Medical Data Visualizer¶
Note¶
This is cool. But I guess I prefer to visualize data with d3, it feels more easier to me. We can also get some interactive charts instead of static images.
Total time: 2h
Damn, why can't I be faster, it's only about Google and reading document
Problem description¶
Content of this section is copied and reformatted from this repl.it, provided by FCC here.
Assignment¶
In this project, you will visualize and make calculations from medical examination data using matplotlib, seaborn, and pandas. The dataset values were collected during medical examinations.
Data description¶
The rows in the dataset represent patients and the columns represent information like body measurements, results from various blood tests, and lifestyle choices. You will use the dataset to explore the relationship between cardiac disease, body measurements, blood markers, and lifestyle choices.
File name: medical_examination.csv
| Feature | Variable Type | Variable | Value Type |
|---|---|---|---|
| Age | Objective Feature | age | int (days) |
| Height | Objective Feature | height | int (cm) |
| Weight | Objective Feature | weight | float (kg) |
| Gender | Objective Feature | gender | categorical code |
| Systolic blood pressure | Examination Feature | ap_hi | int |
| Diastolic blood pressure | Examination Feature | ap_lo | int |
| Cholesterol | Examination Feature | cholesterol | 1: normal, 2: above normal, 3: well above normal |
| Glucose | Examination Feature | gluc | 1: normal, 2: above normal, 3: well above normal |
| Smoking | Subjective Feature | smoke | binary |
| Alcohol intake | Subjective Feature | alco | binary |
| Physical activity | Subjective Feature | active | binary |
| Presence or absence of cardiovascular disease | Target Variable | cardio | binary |
Tasks¶
Create a chart similar to examples/Figure_1.png, where we show the counts of
good and bad outcomes for the cholesterol, gluc, alco, active, and
smoke variables for patients with cardio=1 and cardio=0 in different panels.
Use the data to complete the following tasks in medical_data_visualizer.py:
- Add an
overweightcolumn to the data. To determine if a person is overweight, first 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. - Normalize the data by making 0 always good and 1 always bad. If the value of
cholesterolorglucis 1, make the value 0. If the value is more than 1, make the value 1. - Convert the data into long format and create a chart that shows the value
counts of the categorical features using seaborn's
catplot(). The dataset should be split by 'Cardio' so there is one chart for eachcardiovalue. The chart should look likeexamples/Figure_1.png. - Clean the data. Filter out the following patient segments that represent
incorrect data:
- diastolic pressure is higher than systolic (Keep the correct data with
(df['ap_lo'] <= df['ap_hi'])) - height is less than the 2.5th percentile (Keep the correct data with
(df['height'] >= df['height'].quantile(0.025))) - height is more than the 97.5th percentile
- weight is less than the 2.5th percentile
- weight is more than the 97.5th percentile
- diastolic pressure is higher than systolic (Keep the correct data with
- Create a correlation matrix using the dataset. Plot the correlation matrix
using seaborn's
heatmap(). Mask the upper triangle. The chart should look likeexamples/Figure_2.png.
Any time a variable is set to None, make sure to set it to the correct code.
Solution¶
In [1]:
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
Import data¶
In [2]:
df = pd.read_csv("./medical_examination.csv")
Add 'overweight' column¶
In [3]:
df["overweight"] = np.where(
df["weight"] / (df["height"] * df["height"]) * 10000 > 25,
1,
0,
)
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.
In [4]:
df["cholesterol"] = np.where(df["cholesterol"] == 1, 0, 1)
df["gluc"] = np.where(df["gluc"] == 1, 0, 1)
Draw Categorical Plot¶
In [5]:
def draw_cat_plot():
