The Python code performs various operations on a crime dataset. It begins by importing the necessary libraries, such as pandas for data manipulation, and suppresses warning messages. The dataset is loaded into a Pandas DataFrame named crime_df from a CSV file. Information about the DataFrame, including its dimensions and column names, is then displayed. The code checks for the presence of NULL values in the dataset and identifies that there are some. Subsequently, it examines the distribution of values in the ‘SHOOTING’ column and removes this column from the DataFrame. Rows containing any NULL values are dropped, resulting in a cleaned DataFrame named cleaned_crimedf.
The code proceeds to handle temporal data, converting the ‘OCCURRED_ON_DATE’ column from string format to a timestamp and splitting it into separate ‘DATE’ and ‘TIME’ columns. The first five rows of the cleaned DataFrame are then displayed. Grouping the data by date, the code generates a new DataFrame (crime_count_by_date) that represents the count of crimes for each day, sorting the results in descending order based on the count. Finally, the first five rows and general information of this grouped DataFrame are printed, providing insights into the temporal distribution of crimes in the dataset.