> For the complete documentation index, see [llms.txt](https://jen-hsuan-hsieh.gitbook.io/python/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://jen-hsuan-hsieh.gitbook.io/python/chapter-2courses/21python-for-data-science-and-machine-learning-bootcamp/217python-for-data-visualization-seaborn/2172distribution-plot.md).

# 2.1.7.2.Distribution Plots

## 0. Distribution Plots的繪圖種類

* sns.distplot (kde, bin)
* sns.jointplot
* sns.pairplot
* sns.rugplot

## 1. 使用library

```
import seaborn as sns
```

* 將圖表直接嵌入到Notebook之中

```
%matplotlib inline
```

* 讀入資料

```
tips = sns.load_dataset('tips')
tips.head()
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-19%20上午9.13.50.png)

## [2.Distribution plot](https://seaborn.pydata.org/tutorial/distributions.html)

* [等義於使用pandas內建的plot.hist或是plot(kind = 'hist')](https://jenhsuan.gitbooks.io/python/content/2323.html)

  ```
    tips['total_bill'].hist(bins = 30)
    tips['total_bill'].plot(kind='hist', bins = 30)
    tips['total_bill'].plot.hist(bins = 30)
  ```
* kde (Kernel distribution estimation)預設為true
  * 也可以設為False

    ```
    sns.distplot(tips['total_bill'], kde=False)
    sns.distplot(tips['total_bill'])
    ```

    ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-19%20上午9.08.28.png)
* bin

```
sns.distplot(tips['total_bill'], kde=False, bins=30)
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-19%20上午9.09.22.png)

## [3. Joint plot](https://seaborn.pydata.org/generated/seaborn.jointplot.html)

* 比較資料來源中的兩個屬性之間的相關性或非相關性
  * 繪圖前可以先設定背景的方格線

    ```
    sns.set_style('whitegrid')
    ```
  * 例如比較資料中**帳單金額**與**小費**間的關聯性

    ```
    sns.jointplot(x='total_bill',y='tip',data=tips)
    ```

    ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-19%20上午9.16.04.png)
  * 用顏色深淺來代表相關性 (顏色越深, 相關性越高)

    ```
    sns.jointplot(x='total_bill',y='tip',data=tips,kind='hex')
    ```

    ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-19%20上午9.18.35.png)
  * 線性回歸圖

    ```
    sns.jointplot(x='total_bill',y='tip',data=tips,kind='reg')
    ```

    ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-19%20上午9.20.14.png)
  * kde (Kernel distribution estimation)

    ```
    sns.jointplot(x='total_bill',y='tip',data=tips,kind='kde')
    ```

    ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-19%20上午9.21.39.png)

## 4.Pair plot

* 可以將資料來源中的數值欄位一一比較, 畫出關聯圖
  * 輸入資料來源

    ```
    sns.pairplot(tips)
    ```

    ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-19%20上午9.26.26.png)
  * 加入類別欄位的比較

    * 例如比較性別在資料上的差異

    ```
    sns.pairplot(tips,hue='sex')
    ```

    ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-19%20上午9.23.40.png)
  * 調整色系

    ```
    sns.pairplot(tips,hue='sex',palette='coolwarm')
    ```

    ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-19%20上午9.27.25.png)

## [5.Rug plot](https://seaborn.pydata.org/generated/seaborn.rugplot.html)

* 以stick的方式表現資料

```
sns.rugplot(tips['total_bill'])
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-19%20上午9.30.46.png)