Back when the first iteration was released i was in college having no idea about what a dataframe is now I'm a developer and still watching your videos. Thanks Keith for being a part of my learning journey❤
I'm totally new to Python and learn the basics from the Corey Schafer. And now moved here to learn Pandas. I'm on right track? My goal is to become data engineer and then data scientist.
Strongly resonating with another comment here I recall watching your tutorials in my first year of college. I just graduated recently and became research software engineer. Your videos have been pivotal for all the stuff I've done :)
Would love to have a follow up video on seaborn from this guy with these same csv files shown. the parquet and excel files do not seem to want to copy paste from the browser when you select raw
Did anyone notice how our keith has been sneaking a quick peek to the right at the beginning in the last few videos? 😂 Seriously though, loving the content!"
I really appreciate your efforts , you are delivering such a best content related to python and its libraries. I saw your first dataset cleaning with pandas and i truly loved your live tutorial . Please come with more real word pandas dataset cleaning live tutorials which helps junior developer lime me a lot. Once again Thanks for sharing this type of content
00:01 Complete Python Pandas Data Science Tutorial 02:12 Setting up virtual environment for data science project 07:03 Exploring DataFrame Functions 09:26 Learn how to load CSV files in pandas 14:12 Accessing and filtering data in Pandas 16:31 Understanding data slicing and indexing in Pandas 21:08 Accessing and manipulating data in Pandas 23:18 Iterating through rows in Pandas can be done but may affect performance. 27:45 Advanced conditional filtering based on string operations 29:59 Filtering data using regular expressions in pandas 34:18 Adding and removing columns in Pandas data frame 36:34 Using inplace parameter in Pandas for modifying data in place 41:22 Extracting specific data fields from a Pandas dataframe 43:40 Convert date objects to datetime type for easy manipulation 48:14 Custom functions using Lambda for data manipulation 50:40 Merging and concatenating data at scale. 55:24 Data frame manipulation for filtering and combining data. 57:52 Merging data frames and handling null values 1:02:26 Handling missing data using pandas dropna method 1:04:44 Analyzing Olympic athlete data using Pandas in Python 1:09:03 Pivot tables convert data into a useful format. 1:11:32 Analyzing Popular Birthdates of Olympic Athletes in Python Pandas 1:16:54 Ranking heights of individuals using Python Pandas 1:19:16 Utilizing rolling functions in Pandas for cumulative sums and other calculations 1:24:22 Using specific data types in Pandas like string types within Pi Arrow can optimize performance at scale. 1:27:00 Using Pandas to filter and pivot data in Python 1:32:11 Explore Olympics dataset and pandas functionalities 1:33:52 Wrap up and thank viewers for watching Crafted by Merlin AI.
Awesome video, right speed and comprehensive. My thanks to you for taking the time to do this - am sure it was hours and hours of work and I truly appreciate your effort
Great tutorial. You keep teaching new things all the time with practicao examples and speak just the exact amount not to make it boring. Good job. I wait for sklearn, np, matplotlib, sns, streamlit tutorials 😂
I always love your content because of the ease of understanding ❤ I've been hearing alot of the polars library but there's limited content on it. Please if possible do something on it
This is great content! Please make a similar tutorial or recommend one that relates to using vectorization via Numpy arrays. I have applications that do what I need them to do, but involve nested loops that iterate over millions of rows of data. I really need to move away from these loops to improve execution time.
1:14:00 Yes this is true! I analyzed an Olympic dataset for a college final project, and we used the fact that the plurality of NHL players are born in Jan-March to pitch our analysis proposal.
Thanks. While watching your introduction, I start to wonder if you're going to do a video on NumPy, especially when a major version has released. No hurry, please take your time. Thanks in advance.
