Solution

time_series_visualizer.py

@@ -1,53 +1,112 @@
+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 = None
+df = pd.read_csv("fcc-forum-pageviews.csv")
 
 # Clean data
-df = None
+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_bar = None
+    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')