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datagy
Canada
เข้าร่วมเมื่อ 29 มี.ค. 2020
I'm Nik and I teach Python and other data analysis tools such as Excel, SQL, and PowerBI here on my channel and over at my website at datagy.io.
Check out my comprehensive guide to getting started with Python for data science here: datagy.io/introduction-to-python-for-data-science/
Check out my comprehensive guide to getting started with Python for data science here: datagy.io/introduction-to-python-for-data-science/
Polars (but FASTER!) - Using Nvidia cuDF with Polars for INSANE speeds!
Nvidia just dropped their new cuDF Polars integration for massive speed savings! With a 10GB+ file, you can expect 13x speed improvements!
To install this version of Polars, run the following command:
pip install polars[gpu] --extra-index-url=pypi.nvidia.com
Let me know if you'd like to see more Polars content in the comments below!
0:00 Introduction
1:08 Downloading data
3:15 Getting size of data
4:18 Running simple analysis
5:45 Running a groupby in Polars
6:55 Interoperability with CPU DataFrames
To install this version of Polars, run the following command:
pip install polars[gpu] --extra-index-url=pypi.nvidia.com
Let me know if you'd like to see more Polars content in the comments below!
0:00 Introduction
1:08 Downloading data
3:15 Getting size of data
4:18 Running simple analysis
5:45 Running a groupby in Polars
6:55 Interoperability with CPU DataFrames
มุมมอง: 192
วีดีโอ
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Calculate Correlation in Python and Create a Correlation Matrix in Seaborn!
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Create AMAZING Maps with Python! (GeoPandas Tutorial for Choropleth Maps)
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Learn how to create random and fake data in Python, including how to create mock datasets with Python, random, faker, and Pandas! 0:00 Generate Random Values in Python 4:46 Generate Normal (Gaussian) Distribution in Python 6:52 Choosing Random Values in Python 8:56 Reproducing Random Values in Python 10:07 Generate a Mock Dataset in Python 13:20 Using Faker in Python to Generate Fake Data ✅ Fol...
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Learn how to use the powerful Python filter() Function to filter lists, tuples, dictionaries, and more! Want a written a tutorial instead? Check out the tutorial here: datagy.io/python-filter/ 0:00 Introduction 0:13 Python filter() Function 1:20 Filtering Lists with a For Loop 2:45 Filtering Lists with the Filter() Function 5:37 Using Python Filter() With Lambda Functions 6:42 Practical Example...
Python Map Function
มุมมอง 5K2 ปีที่แล้ว
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Automate Excel Reporting with Python! Combine Workbooks and Add Charts with Pandas and Openpyxl
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Python Collections Library OrderedDict - Intermediate Python Tutorial
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thanks a lot!
very useful. tnx
Im getting additional information in while finding unique values for a column in dataset how to fix this
I am new to matplotlib and this video helped a lot with my very first project. Thank you datagy for making programming less scary! :) One question though, how can I rotate the text on the x axis?
you absolutely slayed with this tutorial!
Thanks so much!!
my dict is the defaultdict
Ver nice tutorial i have been looking for that!!!
very helpful, thank you
If you getting error while executing the line df.corr() it is probably due to the new version of python in this we need to give only numeric value so instead execute the below line df.corr(numeric_only=True)
Thank you ......😊😊😊 you are Godsent
how do you do it
i still didn't understand bins like what are bins??
Each bin is a range of values, say 0-5 or 6-10. If you have a value of 1, it’ll fall into that first bin and get counted once. The histogram then shows how many records fall into each range of values (the bins).
how to fix it in app.py? Please give me instrustion File "d:\Python\WeatherApp_Flask\app.py", line 6, in <module> @app.route('/', methods) ^^^^^^^ NameError: name 'methods' is not defined
There are SO many, quite frankly TOO many instructional videos, and they are tedious, too long, and unclear, but YOURS is EXACTLY on point
Thank you so much! I really appreciate it.
How do i make a map of a different country?
wow am subscribe right now
Guys he is the best and simplest , best one seriously , just trust and watch till the end .