@@KeithGalli dude this video was exactly what i was looking for as someone relatively new to python trying to get into data science. NumPy and Seaborn would be good follow up videos if you used the same data. The CSV files seemed to copy paste well from the browse but the parquet and excel did not want to and made me load as a .txt at that point i just crossed my fingers hoping you would use the csv and 20 mins in so far you have great video so far. excellent focus on detail great beginner level examples and functions...tried datawars and datacamp before coming here...thank you truely..
@@NewsChannel-y4g Happy to hear that!! Yeah I think that because Excel & Parquet files aren't human readable in their raw form, it doesn't let you copy & paste the URL in the same way as CSV. It is a good test to be able to read those files though, so I recommend that you try downloading them (there's a download raw file button on Github) and then reading them in locally with your code. You'll probably want to move the files from your downloads folder to the same location as your notebook file and then you should be able to load it in with a command like pd.read_excel('./olympics-data.xlsx') & pd.read_parquet('./results.parquet') respectively. That being said, I plan to continue using CSV files in most of my videos so you should be fine with the method you have been using. Not sure if I'll use the same data, but I hope to do some videos that incorporate NumPy & Seaborn in the not-so-distant future. Keep up the good work!
bro axis 0 is horizontal frame and axis 1 is vertical frames but the function works when applied vertically by using axis 1 which is weird but thats how it works i guess
I was asked in an interview - how to import multiple files at once in pandas instead of importing files one by one if there are so many files. Is there a quicker way? how to accomplish that in pandas?
I recommend checking out this video: ruclips.net/video/MeyVptCRubI/видео.htmlsi=RqO--khHDJdNRI0a A real-world project (an actual consulting project of mine) that you can follow along with that uses LLMs.
@@KeithGalli Thank you for responding. I did go through this and it was pretty cool. Just wanted to know if you might consider a full tutorial on langchains and LLMs. Also, love your channel.
what is this workstation called? It seems ideal for experimentation. I'm currently using VS Code and can't test individual lines like you are. Or maybe you can in VS Code, I'm jut new!
At 30:40, the code for athletes that start and end with the same letter throws an error. Can anyone suggest me the correct solution? I tried str.extract but I can't include the na=False since it's throwing an error. Wrong code - bios[bios["name"].str.contains(r'^(.).*\1$', na=False)] Correct code -??
I just double checked and I see that a warning pops up (but it's not actually an error). You can ignore the warning. That being said, you might not see results because the names start with an uppercase letter & end with a lowercase letter. You can fix this by passing case=False into.your str.contains() method (see below) Correct code: start_end_same = bios[bios['name'].str.contains(r'^(.).*\1$', na=False, case=False)]
@@KeithGalli Hey thanks for the correction! One more thing I wanted to mention. At 48:22 when the result pops up, I can still see the rows whose height_cm is 'NaN', the height_category is showing to be 'Tall'. So I tweaked your code a little bit: Existing code: bios['height_category'] = bios['height_cm'].apply(lambda x: 'Short' if x < 165 else ('Average' if x < 185 else 'Tall')) New code: bios['height_category'] = bios['height_cm'].apply(lambda x: 'Short' if x < 165 else ('Average' if x < 185 else 'Tall' if x >= 185 else 'NA')) This will show those rows whose height_cm have no information(NaN), the corresponding height_category to be 'NA'. Similarly, this issue occurs again at 50:29. My intent is not to pinpoint your mistakes but just to educate anyone who's a newbie to Python! Love your work always!
use metioned in 15:00 minutes of the video, press control and enter for changing sample that is random . i tried it in my pycharm but it did not work what should i do for this ?
When I said ctrl + enter, within a Jupyter notebook that just re-runs my current code cell thus producing a new sample row from the dataframe. In your pycharm editor you should be able to just re-run your code and if you print out the sample, you'll see it change.
Back when the first iteration was released i was in college having no idea about what a dataframe is now I'm a developer and still watching your videos. Thanks Keith for being a part of my learning journey❤
I'm totally new to Python and learn the basics from the Corey Schafer. And now moved here to learn Pandas. I'm on right track? My goal is to become data engineer and then data scientist.
