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68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
import pandas as pd
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import seaborn as sns
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import matplotlib.pyplot as plt
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import numpy as np
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# Import data
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df = pd.read_csv("medical_examination.csv")
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# Add 'overweight' column
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df['overweight'] = (df['weight']/((df['height']/100)**2) > 25).astype('int8')
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# 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.
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df['cholesterol'] = (df['cholesterol'] != 1).astype('int8')
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df['gluc'] = (df['gluc'] != 1).astype('int8')
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# Draw Categorical Plot
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def draw_cat_plot():
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# Create DataFrame for cat plot using `pd.melt` using just the values from 'cholesterol', 'gluc', 'smoke', 'alco', 'active', and 'overweight'.
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df0 = df[df['cardio'] == 0]
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df0long = pd.melt(df0, value_vars=['active', 'alco', 'cholesterol', 'gluc', 'overweight', 'smoke'])
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df1 = df[df['cardio'] == 1]
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df1long = pd.melt(df1, value_vars=['active', 'alco', 'cholesterol', 'gluc', 'overweight', 'smoke'])
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# Draw the catplot with 'sns.catplot()'
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fig, axs = plt.subplots(ncols=2, figsize=(15, 5))
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plot0 = sns.countplot(data=df0long, x='variable', hue='value', ax=axs[0]).set(title='cadio = 0', ylabel="total")
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axs[0].legend([],[], frameon=False)
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plot1 = sns.countplot(data=df1long, x='variable', hue='value', ax=axs[1]).set(title='cadio = 1', ylabel="total")
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sns.move_legend(axs[1], "right", bbox_to_anchor=(1.15, 0.5))
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# Do not modify the next two lines
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fig.savefig('catplot.png')
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return fig
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# Draw Heat Map
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def draw_heat_map():
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# Clean the data
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global df
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df = df[df['ap_lo'] <= df['ap_hi']]
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df = df[(df['height'] >= df['height'].quantile(0.025)) & (df['height'] <= df['height'].quantile(0.975))]
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df = df[(df['weight'] >= df['weight'].quantile(0.025)) & (df['weight'] <= df['weight'].quantile(0.975))]
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# Calculate the correlation matrix
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def abs_zero(x):
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for i in range(len(x)):
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if x[i] == 0:
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x[i] = abs(x[i])
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return x
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corr = df.corr().round(decimals=1).transform(abs_zero)
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# Generate a mask for the upper triangle
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mask = np.triu(np.ones_like(corr, dtype=bool))
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# Set up the matplotlib figure
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fig, axs = plt.subplots(ncols=1, figsize=(10, 10))
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# Draw the heatmap with 'sns.heatmap()'
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sns.heatmap(corr, mask=mask, annot=True, fmt=".1f")
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# Do not modify the next two lines
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fig.savefig('heatmap.png')
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return fig
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