Hope you all are all staying safe! In this video we'll be learning a lot about working with dates and time-series data in Pandas, and also look at doing some basic plotting. In the next video, we'll be learning how to load in data to Pandas from different (Excel, JSON, SQL, etc). Let me know if there is anything else you'd like me to cover in the Pandas series. I will likely be taking a break from this series after the next two videos are released just so I can focus on some different topics.
Hey Corey Will you be able to cover some machine learning libraries like scikit learn,tensorflow,...? Because matplotlib and pandas that you have covered so far is related to machine learning and we can put this libraries into practical
@@kaaaaasraa49563 I'll do my best. It's a very large topic to cover, so I've been a bit intimidated to tackle such a large course, but I know many people are wanting that, so I'll do my best to start working on something soon
Hey Corey, i was wondering if you could help do a complete EDA on one dataset ,kaggle or from anywhere in which you could cover the entire operations that we've learnt in one complete tutorial video with like a real world dataset. That could be like the end of this series video or something.
3:50 convert to datetime using to_datetime 7:45 parse dates while loading data from csv 11:20 using day_name on whole series using dt class 12:20 create column of dayname 13:20 min and max methods on datetime series 15:00 filtering by dates as string 17:20 filter by to_datetime 18:40 set date as index 19:21 filter data by just passing the date in brackets 20:05 using a slice to get specific dates data 21:00 calculating average of a slice(timeframe) 22:05 getting max value of a column on a given day 23:50 resampling(breaking down by days) a whole column into a new variable 27:00 plotting with matplotlib in pandas 28:28 resampling df with multiple columns 30:55 using agg to apply different function on different columns while resampling
For all those getting the following error: *FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.* Just do this: *from datetime import datetime* and change the *pd.datetime.strptime()* to: *datetime.srtptime()* and it should work the same.
I used the following import datetime as dparser d_parser = lambda fn : dparser.datetime.strptime(fn, '%Y-%m-%d %I-%p') this also worked. is there any advantage of your method over mine ?
@@mrrohitnayak1 its the same thing, u just named datetime as dparser, His code would call datetime.datetime.strptime() and yours call dparser.datetime.strptime()
While there are already other excellent pandas tutorials on TH-cam your videos IMHO stand out for their carefully crafted examples, clarity of explanation, information density and relevance to practical applications. I'd really love this series to be continued and cover some of the more advanced topics.
For anyone getting error in the first part of the video, this is what worked for me d_parser = lambda x: pd.to_datetime(x, format='%Y-%m-%d %I-%p') df = pd.read_csv('data/ETH_1h.csv', parse_dates=['Date'], date_parser=d_parser)
@@rafabws for dfx.loc['2020-01':'2020-02'] , I get KeyError: 'Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is not allowed.; What to do with it? plz help
@@user-testo21347 I was getting same error too and I believe I found the solution. For some reason our indexes are not in order. For this reason, if we sort our indexes by writing the code dfx.sort_index(inplace=True) before writing the code dfx.loc['2020-01':'2020-02'], we get rid of the error.
@@user-testo21347 I was getting same error too and I believe I solved it. For some reason our indexes are not in order. For this reason, if we sort our indexes by writing the code dfx.sort_index(inplace=True) before writing the code dfx.loc['2020-01':'2020-02'], we get rid of the error.
To anyone getting the message that 'date_parser' is deprecated and will be removed in a future version... You no longer need to pass in a function, just the date string formats in the argument date_format='%Y-%m-%d %I-%p' example: df = pd.read_csv('ETH_1h.csv', parse_dates=['Date'], date_format='%Y-%m-%d %I-%p')
Hi Corey, thank you so much for taking the time and putting this series together. This is one of the most structured series on panda on the web. I feel confident in pandas after watching the series. We would love to see something similar on the NumPy. Please, Corey...put together a Numpy series for us.
