Page-View-Time-Series-Visualizer

certification python project from freecodecamp

1. using Python to visualize time series data using a line chart, bar chart, and box plots.

2. the data visualizations will help us understand the patterns in visits and identify yearly and monthly growth of page views each day on the freeCodeCamp.org forum from 2016-05-09 to 2019-12-03.

3. import libaries

   library

    import matplotlib.pyplot as plt
    import pandas as pd
    import seaborn as sns
    from pandas.plotting import register_matplotlib_converters
    register_matplotlib_converters()
    

4. Import data (Make sure to parse dates. Consider setting index column to 'date')

   import data

    df = pd.read_csv("fcc-forum-pageviews.csv", index_col="date", parse_dates=["date"])
    

5. 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

   clean data

    df = df[(df["value"] >= df["value"].quantile(0.025)) & (df["value"] <= df["value"].quantile(0.975))]
    

6. create a draw_line_plot function that uses Matplotlib to draw a line chart. 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

   draw_line_plot

    def draw_line_plot():
        - draw line plot
        fig, ax = plt.subplots(figsize=(12,6))
        ax.plot(df.index, df["value"], color="red", linewidth=1)
        ax.set_xlabel("Date")
        ax.set_ylabel("Page Views")
        ax.set_title("Daily freeCodeCamp Forum Page Views 5/2016-12/2019")
    
          
    - save image and return fig
    fig.savefig('line_plot.png')
    return fig
    

   line plot

   

7. create a draw_bar_plot function that draws a bar chart. 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

   draw_bar_plot

    def draw_bar_plot():
        - copy and modify data for monthly bar plot
        df_bar = df.copy(deep=True)
        df_bar["Months"] = df_bar.index.month
        df_bar["tahun"] = df_bar.index.year
        df_bar["bulan_angka"] = df_bar.index.month
        df_bar = pd.DataFrame(df_bar.groupby(["tahun", "Months", "bulan_angka"])["value"].mean())
        df_bar.reset_index(inplace=True)
                   
   
   draw bar plot
   
   fig, ax = plt.subplots(figsize = (14,10))
   ax = sns.barplot(data = df_bar,
   x = "tahun",
   y = "value",
   hue = "Months",
   hue_order=['January', 'February', 'March', 'April', 'May', 'June',
   'July', 'August', 'September', 'October', 'November', 'December'],
   palette = "bright")
   sns.move_legend(ax, "upper left")
   ax.set_xlabel("Years")
   ax.set_ylabel("Average Page Views")
   
   save image and return fig
   fig.savefig('bar_plot.png')
   return fig
   
    

   bar plot

   

8. create a draw_box_plot function that uses Seaborn to draw two adjacent box plots. 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 y axis are labeled correctly

   draw_box_plot

    def draw_box_plot():
        df_box = df.copy(deep=True)
        df_box.reset_index(inplace=True)
        df_box['year'] = [d.year for d in df_box.date]
        df_box['month'] = [d.strftime('%b') for d in df_box.date]
        df_box["month_angka"] = [d.strftime("%m") for d in df_box["date"]]
    
   
    - draw box plots (using Seaborn)
    fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15,6))
    p = sns.boxplot(data = df_box, 
                x = "year", 
                y = "value", ax=ax[0])
    p.set_title("Year-wise Box Plot (Trend)")
    p.set_xlabel("Year")
    p.set_ylabel("Page Views")
   
    q = sns.boxplot(data = df_box.sort_values(by="month_angka"), 
                    x = "month", 
                    y = "value", 
                    ax=ax[1])
    q.set_title("Month-wise Box Plot (Seasonality)")
    q.set_xlabel("Month")
    q.set_ylabel("Page Views")
    
   
    - save image and return fig
    fig.savefig('box_plot.png')
    return fig
    

   box plot

   

9. put them together in time_series_visualizer.py file

10. call via main.py