# 2.1.5.Python for Data Visual Visualization - Pandas Built-in Data Visualization

![](/files/-M4M0UfyNEBeGoUKDZlD)

## 1.使用library

```
import numpy as np
import pandas as pd
```

* 將matplotlib的圖表直接嵌入到Notebook之中

```
%matplotlib inline
```

## 2.資料判讀與讀取

* 讀取資料格式 (df1)
  * 由於df1的資料有時間這個索引值, 因此必須指定index為0

    ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午9.38.34.png)
  * 讀取資料

    ```
    df1 = pd.read_csv('df1', index_col = 0)
    df1.head()
    ```

    ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午9.47.12.png)
* 讀取資料格式 (df2)
  * 由於此資料沒有索引值, 不需指定index ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午9.42.39.png)
  * 讀取資料

    ```
    df2 = pd.read_csv('df2')
    df2.head()
    ```

    ![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午9.55.00.png)

## 3.Setting before plot

* 一般會配合[Matplotlib](https://jenhsuan.gitbooks.io/python/content/test.html)一起使用
  * 設定圖的大小

    ```
    plt.figure(figsize=(10,6))
    ```

## 4.Histogram plot

* 將df1的資料畫成長條圖

```
df1['A'].hist(bins = 30)
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.00.58.png)

* 也可以用

```
df1['A'].plot(kind='hist', bins = 30)
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.03.32.png)

* 可以搭配filter使用, 例如

```
df[df[B] == 1][A].plot(kind='hist', bins = 30)
```

* 用hist比較不同性質的資料

```
df = pd.read_csv('loan_data.csv')
df.head()
plt.figure()
df[df['credit.policy']==1]['fico'].plot(kind = 'hist', bins = 30, color = 'blue', alpha = 0.5, label = 'Credit.Policy=1')
df[df['credit.policy']==0]['fico'].plot(kind = 'hist', bins = 30, color = 'red', alpha = 0.5, label = 'Credit.Policy=0')
plt.legend()
plt.xlabel('FICO')
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-06-30%20下午12.45.24.png)

## 5.Area plot

* alpha為透明度參數

```
df2.plot.area(alpha = 0.4)
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.08.24.png)

## 6.Line plot

* 基本用法

```
df1.plot.line(x=df1.index, y = 'B')
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.12.02.png)

## 6.Bar plot

* 基本用法

```
df2.plot.bar(alpha = 0.4)
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.11.12.png)

* stacked

```
df2.plot.bar(alpha = 0.4, stacked = True)
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.11.07.png)

## 7.Scatter plot

* 基本用法

```
df1.plot.scatter(x='A', y='B')
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.15.14.png)

* 將原始資料作為色調值的來源

```
df1.plot.scatter(x='A', y='B', c='C', cmap='coolwarm')
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.15.19.png)

* 將原始資料作為尺度的來源

```
df1.plot.scatter(x='A', y='B', s=df1['C']*100)
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.17.07.png)

## 8.Box plot

* 基本用法, 關於boxplot的定義可以參考[此篇筆記](https://jenhsuan.gitbooks.io/python/content/217python-for-data-visualization-seaborn/2173categorical-plots.html)

```
df2.plot.box()
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.21.19.png)

* 將兩資料的box plot畫在一起

```
df1[['A','B']].plot.box()
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.45.47.png)

## 9.Density plot

* 基本用法

```
df2.plot.density()
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.25.12.png)

* 改變線條的style (width, size)

```
df2.plot.density(lw=5,ls='--')
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.49.54.png)

## 10.Hexbin plot

* 基本用法

```
df = pd.DataFrame(np.random.randn(1000, 2), columns=['a', 'b'])df.plot.hexbin(x='a', y='b', gridsize=25, cmap='coolwarm')
```

![](https://github.com/jenhsuan/python/tree/8fc9c0b8df4ccd709d3078c2d8842af0932de09d/assets/螢幕快照%202018-05-27%20下午10.25.56.png)


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