4 new time-saving tricks in pandas
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In the last 20 months, the pandas library has been updated 10 times, introducing hundreds of new features, bug fixes, and API changes. In this video, I’ll show you 4 new pandas tricks that will make your life easier!
1:18 Create a datetime column from a DataFrame
4:24 Create a category column during file reading
7:45 Convert the data type of multiple columns at once
9:48 Apply multiple aggregations on a Series or DataFrame
13:14 Bonus: Download the official pandas cheat sheet
== RELATED VIDEOS ==
My entire pandas video series: https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y
Working with dates and times: https://www.youtube.com/watch?v=yCgJGsg0Xa4&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=25
Using the category data type: https://www.youtube.com/watch?v=wDYDYGyN_cw&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=21
Changing data types: https://www.youtube.com/watch?v=V0AWyzVMf54&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=13
Using groupby: https://www.youtube.com/watch?v=qy0fDqoMJx8&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=14
== OTHER RESOURCES ==
Jupyter notebook on GitHub: https://github.com/justmarkham/pandas-videos
pandas release notes: http://pandas.pydata.org/pandas-docs/stable/whatsnew.html
pandas cheat sheet: https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf
== JOIN DATA SCHOOL ==
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Newsletter: http://www.dataschool.io/subscribe/
Blog: http://www.dataschool.io/
Patreon: https://www.patreon.com/dataschool
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NEW FOR 2019: Learn my top 25 pandas tricks! https://www.youtube.com/watch?v=RlIiVeig3hc
Hi, please can you please make a video on how one can remove or drop a number of columns with similar names from a dataframe.
If a video won't be possible, pwould you please send me through mail.
Thanks, your videos have been of great help.
Email: olakunlebalogun247@gmail.com
Amazing!You are great
can you please make couple of vedios of real world data analysis problem using pandas?
At 9:30, tried using inplace=True instead of assignment. No warning, yet the drinks' columns' dtypes were still int64 instead of float64.
drinks.astype({'beer_servings':'float','spirit_servings':'float'},inplace=True)
ver. 0.24.2
Hi Kevin,
Thanks for the wonderful vedio… I
have question regarding your Tip 3. i have tried below code snippet; but unfortunately Column Continent is not converting to Category data type. why there is strange behavior. it is converting as expected in case of beer_servings to Float
drinks.astype({'beer_servings':'float'},{'continent':'category'}).dtypes
Output:
country object
beer_servings float64
spirit_servings int64
wine_servings int64
total_litres_of_pure_alcohol float64
continent object
dtype: object
great lesson, thank you!
Thank you. I imagine you can do number 3 during the load as well: drinks = pd.read_csv('http://bit.ly/drinksbycountry', dtype={'beer_servings':'float', 'spirit_servings':'float'})
Is there a systematic way for pandas to determine which dataframe column may be helpful to convert into categories, or will that generally be contextual?
Thank you, Kevin. You are great! But honestly, I like your other style which you are writing codes during your videos. it is a lot more helpful.
fantastic! Thank you very much indeed for your time!
thank you Kevin! These are great videos that I learned a lot from. I have a question, why does pd.to_datetime(df[['month','day','year']]) has DOUBLE [[]] rather than SINGLE []??? I tried using ['month','day','year'], pandas gave me a error and I can't figure out what it meant.
how to convert data type of object to int? Do not know why my data shows numbers as object data type. Thank you!
Thank you Kevin, your videos are amazing. i got confident and interested to learn pandas and practice it.
Thanks Kevin ,
Please create a video for Vlookup operations in Pandas
Thank you for your video,very happy to see you again.
Pls update more often
welcome back to here, Love to see your video!
How to create datetime columns during csv file reading? Thanks for the video!
Woooohooo you are back : D
Thanks for your content, amazing as always.
Thanks a lot , Kevin…
If you enjoyed this video, you should definitely check out my other new video, "5 new changes in pandas you need to know about": https://www.youtube.com/watch?v=te5JrSCW-LY&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=33
Thanks for watching! 🙂
Another useful video Thanks
Thanks for your awesome videos. For me it is always the go to for the 'How was that again?'
moments;)
Thanks a ton! Your videos have helped me a lot.
Welcome back! finally got some new videos to feed myself 😛
It's been a long time see you again Kevin. I am happy to see you again!
Hello Kevin. Love the video series. I am wondering, many videos are microscopic views of pandas. I am wondering if you can do a video series that is more macroscopic. For example: 1) How to read multiple csv files from one folder and iterate over them some function/program and then shooting it out. 2) Common problems to debug in pandas like when a csv has characters that pandas can't read. These big picture problems I find to be the most troublesome.
Your last trick is cool but talking about df.describe() I have used pandas-profiling package and it is awesome and gives you more insight. Good for EDA.
Thanks for sharing these tricks
Thank you, Kevin.
Best regards!
Welcome back 🙂