In this Kaggle Session, we covered the usage of pandas, a nice python package… import pandas as pd df=pd.read_csv('gdrive/My Drive/data.csv') Done! How should I reduce the computing time in pandas on Kaggle? DS3 at UCSD starts holding Kaggle Sessions! Follow our Data Science Student Society Page!Stay up-to-date with DS3 Online Content’s publications! Right click on the file that you need to import and select çopy path. It would be nice to have you there!In this Kaggle Session, we covered the usage of pandas, a nice python package for data analysis.
We use cookies to improve your experience. ).Polo hasn’t been practiced olympically since 1936, and the same goes for Aeronautics. I’m guessing we all expected the same sport to come up on top and, unsurprisingly, it did. Garima Jain Garima Jain.
(Just for additional reference it is titanic data from Kaggle which is here.)
Ask Question Asked 1 month ago. I also wish to see which ones have been deprecated.The following snippet will be generally useful any time we need to see when something arose for the first time, especially if we want to see an abnormal increase in a variable.The graph shows us how many sports were practiced in the Olympics for the first time for each year. Or, in other words, how many sports were introduced each year:So even though a lot of sports where there before 1910, and most where introduced before 1920, there have been many relatively new introductions. Tug-of-war practitioners, Basketball players and Rugby players are all heavy.It’s quite interesting to see there’s so much variation in Basketball and Rugby players, going from 59 to 156 kg, whereas most tug of war players are over 80 kilos.Then I just plotted the mean weight for each sport, and found that it followed a normal distribution:The height has a similar, normal distribution, but its variance is a lot smaller, being highly concentrated in the mean:Next I set out to graph all individual means, in an ordered scatter plot, to see whether there were any outliers.In fact, the ‘heaviest’ sport is quite the outlier with respect to the rest of the graph. However, the plot reveals an even bigger difference between ‘outliers’ and people near the mean. Check out our schedule and topics for this Winter Quarter!Now it is our solution notebook for this Kaggle Session!Interested in more?
Feel free to join us! Then import as usual in pandas, using this copied path. Specifically, the year. It would be nice to have you there! As a follow up, I’m thinking of training a small Machine Learning model to predict an athlete’s sex based on the sport, weight and height columns, tell me what model you’d use!And if you feel anything in this article was not properly explained, or is simply wrong, please also let me know, as I’m learning from these as well!If you wish to go deeper into Statistical Analysis with Python, I highly recommend this I am sorry that this post was not useful for you! To do this, we used Python’s Pandas framework on a Jupyter Notebook for Statistical Analysis and Data Processing, and the Seaborn Framework for visualiation. When doing Statistical Analysis, curiosity and intuition are two of a Data Scientist’s most powerful tools. Keep in mind however, the same code would work for either by just switching the ‘Sex’ filter.As you can see, if I group by sport I can take the min, max and average weight and height for each sport’s players.I then looked at the top 5 heaviest sports, and found this (in kilograms):Not too unexpected, right? Looking at the data, I see there were many new sports introduced in 1936, and afterwards they were always brought in small (less than five sports) sets.An analogous analysis for deprecated sports (where max year is not recent) shows this list of sports, most of which I’ve never heard of (though that’s by no means a good metric of whether a sport is popular!
That’s all for today, folks! Machine Learning Tutorials, and Data-Driven Rambling I'd need to send requests to login. Hello everybody! This time Alpine skiing comes up as the least one. This is accentuated by the fact that most people do not really deviate a lot from it.For the lightest sports, the results can be obtained using the previously generated variable, The results (omitting the heaviest ones, since we already saw those) are the following:As you can see, Gymnastics athletes, even the male ones, are by far the lightest players! In the two previous Kaggle tutorials, you learned all about how to get your data in a form to build your first machine learning model, using Exploratory Data Analysis and baseline machine learning models.Next, you successfully managed to build your first machine learning model, a decision tree classifier.You submitted all these models to Kaggle and interpreted their accuracy. I've been trying different methods to import the SpaceX missions csv file on Kaggle directly into a pandas DataFrame, without any success. submission = pandas.DataFrame({“PassengerId”: titanic[“PassengerId”], “Survived”: predictions}).astype(int) submission.to_csv(‘Kaggle.csv’) In the last post, I had to go into my google drive and manually delete the index column (like a chump). For this post, I will use data from the Quora Insincere Question Classification on Kaggle, and we need to create some numerical features like length, the number of punctuations, etc.
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