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I don’t understand what is the issue, my code doesn’t even run, but to me it seems fine
→ poetry add matplotlib
Using version ^3.8.1 for matplotlib
Updating dependencies
Resolving dependencies…
The current project’s Python requirement (>=3.7,<4.0) is not compatible with some of the required packages Python requirement:
- matplotlib requires Python >=3.9, so it will not be satisfied for Python >=3.7,<3.9
Because no versions of matplotlib match >3.8.1,<4.0.0
and matplotlib (3.8.1) requires Python >=3.9, matplotlib is forbidden.
So, because root depends on matplotlib (^3.8.1), version solving failed.
• Check your dependencies Python requirement: The Python requirement can be specified via the python or markers properties
For matplotlib, a possible solution would be to set the `python` property to ">=3.9,<4.0"
https://python-poetry.org/docs/dependency-specification/#python-restricted-dependencies,
https://python-poetry.org/docs/dependency-specification/#using-environment-markers
exit status 1
/home/runner/boilerplate-page-view-time-series-visualizer-1/time_series_visualizer.py:38: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
df_bar = df_bar.groupby([‘year’,‘month’]).value.mean().unstack()
Your code so far
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’,index_col=‘date’)
df.index=pd.to_datetime(df.index)
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,ax=plt.subplots(figsize=(15,5))
plt.plot(df.index,df.value,color='firebrick')
plt.title('Daily freeCodeCamp Forum Page Views 5/2016-12/2019')
plt.xlabel('Date')
plt.ylabel('Page_Views')
# Save image and return fig (don't change this part)
fig.savefig('line_plot.png')
return fig
def draw_bar_plot():
# Copy and modify data for monthly bar plot
df_bar=df.copy()
df_bar[‘month’]=df_bar.index.month_name()
df_bar[‘year’]=df_bar.index.year
month_list=[“January”, “February”, “March”, “April”, “May”, “June”, “July”, “August”, “September”, “October”, “November”, “December”]
df_bar[‘month’]=df_bar.month.astype(‘category’).cat.set_categories(month_list,ordered=True)
df_bar = df_bar.groupby(['year','month']).value.mean().unstack()
# Draw bar plot
fig = df_bar.plot.bar(figsize=(12,6),legend=True,xlabel='Years', ylabel='Average Page Views').figure
ax=plt.gca()
ax.legend(title='Months')
plt.show()
# 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_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]
months_list= [‘Jan’, ‘Feb’, ‘Mar’, ‘Apr’, ‘May’, ‘Jun’, ‘Jul’, ‘Aug’, ‘Sep’, ‘Oct’, ‘Nov’, ‘Dec’]
df[‘month’]=df_box.month.astype(‘category’).cat.set_categories(months_list,ordered=True)
# Draw box plots (using Seaborn)
fig,ax=plt.subplots(1,2,figsize=(15,5))
ax[0]=sns.boxplot(x=df_box['year'],y=df_box['value'],ax=ax[0])
ax[0].set_title('Year-wise Box Plot (Trend)')
ax[0].set_xlabel('Year')
ax[0].set_ylabel('Page Views')
ax[0].set_yticks(range(0, 220000, 20000))
ax[1]=sns.boxplot(x=df_box['month'],y=df_box['value'],ax=ax[1],order=months_list)
ax[1].set_title('Month-wise Box Plot (Seasonality)')
ax[1].set_xlabel('Month')
ax[1].set_ylabel('Page Views')
ax[1].set_yticks(range(0, 220000, 20000))
# Save image and return fig (don't change this part)
fig.savefig('box_plot.png')
return fig
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Challenge Information:
Data Analysis with Python Projects - Page View Time Series Visualizer
