Step-by-Step Data Cleaning in Python with Pandas | Jupyter Notebook Tutorial | CSV to visualization

A Step-by-Step Data Cleaning in Python: Removing Duplicates, Nulls & Visualizing Data. Master Data Cleaning with Python Pandas: CSV File to Visualization in Jupyter Notebook.

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In this video, I’ll show you how to clean data using Python, Pandas, and Jupyter Notebook. We'll start by loading a CSV file, then tackle common data issues like duplicates and missing values. I'll also guide you through dropping unnecessary data and creating visualizations such as pie charts and bar charts. Plus, we’ll create a new variable to categorize age groups, giving you practical tools for effective data cleaning.

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Timestamps:
00:00 Intro to Data Cleaning
02:04 Reviewing the dataset
03:07 Upload dataset (CSV) to Jupyter notebook
04:07 Create a new Workbook and renaming the workbook (document)
04:45 Import libraries into your workbook to start coding
05:02 Python code to import the CSV file to Jupyter notebook & print out CSV dataset
06:26 Check information on the dataset
07:11 Review the variable names for misspellings, etc.
07:53 Drop a column by using python code
08:23 Create a new column naming it "Age Group" to group the ages in my dataset.
09:10 Checking for null values or N/A values
09:50 Dropping N/A (null) values
10:37 Checking for any duplicate values within the dataset
11:25 Dropping duplicates in the dataset using python code
11:54 Looking at the description of the dataset for "count, mean, std, min, max, etc."
12:27 Starting visualization
13:01 Pie chart visualization
16:19 Bar chart visualization
19:26 Pie chart visualization to compare another variable, "Season"
20:58 Bar chart visualization to review the variable "Age Group"
21:55 Bar chart visualization to review the variable "Age Group & Gender"
23:08 Thank you, and I hope you enjoyed the video. Please like and subscribe.

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The dataset used is "customer data on purchases" for a clothing store.
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#datacleaning #pythontutorial #pandas #jupyternotebook #dataanalysis #datavisualization #dataanalyst

[ChrismasPikachu]

The step-by-step approach makes data cleaning with Pandas so much easier to follow. The examples were super helpful—especially handling missing values and duplicates

[leandro-oc]

watching this video gains the skills to efficiently clean and visualize real-world datasets, making it an essential resource for anyone working with data in Python.

[kenshin]

Your step-by-step approach using Python, Pandas, and Jupyter Notebook makes it so accessible for anyone looking to enhance their data skills. Looking forward to applying these techniques in my own projects! Keep up the great work!

[moustafaAbdelfattah-qx]

This tutorial was incredibly clear and practical! The step-by-step approach made it easy to follow along, especially the way you handled missing values and outliers. The transition from raw CSV data to visualization was seamless—really appreciated the explanations behind each Pandas function. Looking forward to more content like this

[Babulu]

This is a great resource for anyone looking to improve their data analysis skills. Thanks for sharing.

[rpleps]

Great tutorial! Your step-by-step explanation of data cleaning with Pandas is really helpful, especially for beginners. The practical demonstration in Jupyter Notebook makes it easy to follow along. Looking forward to more content like this!

[DwiPutri-ut]

Really love the way you guide the data cleaning process from start to visualization! The techniques for dropping columns and handling null values are explained in detail, and the example dataset used is very relevant. Plus, the visualizations with pie charts and bar charts really help clarify the data. Thanks for this concise and valuable tutorial!

[tarektraek]

I finally found a simple and reliable way to clean data without the complexity of a simple and nice explanation

[chaosknight]

The step-by-step breakdown of data cleaning in Python using Pandas was super helpful. The Jupyter Notebook demo made it easy to follow along, especially the handling of missing values and data transformation. Looking forward to more content like this!

[karsisilva]

Great video on the importance of data cleaning! It’s crucial for ensuring the accuracy and reliability of the data we use for decision-making. I especially liked the breakdown of key techniques like removing duplicates, handling missing values, and using box plots for outliers. Data cleaning is often overlooked, but this video really highlights its significance in preparing data for analysis. Looking forward to learning more about the practical steps in your next video. Keep it up!

[joshtzy]

Great tutorial! The step-by-step breakdown makes data cleaning so much easier to understand. Pandas is such a powerful tool!

[NoureddineOsama]

Your video is brief, simple to comprehend, practical, and direct. I appreciate your honest efforts. Keep up the fantastic job,

[Iam-okoc]

Ohh this is kinda similar to what my teacher teach me in the stats in using an excel for solving problems rather than computating manually. Thanks a lot.

[donbig]

Fantastic tutorial! The step-by-step approach made data cleaning with Pandas so much easier to understand. I really appreciate how you broke down each process, from handling CSV files to creating visualizations. Super helpful and well-explained .

[DLDWL_]

"Excellent tutorial on data cleaning in Python! Your step-by-step approach, clear explanations, and visualizations make it easy to understand and apply. Thanks for sharing!"

[myarea]

Great breakdown of the data cleaning process! Properly handling missing values and duplicates is key to ensuring accurate visualizations. Excited to see the insights from the cleaned dataset!

[ahmadhammad]

I always had a problem with data cleaning process in Python, it always confuses me so much, but thank you so much, you have a very simple easy way for techniques for dropping columns and handling null values. it really helps me so much

[darksolstice]

This is very helpful. Thank you for the step by step guide on how to clean data in Python with Pandas. It's just what I'm looking for!

[TaiNguyen-fukd]

Excellent information and simple and clear explanation . Thank you for sharing, madam!!!

[dato]

Great tutorial! The step-by-step explanation makes data cleaning much easier to understand. Thanks for sharing!