03_WXN_matplotlib.Rmd

# Python作图 {#Python_plot_WXN}

## 绘图基础

###  Figure和Subplot


```python
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from numpy.random import randn
```


```python
x = [1, 3, 5, 7, 9, 10, 23, 45, 45, 56]
y = [2, 4, 6, 8, 11, 12, 23, 45, 56, 78]


fig = plt.figure()  # 创建一个Figure
ax1 = fig.add_subplot(2, 2, 1)  # 创建4个图的Figure对象，最后的1为选中第一个
ax2 = fig.add_subplot(2, 2, 2)
ax3 = fig.add_subplot(2, 2, 3)
ax4 = fig.add_subplot(2, 2, 4)

ax1.hist(x, y)  # 在第一图中绘制直方图
ax2.scatter(x, y)  # 散点图
ax4.plot(x, y)  # 线图

plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_4_0.png")
```



```python
fig, axes = plt.subplots(2, 3)  # 创建一个Figure 绘制2 X3 图
fig
```




```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_5_0.png")
```




```python
axes
```




    array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f8436a71780>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7f842f2438d0>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7f842f27d9e8>],
           [<matplotlib.axes._subplots.AxesSubplot object at 0x7f842f236978>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7f842f1f19b0>,
            <matplotlib.axes._subplots.AxesSubplot object at 0x7f842f199710>]], dtype=object)



```
创建2 X 3图像，可以相当于对二维数组进行索引
参数            说明
nrows           subplot行数
ncols           subplot列数
sharex          所有图使用相同的x轴
sharey          所有图使用相同的y轴
subplot_kw      用于创建各subplot的关键字字典
```|

### 调整subplot周围间距

```
subplots_ajust(left=None,bottom=None,right=None,top=None,wspace=None,hspace=None)
wspace和hspace控制宽度和高度
```


```python
fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)
for i in range(2):
    for j in range(2):
        axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_9_0.png")
```



```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_9_1.png")
```



```python
fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)
for i in range(2):
    for j in range(2):
        axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)
plt.subplots_adjust(wspace=2, hspace=0.5)
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_10_0.png")
```



```python
fig
```




```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_11_0.png")
```



### 颜色 标记和线型


```python
"""
绘制绿色虚线
ax.plot(x,y,'g--')
另一种方式
ax.plot(x,y,linestyle='--',color='g')
标记点（maker）
"""
fig, axes = plt.subplots(1, 2)

axes[0].plot(randn(10), 'g--')  # green ---
axes[1].plot(randn(10), 'ko--')  # k：black o：圆点

plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_13_0.png")
```


```
character	description
'-'	solid line style
'--'	dashed line style
'-.'	dash-dot line style
':'	dotted line style
'.'	point marker
','	pixel marker
'o'	circle marker
'v'	triangle_down marker
'^'	triangle_up marker
'<'	triangle_left marker
'>'	triangle_right marker
'1'	tri_down marker
'2'	tri_up marker
'3'	tri_left marker
'4'	tri_right marker
's'	square marker
'p'	pentagon marker
'*'	star marker
'h'	hexagon1 marker
'H'	hexagon2 marker
'+'	plus marker
'x'	x marker
'D'	diamond marker
'd'	thin_diamond marker
'|'	vline marker
'_'	hline marker

The following color abbreviations are supported:

character	color
‘b’	blue
‘g’	green
‘r’	red
‘c’	cyan(青色)
‘m’	magenta（紫红色）
‘y’	yellow
‘k’	black
‘w’	white

https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html#matplotlib.pyplot.plot
```

### 刻度、标签和图例


```python
fig = plt.figure()  # 创建一个Figure
ax = fig.add_subplot(1, 1, 1)
ax.plot(randn(100))
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_16_0.png")
```



```python
"""修改上图的轴"""

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(randn(100))

ticks = ax.set_xticks([0, 25, 50, 75, 100])  # 设置刻度
labels = ax.set_xticklabels(
    ['first', 'second', 'third', 'forth', 'fifth'], rotation=30, fontsize='small')  # 设置x轴标签

ax.set_title('my first matplot plot')  # 设置图片标题

ax.set_xlabel('Stages')  # 设置x轴名称

plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_17_0.png")
```


