Wednesday – November 8,2023.

In todays analysis, I wrote the code to perform text analysis on specific columns of an Excel dataset to count the frequencies of words in those columns. Here’s a step-by-step explanation of the code:

1. Import the necessary libraries:
   • import pandas as pd: Imports the Pandas library and assigns it the alias ‘pd’ for working with data.
   • from collections import Counter: Imports the Counter class from the collections module, which is used to count the frequency of words.
2. Define the column names you want to analyze:
   • columns_to_analyze: A list containing the names of the columns you want to analyze for word frequencies. In this code, the columns specified are ‘threat_type’, ‘flee_status’, ‘armed_with’, and ‘body_camera.’
3. Specify the file path to your Excel document:
   • directory_path: Specifies the file path to the Excel file you want to analyze. Make sure to update this path to your Excel file’s location.
4. Load your data into a DataFrame:
   • df = pd.read_excel(directory_path): Reads the data from the Excel file specified by ‘directory_path’ into a Pandas DataFrame named ‘df.’
5. Initialize a dictionary to store word counts for each column:
   • word_counts = {}: Creates an empty dictionary named ‘word_counts’ to store the word counts for each specified column.
6. Iterate through the specified columns:
   • The code uses a for loop to go through each column specified in the columns_to_analyze list.
7. Retrieve and preprocess the data from the column:
   • column_data = df[column_name].astype(str): Retrieves the data from the current column, converts it to strings to ensure consistent data type, and stores it in the ‘column_data’ variable.
8. Tokenize the text and count the frequency of each word:
   • The code tokenizes the text within each column using the following steps:
     • words = ' '.join(column_data).split(): Joins all the text in the column into a single string, then splits it into individual words. This step prepares the data for word frequency counting.
     • word_counts[column_name] = Counter(words): Uses the Counter class to count the frequency of each word in the ‘words’ list and stores the results in the ‘word_counts’ dictionary under the column name as the key.
9. Print the words and their frequencies for each column:
   • The code iterates through the ‘word_counts’ dictionary and prints the word frequencies for each column. It displays the column name, followed by the individual words and their counts for that column.

The code provides a word frequency analysis for the specified columns in your dataset, making it easier to understand the distribution of words in those columns. This can be useful for identifying common terms or patterns in the data.