Thank you for your response,
If I comment out the below lines my code will run locally in Visual Studio.
#fig.savefig(‘heatmap.png’)
fig.savefig(‘catplot.png’)
However, if I run the code below it throws an error
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
Import data
df = pd.read_csv(“medical_examination.csv”)
#calculate their BMI by dividing their weight in kilograms by the square of their height in meters.
If that value is > 25 then the person is overweight. Use the value 0 for NOT overweight and the value 1 for overweight.
df[‘overweight’] = df.apply(lambda x: 1 if(x.weight / ((x.height / 100)**2) > 25) else 0, axis=1)
Normalize data by making 0 always good and 1 always bad.
If the value of ‘cholesterol’ or ‘gluc’ is 1, make the value 0.
If the value is more than 1, make the value 1.
df[‘gluc’] = df.apply(lambda p: 1 if(p.gluc > 1) else 0, axis=1)
df[‘cholesterol’] = np.where(df.cholesterol > 1, 1,0)
Draw Categorical Plot
def draw_cat_plot():
# Create DataFrame for cat plot using pd.melt using just the values from ‘cholesterol’, ‘gluc’, ‘smoke’, ‘alco’, ‘active’, and ‘overweight’.
df_cat= sorted([‘cholesterol’, ‘gluc’, ‘smoke’, ‘alco’, ‘active’,‘overweight’])
# Group and reformat the data to split it by 'cardio'. Show the counts of each feature. You will have to rename one of the columns for the catplot to work correctly.
df_cat = df.melt(id_vars='cardio', value_vars=df_cat)
# Draw the catplot with 'sns.catplot()'
sns.catplot(x='variable', col='cardio', hue='value',kind='count', data=df_cat).set_axis_labels('variable', 'Total')
# Get the figure for the output
fig = df_cat.plot.bar
# Do not modify the next two lines
fig.savefig('catplot.png')
return fig
draw_cat_plot()
Draw Heat Map
def draw_heat_map():
# Clean/filter the data
df_heat = df.loc[(df[‘ap_lo’] <= df[‘ap_hi’]) &
(df[‘height’] >= df[‘height’].quantile(0.025)) &
(df[‘height’] <= df[‘height’].quantile(0.975)) &
(df[‘weight’] >= df[‘weight’].quantile(0.025))&
(df[‘weight’] <= df[‘weight’].quantile(0.975))]
# Calculate the correlation matrix
corr = df_heat.corr()
# Generate a mask for the upper triangle
mask = np.zeros_like(corr)
mask[(np.triu_indices_from(mask))]=True
# Set up the matplotlib figure
fig, ax = plt.subplots(figsize=(10,10))
# Draw the heatmap with 'sns.heatmap()'
ax = sns.heatmap(corr, vmin=0, vmax=0.25, annot=True, fmt='.1f', linewidths=0, square=True, mask=mask)
# Do not modify the next two lines
fig.savefig('heatmap.png')
return fig
Any help would be greatly appreciated, I have run out of ideas.
