Python: Pandas Tutorial | Intro to DataFrames


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Intro tutorial on how to use Python Pandas DataFrames (unfold sheet) library. Intro to statistical knowledge evaluation and data science utilizing array operations.

RELATED VIDEOS
► Numpy Intro: https://youtu.be/8Mpc9ukltVA
► Numpy Intro Jupyter nb: https://youtu.be/AAS8yoKuK7M
► Pandas Intro: https://youtu.be/e60ItwlZTKM
► Pandas Import Data: https://youtu.be/x2Shyoif3ls
► Pandas Selecting & Filtering: https://youtu.be/DCE6t3vNfvM
► Pandas Time Series: https://youtu.be/QQy_zD-LE-4
► Pandas and MatPlotLib: https://youtu.be/ALX88JzeQnk
► Matplotlib Intro: https://youtu.be/MbKrSmoMads

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► Code: https://bit.ly/Pandas-nb
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► Thank me on Patreon: https://www.patreon.com/joeyajames

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Comment List

  • Joe James
    November 10, 2020

    Such a nice video! If all videos could be like that it would be heaven
    Thank you @Joe James

  • Joe James
    November 10, 2020

    thankyou james

  • Joe James
    November 10, 2020

    Gets the point very quick! Awesome tutorial!

  • Joe James
    November 10, 2020

    Too many ads

  • Joe James
    November 10, 2020

    Thanks! So concise 😀

  • Joe James
    November 10, 2020

    If we have 4 columns like [week, fruits, vegetables, flowers], how to create a dataframe like df['fruits'] where week==36

  • Joe James
    November 10, 2020

    Fantastic intro. Thank you very much!

  • Joe James
    November 10, 2020

    Excellent! Thank you.

  • Joe James
    November 10, 2020

    good job

  • Joe James
    November 10, 2020

    Hey Joe, thanks for the vid.
    Just a question. I need to delete complete rows out of a dataframe depending on a filename which is a part of the dataframe (the df contains informations about specific files).
    I got a for iteration over a list which tells me the filenames of the rows I need to clear. How can I track those rows and how can I delete those? Any ideas or helpful links?
    Have a great day!

  • Joe James
    November 10, 2020

    wtf will dislike thus video?

  • Joe James
    November 10, 2020

    This is one heck of presentation tat can understandable for freshers easily

  • Joe James
    November 10, 2020

    Detailed and comprehensive explanation about pandas for beginners.
    Thank you James!

  • Joe James
    November 10, 2020

    can I get the link of data that you are using?

  • Joe James
    November 10, 2020

    This has been SO helpful, thank you for putting so much thought into breaking it all down to the basics without assuming previous knowledge for us beginners!

  • Joe James
    November 10, 2020

    Is there any built-in function to remove the duplicates of rows in the df?

  • Joe James
    November 10, 2020

    Great video! thanks for sharing!

  • Joe James
    November 10, 2020

    Hi Joe. Thanks for the excellent intro to PANDAS
    .
    One point though. There appears to be a bug introduced to your code (https://github.com/joeyajames/Python/blob/master/Pandas/pandas_weather.py) on GIT Hub:

    # 11. iterate a df
    header("11. iterate rows of df with a for loop")
    for index, row in df.iterrows():
    print (index, row["month"], row["avg_high"])

    This should read:

    # 11. iterate a df
    header("11. iterate rows of df with a for loop")
    for index, row in df.iterrows():

    print (index, row["month"], row["av_hi"])

    The version as is, causes a KeyError exception.

    It's as though the column name change in example 10 isn't taken into account?

  • Joe James
    November 10, 2020

    Thanks Joe. As usual excellent tutorial.

  • Joe James
    November 10, 2020

    super easy tutorial should watch

  • Joe James
    November 10, 2020

    Awesome

  • Joe James
    November 10, 2020

    One question i do have, when you've imported the text file, how does the pd.read_csv know where to put all the rows and columns? does it just have to read it exactly from the text file? Meaning, your text file is pre formatted that way? For example, if i just gave a text file with random data and imported it, what would happen?

  • Joe James
    November 10, 2020

    Great concise video. Preparation does one well.

  • Joe James
    November 10, 2020

    What an awesome explanation, Thanks for this.

  • Joe James
    November 10, 2020

    I heard that pandas eat people with two first names.

  • Joe James
    November 10, 2020

    Exactly what I was looking for. Thanks, Joe! 😀

  • Joe James
    November 10, 2020

    Very well presented, easy to follow.

    Thank you very much Mr.Joe James.

  • Joe James
    November 10, 2020

    @12:54 Columns 0 and 3 (not 0 and 3) (because method called is df.iloc[3:5, [0, 3]] 🙂

  • Joe James
    November 10, 2020

    Excellent video. Thank you for your time and expertise. 5/5

  • Joe James
    November 10, 2020

    Very nice, thank you alot!

  • Joe James
    November 10, 2020

    Hey guys! For the last example (19:17 – Write to csv) you can easily choose the separator, and not just accept the default one (coma ","). This is achieved by using the "sep" option:

    df.to_csv("my_csv_file", sep=";")

  • Joe James
    November 10, 2020

    very nice

  • Joe James
    November 10, 2020

    To remind myself how stupid I really am, I will try to do complex conditional matching with Pandas.

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