The concept of resample is so damn crazy!!! I struggle so much in other systems with this kind of aggregation and it's easy like nuts in pandas... Corey, you are great!
if you get this error (KeyError: 'Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is not allowed.') try sorting your data before slicing. df = df.sort_index()
THank you so much. After this, can you slice one day of data? like at 22:44 df["2020-01-01"]? I got this error: KeyError: '2020-01-01'. I also tried df.loc["2020-01-01"], same error.
I want to point out something. I like your videos. There is nothing boring about them. But I do not have much time so I watch them at 2x. Your normal talking speed is not slow but very clear so I can easily understand you at 2x too.
Gave a thumbs up before even started watching the tutorial..and I'm not disappointed at all. Keep up the good work Corey!!! BTW you are the only TH-camr getting likes from me.
Thanks a lot for this whole series Corey!! I've watched 3/4 other tutorials between youtube and other platforms and yours were the best by far, especially due to the fact that I'm from Italy so it was easier to follow your explanations step by step I can't wait for your plotting with Pandas tutorials too, those are gonna be pretty useful! Have a nice day a stay safe ;)
Hi Corey..I have referred to various resources to learn Pandas properly on TH-cam. And your tutorials are the best I could find. And looking forward to learn more from your matplotlib tutorials. Thanks for creating top class content!!
I loved the point Corey made about how much easier it is to use date as your index, however while following along I got a warning this warning: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.
Also to get the mean for resampling, the default for the mean function was changed. So to get the weekly resampling at around 30:05 of the video: df.resample('W').mean() will give an error do this instead: df.resample('W').mean(numeric_only=True)
Thank you Corey for the wonderful tutorials. No paid courses can come close to the way you present the depth of the concept. Thank you for making it free and a big thank you for keeping up the good work.
Please show more *errors and its corrections* for future vids. The fixes are really beneficial! Thanks for recording, editing, uploading and sharing! 👍
20:32 if you are getting error here then you first have to sort the index and use loc: # Sort the index df = df.sort_index() # Select data between January and February of 2020 df.loc['2020-01':'2020-02']
Hi Corey! Your series on pandas is really informative and helpful. Can you make a similar series for numpy? I believe that's a topic that will be of interest to many of your viewers.
After using d_pars = lambda x: datetime.strptime(x, '%Y-%m-%d %I-%p') i got this warning: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead. I mean, i still get correct output , but if u wanna remove this warning just do: from datetime import datetime then d_pars = lambda x: datetime.strptime(x, '%Y-%m-%d %I-%p'). Treat it like curiosity or sth Btw, great videos, sir!
Your every video is soo nice and perfect. The explanation is very friendly and in a proper sequence. Your channel works for me as a one-stop-shop. Thank you soo much man.❤️❤️ Love from India 🇮🇳
All I can say you are awesome. The way you explained 😍✌️✌️✌️✌️. You have the best play list for pandas on web. Your efforts are truly appreciated 👍👍👍👍👍👍
great job Corey, I was going to go through Wes McKinney's book but looking at the pandas section I realized that I already know most of it by now form your series. Pls do the data plotting soon! ;)
Well done! I didn't know that I can pass in a dict in agg. Always passed in a list and then selected the column-aggregation tuples that I needed, which is quite resource intensive and hard to read. Also I did not know I could make datetime comparisons to a string. Thank you.
As usual your videos are great ! You explain things very clearly. Please start videos on Machine Learning and Artificial Intelligence soon. Earnest request !
For those getting an error for accessing the datetime index, you need to sort it first now. So instead of df['2020'] make it df.sort_index()['2020'] and the same for slicing it
I got the same message... use this instead: import datetime d_parser = (lambda x: datetime.datetime.strptime(x,'%Y-%m-%d %I-%p') ) df = pd.read_csv('./data/ETH_1h.csv', parse_dates=['Date'], date_parser=d_parser)
Yes, get the same. it works like this: import datetime df = pd.read_csv('data/ETH_1h.csv', parse_dates=['Date'], date_parser=lambda x: datetime.datetime.strptime(x, '%Y-%m-%d %I-%p'))
I got an error with the lambda function trying to parse the dates while loading from csv. The error stated pandas no longer supports pd.datetime. After checking the comments I tried @larc99's suggestion but still got an error as it was expecting a string rather than a function for the format. What gave the same output and I think is much simpler (without the lambda function) turned out to be: df = pd.read_csv('data/ETH_1h.csv', parse_dates=['Date'], date_format='%Y-%m-%d %I-%p')
Fantastic series! Thank you for providing this awesome content. I have been using Pandas quite a bit lately to process data, one thing I am struggling with and would really find helpful would be a video on taking these Pandas notebooks and functions and converting them into actual Python apps that can be reused on other projects. Pandas can really be like magic sometimes and being able to pass data into a Python app that could apply these functions would be extremely powerful, I have yet to find any good tutorials or reading on this subject, one from the master would be greatly appreciated!!
