thank u saw much for this playlist especially the first two videos "data exploration & data cleaning ", u saved so much time for me and i learned alot .
Thanks for sharing. Missing values is really a critical problem in data engineering which needs to be addressed as early as possible in the analysis chain.
the biggest plot twist of this year is at 2:32 .... for all this time i thought isnull() will show all null value(inclu. Na,Nan,none, etc) 👏👏 that's a useful information
Hey Misra, I am loving and enjoying to learn from you. The way you help understand concepts, I adore it. Please keep doing your best and smile! P.S. I upgraded my Streamlit apps to a great deal after looking at your small yet powerful videos. All thanks to "Session State", because of which I found you. Cheers :)
Hi Misra I am trying to write a code which gives missing values feature name and missing value count in form of dict, but mt code is not working. Kindly help. a = df.isnull().sum() miss = {} for i in range(len(a)): if a.values[i]>0: miss = {a.index[i] : a.values[i]} print(a.index[i])
This is the most irrelevant video on missing values. You didn't even talk about the categories of missing values mcar,mnar,mar . Neither u talked about mean mode median,ffill, bfill or drop or fillna...interpolate is another thing you did talk about..just money minded speaking about your course in end...so sad😢