### 添加图例legend

```
https://matplotlib.org/api/legend_api.html?highlight=legend#module-matplotlib.legend
‘best’	        0
‘upper right’	1
‘upper left’	2
‘lower left’	3
‘lower right’	4
‘right’	        5
‘center left’	6
‘center right’	7
‘lower center’	8
‘upper center’	9
‘center’	    10
bbox_to_anchor=（0.5,0.8）
bbox_to_anchor被赋予的元组中，第一个数值用于控制legend的左右移动，值越大越向右边移动，
第二个数值用于控制legend的上下移动，值越大，越向上移动

```


```python
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)

ax.plot(randn(10), 'k', label='one')  # 画一条线，k黑色
ax.plot(randn(10), 'g--', label='two')  # 画第二条线，g绿色 - -类型
ax.plot(randn(10), 'ro--', label='three')  # 画第三条线红色 ，类型 ...

ax.legend(loc=0, bbox_to_anchor=(1, 1))


plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_19_0.png")
```


### 注解


```python
x = [2, 4, 6, 8, 10, 12]
y = [1, 3, 5, 7, 9, 11]
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(x, y, 'r--')

ax.text(2, 4, 'hello python')
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_21_0.png")
```



```python
ax = plt.subplot(111)

t = np.arange(0.0, 5.0, 0.01)
s = np.cos(2 * np.pi * t)
line, = plt.plot(t, s, lw=2)

plt.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
             arrowprops=dict(facecolor='blue')  # xy=（要注释的点），xytext=(注释在图中的位置)
             )

plt.ylim(-2, 2)
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_22_0.png")
```


### 图片保存


```python
x = [2, 4, 6, 8, 10, 12]
y = [1, 3, 5, 7, 9, 11]
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(x, y, 'r--')

ax.text(2, 4, 'hello python')

# bbox_inches减除当前图片周围空白部分
plt.savefig('figpath.jpg', dpi=300, bbox_inches='tight')
```

## 绘图实例

### 绘制散点图


```python
x = [1, 2, 3, 4, 5, 6]
y = [1, 4, 9, 16, 25, 36]
plt.scatter(x,  # x轴数据
            y,  # y轴数据
            s=20,  # 设置点的大小
            c='green',  # 设置点的颜色
            marker='s',  # 设置点的形状
            alpha=0.9,  # 设置点的透明度
            linewidths=0.8,  # 设置散点边界的粗细
            edgecolors='red'  # 设置散点边界的颜色
            )

plt.title('simple scatter plot')
plt.xlabel('X')  # x轴名称
plt.ylabel('Y')

plt.show()  # 展示绘图
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_27_0.png")
```


### 折线图


```python
x = [1, 2, 3, 4, 5, 6]
y = [1, 4, 9, 16, 25, 36]

plt.plot(x,  # x轴数据
         y,  # y轴数据
         linestyle='-',  # 折线类型
         linewidth=2,  # 折线宽度
         color='blue',  # 折线颜色
         marker='o',  # 点的形状
         markersize=8,  # 点的大小
         markeredgecolor='black',  # 点的边框色
         markerfacecolor='red')  # 点的填充色


# 添加标题和坐标轴标签
plt.title('line plot')
plt.xlabel('X')
plt.ylabel('Y')

plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_29_0.png")
```



```python
import numpy as np
import matplotlib.pyplot as plt


t = np.arange(0., 5., 0.2)

t
```




    array([ 0. ,  0.2,  0.4,  0.6,  0.8,  1. ,  1.2,  1.4,  1.6,  1.8,  2. ,
            2.2,  2.4,  2.6,  2.8,  3. ,  3.2,  3.4,  3.6,  3.8,  4. ,  4.2,
            4.4,  4.6,  4.8])




```python
# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g')
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_31_0.png")
```



```python
# 同一效果
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(t, t, 'r--')
ax.plot(t, t**2, 'bs')  # 'bs'表示blue square marker
ax.plot(t, t**3, 'g')
fig
```




```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_32_0.png")
```



### 直方图


```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

plt.hist(np.random.randn(50),  # 绘图数据
         bins=50,  # 指定直方图的条形数
         color='red',  # 指定填充色
         edgecolor='k')  # 指定直方图的边界色k :black
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_34_0.png")
```


### 直条图


```python
x = [1, 2, 3, 4, 5, 6]
y = [1, 4, 9, 16, 25, 36]

plt.bar(x, y,
        color='steelblue',
        alpha=0.8)

plt.title('bar plot')

plt.ylim([0, 40])
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_36_0.png")
```



```python
x = [1, 2, 3, 4, 5, 6]
y = [1, 4, 9, 16, 25, 36]

plt.barh(x, y,
         color='steelblue',
         alpha=0.8)

plt.title('bar plot')

plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_37_0.png")
```


