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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.

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