Aggregation in Pandas DataFrame in Python | Aggregating each of the variables separately
If you have already learned to make the descriptive statistical summary of DataFrame then the time comes to understand how can you use the statistical aggregation functions like:- sum, min, max, mean, average, product, median, standard deviation, variance, argmin, argmax, percentile, cumprod, cumsum, and corrcoef separatly on each of the variable or on a group of variables independently, and that's where we bring you this video to learn the technique in simplified and step by step way.
This video covers the following:
00:00 - Introduction
00:16 - Recap of Descriptive statistical summary video
01:09 - Understanding the need of different type of aggregation for each of the variable separately
02:06 - Understanding DataFrame aggregation function syntax and its arguments
05:24 - How to use one aggregation function on all of the variables in a DataFrame
06:06 - How to use multiple different aggregation functions on all of the variables in a DataFrame
06:49 - How to use multiple aggregation functions on one or multiple variables in a DataFrame
08:54 - How to use one or more aggregation function/s on one or more variables separately/individually
The list of aggregation functions which you can use from numpy are:
sum, min, max, mean, average, product, median, standard deviation, variance, argmin, argmax, percentile, cumprod, cumsum, and corrcoef
If you missed to watch the descriptive statistical summary video, don't worry, you can still watch that using below link:
• Descriptive Statistical Summary Using...
You can find the data used in the video, look for the file supermarket_sales_data.xlsx at below link:
https://github.com/LEARNEREA/Matplotlib
You can find the script created in this video at below link:
https://github.com/LEARNEREA/Matplotl...
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