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)

@wenkanglee9596

5 ай бұрын

gosh you're observant

@ronnelsupnet9850

5 ай бұрын

Thank you!

@rhodaime79

5 ай бұрын

My goodness. You saved me. I’ve been at this for about an hour. Thank you 🙏 thank you 🙏

@DevanshAsawa

5 ай бұрын

Thanks a lot dude !!!!!! Helped a lot !!!!!!!

@rnjesus9950

4 ай бұрын

Legend.

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

@rozakhan2811

Жыл бұрын

Hey tell me how can I do it too ri8 now I'm working as a customer support executive please help me to grow..

@dywa_varaprasad

Жыл бұрын

hey Rahul, how do you learn DA ? Can you share your experience it will be helpful for us!!

@sandeepthukral3018

11 ай бұрын

Hi bro is this course sufficient for beginner to land a job

@abdullahalmahfuz6700

10 ай бұрын

Is this a spam comment?

@TamilTigers001

7 ай бұрын

​@rozakhan2811 skills need is a basic thing...what you want..in that be strong..And way of Alex Teach Videos are Effective..

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)

@user-tm7uw4os1n

3 ай бұрын

This helps! Thank you so much!

@nataliarobinson5671

3 ай бұрын

Thank you very much

@OmarRabeh

3 ай бұрын

thank you very much

@OkallTheAnalyst

3 ай бұрын

Thank you!

@janrelleelam3628

Ай бұрын

OMG! Thank you so very much. I have been trying to figure this out for about four days now. I figured out the phone number issue and then how to split the address, but for the life of me splitting the address into named columns with the changes committed the df was not working. THANK YOU!

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 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.

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.

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.

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.

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

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.

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.

@AlexTheAnalyst

Жыл бұрын

Glad to hear it!

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

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

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

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

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

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.

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

@AlexTheAnalyst

Жыл бұрын

Glad to do it! :D

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.

@millenniumkitten4107

10 ай бұрын

If you want to replace the empty values in No Not Contact you'll need to use df["Do_Not_Contact"].astype(str).replace("","N") Technically those values are not empty, they are NaNs which is why replace is giving them 'NNN' instead of just the one 'N'. It's treating it as if NaN equals three blank spaces

@atomicafk8704

9 ай бұрын

that's what i've noticed too, great work

@jameslindsay4705

6 ай бұрын

You are a genius, thanks :)

@jaldaamol46

2 ай бұрын

Thanks man, this worked.

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

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

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

@AlexTheAnalyst

Жыл бұрын

Glad to help!

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

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

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

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.

@ivanovalle9764

3 ай бұрын

Where did you learn that you could use a dictionary format to replace multiple values in one line? this is really useful, thanks!

@yanpaucon1043

26 күн бұрын

Thank You. 34:16 is really helpful. I appreciate your kindness.

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

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'})

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

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.

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

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)

@arpandebnath6115

10 ай бұрын

df[["Street_Address", "State", "Zip_Code"]] = df["Address"].str.split(pat=',', n=2, expand=True) use this you have to include pat

@toni_munoz

6 ай бұрын

thank you!

@warinside7831

5 ай бұрын

what does that exactly?

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.

@uchindamiphiri1381

6 ай бұрын

mine didnt work lol

Your explanation was super cool

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

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

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

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)

@ahmadfadlanamin9286

11 ай бұрын

Thanks!

@vigneshwarsekar8351

10 ай бұрын

Hi, Thanks, If I try this, Index 2 , 11 and 17 becomes NAN when originally they are in correct format, Kindly help

@vigneshwarsekar8351

10 ай бұрын

Thanks a ton, been looking for it for almost a week

@manishaarya247

8 ай бұрын

Thanxxxxxsss aaa lotttt🙌

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

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

Your work are amazing. Thank you so Much

Thanks Alex, Please post more videos.

You are great, Alex. Your teaching skills excellent.

@AlexTheAnalyst

9 ай бұрын

Thanks! 😃

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

@AlexTheAnalyst

Жыл бұрын

Glad to hear it! :D

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

@DreaSimply21

7 ай бұрын

This helped me, thank you! However, what does '"n" mean?

@bobojonkasymov2279

6 ай бұрын

n=2 parameter indicates that the split should occur at most two times, producing three resulting parts.@@DreaSimply21

@championsadiq7411

4 ай бұрын

Thank you for this. It helped me a great deal

Great video mam, need more this type of tutorials

Amazing explanations!

