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Hello,
for some reason the graph my program returns is the wrong datatype leading to an error in the testing module:
AttributeError: 'NoneType' object has no attribute 'get_texts'
I solved the issue for very similar problems in the test module by just doing fig = fig.fig.
For some reason the legend is however diffrent from the X/Y label.
Additionally the scale on my boxplot is of. How can I fix that? (like is there away I can tell pyplot to use a fixed y scale?)
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")
df['date'] = pd.to_datetime(df['date'])
df = df.set_index("date")
# Clean data
df = df.loc[((df["value"]>=df["value"].quantile(0.025))&(df["value"]<=df["value"].quantile(0.975)))]
def draw_line_plot():
# Draw line plot
fig = plt.figure(figsize= (1,1))
plt.xticks(df["value"],df.index.values)
plt.plot(df["value"],"r")
plt.xlabel("Date")
plt.ylabel("Page Views")
plt.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():
# Copy and modify data for monthly bar plot
df_bar = df.resample("M").sum()
df_bar["count"]= df.resample("M").count()
df_bar = df_bar.reset_index()
df_bar_extend = pd.DataFrame({"date": ["2016-01-31","2016-02-27","2016-03-30","2016-04-30"], "value": [0,0,0,0],"count": [1,1,1,1]})
df_bar = pd.concat([df_bar_extend, df_bar], ignore_index=True)
df_bar['date'] = pd.to_datetime(df_bar['date'])
df_bar["Years"]= df_bar["date"].dt.year
df_bar["Months"]= df_bar["date"].dt.month
df_bar = df_bar.set_index("date")
monthnames = ["January","February","March","April","Mai","June","July","August","September","October","November","December"]
for i in range(1,13):
df_bar.loc[df_bar["Months"]==i,"Months"] = monthnames[i-1]
df_bar["Average Page Views"]=df_bar["value"]/df_bar["count"]
fig = sns.catplot(x="Years", y= "Average Page Views",hue= "Months",data=df_bar,kind = "bar")
# Draw bar plot
# Save image and return fig (don't change this part)
fig=fig.fig
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 = df_box.reset_index()
df_bar_extend = pd.DataFrame({"date": ["2016-01-31","2016-02-27","2016-03-30","2016-04-30"], "value": [0,0,0,0]})
df_box = pd.concat([df_bar_extend, df_box], ignore_index=True)
df_box['date'] = pd.to_datetime(df_box['date'])
df_box = df_box.set_index("date")
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,ax = plt.subplots(1,2,figsize=(15,10))
ax1 = sns.boxplot(x=df_box["Year"],y=df_box["value"],ax =ax[0])
ax1.set_ylabel("Page Views")
ax1.set_title("Year-wise Box Plot (Trend)")
ax1.set_ylim(0,200000)
ax2 = sns.boxplot(x=df_box["month"],y=df_box["value"],ax =ax[1])
ax2.set_ylabel("Page Views")
ax2.set_xlabel("Month")
ax2.set_title('Month-wise Box Plot (Seasonality)')
ax2.set_ylim(0,200000)
# Save image and return fig (don't change this part)
fig.savefig('box_plot.png')
return fig
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Challenge: Page View Time Series Visualizer
Link to the challenge: