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116 lines
6.7 KiB
Markdown
116 lines
6.7 KiB
Markdown
---
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id: 5e46f7f8ac417301a38fb92a
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title: 医疗数据可视化工具
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challengeType: 10
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forumTopicId: 462368
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dashedName: medical-data-visualizer
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---
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# --description--
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You will be <a href="https://gitpod.io/?autostart=true#https://github.com/freeCodeCamp/boilerplate-medical-data-visualizer/" target="_blank" rel="noopener noreferrer nofollow">working on this project with our Gitpod starter code</a>.
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我们仍在开发 Python 课程的交互式教学部分。 目前，你可以在 YouTube 上通过 freeCodeCamp.org 上传的一些视频学习这个项目相关的知识。
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- <a href="https://www.freecodecamp.org/news/python-for-everybody/" target="_blank" rel="noopener noreferrer nofollow">每个人视频课程的 Python</a> (14小时)
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- <a href="https://www.freecodecamp.org/news/how-to-analyze-data-with-python-pandas/" target="_blank" rel="noopener noreferrer nofollow">如何使用 Python Pandas 分析数据</a>（10 小时）
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# --instructions--
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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。
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## 数据说明
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数据集中的行代表患者，列代表身体测量、各种血液检查的结果和生活方式等信息。 您将使用该数据集来探索心脏病、身体测量数据、血液标志物和对生活方式的选择之间的关系。
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文件名：medical_examination.csv
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| 项目 | 变量类型 | 变量名 | 变量值类型 |
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|:--------:|:----:|:-------------:|:---------------------:|
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| 年龄 | 客观特征 | `age` | int (days) |
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| 身高 | 客观特征 | `height` | int (cm) |
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| 体重 | 客观特征 | `weight` | float (kg) |
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| 性别 | 客观特征 | `gender` | 分类编码 |
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| 收缩压 | 检测特征 | `ap_hi` | int |
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| 舒张压 | 检测特征 | `ap_lo` | int |
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| 胆固醇 | 检测特征 | `cholesterol` | 1：正常，2：高于正常，3：远远高于正常值 |
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| 血糖值 | 检测特征 | `gluc` | 1：正常，2：高于正常，3：远远高于正常值 |
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| 吸烟问题 | 主观特征 | `smoke` | binary |
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| 饮酒量 | 主观特征 | `alco` | binary |
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| 体育活动 | 主观特征 | `active` | binary |
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| 是否有心血管疾病 | 目标变量 | `cardio` | binary |
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## 任务
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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.
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在 `medical_data_visualizer.py` 中使用数据完成以下任务：
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- 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.
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- 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`.
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- 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`。
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- 清理数据。 过滤掉以下代表不正确数据的患者段：
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- 舒张压高于收缩压（使用 `(df['ap_lo'] <= df['ap_hi'])` 保留正确的数据）
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- 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）
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- 身高超过第 97.5 个百分位
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- 体重小于第 2.5 个百分位
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- 体重超过第 97.5 个百分位
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- 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`。
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每当变量设置为 `None` 时，请确保将其设置为正确的代码。
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Unit tests are written for you under `test_module.py`.
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## Instructions
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By each number in the `medical_data_visualizer.py` file, add the code from the associated instruction number below.
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1. Import the data from `medical_examination.csv` and assign it to the `df` variable
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2. Create the `overweight` column in the `df` variable
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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`.
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4. Draw the Categorical Plot in the `draw_cat_plot` function
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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.
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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.
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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()`
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8. Get the figure for the output and store it in the `fig` variable
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9. Do not modify the next two lines
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10. Draw the Heat Map in the `draw_heat_map` function
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11. Clean the data in the `df_heat` variable by filtering out the following patient segments that represent incorrect data:
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- height is less than the 2.5th percentile (Keep the correct data with `(df['height'] >= df['height'].quantile(0.025))`)
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- height is more than the 97.5th percentile
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- weight is less than the 2.5th percentile
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- weight is more than the 97.5th percentile
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12. Calculate the correlation matrix and store it in the `corr` variable
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13. Generate a mask for the upper triangle and store it in the `mask` variable
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14. Set up the `matplotlib` figure
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15. Plot the correlation matrix using the method provided by the `seaborn` library import: `sns.heatmap()`
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16. Do not modify the next two lines
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## 开发
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Write your code in `medical_data_visualizer.py`. For development, you can use `main.py` to test your code.
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## 测试
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The unit tests for this project are in `test_module.py`. 为了你的方便，我们将测试从 `test_module.py` 导入到 `main.py`。
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## 提交
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复制项目的 URL 并将其提交给 freeCodeCamp。
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# --hints--
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它应该通过所有的 Python 测试。
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```js
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```
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# --solutions--
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```py
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# Python challenges don't need solutions,
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# because they would need to be tested against a full working project.
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# Please check our contributing guidelines to learn more.
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```
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