Wednesday – November 1,2023.

Today, I wrote a code in Python and used the pandas and collections libraries to analyze data from an Excel file. Here’s a simple explanation of what it does:

1. It starts by importing two libraries: “pandas” (commonly used for data analysis) and “Counter” from “collections” (used for counting elements in a list).
2. The code specifies the names of the columns you want to analyze from an Excel file. These columns include information like “threat_type,” “flee_status,” “armed_with,” and others.
3. It sets the file path to the location of your Excel document. You need to replace this path with the actual path to your Excel file.
4. The code uses “pd.read_excel” to load the data from the Excel file into a DataFrame (a table-like structure for data).
5. It initializes a dictionary called “word_counts” to store word frequencies for each of the specified columns.
6. The code then goes through each of the specified columns one by one. For each column:

• • It retrieves the data from that column and converts it to strings to ensure uniform data type.
  • It breaks the text into individual words (tokenizes it) and counts how many times each word appears in that column.
  • These word counts are stored in the “word_counts” dictionary under the column’s name.
  • Finally, the code prints the words and their frequencies for each of the specified columns. It goes through the “word_counts” dictionary and displays the words and how many times they appear in each column.

In summary, this code reads data from an Excel file, tokenizes the text in specific columns, and counts the frequency of each word in those columns. It then prints out the word frequencies for each column, which can be useful for understanding the data in those columns.