Your last pandas tutorial helped save me hours and hours of work. Don't ever forget that you are AWESOME!!!!
People wait for new episodes on Netflix but legends wait for Keith's new tutorials 😎
Bro's content is still the best out there after 5 years
Strongly resonating with another comment here
I recall watching your tutorials in my first year of college. I just graduated recently and became research software engineer. Your videos have been pivotal for all the stuff I've done :)
Awesome stuff! Congrats on the new role. Keep up the good work 😎
@@KeithGalli Thank You!!
Absolutely amazing! A hint: make a Python Pandas Advanced Tutorial more focused on graphics.
Would love to have a follow up video on seaborn from this guy with these same csv files shown. the parquet and excel files do not seem to want to copy paste from the browser when you select raw
No way- I just watched your other video on this the other day! Crazy!
this is honestly the best tutorials i have ever seen, really looking forward to new learnings
Did anyone notice how our keith has been sneaking a quick peek to the right at the beginning in the last few videos? 😂 Seriously though, loving the content!"
It's an honour to me to be among the first viewers of this excellent tutorial!
I really appreciate your efforts , you are delivering such a best content related to python and its libraries. I saw your first dataset cleaning with pandas and i truly loved your live tutorial . Please come with more real word pandas dataset cleaning live tutorials which helps junior developer lime me a lot. Once again Thanks for sharing this type of content
00:01 Complete Python Pandas Data Science Tutorial
02:12 Setting up virtual environment for data science project
07:03 Exploring DataFrame Functions
09:26 Learn how to load CSV files in pandas
14:12 Accessing and filtering data in Pandas
16:31 Understanding data slicing and indexing in Pandas
21:08 Accessing and manipulating data in Pandas
23:18 Iterating through rows in Pandas can be done but may affect performance.
27:45 Advanced conditional filtering based on string operations
29:59 Filtering data using regular expressions in pandas
34:18 Adding and removing columns in Pandas data frame
36:34 Using inplace parameter in Pandas for modifying data in place
41:22 Extracting specific data fields from a Pandas dataframe
43:40 Convert date objects to datetime type for easy manipulation
48:14 Custom functions using Lambda for data manipulation
50:40 Merging and concatenating data at scale.
55:24 Data frame manipulation for filtering and combining data.
57:52 Merging data frames and handling null values
1:02:26 Handling missing data using pandas dropna method
1:04:44 Analyzing Olympic athlete data using Pandas in Python
1:09:03 Pivot tables convert data into a useful format.
1:11:32 Analyzing Popular Birthdates of Olympic Athletes in Python Pandas
1:16:54 Ranking heights of individuals using Python Pandas
1:19:16 Utilizing rolling functions in Pandas for cumulative sums and other calculations
1:24:22 Using specific data types in Pandas like string types within Pi Arrow can optimize performance at scale.
1:27:00 Using Pandas to filter and pivot data in Python
1:32:11 Explore Olympics dataset and pandas functionalities
1:33:52 Wrap up and thank viewers for watching
Crafted by Merlin AI.
Awesome video, right speed and comprehensive. My thanks to you for taking the time to do this - am sure it was hours and hours of work and I truly appreciate your effort
Hey Keith! Big fan of your work! Keep it going brother!
Great tutorial. You keep teaching new things all the time with practicao examples and speak just the exact amount not to make it boring. Good job. I wait for sklearn, np, matplotlib, sns, streamlit tutorials 😂
Thanks for uploading new video about Pandas. I learn a lot from you. Can't wait to watch your next videos 🤩
Good stuff man! Keep up the good work!
I always love your content because of the ease of understanding ❤
I've been hearing alot of the polars library but there's limited content on it. Please if possible do something on it
Great you're back!
Keith, your tutorial is a game-changer!
Your content is top-notch. Can't wait for more!
❤️ from 🇵🇰
Well done Keith . Please do more videos about Data Analysis .
