Page View Time Series Visualizer

The objective of this project is to provide a walkthrough of visualize a time series of data using a line chart, bar chart, and box plots. We will do this by using 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 aid in understanding the patterns of visits while identifying yearly and monthly growth.

Table of Content

• Introduction
• Conditions
• Impoting CSV
• Cleaning Data
• Visualuzation
  • Line Chart
  • Bar Chart
  • Box Plot
• Conclusion

Introduction

We will be using the data given to us to complete the following tasks:

1. Using Pandas to import the data from "fcc-forum-pageviews.csv" and setting the index to the "date" column.
2. Cleaning 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. Creating a draw_line_plot function that uses Matplotlib to draw a line chart similar to "examples/Figure_1.png". The title will be labeled as "Daily freeCodeCamp Forum Page Views 5/2016-12/2019". The label on the x-axis will be "Date" and the label on the y-axis will be "Page Views".
4. Creating a draw_bar_plot function that draws a bar chart similar to "examples/Figure_2.png". It will show an average daily page views for each month grouped by year. The legend will show month labels and have a title of "Months". On the chart, the label on the x axis will be "Years" and the label on the y-axis will be "Average Page Views".
5. Creating a draw_box_plot function that uses Seaborn to draw two adjacent box plots similar to "examples/Figure_3.png". These box plots will 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)". Making 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.

Conditions

For each chart, we will be using a copy of the data frame. Unit tests are written under test_module.py.

The boilerplate also includes commands to save and return the image.

We start off by having set factors ready for us to work off of. Here we have an data from an excel spreadsheet of page views from the freeCodeCamp forum page.

We are also provided with graph examples of how our outcomes should appear for our line chart, bar chart, and box chart.

Importing CSV

Import data while making sure to parse dates. We can consider setting index column to 'date'. We may search for the appropriete usage of pandas for importing CSV files by the following documentation. We can then see how index_col and parse_dates are the most appropeite to use for meeting our condtions.

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

Cleaning Data

Setting conditions to find the top 2.5% and bottom 2.5% (97.5%) of the datasets.

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

Visualization

Line Chart

Creating line graph and size:

def draw_line_plot():
      fig, ax = plt.subplots(figsize=(10, 5))
      ax.plot(df.index, df['value'], 'r', linewidth=1)

Setting the labels of the graph:

      ax.set_title('Daily freeCodeCamp Forum Page Views 5/2016 - 12/2019')
      ax.set_xlabel('Date')
      ax.set_ylabel('Page views')

Setting the input to save and return the graph:

      fig.savefig('line_plot.png')
      return fig

Line Plot Solution

def draw_line_plot():
  fig, ax = plt.subplots(figsize=(10, 5))
  ax.plot(df.index, df['value'], 'r', linewidth=1)
  ax.set_title('Daily freeCodeCamp Forum Page Views 5/2016 - 12/2019')
  ax.set_xlabel('Date')
  ax.set_ylabel('Page views')
  fig.savefig('line_plot.png')
  return fig

Output returns:

Bar Chart

Here is a pandas documentation to help in plotting bar chart.

plot.bar and determining the type of string under the

Preparing data while sorting values by month and year:

def draw_bar_plot():
    df["month"] = df.index.month
    df["year"] = df.index.year

Average daily page views for each month grouped by the year:

    df_bar = df.groupby(["year", "month"])["value"].mean()
    df_bar = df_bar.unstack()

Setting up bar graph to be plot:

    fig = df_bar.plot.bar(legend=True, figsize = (13, 6), ylabel="Average Page Views", xlabel="Years").figure
    plt.legend(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'])
    plt.xticks(fontsize = 10)
    plt.yticks(fontsize = 10)

For saving graph and returning figure:

    fig.savefig('bar_plot.png')
    return fig

Output return:

Box Plot

Perparing data for box plots:

def draw_box_plot():
    df_box = df.copy()
    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]

Sorting months starting at Janurary:

    df_box["month_num"] = df_box["date"].dt.month
    df_box = df_box.sort_values("month_num")

Drawing of box plots for the yearly and monthly values:

    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10,5))
    axes[0] = sns.boxplot(x=df_box["year"], y=df_box["value"], ax = axes[0])
    axes[1] = sns.boxplot(x=df_box["month"], y=df_box["value"], ax = axes[1])

Labels for the yearly and monthly plots:

    axes[0].set_title("Year-wise Box Plot (Trend)")
    axes[0].set_xlabel("Year")
    axes[0].set_ylabel("Page Views")

    axes[1].set_title("Month-wise Box Plot (Seasonality)")
    axes[1].set_xlabel("Year")
    axes[1].set_ylabel("Page Views")

Output return:

Conclusion

From the trends of our graphs, it can be observed that the FreeCodeCamp site has gained popularity and growth as time progresses. More specifically, we can see that when we observe the months within the year, there is also an increase in activity towards the middle-to-end of the year.