How to solve error 'Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is not allowed.' while using df['2020-01': '2020-02'] at timestamp 20 :17
Values the time of viewers. Not sure if you really speak so fast or just increase speed in the first and last few mins of video. I just like the way it goes. Thanks for the great tutorial.
Let me echo what plenty of others have said -- your videos are exceptionally well done and very understandable!! I knew virtually nothing about Python about two months ago and now I can do plenty of rudimentary, but useful stuff with it. In working through this tutorial with data that I've exported from ThingSpeak, I get to the point of having pandas successfully (apparently) converting the str by using: df['created_at'] = pd.to_datetime(df['created_at']). I get no errors and it seems to recognize the datetime group automatically without providing formatting schema. However, when I try to prove that it's now a datetime via the .day_name() method it throws an error and tells me my attribute is a datetime.datetime object and doesn't allow me to use that method. I get the same results if I convert the created_at column via the read_csv() method at the initial import. From forum searches I'm concluding that it got converted differently, than in your example, into a datetime object. I don't totally understand what that means, but if I try different methods on those objects, I can manage to arrive at the same destination, which is to return the day of the week for a given date. I used this strftime() method: print(datetime.strftime(df.loc[20001,'created_at'],'%A')) IDK if you're still responding to comments on a 2 yo vid, but in case others ran into this problem, that's how I solved it. I'd be great to have a video on the datetime library (module ?) and how that's different and how to best use it. Maybe you've already done that, so I'll look for that next in your extensive list of videos, but if not, just a suggestion. Again - thanks for all the amazing education efforts!!! cheers
lol -- and of course I now see a tutorial on this: Python Tutorial: Datetime Module - How to work with Dates, Times, Timedeltas, and Timezones. checking that out next!! 🤓
date_parser didnt work for me... instead I used the date_format, like this: df = pd.read_csv('ETH_1h.csv', parse_dates=['Date'], date_format='%Y-%m-%d %I-%p')
Fantastic series on Pandas. I have no words to express how i am thankful to you for this series. You are a great teacher. I am yet to check on the 11th video on this series and up next to the Matplotlib series (definetely). You are awesome. A request, could you please plan to do Numpy series as well.
I like all the videos u upload and u are the reason I started development and became fond of python. Can u please make some videos on how to write unit testing in python?
Hope you all are all staying safe! In this video we'll be learning a lot about working with dates and time-series data in Pandas, and also look at doing some basic plotting. In the next video, we'll be learning how to load in data to Pandas from different (Excel, JSON, SQL, etc). Let me know if there is anything else you'd like me to cover in the Pandas series. I will likely be taking a break from this series after the next two videos are released just so I can focus on some different topics.
Hey Corey
Will you be able to cover some machine learning libraries like scikit learn,tensorflow,...?
Because matplotlib and pandas that you have covered so far is related to machine learning and we can put this libraries into practical
@@kaaaaasraa49563 I'll do my best. It's a very large topic to cover, so I've been a bit intimidated to tackle such a large course, but I know many people are wanting that, so I'll do my best to start working on something soon
Hey guys checkout dfcleaner package on pypi
Hey Corey, i was wondering if you could help do a complete EDA on one dataset ,kaggle or from anywhere in which you could cover the entire operations that we've learnt in one complete tutorial video with like a real world dataset. That could be like the end of this series video or something.