Awesome content as always 🔥🔥🔥
Thank you for making this video. Keep rocking!
Hey I love these vids... Keep them coming! Love from Mexico buddy
You are doing a great job. Well-done
This is great content! Please make a similar tutorial or recommend one that relates to using vectorization via Numpy arrays. I have applications that do what I need them to do, but involve nested loops that iterate over millions of rows of data. I really need to move away from these loops to improve execution time.
1:14:00 Yes this is true! I analyzed an Olympic dataset for a college final project, and we used the fact that the plurality of NHL players are born in Jan-March to pitch our analysis proposal.
Cool to hear that you have validated this with data! 💯
i have been waiting for this. thank you teacher
You are very welcome!
Just WOW. Great tutorial!
Glad you liked it!!
Well-done man
Hey , Thanks for the amazing tutorial.
nice 2:00 pm course for me thanks alot
good its the update of the old video . Excellent!!!
Köszönöm a videót.
Amazing video!
Me parece increible como hay gente capaz de almacenar tanta informacion, muchas gracias por el video!!
Thanks. While watching your introduction, I start to wonder if you're going to do a video on NumPy, especially when a major version has released. No hurry, please take your time. Thanks in advance.
Good suggestion. I need to do some more research into the new release, but an updated NumPy video is definitely a possibility!
@@KeithGalli dude this video was exactly what i was looking for as someone relatively new to python trying to get into data science. NumPy and Seaborn would be good follow up videos if you used the same data. The CSV files seemed to copy paste well from the browse but the parquet and excel did not want to and made me load as a .txt at that point i just crossed my fingers hoping you would use the csv and 20 mins in so far you have great video so far. excellent focus on detail great beginner level examples and functions...tried datawars and datacamp before coming here...thank you truely..
@@NewsChannel-y4g Happy to hear that!! Yeah I think that because Excel & Parquet files aren't human readable in their raw form, it doesn't let you copy & paste the URL in the same way as CSV. It is a good test to be able to read those files though, so I recommend that you try downloading them (there's a download raw file button on Github) and then reading them in locally with your code. You'll probably want to move the files from your downloads folder to the same location as your notebook file and then you should be able to load it in with a command like pd.read_excel('./olympics-data.xlsx') & pd.read_parquet('./results.parquet') respectively. That being said, I plan to continue using CSV files in most of my videos so you should be fine with the method you have been using. Not sure if I'll use the same data, but I hope to do some videos that incorporate NumPy & Seaborn in the not-so-distant future. Keep up the good work!
Tysm
Brilliant, thank you.
how to split dataframe for example i want dataframe for every sport or country great video :)
Bro I got a job after your first video!
That's awesome!! Nice work 💪
Thanks Man ❤
very helpful, thx K.
Love from Madagascar
pointers 38:16
continue from filtering data 24:12
Awesome!
Muy bueno, gracias por todo!
Very good, thank you for everything!
¡Por supuesto! Estoy feliz que te gustó 🙂
Hey Keith, it was a nice promo. From Bangladesh 🇧🇩
Glad you liked the promo!!
🔥 Thanks!
Amazing!
🤠
awesome video
Beast mode
Very Nice Mr. Galli. Can u pl do one in polars too???
wake up babe new keith drop
Lolzzzz
Great work bro !! where do you live in boston. I am from boston too
Could you please tell me how to teach pandas after this course, what topics should be covered and what's the best way to teach that?
bro axis 0 is horizontal frame and axis 1 is vertical frames but the function works when applied vertically by using axis 1 which is weird but thats how it works i guess
dude just woke up to gave another PANDA
😎
Thank you sir.
You are very welcome!
I was asked in an interview - how to import multiple files at once in pandas instead of importing files one by one if there are so many files. Is there a quicker way? how to accomplish that in pandas?
Cool!!!!
😎
Great video keith.......:) bye
Hi keith please make a video on pyspark
OMG beside this great video, you really are the young version of Christian Bale man hhh
Haha I'll take it 😎
Thanks boss
Can you release polars course?