Excellent
you're the best!!! thank you
Hey you're good! Learnt a few good things here
Thanks so much!
@@datagy glad you're still reading. I just discovered you. You dont seem to have posted anything for a year.
@WildRover1964 I hope to do a few videos soon! And hopefully get back into a rhythm with it!
thanks for the tut, it is so useful and finally I made my first project
nice!
Thanks for your Video ! Can you please let me know if we can put those collapse and expand functionality in actually generated pivot table in excel using python ??
thanks
Challenge solution: df.iloc[df.ShareWomen.argmax(), 0:8] Returns all column values for the highest share of women
Or write a simple function: a = df.iloc[df.ShareWomen.argmax(),3] b = df.iloc[df.ShareWomen.argmax(),7] print(f" Category: {a} , Women share: {b}")
len(df) doesn't work. TypeError: 'DataFrame' object is not callable
Nevermind. It worked when I quit Jupyter notebook and reloaded the notebook.
Can you define normalize separately for rows and columns, say I want numbers in the columns total and percentages in the row totals?
HI, what if you want to create a similar function with takes 2 values? such that the 2 values will be 2 columns rather than using one column. The def function to take 2 columns instead of one, then use apply function
how to label select points using data?
This styling passes into csv and excel files?
Unfortunately not! For that, you can use other libraries like openpyxl :)
Great content, thank you for this contribution!
Thank you so much!
hi, great work, explaining subject so easily to all, wondaful work, keep it up, could you explain OHLCV data, apply, map functions, user defined functions to to find the trend on the High, Low columns, to get buy signals and sell signals by not using any price lagging indicators.
Hi, you are doing awesomely work, explaining subject so easily to all, wondaful work, keep it up, could you explain OHLCV data, apply, map functions, user defined functions to to find the trend on the High, Low columns, to get buy signals and sell signals by not using any price lagging indicators.
This is exactly what ive been struggling with for these past couple of weeks.Im teaching myself data analytics and this video is making things so much better
Thanks so much!!
7:18 ... The population growth rate math has a mistake. It should be (pop 21 - pop 20)) / pop 20. Currently it devides by pop 21.
Succinct and super effective. Thank you so much
Thanks so much!
Amazing Stuff , was looking for the same since some days and you are a true star, Thank you so much
Thanks so much!!
Can we set clean transitions on this jinja data returns??
This is awesome, very detailed explanation. How can we bring the city lines inside the map?
Great video! Take a look at my Pandas tutorial if you want.
I think you should divide by population of 2020 to measure the variation. Still very instructive
Thank you for making this video. I finally understand how to do groupby. I have a question related to “agg”. Why did you apply numpy with aggregation functions?
Great question! That was just to show that you can use different functions inside of agg. You can even use custom ones!
Great presntation. I am building a trading bot using algorythm that deals with conditional columns (candlestick lengths of previous columbs) This is the video I was looking for. Thanks for the vid. Very clear and informative. Exactly what I was looking for.
It's nice to know the basics, but as an experienced user I got tired of typing the same things over and over... So I built the open source buckaroo data table so you never have to look up pd.set_option again. Take a look, it has sortable columns, summary stats, histograms, and a performant table built in. It works in Jupyter with pandas and polars. Try it with "pip install buckaroo" then import buckaroo #buckaroo becomes the default display method for dataframes in Jupyter
Great video! Thank you!
I really did enjoy this video and tutorial.. I will keep looking for the video I meant to learn in this channel afterward. THANK YOU VERY MUCH
still confused why this video is having so less likes, you have cleared the map function in a short and much clear video , nice work bro
Thanks so much!!
I was banging my head trying to figure out how to make a correlation matrix and heatmap with plotly...your solution was much simpler and worked perfectly for me. You're a God send, thank you!!
Great tutorial, thanks
Thanks so much!
The Toronto ON and Canada values are hard coded hence this code doesn't work for any other cities
Ah shoot! Thanks for pointing that out!
@@datagy how to fix that?
this is awesome very interesting thanks for teaching🙂🙂
Thanks so much!!