Please help with numpy
I bless the day I came across your channel
3:50 convert to datetime using to_datetime
7:45 parse dates while loading data from csv
11:20 using day_name on whole series using dt class
12:20 create column of dayname
13:20 min and max methods on datetime series
15:00 filtering by dates as string
17:20 filter by to_datetime
18:40 set date as index
19:21 filter data by just passing the date in brackets
20:05 using a slice to get specific dates data
21:00 calculating average of a slice(timeframe)
22:05 getting max value of a column on a given day
23:50 resampling(breaking down by days) a whole column into a new variable
27:00 plotting with matplotlib in pandas
28:28 resampling df with multiple columns
30:55 using agg to apply different function on different columns while resampling
Would it be possible for you to tell me how can I download ETH_1h.csv file? I found just row data on bit.ly/Pandas-10. Thanks in advance.
😊
Wow! Thk you very much! I appreciated a lot!
For all those getting the following error: *FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.*
Just do this:
*from datetime import datetime*
and change the *pd.datetime.strptime()* to:
*datetime.srtptime()*
and it should work the same.
I used the following
import datetime as dparser
d_parser = lambda fn : dparser.datetime.strptime(fn, '%Y-%m-%d %I-%p')
this also worked. is there any advantage of your method over mine ?
@@mrrohitnayak1 its the same thing, u just named datetime as dparser, His code would call datetime.datetime.strptime() and yours call dparser.datetime.strptime()
@@alenjose3903 any advantage in using his method over mine or vice versa
@@mrrohitnayak1 no its the same, u can name it anything you want.
Consider changing 'datetime.srtptime()' to 'datetime.strptime()'
While there are already other excellent pandas tutorials on TH-cam your videos IMHO stand out for their carefully crafted examples, clarity of explanation, information density and relevance to practical applications.
I'd really love this series to be continued and cover some of the more advanced topics.
For anyone getting error in the first part of the video, this is what worked for me
d_parser = lambda x: pd.to_datetime(x, format='%Y-%m-%d %I-%p')
df = pd.read_csv('data/ETH_1h.csv', parse_dates=['Date'], date_parser=d_parser)
or pass the date format directly
df = pd.read_csv("data/ETH_1h.csv",parse_dates=['Date'],date_format = "%Y-%m-%d %I-%p")
@@rafabws for dfx.loc['2020-01':'2020-02'] , I get KeyError: 'Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is not allowed.; What to do with it? plz help
@@user-testo21347 I was getting same error too and I believe I found the solution. For some reason our indexes are not in order. For this reason, if we sort our indexes by writing the code dfx.sort_index(inplace=True) before writing the code dfx.loc['2020-01':'2020-02'], we get rid of the error.
@@user-testo21347 I was getting same error too and I believe I solved it. For some reason our indexes are not in order. For this reason, if we sort our indexes by writing the code dfx.sort_index(inplace=True) before writing the code dfx.loc['2020-01':'2020-02'], we get rid of the error.
@@user-testo21347 I too got this error. if you have solution pls share it with me
To anyone getting the message that 'date_parser' is deprecated and will be removed in a future version...
You no longer need to pass in a function, just the date string formats in the argument date_format='%Y-%m-%d %I-%p'
example:
df = pd.read_csv('ETH_1h.csv', parse_dates=['Date'], date_format='%Y-%m-%d %I-%p')
Hi Corey, thank you so much for taking the time and putting this series together. This is one of the most structured series on panda on the web. I feel confident in pandas after watching the series. We would love to see something similar on the NumPy. Please, Corey...put together a Numpy series for us.
This is the only channel I can’t wait for the new video to drop. Two weeks and waiting but who’s counting. ☺️
The concept of resample is so damn crazy!!! I struggle so much in other systems with this kind of aggregation and it's easy like nuts in pandas...
Corey, you are great!
Yesterday i was searching for any tutorial that explains dates and times in pandas and i couldn't find a good material
Thanks man
These are the most developed, best explained videos on how to use pandas - thank you Corey for putting these together!
