Fun Friday Code Blog

Understanding Python Code: Data Manipulation with Pandas

Understanding Python Code: Data Manipulation with Pandas

Let's dive into a detailed explanation of the provided Python code which involves manipulating movie data using the Pandas library. This is especially useful for a new Python programmer looking to understand the intricacies of data manipulation.

Code Breakdown:

import pandas as pd
import numpy as np
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 = 5

col = m.columns

redefine = (m[col]
.loc[:,'genres']
.str.split('|', expand=True)
.stack()
.value_counts()
.reset_index()
.rename(columns={'index':'Genre', 0:'Count'})
.style.bar(subset=['Count'], color='lightblue')
.set_caption('Genres')
)
redefine

Step-by-step Analysis:

  1. Importing Libraries: We begin by importing the necessary libraries. 'pandas' is a popular library for data manipulation, and 'numpy' for numerical operations.
  2. Reading Data: A URL link pointing to a CSV file containing movie data is stored in the variable 'loc'. The pd.read_csv() function is used to read the CSV file into a DataFrame, which is a 2-dimensional labeled data structure in Pandas.
  3. Setting Display Options: The subsequent lines set display options for the DataFrame. The maximum number of columns to display is set to 'None', meaning all columns will be displayed. The minimum number of rows to display is set to '5'.
  4. Getting Columns: We extract the column names of our DataFrame 'm' and store them in the variable 'col'.
  5. Data Manipulation: The core of our code begins here.
    • The '.loc' method extracts the 'genres' column.
    • The genres in the dataset are separated by the pipe ('|') character. We use the 'str.split()' method to split them and create separate columns.
    • The '.stack()' method reshapes the DataFrame, transforming the separate genre columns into a single column with multiple rows for each genre per movie.
    • We then count the occurrence of each genre using 'value_counts()'.
    • The '.reset_index()' method is used to reset the index of the DataFrame. The genre names which were previously the index, become a separate column.
    • We rename the columns for clarity.
    • Finally, we apply a bar style to the 'Count' column for a visual representation of each genre's count.

This simple breakdown serves to give a new Python programmer insight into the power of the Pandas library and how it can be employed for intricate data manipulation tasks.

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