1. Import the necessary libraries: Import the “pandas” library and assign it the alias ‘pd’ for data manipulation. Import the “matplotlib. pyplot” library and assign it the alias ‘plt’ for data visualization.
2. Load the Excel file into a DataFrame: Specify the file path to the Excel file that you want to load (update this path to your Excel file’s location).
Specify the name of the sheet within the Excel file from which data should be read. Use the pd.read_excel function to read the data from the Excel file into a Pandas DataFrame named ‘df.’
3. Drop rows with missing ‘race,’ ‘age,’ or ‘gender’ values: Remove rows from the DataFrame where any of these three columns (race, age, gender) have missing values.
4.Create age groups: Define the boundaries for age groups using the ‘age_bins’ variable. Provide labels for each age group, corresponding to ‘age_bins,’ using the ‘age_labels’ variable.
5. Cut the age data into age groups for each race category: Create a new column ‘Age Group’ in the DataFrame by categorizing individuals’ ages into the age groups defined in ‘age_bins’ and labeling them with ‘age_labels.’
6. Count the number of individuals in each age group by race and gender: Group the data by race, gender, and age group.Count the number of individuals in each combination.Use the unstack() function to reshape the data, making it more suitable for visualization.Fill missing values with 0 using fillna(0).
7. Calculate the median age for each race and gender combination: Group the data by race and gender. Calculate the median age for each combination.
8. Print the median age for each race and gender combination: Print a header indicating “Median Age by Race and Gender.” Print the calculated median age for each race and gender combination.
9. Create grouped bar charts for different genders: The code iterates over unique gender values in the DataFrame.
10. For each gender: Subset the DataFrame to include only data for that gender. Create a grouped bar chart that displays the number of individuals in different age groups for each race-gender combination.
Set various plot properties such as the title, labels, legend, and rotation of x-axis labels. Display the plot using plt.show().