Month: December 2023

1. Convert Dates to Datetime Objects
– ‘pd.to_datetime()’ is used to convert the ‘issued_date’ and ‘expiration_date’ columns in the DataFrame `data` to datetime objects.
– ‘errors=’coerce” is employed to handle any parsing errors, converting problematic entries to NaT (Not a Time).

Code:

data[‘issued_date’] = pd.to_datetime(data[‘issued_date’], errors=’coerce’)

data[‘expiration_date’] = pd.to_datetime(data[‘expiration_date’], errors=’coerce’)

2. Calculate Duration
– The ‘duration’ column is created, representing the number of days between ‘expiration_date’ and ‘issued_date’.

Code:

data[‘duration’] = (data[‘expiration_date’] – data[‘issued_date’]).dt.days

3. Visualization – Number of Permits Issued Over Time
– A new column ‘issued_year’ is created, extracting the year from the ‘issued_date’.
– A bar plot (‘sns.countplot()’) is generated using Seaborn to visualize the count of permits issued each year.
– Matplotlib functions are then utilized to add labels, title, and rotate the x-axis ticks for better readability.
– Finally, ‘plt.show()’ displays the resulting bar chart.

Code:

data[‘issued_year’] = data[‘issued_date’].dt.year

plt.figure(figsize=(10, 6))

sns.countplot(data=data, x=’issued_year’)

plt.title(‘Number of Permits Issued Over Time (by Year)’)

plt.xlabel(‘Year’)

plt.ylabel(‘Number of Permits’)

plt.xticks(rotation=45)

plt.tight_layout()

plt.show()