page-view-time-series-visualizer/time_series_visualizer.py

import calendar

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

register_matplotlib_converters()

# Import data (Make sure to parse dates. Consider setting index column to 'date'.)
df = pd.read_csv("fcc-forum-pageviews.csv")

# Clean data
df = df[(
    df['value'] >= df['value'].quantile(0.025))
    & (df['value'] <= df['value'].quantile(0.975))]


def draw_line_plot():
    # Draw line plot
    fig, axs = plt.subplots(ncols=1, figsize=(20, 10))
    sns.lineplot(df["value"], estimator=None).set(
        xlabel="Date",
        ylabel="Page Views",
        xticklabels=df.index,
        title='Daily freeCodeCamp Forum Page Views 5/2016-12/2019')

    # Save image and return fig (don't change this part)
    fig.savefig('line_plot.png')
    return fig


def draw_bar_plot():
    df = pd.read_csv(
        "fcc-forum-pageviews.csv", parse_dates=True, index_col=[0])
    df = df[
        (df['value'] >= df['value'].quantile(0.025))
        & (df['value'] <= df['value'].quantile(0.975))]
    # Copy and modify data for monthly bar plot
    df['year'] = df.index.year
    df['month'] = df.index.month

    # Draw bar plot
    df.groupby(['year', 'month']).mean()
    df_bar = df.groupby([df.index.year, df.index.month]).mean()
    df_bar['month'] = df_bar['month'].apply(
        lambda x: calendar.month_name[int(x)])
    df_bar = df_bar.astype({'year': 'int'})
    fig, axs = plt.subplots(ncols=1, figsize=(20, 10))
    sns.barplot(
        data=df_bar,
        x='year',
        y='value',
        hue='month',
        hue_order=[
                 'January',
                 'February',
                 'March',
                 'April',
                 'May',
                 'June',
                 'July',
                 'August',
                 'September',
                 'October',
                 'November',
                 'December']).set(
        xlabel='Years', ylabel='Average Page Views')

    # Save image and return fig (don't change this part)
    fig.savefig('bar_plot.png')
    return fig


def draw_box_plot():
    # Prepare data for box plots (this part is done!)
    df = pd.read_csv("fcc-forum-pageviews.csv", parse_dates=True, index_col=0)
    df = df[(df['value'] >= df['value'].quantile(0.025))
            & (df['value'] <= df['value'].quantile(0.975))]
    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]

    # Draw box plots (using Seaborn)
    fig, axs = plt.subplots(ncols=2, figsize=(24, 10))
    sns.boxplot(data=df_box, ax=axs[0], x="year", y="value").set(
        title="Year-wise Box Plot (Trend)", ylabel="Page Views", xlabel="Year")
    sns.boxplot(
        data=df_box,
        ax=axs[1],
        x="month",
        y="value",
        order=[
            "Jan",
            "Feb",
            "Mar",
            "Apr",
            "May",
            "Jun",
            "Jul",
            "Aug",
            "Sep",
            "Oct",
            "Nov",
            "Dec"]).set(
        title="Month-wise Box Plot (Seasonality)",
        ylabel="Page Views",
        xlabel="Month")

    # Save image and return fig (don't change this part)
    fig.savefig('box_plot.png')
    return fig