Python Pandas For Your Grandpa Wanna learn Pandas? I did. And I consolidated everything I learned into a 43 videos spanning roughly three hours of content, including 23 lecture videos (~2 hrs) and 20 challenge videos (~1 hr). Course Curriculum Introduction 1.1 Introduction Series 2.1 Series Creation 2.2 Series Basic Indexing 2.3 Series Basic Operations 2.4 Series Boolean Indexing 2.5 Series Missing Values 2.6 Series Vectorization 2.7 Series apply()
Introduction Hey, thanks for checking out my course - Python Pandas for your Grandpa, so easy your grandpa could learn it! Things To Know. I’m using Pandas version 1.20.0 If you’re using a later version, it probably doesn’t matter. (Most of what I teach is unlikely to break for at least a few years). I’m using Google Colab as my IDE. You don’t need to use Google Colab, but if you want to, it’s a really awesome, free way to run Python directly inside your browser with essentially zero maintenance and zero hassle setting things up.
Pandas is a vast library of data wrangling tools, but all those tools are centered around two fundamental data structures: Series and DataFrame. If you imagine a table of data, you can think of each column as a Series and the structured collection of every column as a DataFrame. If you know NumPy, you might be wondering, what’s the difference between a Series and a Numpy 1-d array? After all, they both represent a 1-dimensional set of values.
Setup Given, two Series bees and knees, if the ith value of bees is NaN, double the ith value inside knees. import numpy as np import pandas as pd bees = pd.Series([True, True, False, np.nan, True, False, True, np.nan]) print(bees) ## 0 True ## 1 True ## 2 False ## 3 NaN ## 4 True ## 5 False ## 6 True ## 7 NaN ## dtype: object knees = pd.
Setup After accidentally leaving an ice chest of fish and shrimp in your car for a week while you were on vacation, you’re now in the market for a new vehicle. Your insurance didn’t cover the loss, so you want to make sure you get a good deal on your new car. Given a Series of car asking_prices and another Series of car fair_prices, determine which cars for sale are a good deal.
Setup You suspect your local grocery’s been price gouging the ground beef. So, you and some friends decide to track the price of ground beef every day for 10 days. You’ve compiled the data into a Series called beef_prices, whose index represents the day of each recording. Determine which day had the biggest price increase from the prior day. (Note that the index of beef_prices represents the day tracked, and it’s not in order.