For those struggling with the regular expression at 14:57 , you might need to explicitly assign regex = True (based on the FutureWarning displayed in the video). That is: df['Phone_Number'] = df['Phone_Number'].str.replace('[^a-zA-Z0-9]', '', regex=True)

Fan from India I just got 2 offers from very good companies thanks to your videos and it helped me transition from a customer success support to Data Analyst

For splitting the address at 21:29, you may want to add a named parameter to the value of 2, as in n=2: df[["Street_Address", "State", "Zip_Code"]] = df["Address"].str.split(',', n=2, expand=True)

This is one of the best videos regarding data cleaning I have ever watched. Really crisp and covers almost all the important steps. It also dives deep into concepts that are really important, but you rarely see anybody applying them. Must watch for everybody, who is looking to get into data field or are already in the field.

I like how in some of your videos you show us the long way and then the short cut, instead of just showing the short cut. I think that way gives the person who is learning a better breakdown of what they are doing.

Thanks for this content, this was so helpful!! I think i have some optimizations, correct me if im wrong :D 27:04 instead of calling the replace function multiple times, you can create a mapping just like: replace_mapping = {'Yes': 'Y', 'No': 'N'} and call it like: df = df.replace(replace_mapping), so you dont have to specify mapping for each column and need to call .replace() just once. 34:16 instead of the for loop + manually dropping row per row, you can make use of the .loc function like: df = df.loc[df["Do_Not_Contact"] == "N"] in order to filter the rows based on filter criterium.

instead of applying lambda function to convert Phone_Number column elements to string , we can also use df['Phone_Number'] = df['Phone_Number'].astype(str) and use dictionary as an argument to be passed inside replace method to avoid Yes becoming YYes df['Paying Customer']= df['Paying Customer'].replace({'Y':'Yes','N':'No'})

I really like when you make mistakes, because it tells that no one perfect. I sometimes anxious when I watch tutorials and they seem to be so good. You also implicate the struggles that you experiencing throughout the process is real. Thanks for the tutorial Alex.

Some of the phone numbers are removed while doing the formatting. If you look in the excel file, you'll see that some of the numbers are strings and some are integers. When you run the string method during the formatting, it replaces the numeric values with NaN and they are later removed completely. If you want to avoid losing that data you'll need to use df["Phone_Number"] = df["Phone_Number"].astype(str) before formatting. You also won't need to convert to string in the lambda after doing this.

I enjoyed working on this project. Thank you Alex and a huge thank you to those guys who helped in the struggling minutes!

If you're getting an error when trying to split the address, this is what worked for me; I had to remove the number of values to look for. df[["Street_Address", "State", "Zip_Code"]] = df["Address"].str.split(',', expand=True)

Thank you sir, you can't imagine how i fill confident in cleaning data after completing this video with real data practices. Thank you once again.

Found this REALLY helpful! I love how you walk us through mistakes as well as explain WHY you do what you do throughout your videos. It adds so much value to each video. As always, THANK YOU ALEX!!

I am studying Data Collection and Data Visualization at Kings College, your channel is reccomned by our lecturers to understand data cleaning.

Alex your are the GOAT! for real thank you for all the tutorials and your help for everyone who want's to become a data analyst1

I've been struggling with Pandas a bit and this video cleared some things for me! what frustrates me from the way my teachers would teach Pandas, their solutions are sometimes too efficient, in the sense that a student that started from zero who's taking an exam, will never be able to come up with these hyper efficient and elegant one-liners in their code. what I appreciate in your video is how you achieve the same results, but in a way that a beginner can easily remember and apply on an exam. thank you! I'll be checking out more of your videos.

thank you for your work Alex! I went through the entire video 1 by 1 twice and I can tell I learned a lot from this video , finally understanding why we need to learn Loops etc. and how simple cleaning methods work on Jupyter.

at 15:19 i would like to say something. in the new version from jupyter, if u write the code from alex the data will be same. To fix this, u can input regex = True after the ''. CODE: df['Phone_Number'].str.replace('[^a-zA-Z0-9]', '', regex = True). But overall i can't say anything except thank u alex for this awesome tutorial !!!!

I discovered that replace() has an argument regex (regular expression). It is set as regex = True but when we change it to regex = False, it only looks for exact matches, meaning it won't change 'Yes' to 'Yeses', only 'Y' to 'Yes'. We can write df["Paying Customer"].replace('Y', 'Yes', regex = False) and it will work as expected.

