Just wanted to say thank you for the incredibly simple yet comprehensive video. I stumbled upon the pivot() method while working through data visualisation, and I wasn't 100% sure how it worked. After this watching this video, everything is much more clearer and understandable to me.
@@SoulSheIS the pivot_table method is a bit more flexible. Essentially, if you have duplicates in your data, the pivot_table method is necessary. Similarly, it allows you to use multiple indices. Historically, the pivot_table method is built on pivot, but was introduced to not break previous functionality. Hope this helps!
Thanks Amri! I'm glad it was helpful! I'm not able to find any formal documentation on this, but you can pass in many different kinds of functions, including numpy's functions.
Hi Parth, Thanks for your comment! I'd say, and there's probably much better ways to do this, but one way you could do this is to filter a string version of the column: df = df[df['colname'].astype('str').str.contains('5555')]
You may not need to pivot anything to create the histogram, as the histogram will create bins for you automatically. I’d suggest checking out my video on histograms: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-m0y5uqU--YQ.html
@@datagy Table3TEST3= PL.pivot_table(values =( 'ID','IN'),index = 'SEGMENT', columns='CUTOFF',dropna= False, aggfunc = {'ID':'count','IN':sum}) I have used dropna=False but still not working files uploaded in this link: drive.google.com/drive/folders/1Pnc4YMjqL0loclfX-5Nxi3KTpxFd1ffm?usp=sharing
You can convert the column to integers by apply the .astype('int') method on the column of the resulting dataframe :). Such as: pivot['col'] = pivot['col'].astype('int')
hello datagy!! two questions, how can I calculate a weighted average on the pivot table?? Second, after results are printed, how can I sort values within a column? thanks for the great videos!!
Hi Ricardo! Thanks so much for your comment! To sort your data, you'd technically want to sort it before printing it (or in the print function). You can sort data. Such as print(df.sort_values(by=['colname'])). I have a video on this topic here: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-41XLTQI1qzI.html To create a weighted average you can follow along with my written tutorial here: datagy.io/pandas-weighted-average/ It'll walk you through a number of different ways in which you can calculate a weighted average (including using Pandas).
thanks for this useful information , i am trying to add pivot on function and add this function using xlwings on excel , the function works good , but i need it come with porders GUI instead off normal cells
Nik, thank you for such a clear explanation of the syntax and all the features! It’s not the first of your videos that I have seen, and each time I am amazed how simply Python might be clarified. Good luck and a lot of inspiration for your new videos!
@@tobecontinued...5712 Ah, you want the values broken out by those categories? So each section lance, termine, provisoire (forgive my lack of accents) would have numeric values beside it?
I normally get my info on youtube videos and go. This video quickly/easily helped me solve something I'd been struggling with! I had a groupby that I couldn't figure out how to transpose. PIVOT is what I wanted! Thanks!
Hi can you pls let me know how to create pivot tables with mode as function, no mean. I looking a way to analyse data by pivoting on object data type with mode function. Kindly do the needful.
Hi there! Thanks so much for your great question! Import mode from scipy.stats using: from scipy.stats import mode Then, in your pivot table, as the aggfunc= use the following: aggfunc=lambda x: x.mode().iat[0] Just tested it and it works well :)
