In this article, I will offer an opinionated perspective on how to best use the Pandas library for data analysis. My objective is to argue that only a small subset of the library is sufficient to complete nearly all of the data analysis tasks that one will encounter. This minimally sufficient subset of the library will benefit both beginners and professionals using Pandas. Not everyone will agree with the suggestions I lay forward, but they are how...

In this challenge, you will recreate the Tesla Cybertruck unveiled last week using matplotlib. All challenges are available to be completed in your browser in a Jupyter Notebook now thanks to Binder (mybinder.org).

Challenge

Use matplotlib to recreate the Tesla Cybertruck image above.

Extra Challenge

Add animation so that it drives off the screen.

I’m still working on this challenge myself. My current recreation is below:

 

Become a pandas...

This post presents a solution to Dunder Data Challenge #5 — Keeping Values Within the Interquartile Range.

All challenges may be worked in a Jupyter Notebook right now thanks to Binder (mybinder.org).

Solution

We begin by finding the first and third quartiles of each stock using the quantile method. This is an aggregation which returns a single value for each column by default. Set the first parameter, q to a float between 0 and 1 to...

Selecting subsets of data in pandas is not a trivial task as there are numerous ways to do the same thing. Different pandas users select data in different ways, so these options can be overwhelming. I wrote a long frou-part series on it to clarify how its done. For instance, take a look at the following options for selecting a single column of data (assuming it’s the first column):

• df[‘colname’]
• df[[‘colname’]]
• df.colname
• df.loc[:,...

In this challenge, you are given a table of closing stock prices for 10 different stocks with data going back as far as 1999. For each stock, calculate the interquartile range (IQR). Return a DataFrame that satisfies the following conditions:

• Keep values as they are if they are within the IQR
• For values lower than the first quartile, make them equal equal to the exact value of the first quartile
• For values higher than the third quartile, make them equal equal to the exact value of the...

In this post, I detail the solution to Dunder Data Challenge #4 — Finding the Date of the Largest Percentage Stock Price Drop.

Solution

To begin, we need to find the percentage drop for each stock for each day. pandas has a built-in method for this called pct_change. By default, it finds the percentage change between the current value and the one immediately above it. Like most DataFrame methods, it treats each column independently from the others.

If we call it on our current...

In this challenge, you are given a table of closing stock prices for 10 different stocks with data going back as far as 1999. For each stock, find the date where it had its largest one-day percentage loss.

Begin working this challenge now in a Jupyter Notebook with Binder

Begin working this challenge now in a Jupyter Notebook thanks to Binder (mybinder.org). The data is found in the stocks10.csv file with the ticker symbol as a column name. The Dunder Data Challenges Github...

In this tutorial, we will cover an efficient and straightforward method for finding the percentage of missing values in a Pandas DataFrame. This tutorial is available as a video on YouTube.

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Non-intuitive Solution

The final solution to this problem is not quite intuitive for most people when they first encounter it. We will slowly build up to it...