This is really very important to both the beginners and pro. Kudos!!

And I was already looking for some Pandas tutorial. Thank you, Alex, this was much needed. :)

man lets go,you are our hero who can not afford paid courses

This is the best video I have ever watched on data cleaning using pandas.. even the mistakes were good to learn from.

After making it this far through the course over the last 2 months, looking at these last 4 videos I'm getting strong final exam vibes. Python has not felt intuitive to me at all, but I recognize its value. I guess it feels like taking Spanish 1 and having Spanish 2 tests. I'm definitely looking forward to applying what I've learned here to solidify the lessons more. I'm contracting for a company already and writing a proposal for them to transition to My SQL Server. I guess the fact that I feel overwhelmed with all the info means I'm actually learning how little I actually know, which is a good thing for growth in the long run. Rambling here, but I am incredibly thankful for the course, Alex.

Hello Alex, thank you for such a wonderful tutorial . I have one suggestion regarding the last part where you are filtering # Filtering the Data with "Do_Not_Contact" Column with N and " " Filter1 = df["Do_Not_Contact"]=="N" Filter2= df["Do_Not_Contact"]=="" df[Filter1 | Filter2]

Thank you for this video. I just finished this part of the data analytics course and I definitely learned something new and helpful.

Alex, I loved the Video. It have Correct Explanation. Thank you so much for your Video. There is a Small Mistake while you are typing #Another Way to drop null value df.dropna(subset='Column_name',inplace = True). I hope you will notify the Error. Thank you. Have a Great day!

For the address column: df[["Street_Address", "State", "Zip_Code"]] = df["Address"].str.split(",", n=2, expand = True). Defining only 2 was giving me an error. so i had to change it to n=2

Also, to clean the Do_Not_Contact field, one can use: df['Do_Not_Contact'] = df['Do_Not_Contact'].replace({'N': 'No', 'Y': 'Yes'})

Simply amazing! Well-explained and comprehensive. Loved it!

Best video available on internet so far for data cleaning in Pandas. Best explanation. 😇😇

Hey alex, we don't need to take any course because you are there 😉 I am doing your bootcamp of becoming a data analyst

Very well done! Great video. I am working on analyzing and cleaning scraped data from web and this guide is helpful, especially where you mentioned the mistakes.

My fav thing to do in pandas, thanks for making tutorial.

Thank you so much sir i have start my data cleaning from you From india 💌

Thank you, this is most elborative and simplest videos i saw

thanks for your effort making this amazing video. It helps me alot. I've been struggling on Data cleaning and your video is helpful

Oh my.. I am going to watch every single video you created..

Great Pandas data cleaning video. Thank you very much for sharing your knowledge.

Hey, Alex, I just Started your Pandas Tutorial, and I was waiting for Data Cleaning video, when i open my KZfaq, First your Video is seen.. This is boon for me 😇🥺 Thanks, I hope you will Upload Matploib, Numpy and Many More Libraries video ❤🤗

Thank you Alex. That Lambda example is going to be very useful.

Thank you Alex. Your videos are very helpful. Now I can resume cleaning my data.

Using regular expressions for manipulating data is beneficial because it allows you to change strings as needed, especially when dealing with different types of strings.

Thanks a lot Alex for the video ! This was exactly what I was looking for. May I request you to try and upload video on how to write Python ETL code which uses table in a cloud database like snowflake, saves it in a csv format, transforms it and then again uploads it on snowflake. And all these steps are being captured in a log file which is in txt format !

Thank you Alex for this detailed breakdown. Just a side note for those who don't like to use loops e.g. for, while For 31:00, you could do the following code 'df.drop(df[df['Do_Not_Contact'] == 'Y'].index, inplace=True'

Hi Alex, idk if you will see this comment. So I was doing the same codes, and I noticed when you eliminated the characters for the phone numbers at 14:57 you also deleted the phone numbers that did not have any characters in them. You can see that at index 3 for Walter White, before he had a phone number but after he had NaN. If you can tell me how to correct it, it would be very great. I also never commented on your videos, but i like them very much, they are very good, and helpful. Thanks for everything

Thank you Alex for this video on data cleaning with pandas. It is very detailed and explanatory

Your explanation was super cool

You are great, Alex. Your teaching skills excellent.

If the df["Phone_Number"].replace('[^a-zA-Z0-9]', ''") is not working for you. Try, df["Phone_Number"].replace('[^a-zA-Z0-9]', ''", regex=True)

Thanks Alex, Please post more videos.