Thanks for this absolutely great video.

Very helpful, and well explained.

Thank you so much, Alex. You are the Best

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!

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

Thank you for this very useful video!

Super Explanation Thanks

Thank you, great video!

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)

@DreaSimply21

7 ай бұрын

OMG Thank youuuu!!! I knew someone on here had to know the answer to how to use regex lol.

@bolajiawofuwa8116

5 ай бұрын

Thanks

Thank you Alex! 🙏

Really enjoyed the video

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

@AlexTheAnalyst

Жыл бұрын

In the future, yes :)

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

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.

Alex i have a question regarding the part in 18:50 where you change the phone number column into string using the str() inside the lambda , can i get the same result using first df["Phone_Number"].astype() and then do the lambda ? or is there a nuance and it works only using str() ? Thanks for the great work !

very well explained video thank youuuu

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

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 !

@MehmoodAyazKhan

10 ай бұрын

vouching for this @Alex. It'd be really appreciated TIA

Yesss love these vids

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

Thank you so much this awesome 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?😄

Amazing Video

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

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

@AlexTheAnalyst

Жыл бұрын

Do it! I try my best to bring the best free content I can :)

Hey alex, could you please expand in detail about the lambda function? thank you.

Thanks for this

Thanks brother!❤

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

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

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?

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

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 😅

really helpful

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'

@LuisRivera-oc6xh

Жыл бұрын

I'd say that's complicating the code. You can simply do df = df[df['Do_Not_Contact'] != "Y"]

@vickygalih5571

10 ай бұрын

@@LuisRivera-oc6xh i literally use this at the first time learning pandas myself

@ghanem87

9 ай бұрын

df = df.drop(df[df['Do_Not_Contact'] == 'Y'].index) df = df.drop(df[df['Do_Not_Contact'] == ''].index) OR df = df[df['Do_Not_Contact'] == 'N']

Still Helpful Thanks

Great stuff ! Do a collab with Rob Mulla !

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

@GlennLee-qz4st

5 ай бұрын

Not sure if you're still looking for a solution, but from some online searching, I found a solution to avoid deleting phone numbers that did not have any error/contain no characters, by adding .astype(str) before .str.replace, this seems fix the issue and the code should look something like this: df["Phone_Number"] = df['Phone_Number'].astype(str).str.replace('[^a-zA-Z0-9]','',regex=True) Also note you'll have to add in regex=True manually. Maybe it's deleting as it somehow interpret whole number as non-numeric and deleting it erroneously, not 100% sure tho, still a beginner, and it might cause issue with other types of data.

Sir, in your opinion : Jupyter vs Pycharm? Which is better for data cleaning ?

thank you

Perfect 👍

Question is it bad that i dont specify the .str in this df["Do_Not_Contact"].replace("Y", "Yes")

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)

For phone number why don't you convert each record into str first, and then when you apply the reg expression, you can get rid of Nan and Na all together with other stuff?

Hi Alex! Want to ask whats the point of data cleaning and visualization on pandas if there is PowerBI? PBI is more of a click and drag interface and much more user friendly compared to pandas if its just Cleaning and Visualizing stuff. Is pandas much speedier in terms of raw performance compared to PBI?

@lazy_cat_traveller

9 ай бұрын

Not the author but your question is effectively - why should we use Python/R/other programming language vs Excel/PowerBI software for the aforementioned purpose. Several reasons, but the main one - python/r would be much more suitable when working with big amount of data (aka the size of which is measured in GB and not MB). Trying to use excel/powerbi in such cases would lead to pretty long and painful struggles of programs to try to execute what you want from them and can sometimes straight up lead to the loss of data. But if you just want to clean monthly sales report for instance, then yes Excel/Powerbi would be a more user-friendly option.

THank you for the video When trying to filter using the DNC column, Couldn't we have done df = df[df['Do_Not_COntact'] !== 'Y']

Python is so fun

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.

I have a lot of questions. How do we ask questions privately?

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

@AlexTheAnalyst

Жыл бұрын

lol for sure!

The pd.read_excel(r[filenamem])

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

enjoying the video and appreciate this is a beginner video, but for 11:56, i have written a function to handle non alphabetical chars from start and end of column def remove_special_characters(text): if isinstance(text, str): return re.sub(r'^[^a-zA-Z0-9]+|[^a-zA-Z0-9]+$', '', text) else: return text df['Last_Name'] = df['Last_Name'].apply(lambda x: remove_special_characters(x))