Data Cleaning after Identifying Data Problems in Pandas

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  • Опубликовано: 2 окт 2024

Комментарии • 2

  • @ajithdevadiga9939
    @ajithdevadiga9939 5 месяцев назад +1

    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 :-)

  • @abdullahhanif4541
    @abdullahhanif4541 2 месяца назад

    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