Learn Pandas in 20 minutes!
Вставка
- Опубліковано 27 вер 2024
- ►► All you need to know about Pandas in one place! Download my Pandas Cheat Sheet (free) - misraturp.gumr...
In this video we go through the fundamentals of Pandas and see some common functions that I, as a data scientist, use very often.
The dataset: www.kaggle.com...
Some things we learn are:
* difference between a dataframe and Series objects in Pandas
* how to filter dataframes
* how to combine multiple datasets
* working with Strings/text data
* plotting data
* groupby, merge, concat, value_counts, sort_values, head, plot, describe and much more!
👇Learn how to complete your first real-world data science project
Hands-on Data Science: Complete Your First Portfolio Project
www.misraturp....
7:21 ~ Negation
9:10 column name change
10:31 reset index
12:30 lowercase letter
15:14 concatenate dataframes
15:36 merge dataframes
Thank you!
hello, I am new to python and pandas, just finished my elementary course, and it got me mad. But actually, with you, this video is straightforward, simple, easy, and fun. thanks a lot
Glad I could help!
I am a #450 liker. I really appreciate what you are doing. Many thanks dear.
You are helping a master degree data analyst from Iraq :)
That's great to hear, thank you Ahmed!
You're amazing. I have an interview on data manipulation. Your tutorial is on point. Thank you Misra.
Awesome to hear!
00:02 Pandas is fun and useful, learning the fundamentals and common functions.
01:58 Learn how to write data from a Pandas data frame to a CSV file.
04:04 Pandas can return Series or DataFrame objects based on the data requested.
06:07 Understanding and applying conditions in Pandas dataframes
08:09 Adding new columns and renaming columns in Pandas
10:20 Resetting index and sorting values in Pandas
12:22 Convert names to lowercase and assign to dataframe
14:13 Concatenate data frames using Pandas.
16:12 Merge and aggregation in Pandas
18:14 Pandas provides value counts and group by functions for data exploration and cleaning.
20:09 Pandas makes plotting simple but may require customization for better visuals
22:07 Histograms are useful for detecting outliers and data issues.
Very Nice and Simple Explanation. Could be used as a quick reference for beginners. 👍👍👍👍
Thanks...very crisp and clear!!!!
sabaha kadar anlat dinleyelim Mısra Reyissss
you are a blessing from above ? Great video !!!!
great explanation. calm and relaxing. you have a new sub!
Welcome!
Thank you Misra great content it helps me a lot in my FYP
Great to hear!
Amazing videos! Keep up the great work!
Thanks!
Thanks! Most usefull for me. New subscriber and awaiting for more. The best!
You're welcome and thank you!
perfect!
thanks a lot.
very helpful, Thanks :)
Nicely explain mam... Thanks!!!
Superb ur explanation is awesome!! I will surely buy ur course.
Thank you Syed! I'm happy to hear it was useful. And can't wait to welcome you to the course. :)
you look exactly like my prof it is like watching her on youtube hhhhhh thank you so much for the information.
Haha that's a funny coincidence! You are welcome!
What do you do for a living? Are you a full time teacher?
Thank you very much Misra for the great content!
My pleasure!
Great video!!!
Thanks!
Hey, nice video. Can you make a real case data analysis and show us how the analysis impact in a decision take? 🙏
awesome stuff
thanks!
also make video on confidence interval in pandas.
Thank you!
some functions are not working in my code. the .sort_values(by='whatever i put there) is not working. any advice??
Awesome job!
Great overview of Pandas Misra! All the important functions in 20 mins.
Thank you! I'm glad you liked it. :)
cool intro!
Thanks!
Awesome 👌👌👌
Thanks 🤗
thanks for the video. Was very helpful :)
You're very welcome :)
Never stop smiling. You are so sweet 💗
Thank you
Great content! If you want take a look at my Pandas tutorial as well.
Great! thank you Misra
You are very welcome :)
Thanks a lot this is so nice!
You're very welcome!
Thank You...Thank You.... Thank You... Thank Youuuuuuuuuuuuuuuu
You are most welcome :))
Very useful
Thanks!
Great video :)
Thanks!
Thanks, 👍👍👍
You're welcome! :)
I don't get it why did you use +1 in here [ "Parents/children Abroard"] +1]
+1 is the person who the row is about. Parents/children Aboard is that person's parents and children. So to get the total number of people a row represents we have to add 1 to that number.
Like it!
Thank you! Cheers!
Misra, where can I connect with you Ma'am?
Hi Avi, if you have any questions, I can answer them here. :)
Misra please make series or maybe single video covering sql required for data analyst and scientist role so that we know we can land a job surely.It will be greatly helpful for aspiring data science students.If possible Nosql
Hey Avi, thanks for the suggestions. I've noted them down for future video ideas. :)
amazing channel & a beautiful girl !
Who else thinks she moves so fast?😂
All you need to know about Pandas in one place! Download my Pandas Cheat Sheet (free) - misraturp.gumroad.com/l/pandascs
I have given my mail info, but, did not receive any cheat sheet.
Hey @@pavanasopa250, have you checked your spam folder? Because I see that the mail went out to you in my system.
How can I use the code to create a new column?
df['new_column']= df[(df['age']>30) & (df['fare']
Truly amazing tutorials ! You are so great, clear and listenable. Thanks for all your work 🙏 Keep on going 🌞
Thank you Jonathan! :)
Thanks Misra, this is really great video to learn Pandas
You're very welcome :)
very clear explaination
Thank you!
Thank you so much Misra, very much helpful.
How do you create a data frame with label columns after using the value_counts method? For instance, after applying the value_counts method to a column, how do you recreate it into a data frame with two columns(one with records of the original column and the other with the count values eg "names" and "count" columns)
You are very welcome Grivine. :) To create a dataframe out of the value_counts() results, just wrap them into a dataframe creator. It'd look like this:
import pandas as pd
pd.DataFrame(my_df["random_column"].value_counts())
But you'd have to update the column names because as you'll see they'll not be accurate.
@@misraturp I have implemented as follows:
df=pd.DataFrame(innovation_data["Preferred_Chnl_Sch"].value_counts())
df.columns = ['pref', 'count']
df.head()
I get an error, " ValueError: Length mismatch: Expected axis has 1 elements, new values have 2 elements "
I think it is because the dataframe created only has a single column and not two so renaming with the .columns method doesn't recognize one parameter.
How do you create two columns from a single column with the value_counts method?
Ah, I think I know why. You should add .reset_index() at the end of your first line.
Because the resulting dataframe of the first line has 1 index and 1 column. When you use reset_index, it sets the index as a new column and creates a new index, thus you will have 1 index and 2 columns.
This will make it possible for you to rename the columns.