and to embrace the personal touch that handwritten notes bring to our lives.

Click Here for notes

Conclusion:

Handwritten notes are not merely pieces of paper; they are vessels of our thoughts, emotions, and creativity. They allow us to connect on a deeper level, create lasting memories, and express ourselves in a way that digital text cannot replicate. So, let's bring back the beauty of handwritten notes, one stroke of the pen at a time, and embrace the power of personal expression. Together, let's make the world a little brighter with our handwritten notes.

Read More

Operations On Dataframe in Python

Checking data types of each Column in a Data Frame.

• If you want to check the data type of each column, because whenever you have been given a data, you want to really check what is the structure of the data; that means, which variable has which data type?

• In, that case you can use dtypes, because that returns a series with the data type of each column and the syntax would be you use dtypes along with the Data Frame name.

• So, Data Frame.dtypes will give you a series with the data type of each column

Here is the syntax.

Syntax = DataFrame.dtypes

                                      

Count of unique data types

• So, now we have an overall idea about what are the data types that we are going to work with using the cars_data. 

• There is also an option where you can get the count of unique data types available in your Data Frame.

• So, in that case get_dtype_counts, returns the counts of unique data types in the data frame.

Here is the syntax.

Syntax = get_dtype_counts()

Selecting data based on data types

• So, now we also have an overall idea about the count of unique data types that we are going to handle with. 

• So, now, we know about how to get the data type of each variables. So, there might be cases where you want to perform the operations only on a numerical data type.

• Similarly, there can be cases where you are going to work with only categorical data type.

Here is the syntax.

Syntax = pandas.DataFrame.select.dtypes()

Join us: https://t.me/jupyter_python

Like us: https://www.facebook.com/pirawenpython

Read More