Since I stumbled on Corey Schafer videos, it has become my favourite TH-cam Channel
this whole series is so beautifully explained that if one is little bit aware of python and its syntax whole series can be watched without volume .
if you get this error (KeyError: 'Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is not allowed.') try sorting your data before slicing.
df = df.sort_index()
THank you so much. After this, can you slice one day of data? like at 22:44 df["2020-01-01"]? I got this error: KeyError: '2020-01-01'. I also tried df.loc["2020-01-01"], same error.
Thanks brother 👍
@@looklook6075 same here
@@looklook6075 same here
@@looklook6075 try df["2020-01-01:2020-01-01"]
I want to point out something. I like your videos. There is nothing boring about them. But I do not have much time so I watch them at 2x. Your normal talking speed is not slow but very clear so I can easily understand you at 2x too.
Very, very, very good. The accurate and straightforward explanations are supported by great examples. Big thanks.
My man Corey on youtube dropping bangers.
Thanks man. Amazing content.
Thank you sir. This is the best explanation out of all the videos I've watched!
There should be a mega-like button for this. Thank you so much!
Gave a thumbs up before even started watching the tutorial..and I'm not disappointed at all. Keep up the good work Corey!!!
BTW you are the only TH-camr getting likes from me.
Right now I'm learning flask from your playlist and it's freaking amazing!
This is the best episode from the series, well done
Thanks a lot for this whole series Corey!! I've watched 3/4 other tutorials between youtube and other platforms and yours were the best by far, especially due to the fact that I'm from Italy so it was easier to follow your explanations step by step
I can't wait for your plotting with Pandas tutorials too, those are gonna be pretty useful! Have a nice day a stay safe ;)
That's great Corey !!
The best lesson about Time series, ever.
Hi Corey..I have referred to various resources to learn Pandas properly on TH-cam. And your tutorials are the best I could find. And looking forward to learn more from your matplotlib tutorials. Thanks for creating top class content!!
I loved the point Corey made about how much easier it is to use date as your index, however while following along I got a warning this warning:
FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.
Also to get the mean for resampling, the default for the mean function was changed. So to get the weekly resampling at around 30:05 of the video:
df.resample('W').mean() will give an error
do this instead:
df.resample('W').mean(numeric_only=True)
where to download this time series data please
@@sahilmehra290 you can find in his github link
This Video was sent by the Data Science Gods. Thank you so much
I learnt so much from this single video. Thank you Corey
Thank you Corey for the wonderful tutorials. No paid courses can come close to the way you present the depth of the concept. Thank you for making it free and a big thank you for keeping up the good work.
I always give you a thumbs up before i even watch the video!
Will you introduce us to machine learning in the near future?
Another great video Corey. Keep up the good work!!!
I really appreciate your detailed explanation on the errors, which help us to select the right corrective action to take. Love your videos.
I'm happy that I found a Awesome channel to learn Python for Data Science Easily!!!! Very Nice and Crystal Clear Explanation!!👌🙂
Extremely useful. Thanks for the video Corey! You have my gratitude.
Please show more *errors and its corrections* for future vids.
The fixes are really beneficial!
Thanks for recording, editing, uploading and sharing! 👍
OMG!!!
You are awesome Corey.
I learned a lot from your videos!
Best tutorial series for pandas ever❤❤❤
Thank you Sir for sacrificing your precious time to teach the World.
We appreciate you .
l Appreciate You.
I used this video to backtrack over some material that I wasn't sure of. This was a great video as is your other instructional videos. Thanks so much.
20:32 if you are getting error here then you first have to sort the index and use loc:
# Sort the index
df = df.sort_index()
# Select data between January and February of 2020
df.loc['2020-01':'2020-02']
Thanks
You are a great teacher, many thanks for putting in all the effort and time in creating such a great series :)
pandas plotting will be useful and intesting! looking forward to it! Thanks Corey!
Hi Corey! Your series on pandas is really informative and helpful. Can you make a similar series for numpy? I believe that's a topic that will be of interest to many of your viewers.