# Create DataFrame for cat plot using `pd.melt` using just the values from
# 'cholesterol', 'gluc', 'smoke', 'alco', 'active', and 'overweight'.
vars = sorted(
["cholesterol", "gluc", "smoke", "alco", "active", "overweight"]
)
df_cat = pd.melt(
df,
id_vars=["cardio"],
value_vars=vars,
)
# 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_cat.value_counts().reset_index(name="total")
# Draw the catplot with 'sns.catplot()'
fig = sns.catplot(
data=df_cat,
x="variable",
y="total",
hue="value",
col="cardio",
kind="bar",
order=vars,
)
fig.set_ylabels("total")
fig.set_xlabels("variable")
fig = fig.fig
# Do not modify the next two lines
# fig.savefig("catplot.png") # Don't save to file
return fig
Draw Heat Map¶
In [6]:
def draw_heat_map():
# Clean 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
# with sns.axes_style("white"):
fig, ax = plt.subplots(figsize=(12, 9))
# Draw the heatmap with 'sns.heatmap()'
ax = sns.heatmap(
corr,
mask=mask,
vmax=0.4,
square=True,
fmt=".1f",
annot=True,
)
# Do not modify the next two lines
# fig.savefig("heatmap.png")
# Don't save to file
return fig
Test¶
In [7]:
import unittest
In [8]:
class CatPlotTestCase(unittest.TestCase):
def setUp(self):
self.fig = draw_cat_plot()
self.ax = self.fig.axes[0]
def test_line_plot_labels(self):
actual = self.ax.get_xlabel()
expected = "variable"
self.assertEqual(
actual, expected, "Expected line plot xlabel to be 'variable'"
)
actual = self.ax.get_ylabel()
expected = "total"
self.assertEqual(
actual, expected, "Expected line plot ylabel to be 'total'"
)
actual = []
for label in self.ax.get_xaxis().get_majorticklabels():
actual.append(label.get_text())
expected = [
"active",
"alco",
"cholesterol",
"gluc",
"overweight",
"smoke",
]
self.assertEqual(
actual,
expected,
"Expected bar plot secondary x labels to be 'active', 'alco', 'cholesterol', 'gluc', 'overweight', 'smoke'",
)
def test_bar_plot_number_of_bars(self):
actual = len(
[
rect
for rect in self.ax.get_children()
if isinstance(rect, mpl.patches.Rectangle)
]
)
expected = 13
self.assertEqual(
actual, expected, "Expected a different number of bars chart."
)
In [9]:
class HeatMapTestCase(unittest.TestCase):
def setUp(self):
self.fig = draw_heat_map()
self.ax = self.fig.axes[0]
def test_heat_map_labels(self):
actual = []
for label in self.ax.get_xticklabels():
actual.append(label.get_text())
expected = [
"id",
"age",
"gender",
"height",
"weight",
"ap_hi",
"ap_lo",
"cholesterol",
"gluc",
"smoke",
"alco",
"active",
"cardio",
"overweight",
]
self.assertEqual(
actual,
expected,
"Expected bar plot legend labels to be months of the year.",
)
def test_heat_map_values(self):
actual = [
text.get_text()
for text in self.ax.get_default_bbox_extra_artists()
if isinstance(text, mpl.text.Text)
]
print(actual)
expected = [
"0.0",
"0.0",
"-0.0",
"0.0",
"-0.1",
"0.5",
"0.0",
"0.1",
"0.1",
"0.3",
"0.0",
"0.0",
"0.0",
"0.0",
"0.0",
"0.0",
"0.2",
"0.1",
"0.0",
"0.2",
"0.1",
"0.0",
"0.1",
"-0.0",
"-0.1",
"0.1",
"0.0",
"0.2",
"0.0",
"0.1",
"-0.0",
"-0.0",
"0.1",
"0.0",
"0.1",
"0.4",
"-0.0",
"-0.0",
"0.3",
"0.2",
"0.1",
"-0.0",
"0.0",
"0.0",
"-0.0",
"-0.0",
"-0.0",
"0.2",
"0.1",
"0.1",
"0.0",
"0.0",
"0.0",
"0.0",
"0.3",
"0.0",
"-0.0",
"0.0",
"-0.0",
"-0.0",
"-0.0",
"0.0",
"0.0",
"-0.0",
"0.0",
"0.0",
"0.0",
"0.2",
"0.0",
"-0.0",
"0.2",
"0.1",
"0.3",
"0.2",
"0.1",
"-0.0",
"-0.0",
"-0.0",
"-0.0",
"0.1",
"-0.1",
"-0.1",
"0.7",
"0.0",
"0.2",
"0.1",
"0.1",
"-0.0",
"0.0",
"-0.0",
"0.1",
]
self.assertEqual(
actual, expected, "Expected differnt values in heat map."
)
In [10]:
if __name__ == "__main__":
unittest.main(argv=["first-arg-is-ignored"], exit=False)
