Data Analysis with Python Projects - Page View Time Series Visualizer

What’s happening:
Hello, guys!
Asking for help to deal with the ‘draw_bar_plot()’ function.
My code works well in Spyder and Jupyter, but when running it on Replit, it returns an error:
‘’’
Traceback (most recent call last):
File “main.py”, line 73, in
draw_bar_plot(df_2)
File “main.py”, line 62, in draw_bar_plot
ax.bar(x + offset, measurement, width, label=attribute)
File “/home/runner/ExpensiveTraumatic
Businesses/venv/lib/python3.10/site-p
ackages/matplotlib/init.py”, line 1442, in inner
return func(ax, *map(sanitize_sequence, args), **kwargs)
File “/home/runner/Expensive Traumatic
ackages/matplotlib/axes/_axes.py”, line 2436, in bar
raise ValueError(f’number of labels ({len(patch_labels)}) ’
ValueError: number of labels (2) does not match number of bars (4).
‘’’
**Here below is my code **
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

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

import os
os.getcwd()

df_raw = pd.read_csv(‘fcc_forum_pageviews.csv’, parse_dates = [‘date’], index_col=‘date’)
df_raw.rename(columns = {‘value’:‘page_views’}, inplace=True)
df_1 = df_raw.applymap(lambda x: x.strip() if isinstance(x, str) else x)

df_1.head()

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.

cond_1 = df_1[‘page_views’] <= df_1[‘page_views’].quantile(0.975)
cond_2 = df_1[‘page_views’] >= df_1[‘page_views’].quantile(0.025)
df_2 = df_1.loc[cond_1 & cond_2]

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.

def draw_line_plot(x, y,):
plt.figure(figsize=(16,5))
plt.plot(x, y, color=‘red’)
plt.title(‘Daily freeCodeCamp Forum Page Views 5/2016-12/2019’)
plt.xlabel(‘Date’)
plt.ylabel(‘Page Views 5/2016-12/2019’)

draw_line_plot(df_2.index, df_2[‘page_views’])

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.

def draw_bar_plot(df):
df = df.reset_index()
df[‘Year’] = df.date.dt.to_period(‘Y’)
df[“Month”] = df.date.dt.to_period(‘M’)
df = df[[‘Year’, ‘Month’, ‘page_views’]]
df_3_gr = df.groupby([‘Year’, ‘Month’], as_index=False).mean().rename(columns={‘page_views’:‘average_page_views’})
df_3_gr[‘month’] = df_3_gr[‘Month’].astype(str).str[-2:]
df_gr = df_3_gr[[‘Year’,‘month’,‘average_page_views’]]
df_piv = df_gr.pivot(index=‘Year’, columns=‘month’).replace(np.nan,0)

x = np.arange(len(df_piv.index.astype(int)))  # the label locations
width = .05  # the width of the bars
multiplier = 0

fig, ax = plt.subplots(layout='constrained')

for attribute, measurement in df_piv.items():
    offset = width * multiplier
    ax.bar(x + offset, measurement, width, label=attribute)
    multiplier += 1

# Add text for labels, and custom x-axis tick labels, etc.
ax.set_ylabel('Average Page Views')
ax.set_xlabel('Year')
ax.set_xticks(x + width + 0.22, df_piv.index)
ax.legend(loc='upper left', title='Month', labels=['Jan', 'Feb', 'Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'])

plt.show()

draw_bar_plot(df_2)

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Challenge: Data Analysis with Python Projects - Page View Time Series Visualizer

Link to the challenge:

The posted repl does not have the tests and you’ve added all your code to main.py. You should have the tests in test_module.py and your code in time_series_visualizer.py like the boilerplate. It’s impossible to debug easily without a complete repl from the project boilerplate.

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