CategoricalDtypes used in Filtering Data

CategoricalDtypes in Pandas


CategoricalDtypes can also be used for filtering.  For example, if we have low, high, and medium survey responses, we can use CategoricalDtypes to filter all responses less than or equal to medium. There are three basic steps!

  1. Create the CategoricalDtype.
  2. Apply the CategoricalDtype to the pandas Series.
  3. Filter the data.
It is just that simple.  Let's look at an example.


When we print the filtered_df, we can see we have our desired output. 


It is just that simple!

Link to Google Colab with the code!



Comments

Post a Comment

Popular posts from this blog

Blog Topics

Image
This platform aims to systematically curate and preserve my favored Python techniques for future reference. This digital journal, accessible globally wherever an internet connection is available, is a convenient and effective learning tool.  Going down a wooded path at night, alone, with your trusted companion by your side, can be scary and daunting.  However, with support from your friends, documenting your path (curating a code base like a blog), and continuously working on the code base can make the process appear as a walk in the park!  In a world dominated by sophisticated language models like OpenAI's GPT, blogging may seem like a relic of the past. But here's a nuanced perspective: this blog serves as an innovative instrument for both teaching and mastering new programming languages. It's about immersing in syntax, applying it, and then translating the information into layman's terms for broader understanding. Each topic presented here is elaborated in a separate
Read more

Drawing Tables with ReportLab: A Comprehensive Example

Image
Tables play an integral part in visualizing and summarizing complex data in a clear, readable format. ReportLab offers a powerful object called `Table` for rendering data in table form within a PDF. By pairing this with `TableStyle`, users can customize tables to fit any styling requirements. Let's break down an example to understand the power and flexibility of ReportLab's table creation capabilities. The first step is to know your table and cast it to a list of list.  I didn't know how to do this off the top of my head, so some quick searching gave me the results! It is a common task for ReportLab users so there are many example out there.  Here is my example:  #Start working with the table budgetinlist = [budget.columns.tolist()] + budget.values.tolist() # Format numbers with commas for i in range(1, len(budgetinlist)):     for j in range(1, len(budgetinlist[i])):         try:             budgetinlist[i][j] = "{:,.0f}".format(float(budgetinlist[i][j]))        
Read more

DataFrame groupby agg style bar

Image
 The goal of the article is to investigate the bar function through the style method of Pandas DataFrame.  So, when we work with DataFrames, we can create a visual within a DataFrame.  What does that mean? We can embed bar charts, sparklines, and mini bar charts in the DataFrame.  This can reduce the amount of cognitive load when reviewing a DataFrame.   Google Colab link with all the code To get started, we are going to import the data: import pandas as pd import numpy as np import pandas_profiling as pp loc = 'https://raw.githubusercontent.com/aew5044/Python---Public/main/movie.csv' m = pd.read_csv(loc) pd.set_option('display.max_columns',None) pd.options.display.min_rows = 10 Next, I want to create a new DataFrame that groups by the rating (e.g., "R" "PG-13"), then calculates the total sum, min, max, and total observations for gross.  content = ( m .groupby('content_rating') .agg({'gross':['sum','min&#
Read more