Great job Corey! Thank you!
You're my savior I was searching for video as this thank you.
I watched this video when the likes for this video are 999 and I hit like right away to make it 1K, because I believe Corey's content!
Corey this series is fantastic, thank you!
You taught me pandas completely free of charge.
Thank you, sir.
After using d_pars = lambda x: datetime.strptime(x, '%Y-%m-%d %I-%p') i got this warning: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead. I mean, i still get correct output , but if u wanna remove this warning just do: from datetime import datetime then d_pars = lambda x: datetime.strptime(x, '%Y-%m-%d %I-%p').
Treat it like curiosity or sth
Btw, great videos, sir!
Your every video is soo nice and perfect. The explanation is very friendly and in a proper sequence. Your channel works for me as a one-stop-shop. Thank you soo much man.❤️❤️
Love from India 🇮🇳
This has been beyond useful! Thank you.
Hei, welcome to my playlists too, Python and R, downloadable source files (see video description)
Thanks Corey!
Your videos are always lit!
😁✌️
I really like how you explain things so clearly. Super helpful for my project. Thank you so much!
All I can say you are awesome. The way you explained 😍✌️✌️✌️✌️. You have the best play list for pandas on web. Your efforts are truly appreciated 👍👍👍👍👍👍
great job Corey, I was going to go through Wes McKinney's book but looking at the pandas section I realized that I already know most of it by now form your series. Pls do the data plotting soon! ;)
What a detailed video !! Thank you man !!! 🤝
Starting at 8:02
You may use this instead
*df = pd.read_csv('data/ETH_1h.csv',parse_dates=['Date'], date_format = '%Y-%m-%d %I-%p')*
Very well-explained. Thanks for this. I am requesting you to make a video on multi index in Pandas.
Great! I love your lectures.
Oh Corey, you legend! Thanks mate, this is some great stuff.
And yet another great lesson. Thanks
You are the best teacher. period
corey man, i feel obligated to say thank you , aaaaaaaaaaaaaaaaaand a big well done sir.
This was an absolute masterpiece!! Thank you so much. I hope you are good and safe
just 11 minutes in the video, i hit the like button as hard as i can !!
Pandas is so easy to work on after seeing this vid.
Wish my coaching teacher taught me this simpler way
Awesome stuff as always
Well done! I didn't know that I can pass in a dict in agg. Always passed in a list and then selected the column-aggregation tuples that I needed, which is quite resource intensive and hard to read. Also I did not know I could make datetime comparisons to a string. Thank you.
Thanks a lot for this pandas series Corey ♥️.
again and again awesome information
Thank you again Corey!!
Loving this series and have learned a lot. The prices of ETH in this video, wow!
டேmn true. $200 vs $4900 at peak and $1700 now
As usual your videos are great ! You explain things very clearly. Please start videos on Machine Learning and Artificial Intelligence soon. Earnest request !
For those getting an error for accessing the datetime index, you need to sort it first now. So instead of df['2020'] make it df.sort_index()['2020'] and the same for slicing it
Yes!, or for accessing the datetime index you can also put df.loc['2020'] but this not work for slicing!
Amazing tutorials man ! thank you so much
Man...you really rock! Thanks a lot for that!
This is so in depth I love it thank you!
:2: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.
I got the same message... use this instead:
import datetime
d_parser = (lambda x: datetime.datetime.strptime(x,'%Y-%m-%d %I-%p') )
df = pd.read_csv('./data/ETH_1h.csv', parse_dates=['Date'], date_parser=d_parser)
Yes, get the same. it works like this: import datetime
df = pd.read_csv('data/ETH_1h.csv', parse_dates=['Date'], date_parser=lambda x: datetime.datetime.strptime(x, '%Y-%m-%d %I-%p'))
Thank you thank you thank you, Finally found the help that i needed!