Many thanks for the dataset+code+video!!! 🔥🔥

If any one is getting an error on df['Address'].str.split(",",2, expand=True), you can omit 2 and use df["Address"].str.split(",", expand=True)

The video I needed to have a realistic practice in data cleaning.thanks

in Last_Name columns we can used replace function in order remove regular expression like ( ./-) code: df["Last_Name"]= df["Last_Name"].str.replace("[./_]","" ,regex= True)

I was intimidated by the Machine learning module but now I am not. Thanks a lot dude

Great video! I enjoyed learning from you! Thanks for making things easier to understand

For explanation purposes, it is great. For getting the final result, I would have done differently though

Great video mam, need more this type of tutorials

I'm in love with ur videos

Thank you a lot, Alex! ^^

Thanks for the video. Helped a lot in understanding Pandas.

Timestamp 32:42. I simply use #Filter out "Do_Not_Contact" == "Yes" df[df['Do_Not_Contact']!='Yes']

Not an analyst (never wanted to be), but it was very interesting. Thanks!

Your work are amazing. Thank you so Much

Thank you Mr Alex

How you are at 23:27 apply the changes and go back to the previous steps in Jupiter notebook

A Glorious Thank You!! Please Keep This UP!!!!

Thank you so much, Alex. You are the Best

Hey Alex, Thanks for the super content ...!

Very helpful, and well explained.

Great video thanks! Can’t help thinking that tools like chatGPT, github copilot al, GPT engineer can pretty much tell you how to/do this all for you so maybe I am wasting my time learning this 😅

thank you very much. your video helped me a lot. good luck

Thanks for this absolutely great video.

Thanks for the detailed tutorial Alex. I was wondering, if i wanted to become a data scientist instead of a data analyst, would you recommend any people in the industry who I should follow? F.e is there an Alex the Data Scientist out there?😄

Thank you soo much sir you're really a great professor 👏❤

Amazing explanations!

Really u fone a good job i became a big fan of u thank u so much for doing this

I not only survived! on 20:46 you can place AND in .replace('nan--' AND 'Na--' , ' '). Thank you 1:1

Yesss love these vids

Super Explanation Thanks

thanks alot for this great video.

which one is better for data cleaning, Pandas or Excel ?

Hello Alex on time of cleaning the Phone_Numder column(14:00 to 21:39 ) the code is executed. But at the table there are no changes . Please help me on this

Thank you for this very useful video!

In the case of column Phone_Number with all the variant of NaN, first "stringuify" the column, and after do the format thing and then replace with nothing all the content of the column when the content contains 2 - Thank you!

My only doubt is, you saw the first 20 rows and decide only \ or .. or _ could be preceding, or only "Nan" or "N/A" is only there in that row, while replacing it. What if the 50th row has "%Mike" as a name or what if "Null" is there one of the columns?? How do we deal with it. Great recap for me other than this. Thank you.

Really enjoyed the video

Thank you Alex! 🙏

great video thank you. when we did the first lambda, the reason was because lambda is faster. so why did we go against using a lambda when it was time to check if the customer can be called or not?

Thank you, great video!

very well explained video thank youuuu

Nice one Alex. Don't forget to add comments to the code! 🙂

Hi Nice explanation. But in this data cleaning you have simply remove NA values. But as per my understanding we need to fill NA values, I am not clear about the logic to fill in. If you can provide video on how to fill NA values it will help us a lot. Thanks Abhinav

Python is so fun

thank you Alex

Instead of striping each symbols one by one in 9:11 i think its better to use characters_to_remove = ['/','...','_'] for x in characters_to_remove: df["Last_Name"] = df["Last_Name"].str.strip(x)

at about 33:54, whoa! unless you were specifically told to do this, you are altering the data! Changing no value to 'N' is a no-no unless you have been told to do so. Otherwise you're adding information that was not there. We don't know if Harry Potter wants to be contacted or not and that's probably for someone above our pay grade to decide! :D

I think you did a good job explaining a lot of these but I'm noticing some fatal errors in both the video and the example notebooks you've provided that the comments confirm with me. For example, the Phone Number string replace around the 15 minute mark in the video doesn't work at all and another viewer commended a correct revision. In addition, the string split for the Addresses around the 23 minute mark also do not work because the StringMethods split function takes from 1-2 positional arguments.

38:36 df[['Street_address','State','Zip_code']]=df['Address'].str.split(" ",n=2, expand=True)