For this project you will visualize time series data using a line chart, bar chart, and box plots. You will use Pandas, Matplotlib, and Seaborn to visualize a dataset containing the number of page views each day on the freeCodeCamp.org forum from 2016-05-09 to 2019-12-03. The data visualizations will help you understand the patterns in visits and identify yearly and monthly growth.

Use the data to complete the following tasks:

1. Use Pandas to import the data from fcc-forum-pageviews.csv. Set the index to the "date" column.

2. Clean the data by filtering out days when the page views were in the top 2.5% of the dataset or bottom 2.5% of the dataset.

3. Create a draw_line_plot function that uses Matplotlib to draw a line chart similar to examples/Figure_1.png. The title should be "Daily freeCodeCamp Forum Page Views 5/2016-12/2019". The label on the x axis should be "Date" and the label on the y axis should be "Page Views".

4. Create a draw_bar_plot function that draws a bar chart similar to examples/Figure_2.png. It should show average daily page views for each month grouped by year. The legend should show month labels and have a title of "Months". On the chart, the label on the x axis should be "Years" and the label on the y axis should be "Average Page Views".

5. Create a draw_box_plot function that uses Searborn to draw two adjacent box plots similar to examples/Figure_3.png. These box plots should show how the values are distributed within a given year or month and how it compares over time. The title of the first chart should be "Year-wise Box Plot (Trend)" and the title of the second chart should be "Month-wise Box Plot (Seasonality)". Make sure the month labels on bottom start at "Jan" and the x and x axis are labeled correctly. The boilerplate includes commands to prepare the data.