I got an error with the lambda function trying to parse the dates while loading from csv. The error stated pandas no longer supports pd.datetime. After checking the comments I tried @larc99's suggestion but still got an error as it was expecting a string rather than a function for the format. What gave the same output and I think is much simpler (without the lambda function) turned out to be:
df = pd.read_csv('data/ETH_1h.csv', parse_dates=['Date'], date_format='%Y-%m-%d %I-%p')
Fantastic series! Thank you for providing this awesome content. I have been using Pandas quite a bit lately to process data, one thing I am struggling with and would really find helpful would be a video on taking these Pandas notebooks and functions and converting them into actual Python apps that can be reused on other projects. Pandas can really be like magic sometimes and being able to pass data into a Python app that could apply these functions would be extremely powerful, I have yet to find any good tutorials or reading on this subject, one from the master would be greatly appreciated!!
How to solve error 'Value based partial slicing on non-monotonic DatetimeIndexes with non-existing keys is not allowed.' while using df['2020-01': '2020-02'] at timestamp 20
:17
Values the time of viewers. Not sure if you really speak so fast or just increase speed in the first and last few mins of video.
I just like the way it goes. Thanks for the great tutorial.
32:28 You could have also used method 'first' for open price within agg function
Thanks a lot for these videos.
Exactly what I was thinking.
At 20.08, calling df['2019'] didn't work for me. Gave an 'Assertion Error'. df.loc['2019'] worked though.
Same here !
@@nesrinebelkadi7702 print(df.loc['2019'])
Let me echo what plenty of others have said -- your videos are exceptionally well done and very understandable!! I knew virtually nothing about Python about two months ago and now I can do plenty of rudimentary, but useful stuff with it.
In working through this tutorial with data that I've exported from ThingSpeak, I get to the point of having pandas successfully (apparently) converting the str by using:
df['created_at'] = pd.to_datetime(df['created_at']). I get no errors and it seems to recognize the datetime group automatically without providing formatting schema.
However, when I try to prove that it's now a datetime via the .day_name() method it throws an error and tells me my attribute is a datetime.datetime object and doesn't allow me to use that method.
I get the same results if I convert the created_at column via the read_csv() method at the initial import.
From forum searches I'm concluding that it got converted differently, than in your example, into a datetime object. I don't totally understand what that means, but if I try different methods on those objects, I can manage to arrive at the same destination, which is to return the day of the week for a given date. I used this strftime() method:
print(datetime.strftime(df.loc[20001,'created_at'],'%A'))
IDK if you're still responding to comments on a 2 yo vid, but in case others ran into this problem, that's how I solved it. I'd be great to have a video on the datetime library (module ?) and how that's different and how to best use it. Maybe you've already done that, so I'll look for that next in your extensive list of videos, but if not, just a suggestion.
Again - thanks for all the amazing education efforts!!!
cheers
lol -- and of course I now see a tutorial on this: Python Tutorial: Datetime Module - How to work with Dates, Times, Timedeltas, and Timezones.
checking that out next!! 🤓
date_parser didnt work for me... instead I used the date_format, like this:
df = pd.read_csv('ETH_1h.csv', parse_dates=['Date'], date_format='%Y-%m-%d %I-%p')
thank you Corey for making this video
This was really helpful. Thanks!
Fantastic series on Pandas. I have no words to express how i am thankful to you for this series. You are a great teacher. I am yet to check on the 11th video on this series and up next to the Matplotlib series (definetely). You are awesome. A request, could you please plan to do Numpy series as well.
df = pd.read_csv('ETH.csv', parse_dates=['Date'], date_format="%Y-%m-%d %I-%p")
Always the best, wonderful.
If anybody gets an error at 10:20 , use date_format= '%Y-%m-%d %I-%p' instead of date_parser = d_parser
thanks so much.
thank you
so what is the use of the lambda express on the line above then ??
Hey Corey, Shall we wait for NumPy series as well after this ?
Good example and coverage 👍🙂
Thank you for the detailed explanation
excellent video with details. Thank you
I like all the videos u upload and u are the reason I started development and became fond of python. Can u please make some videos on how to write unit testing in python?
Hey there. Glad you like the videos! I do have one unit testing video you can find on my channel, but I do plan on doing more in the future