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Co-authored-by: Naomi Carrigan <nhcarrigan@gmail.com>
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curriculum/challenges/chinese/08-data-analysis-with-python/data-analysis-with-python-projects/medical-data-visualizer.md
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# --instructions--
在本项目中，您将使用 matplotlib、seaborn 和 pandas 来对体检数据进行可视化和计算。 数据集的数值是从体检中收集的。
In this project, you will visualize and make calculations from medical examination data using `matplotlib`, `seaborn`, and `pandas`. 数据集的数值是从体检中收集的。
## 数据说明
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## 任务
创建一个类似于 `examples/Figure_1.png` 的图表，其中我们显示 `cholesterol`、`gluc`、`alco`、`active` 和 `smoke` 变量，用于不同面板中 heart=1 和 heart=0 的患者。
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.
在 `medical_data_visualizer.py` 中使用数据完成以下任务：
- 给数据添加一列 `overweight`。 要确定一个人是否超重，首先通过将他们的体重（公斤）除以他们的身高（米）的平方来计算他们的 BMI。 如果该值是 > 25，则此人超重。 使用值 0 表示不超重，使用值 1 表示超重。
- 使用 0 表示好的和 1 表示坏，来规范化数据。 如果 `cholesterol` 或 `gluc` 的值为 1，则将值设为 0。 如果值大于 1，则将值设为 1。
- 将数据转换为长格式并使用 seaborn 的 `catplot()` 创建一个显示分类特征值计数的图表。 数据集应按 “Cardio” 拆分，因此每个 `cardio` 值都有一个图表。 该图表应该看起来像 `examples/Figure_1.png`。
- 给数据添加一列 `overweight`。 要确定一个人是否超重，首先通过将他们的体重（公斤）除以他们的身高（米）的平方来计算他们的 BMI。 如果该值是 > 25，则此人超重。 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 `cholesterol` or `gluc` is `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 each `cardio` value. 该图表应该看起来像 `examples/Figure_1.png`。
- 清理数据。 过滤掉以下代表不正确数据的患者段：
- 舒张压高于收缩压（使用 `(df['ap_lo'] <= df['ap_hi'])` 保留正确的数据）
- 高度小于第 2.5 个百分位数（使用 `(df['height'] >= df['height'].quantile(0.025))` 保留正确的数据）
- 身高超过第 97.5 个百分位
- 体重小于第 2.5 个百分位
- 体重超过第 97.5 个百分位
- 使用数据集创建相关矩阵。 使用 seaborn 的 `heatmap()` 绘制相关矩阵。 遮罩上三角。 该图表应类似于 `examples/Figure_2.png`。
- 使用数据集创建相关矩阵。 Plot the correlation matrix using `seaborn`'s `heatmap()`. 遮罩上三角。 该图表应类似于 `examples/Figure_2.png`。
每当变量设置为 `None` 时，请确保将其设置为正确的代码。
Unit tests are written for you under `test_module.py`.
## Instructions
By each number in the `medical_data_visualizer.py` file, add the code from the associated instruction number below.
1. Import the data from `medical_examination.csv` and assign it to the `df` variable
2. Create the `overweight` column in the `df` variable
3. Normalize data by making `0` always good and `1` always bad. If the value of `cholesterol` or `gluc` is 1, set the value to `0`. If the value is more than `1`, set the value to `1`.
4. Draw the Categorical Plot in the `draw_cat_plot` function
5. Create a DataFrame for the cat plot using `pd.melt` with values from `cholesterol`, `gluc`, `smoke`, `alco`, `active`, and `overweight` in the `df_cat` variable.
6. Group and reformat the data in `df_cat` 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.
7. Convert the data into `long` format and create a chart that shows the value counts of the categorical features using the following method provided by the seaborn library import : `sns.catplot()`
8. Get the figure for the output and store it in the `fig` variable
9. Do not modify the next two lines
10. Draw the Heat Map in the `draw_heat_map` function
11. Clean the data in the `df_heat` variable by filtering out the following patient segments that represent incorrect data:
- 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
12. Calculate the correlation matrix and store it in the `corr` variable
13. Generate a mask for the upper triangle and store it in the `mask` variable
14. Set up the `matplotlib` figure
15. Plot the correlation matrix using the method provided by the `seaborn` library import: `sns.heatmap()`
16. Do not modify the next two lines
## 开发
Write your code in `medical_data_visualizer.py`. For development, you can use `main.py` to test your code.