Learn Pandas in Under 3 Hours | Filtering, Joins, Indexing, Data Cleaning, Visualizations

[@filminsightful]

Man....the quality you are providing free of any penny, is amazing. Salute to your contribution to community man

[@alexrosen8762]

Thanks! If this tutorial is half of the quality compared to the latest SQL tutorial, then it is really worth watching 👌

[@nilosamson5982]

so excited for this. I'm going to kill my procrastination and i will finish this :) thank you Alex.

[@AndrewT]

@2:02:39 I believe it is best practice to avoid using for loops since the pandas operations are built on numpy and are vectorized. You can do something like df.loc[df['Do_Not_Contact'] != 'Y'] to filter out the y's and then set that whole column to N with df['Do_Not_Contact'] = 'N'

[@AlexTheAnalyst]

You're right - definitely could have done it that way

[@xtravengersgaming]

you sir are a GEM !! thank you so much

[@AndrewT]

Great stuff Alex! Thanks for sharing

[@AndrewT]

For merge, I use left_on and right_on when the columns that I am merging on have different names in the two tables.

[@hw7channel571]

Thanks for the knowledge sharing sir. Since when I started watching your JavaScript tutorial, it was understandable and clear.

[@AlexAlex-ei7zf]

Спасибо Вам огромное! Вот это действительно очень полезный урок!

[@AlchemistTasmi]

It is needed ❤

[@Aaron-gv7lf]

Thanks!

[@Stephen-mb2gf]

Another save in my coding playlist

[@NostraDavid2]

Now do it again, but for Polars - the superior dataframe library! It's WAY faster, can handle WAY more data, uses WAY less memory, the API is MUCH cleaner (i.e. more readable) and I truly believe it's the future of dataframe libraries.
I say that after using Pandas for 2 years, and Polars for 2 months. No more abusing the index, when you really just want to do a group_by.

[@yaqubnaqiyev131]

we need matplotib, seaborn that are used only in data analyst, can you do that

[@mulikinatisiddarthasiddu8245]

Alex do you added any new topics in this video or is it same as the one in the bootcamp ? @Alex

[@AlexTheAnalyst]

Same all put into one video

[@NOxhunter]

1:44:42
3 rows which have number in format 0000000000 got Nan value why
And how to fix it?

[@danielnjeru2801]

I downloaded this zip file from GitHub but when I copy the path in python it is returning a not found error, How do I do it?

[@dasarijagath674]

create a folder and keep the xlsx file and pynb in same folder & right click on on the file u want to read copy and placce it in df=pd.read_csv(r"file path") now read it if it works tey to copy the path from folder and paste it next \file name enter i hope this might work

[@AA-kq8on]

hi
is that work in notebook in Microsoft Fabric ???

[@okkaraung9512]

Thank you for the video

[@FromPlanetZX]

Hi All, I need help for th GroupBy section. I'm getting error while applying aggregator on group_by_frame.
group_by_frame = df.groupby('Base Flavor') --> run succesfully
group_by_frame.mean() --> gave error
TypeError: Could not convert ChocolateRocky RoadChocolte Fudge Brownie to numeric

[@elhyjhayqulchat4049]

I also did get an error but i tried this and it worked. I am not sure how it works though. Try placing a column name with integers in the bracket. so i used:
group_by_frame.mean('Flavor Rating')
'Flavor Rating' contains int. You can use any other column which contain integers/numbers.
with this i was able to get the mean values

[@haykinanc9079]

@@elhyjhayqulchat4049 In actuality, the argument you are passing goes into the numeric_only parameter, which takes a boolean value. Thus the “Flavor Rating” value is converted to a boolean value and the mean method is executed.

[@AmandeepSinghP1]

@@elhyjhayqulchat4049 you can also use this if you getting error:-
group_by_frame.mean(numeric_only=True)

[@loroda5375]

@1:48:55 formatting the phone number with lambda function does NOT work, we have to handle if the phone number is missing or not a 10 digit first...
CODE:
import numpy as np
# Define a function to format phone numbers
def format_phone_number(x):
if pd.isna(x) or len(x) != 10:
return np.nan # Return NaN if the phone number is missing or not 10 digits
return x[0:3] + '-' + x[3:6] + '-' + x[6:10]
# Apply the formatting function
df3['Phone_Number'] = df3['Phone_Number'].apply(lambda x: format_phone_number(str(x)))
print(df3)
Courtesy of Copilot
Thank you @AlexTheAnalyst for the video!

[@dark_legions2227]

Hey thank you for making the best content

[@dark_legions2227]

Make a video where you fetch data of the Olympic 2024 medalists using web scraping and display it on the frontend using Flask or Streamlit, with a feature for filtering as well. This project will give many ideas, and there isn't a video like this on RUclips.

[@2011Anurag1]

Hello Alex. This line is not working **fl.groupby('Base Flavor').mean()**.
I see error TypeError: agg function failed [how->mean,dtype->object]. But it is working on your jupyter?

[@nerads]

Use group_by_frame.mean(numeric_only=True)

[@2011Anurag1]

@@nerads Thank you very much

[@xtravengersgaming]

2:03:30
This will work for making 'NNN' to 'N'
df['Do_Not_Contact']=df['Do_Not_Contact'].str.strip(' ').replace(' ', 'N')