Please upload an end to end machine learning project
I recommend checking out this video:
ruclips.net/video/MeyVptCRubI/видео.htmlsi=RqO--khHDJdNRI0a
A real-world project (an actual consulting project of mine) that you can follow along with that uses LLMs.
would like to refresh numpy too with you
What did he say we should click to get copilot to come out please? I am using windows
1:16:15 - This was 20% increase, not 120% increase.
Good catch. It was 120% of the previous day, which is a 20% increase :).
Vote for Numpy guies ❤❤❤❤
you're telling me this isn't kung fu panda 4? bummer
haha I wish
Can you do a course on langchain?
Not a dedicated course, but here's a video I did using Langchain in a real-world project:
ruclips.net/video/MeyVptCRubI/видео.htmlsi=CeGcaKvG6eSAbGpg
@@KeithGalli Thank you for responding. I did go through this and it was pretty cool. Just wanted to know if you might consider a full tutorial on langchains and LLMs.
Also, love your channel.
what is this workstation called? It seems ideal for experimentation. I'm currently using VS Code and can't test individual lines like you are. Or maybe you can in VS Code, I'm jut new!
Make sure you use the ".ipynb" file extension and then in VSCode you will need to install the "Jupyter" extension. Hope this helps you get set up!
why i couldn't download data raw file..kindly say? thats why im stuck...
Not feeling longterm compatibility chat LOL
from where can i download the csv file for practice?
Can anybody help me with practicing pandas . Like is there any website like w3school where I can practice the code
At 30:40, the code for athletes that start and end with the same letter throws an error. Can anyone suggest me the correct solution? I tried str.extract but I can't include the na=False since it's throwing an error.
Wrong code - bios[bios["name"].str.contains(r'^(.).*\1$', na=False)]
Correct code -??
I just double checked and I see that a warning pops up (but it's not actually an error). You can ignore the warning. That being said, you might not see results because the names start with an uppercase letter & end with a lowercase letter. You can fix this by passing case=False into.your str.contains() method (see below)
Correct code:
start_end_same = bios[bios['name'].str.contains(r'^(.).*\1$', na=False, case=False)]
@@KeithGalli Hey thanks for the correction! One more thing I wanted to mention. At 48:22 when the result pops up, I can still see the rows whose height_cm is 'NaN', the height_category is showing to be 'Tall'. So I tweaked your code a little bit:
Existing code: bios['height_category'] = bios['height_cm'].apply(lambda x: 'Short' if x < 165 else ('Average' if x < 185 else 'Tall'))
New code: bios['height_category'] = bios['height_cm'].apply(lambda x: 'Short' if x < 165 else ('Average' if x < 185 else 'Tall' if x >= 185 else 'NA'))
This will show those rows whose height_cm have no information(NaN), the corresponding height_category to be 'NA'.
Similarly, this issue occurs again at 50:29.
My intent is not to pinpoint your mistakes but just to educate anyone who's a newbie to Python!
Love your work always!
use metioned in 15:00 minutes of the video, press control and enter for changing sample that is random . i tried it in my pycharm but it did not work what should i do for this ?
When I said ctrl + enter, within a Jupyter notebook that just re-runs my current code cell thus producing a new sample row from the dataframe. In your pycharm editor you should be able to just re-run your code and if you print out the sample, you'll see it change.
@@KeithGallii am glad u repiled thank u .
46:44 where is the chear sheet did anyone know
I got you! here is the cheat sheet: strftime.org/ (this link can also be found in the video description)
@@KeithGalli thanks
First
🎉🎉
bro be like what does na = false do in chat gpt not here to troll just thought it was intresting
WHAT IS WITH THE SPEEDING OF VIDEOS...?!
I'm not sure if I understand the question, what are you seeing?
Age difference in 5 years😂
🎉🎉🎉🎉🎉 come on
why not using pokemon data this time 🥲