How do I change the data type of a pandas Series?

Have you ever tried to do math with a pandas Series that you thought was numeric, but it turned out that your numbers were stored as strings? In this video, I’ll demonstrate two different ways to change the data type of a Series so that you can fix incorrect data types. I’ll also show you the easiest way to convert a boolean Series to integers, which is useful for creating dummy/indicator variables for machine learning.

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GitHub repository for the series:
“astype” documentation:



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  1. Starting in pandas version 0.19, you can change the data type of multiple columns at once! Learn how to do it here:

  2. Thank you for these videos….the explanation is top notch.

    While converting the item_price to a float (code line 12) did return an average of 7.46, item_price still shows up as object (ran .dtypes command). This is not what i expected. Any reason why this is the case?

  3. Sir,when we replace elements from a series, operation is done but still the dataframe is same ie I can't see the changes on CSV files? I guess it needs a variable to Store the operations?

  4. thanks for such a clear explanation

  5. When i want to read the csv file with sep (;) and convert the 'Height' to float from the start… i try this … dsv = pd.read_csv(('c:/users/dsv/desktop/LungCapData.csv',sep';'), dtype={'Height':float}) … but …Error.

  6. Kristin Kuo 太hardcore

  7. When am converting objective data type to integer

    There is error showing base 10:. '100,00'

    Why so???Plz help me solve this issue

  8. How to change a float column containing NaN values to int column by keeping NaN values as it is?

  9. well done. thank you man

  10. u did not show the dataframe after doing the astype

  11. How can we convert the data type of item_price inplace(as in the original dataset)? I did not find inplace argument in astype method . Please help me on this.

  12. if the column contain non numeric data and if i need to convert them to 0 or NaN and the rest to float, how should I do. In this case the data may not have a certain character like $, instead say there are non valid numbers.
    ex. "10:00", "5.6.7", "> 50"

  13. Firstly, thank you for your videos, they are very helpful. Secondly, I have a question. I haven't watched all your videos yet, so, maybe you have an answer somewhere already (if yes, could you please share a link). So, my question is how to convert floats to integers. I was given a task to carry out the analysis of gun ownership data, and the number of guns is always shown as a float, which is okay, you can still conduct numerical operations, but logically, you don't need the number of guns to be shown as floats, because you cannot own 1.5 or 4.75 guns, right? So, the dataframe just visually does not seem nice with all unnecessary .0 's. I was trying to use .astype(int), but for some reasons was getting errors, maybe I'm doing something wrong? Please advise.

  14. How can i convert non-numeric data to numerical data. can you make a video regarding this.

  15. hello just gone through the video.Its really very helpful. Ty soo for this help
    well i am facing an issue with this particular code.



    its showing error, i want to permanently change the item_price column. can someone pls help?

  16. Just what I needed! Great explanation and visuals! Subscribed!

  17. i really like your videos and plaease help me out on this
    ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). am using Jupyter Notebook

  18. Hi can someone help .I am getting this error "ValueError: Cannot convert non-finite values (NA or inf) to integer
    " while using this code inp0['age'] = inp0.age.astype(int). The dtype if float for the age column.

  19. How to convert object type into int?

  20. Hi everyone. I HAVE A GREAT EXERCISE FOR PANDAS LEARNERS. HOW CAN I GET ONLY CHICKEN BOWL FROM ITEM_NAME AND WITH THE PRICE ONLY ABOVE $10 FROM ITEM_PRICE. by the way this exercise uses order-csv file data which is shown in previous 12th video tutorial. Thank u so much. Please do not forget to write the answer below

  21. Very useful video, helps me out of many bugs, thanks for sharing.

  22. If I show you a CSV would you recommend me some tips to convert it into INT from Object DTYPE ?

  23. thanks for the video. but i have a question, in some cases as type() didn't work.we need to use pd.to_numeric to change the data type of any series.could you brief us about this. where do i use as type to change the data type and where do i use pd.to_numeric to change the data type.

  24. I tried to convert date to float type. It looks values are changed.
    For eg:-31st may changes to 1.45
    Am I on the right path

  25. when to use dtype and when dtypes?

    drinks = pd.read_csv('',dtype = {'beer_servings': float})

    in this code we use d type in first row while in 2nd row its dtypes.
    its bit confusing

  26. Can you make some videos about regular expression in python? That's so hard and I can't find any good video to teach it.

  27. Learning a lot from your videos. Thank you so much.

  28. Please post a video on cleaning the columns of a csv file. Like removing $ or translate 19K into a common format. Thanks

  29. hey! how did you accessing png image. ![ ](name.png) it's not going to work with me.

  30. Hello
    i have dataset in which one numeric column is in string so how can we change it to integer as i'm trying to do it but facing below error ValueError: could not convert string to float: '#VALUE!' or this ValueError: invalid literal for int() with base 10: '#VALUE! …..

  31. Great Videos! So when to use Dictionary over List in Pandas? I am trying to read .xls file after extracting from zip file, and then commit the data to a Database. What would be the best way to do such an event? Any help would be of great help!

  32. Hi, Thank you very much for your time and an efforts to produce easy to follow videos. I have a question: how do I convert values in a series/column from decimal number to binary 16 bits ? and could I apply this conversion for the entire dataframe e.g. for all columns in the data set conver the decimal values into 16 bit binary ?

    Could I still use astype ? Many thanks in advance.

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