Exploratory Data Analysis with Pandas Python

In this video about exploratory data analysis with pandas and python, Kaggle grandmaster Rob Mulla will teach you the basics of how to explore data using python and pandas. Exploratory Data Analysis it a necessary tool for any data scientist. Pandas is a MUST for anyone getting into data science with python. Python is the #1 coding language for data science and has been growing over the years as an essential tool, with Pandas being the main data wrangling module. Kaggle Grandmaster Rob goes over it all in this video. In this video we discuss the basics of how to use explore data including...

Timestamps:
00:00 Introduction
01:00 Imports and reading data
03:35 Data Understanding
06:40 Data Preparation
20:57 Feature Understanding
27:35 Feature Relationships
35:30 Asking a Question about the Data
40:00 Final Thoughts

#Python #Coding #DataScience #Kaggle

[robmulla]

Chapters don't appear to be working for my videos for some reason. Here are the timestamps for the video:

00:00 Introduction
01:00 Imports and reading data
03:35 Data Understanding
06:40 Data Preparation
20:57 Feature Understanding
27:35 Feature Relationships
35:30 Asking a Question about the Data
40:00 Final Thoughts

[saintsaens]

As a begginer in data this really opend my eyes as to how things works. Your explanations are very clear and I can feel how passionate you are. Great video

[saptarshidey]

There are a ton of EDA videos on YouTube. This is one of the best I have ever come across. You just nailed it, Rob.

[grandselenium]

- import data

Data understanding
- filter columns by need
- convert dtype of certain columns
- rename columns
- check isna in columns and dropna on row or column accordingly
- locate duplicated rows in single or multiple columns
- drop duplicated rows from dataset and reset index

Data prep
-univariate analysis of features - kde, histogram, box plot
- use value counts to determine duplicates and unique values in feature
- he creates bar plot for top 10 years introduced to highest # of coasters
- he creates histogram to bin speeds of roller coaster and view their frequency distribution


Feature understanding - scatterplot, pairplot, correlation, groupby
- he creates scatterplot for speed and height with year based hue of points
- he create pairplot to compare correlation between features, alongside hue from material type
- creates a correlation heatmap for selected features

Ask question
- he uses groupby and query to create bar plot with sorted descending data on mean speed of roller coasters by location.

[sa-ptkf]

By far one of the most clear and concise ways of teaching in a computer science related field I've come across in a while. I'll be binging all your tutorials for sure!

[nigelkiernan]

This is a great refresher guide! Very nice coding style and I appreciate you using a simple Kaggle dataset to follow along. Great stuff - thanks!

[Aarron-iopm]

Hi Rob, this was super useful to me as a tired Excel veteran and python beginner. You explain and demonstrate everything so clearly, thank you

[wesleyweel]

The quality of your content is only surpassed by the ease at which it is to assimilate it, keep up the great content Rob, cheers!

[JHornsby]

This is the second one of these I have now watched and coded along with! Genuinely awesome content, so precise and simple to follow. You make daunting tasks (for beginners getting into data) really accessible which is a sign of a great teacher!

[darshantawte]

Lucidly explained. One thing i have learned that in order to be a great Data scientist what matters is your problem solving skills, understanding the business requirements and curiosity to dive deep into data (true to the name data scientist) . There is no need in remembering these codes as long as you know what to look for.

[jackgarn]

This is such an amazing guide! I’m new to data analysis and had limited python exposure and have taught myself most of these things so far by googling or just reading the pandas documentation. Watching someone familiar with the process do it all together was really helpful and gave me a lot of insights as to how I can improve my skills and workflow. Thank you so so much!

[rrestituti]

#1 Data science youtuber!!!
You made easy to understand the basic commands e sintaxes.
Thank you a lot, Rob. 😉

[shoaibahmed]

I completed my certification in data science but could not figure out where to start. Thanks for such a detailed and easy to understand instructions with very useful commands. It helps a lot. Keep it up

[chess]

Perfect stuff what I love about this video is the simplicity and the clearness of the way you talk

[mrahul]

Decade years of experience in churning data in xl and SQL .... Trying to get my hands on python... This video is the best I have come across on the internet. Thank you for letting us know a real life problem getting solved

[chrisosomo]

I can’t get enough of your videos, especially the very hands-on practical approach to learning. Your explanations are clear and easy to follow along with. Please make more of these types of videos. You are definitely makes a change and contributing to the YouTube knowledge pool. Thank you so much.

[adamvoltemar]

This is one of the best content related to Data Analysis and Python/Pandas, I am really glad I found it! Thanks!

[chrismagee]

This is the best reference guide. I always find myself rewatching this whenever I'm cleaning a dataset.

[silver_soul]

I have tried plenty of tutorials by now. This is the most precise and to-the-point tutorial so far. Well done.

[alisonhenley]

Wow, what an informative fun tutorial. Thanks Rob!