Data Cleaning after Identifying Data Problems in Pandas

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This is my course Hands-on Data Science which was released back in 2020 to help aspiring data scientists learn how the end-to-end process of a data science project works. It has been a paid course since it was published but now I believe it is time for it to be publicly accessible.
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00:00 Shallow copy vs Deep Copy
04:13 Correcting data types

[@abdullahhanif4541]

i find your tutorials very easy and understandable however i have one simple question. can you please explain how statistics is important in data science like how it is useful in anyway possible. can you please make a video on it and explain it using dataset

[@ajithdevadiga9939]

Regarding the Payment_type attribute As mentioned in the the data dictionary the payment code options are
A numeric code signifying how the passenger paid for the trip.
1= Credit card
2= Cash
3= No charge
4= Dispute
5= Unknown
6= Voided trip
but the data in the paraquet files have payment codes as [1, 2, 0, 4, 3 ] (even seen in the the old files now updated to paraquet format)
could not understand what the payment code 0 refers to ?
I am doing analysis for yellow_taxi_jan_2024 (january 2024) data
could observe that after payement_type 1 (credit card ) and 2 (cash)
could see high ocurrence of payment_type 0 (around 140K)
is this something to think about or the relevant meta data is not updated in the website ?
if anyone is working on this data kindly share your thoughts regarding this matter,
would appreciate a response
thanks :-)