Exploratory Data Analysis in Pandas | Python Pandas Tutorials
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Published 2023-06-06
In this series we will be walking through everything you need to know to get started in Pandas! In this video, we learn about Exploratory Data Analysis in Pandas.
Dataset in GitHub:
github.com/AlexTheAnalyst/PandasYouTubeSeries/blob…
Code in GitHub: github.com/AlexTheAnalyst/PandasYouTubeSeries/blob…
Favorite Pandas Course:
Data Analysis with Pandas and Python - bit.ly/3KHMLlu
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Coursera Courses:
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📖Tableau Data Visualization - coursera.pxf.io/MXYqaN
Udemy Courses:
📖Python for Data Analysis and Visualization- bit.ly/3hhX4LX
📖Statistics for Data Science - bit.ly/37jqDbq
📖SQL for Data Analysts (SSMS) - bit.ly/3fkqEij
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0:00 Intro
1:51 First Look at Data
3:45 Info()
4:40 Describe()
5:47 Counting all Null Values
7:09 Count of Unique Values
8:15 Sorting on Values
10:40 Correlation between Columns
11:53 Heatmap using Seaborn
14:43 Grouping Data
25:02 Visualizing Grouped Data
26:17 Boxplots for Outliers
29:07 Data Types of Columns
30:41 Outro
All opinions or statements in this video are my own and do not reflect the opinion of the company I work for or have ever
All Comments (21)
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Hello, at minute 24:24, I managed to reverse the range of column names using [5:13][::-1]. The expression [::-1] is used to reverse ranges and it is very useful: df2 = df.groupby('Continent')[df.columns[5:13][::-1]].mean(numeric_only=True).sort_values(by='2022 Population', ascending=False) df2 Thank you very much, Mr. Alex, for these tutorials.
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Man, “Oceania” was so funny 😂, tks for the class!
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This is absolutely top tier content. I can't stress this enough to people new, or going into the DA/DS field: you WILL be exploring and cleaning data sets much more than you will be visualizing and building models. Thanks for this, Alex!
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the correction of df.corr() is: numeric_columns = df.select_dtypes(include=[np.number]) correlation_matrix = numeric_columns.corr correlation_matrix()
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I always enjoy a video from Alex. Making one of the best videos , while some other channels just can be a real headache
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Thanks Alex! Right now i'm applying to my first DA Job and you have no idea how useful your videos have been for me!!
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Oceania is one of the 7 Continents (North America, South America, Europe, Asia, Africa, Oceania, Antartica). It's basically Australia and the countries (islands) around it. Hope that helps!
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I just finished all the videos in you bootcamp playlist few hours ago and I'm excited to do this again..
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We can also write this to save time writing all the column names in the list "df2 = df.groupby('Continent')[df.columns[12:4:-1]].mean(numeric_only=True).sort_values(by='2022 Population', ascending=False) "
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EXCELLENT SUPERB video!! I can't believe it--I'm 6/7 videos away from the end of your FANTASTIC bootcamp series! Wahoo! I learned a lot in this video. :) As for "ending on a low note", hardly Alex lol All your content is uplifting and rewarding! As always, THANK YOU!
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Hi Alex Thank you so much for your support for freshers in the field of data analytics.
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I really enjoyed this introduction to Pandas! Keep up the good work!
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Hello, 100000000 thanks for sharing For the Corealtion part at 11mn df.corr(numeric_only=True) # pass numeric only param to not having error
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Alex, thank you for this great video and everything you do! In order to avoid manual ordering of the population years, there is a way to use df.columns method, by adding reversed. The whole construction looks like df2 = df.groupby('Continent')[list(reversed(df.columns[5:13]))].mean().sort_values(by='2022 Population', ascending=False) And it works )
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I got some error's (using pycharm) that I solved by using "mumeric_only=True". For instance: df.corr(numeric_only=True) and df.groupby("Continent").mean(numeric_only=True)
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Hello, Alex! Once again, thanks a lot for all your hard work! At 13:10 I got an error ValueError: 'box_aspect' and 'fig_aspect' must be positive" Solved it by putting the plt.rcParams BEFORE the sns.heatmap The other problem was that some functions didn't work until I added the parameter numeric_only = True, e.g., df.corr (numeric_only=True) or .mean(numeric_only = True) Hope, it can help someone!
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For those get error in heatmap: import matplotlib.pyplot as plt numeric_columns = df.select_dtypes(include=['float']) sns.heatmap(numeric_columns.corr(), annot=True) plt.rcParams['figure.figsize'] = (20, 7) plt.show()
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Where would l have been without this video .
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Thank you for the Pandas class!
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Thank you so much for this. I really enjoyed it and learned a lot of what I had forgotten a few years ago.
