Consequence the fundamental libraries:
The code imports the “pandas” library for information investigation and the “Counter” course from the “collections” module for tallying components in a list.
Specify the columns to be analyzed:
The code indicates the names of the columns you need to analyze from an Exceed Expectations record. These columns contain data such as “threat_type,” “flee_status,” “armed_with,” and others.
Set the record way to the Exceed Expectations document:
The code sets the record way to the area of your Exceed expectations record. You ought to supplant this way with the real way to your Exceed Expectations file.
Load the information from the Exceed expectations record into a DataFrame:
The code employments the “pd.read_excel” work to stack the information from the Exceed expectations record into a Pandas DataFrame, which may be a table-like structure for data.
Initialize a word reference for word counts:
The code initializes a lexicon called “word_counts” to store word frequencies for each of the desired columns. Each column will have its claim word recurrence counts.
Process each indicated column:
For each column indicated for examination, the code performs the following steps:
It recovers the information from that column and changes it to strings to guarantee uniform data type. This can be imperative for content processing.
It tokenizes the content within the column by breaking it into personal words. Tokenization is the method of part content into smaller units, such as words or phrases.
It tallies how numerous times each word shows up in that column utilizing the “Counter” lesson, and these word counts are put away within the “word_counts” word reference beneath the column’s name.
Print the words and their frequencies:
Finally, the code goes through the “word_counts” lexicon for each indicated column and shows the words and how numerous times they appear in that column. This gives bits of knowledge into the foremost common words or expressions in each column.