Exploratory Data Analysis in Pandas | Python Pandas Tutorials

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:

Favorite Pandas Course:
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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 worked for*

[santiagofajardo]

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 = Population', ascending=False)
df2

Thank you very much, Mr. Alex, for these tutorials.

[satrapech]

the correction of df.corr() is:

numeric_columns =
correlation_matrix = numeric_columns.corr
correlation_matrix()

[pbp]

Man, “Oceania” was so funny 😂, tks for the class!

[JW-puuk]

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!

[rafaelmarques]

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!

[AlastorGarcia]

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!!

[frenamakenson]

Hello,
thanks for sharing
For the Corealtion part at 11mn
df.corr(numeric_only=True) # pass numeric only param to not having error

[OkallTheAnalyst]

Incase you are running into an error at minute 11:12, add numeric_only = True to the corr. i.e df.corr(numeric_only = True).

[kartikgupta]

We can also write this to save time writing all the column names in the list "df2 = Population', ascending=False)
"

[shankarmidatala]

Namaste! I found your tutorials "Simple, Easy to follow, and To the point". Thanks.

[toygar]

For those get error in heatmap:
import matplotlib.pyplot as plt
numeric_columns =
sns.heatmap(numeric_columns.corr(), annot=True)
= (20, 7)
plt.show()

[MaximKazartsev]

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 = Population', ascending=False)

And it works )

[pradiptisimkhada]

I just finished all the videos in you bootcamp playlist few hours ago and I'm excited to do this again..

[sj]

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!

[DuckingDuck-thlt]

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!

[DEDE-ixlg]

I always enjoy a video from Alex. Making one of the best videos, while some other channels just can be a real headache

[ngwamalfred]

Where would l have been without this video .

[sivasagarchakkarai]

If "df.corr()" doesn't work for the same data set were using in this Video. And It throughs an error : could not covert string to float: 'AFG'. Like this, Try : df.corr(numeric_only = True)

[staquatica]

I got some error's (using pycharm) that I solved by using "mumeric_only=True". For instance: df.corr(numeric_only=True) and

[quotesdiary]

Hi Alex
Thank you so much for your support for freshers in the field of data analytics.