### 箱线图


```python
x = [1, 2, 3, 4, 5, 6]
plt.boxplot(x,
            patch_artist=True,  # 箱体添加颜色
            labels=['boxplot'],  # 添加具体的标签名称
            showmeans=True)

# 显示图形
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_39_0.png")
```



```python
np.random.seed(2)  # 设置随机种子
df = pd.DataFrame(np.random.rand(5, 4),
                  columns=(['A', 'B', 'C', 'D']))
df
```



<!--html_preserve-->
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>A</th>
      <th>B</th>
      <th>C</th>
      <th>D</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0.435995</td>
      <td>0.025926</td>
      <td>0.549662</td>
      <td>0.435322</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0.420368</td>
      <td>0.330335</td>
      <td>0.204649</td>
      <td>0.619271</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.299655</td>
      <td>0.266827</td>
      <td>0.621134</td>
      <td>0.529142</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.134580</td>
      <td>0.513578</td>
      <td>0.184440</td>
      <td>0.785335</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.853975</td>
      <td>0.494237</td>
      <td>0.846561</td>
      <td>0.079645</td>
    </tr>
  </tbody>
</table>
</div>
<!--/html_preserve-->



```python
data = []
for i in range(4):
    data.append(df.iloc[:, i])
data
```




    [0    0.435995
     1    0.420368
     2    0.299655
     3    0.134580
     4    0.853975
     Name: A, dtype: float64, 0    0.025926
     1    0.330335
     2    0.266827
     3    0.513578
     4    0.494237
     Name: B, dtype: float64, 0    0.549662
     1    0.204649
     2    0.621134
     3    0.184440
     4    0.846561
     Name: C, dtype: float64, 0    0.435322
     1    0.619271
     2    0.529142
     3    0.785335
     4    0.079645
     Name: D, dtype: float64]




```python
plt.boxplot(data)
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_42_0.png")
```


```
plt.boxplot(x, notch=None, sym=None, vert=None, 
             whis=None, positions=None, widths=None, 
             patch_artist=None, meanline=None, showmeans=None, 
             showcaps=None, showbox=None, showfliers=None, 
             boxprops=None, labels=None, flierprops=None, 
             medianprops=None, meanprops=None, 
             capprops=None, whiskerprops=None)

x：指定要绘制箱线图的数据；
notch：是否凹口的形式展现箱线图，默认非凹口；
sym：指定异常点的形状，默认为+号显示；
vert：是否需要将箱线图垂直摆放，默认垂直摆放；
whis：指定上下须与上下四分位的距离，默认为1.5倍的四分位差；
positions：指定箱线图的位置，默认为[0,1,2…]；
widths：指定箱线图的宽度，默认为0.5；
patch_artist：是否填充箱体的颜色；
meanline：是否用线的形式表示均值，默认用点来表示；
showmeans：是否显示均值，默认不显示；
showcaps：是否显示箱线图顶端和末端的两条线，默认显示；
showbox：是否显示箱线图的箱体，默认显示；
showfliers：是否显示异常值，默认显示；
boxprops：设置箱体的属性，如边框色，填充色等；
labels：为箱线图添加标签，类似于图例的作用；
filerprops：设置异常值的属性，如异常点的形状、大小、填充色等；
medianprops：设置中位数的属性，如线的类型、粗细等；
meanprops：设置均值的属性，如点的大小、颜色等；
capprops：设置箱线图顶端和末端线条的属性，如颜色、粗细等；
whiskerprops：设置须的属性，如颜色、粗细、线的类型等；
```

### 饼图


```python
data = [0.2, 0.3, 0.4, 0.1]
plt.pie(data)
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_45_0.png")
```


```
plt.pie(x, explode=None, labels=None, colors=None, 
        autopct=None, pctdistance=0.6, shadow=False, 
        labeldistance=1.1, startangle=None, 
        radius=None, counterclock=True, wedgeprops=None, 
        textprops=None, center=(0, 0), frame=False)
        
x：指定绘图的数据；
explode：指定饼图某些部分的突出显示，即呈现爆炸式；
labels：为饼图添加标签说明，类似于图例说明；
colors：指定饼图的填充色；
autopct：自动添加百分比显示，可以采用格式化的方法显示；
pctdistance：设置百分比标签与圆心的距离；
shadow：是否添加饼图的阴影效果；
labeldistance：设置各扇形标签（图例）与圆心的距离；
startangle：设置饼图的初始摆放角度；
radius：设置饼图的半径大小；
counterclock：是否让饼图按逆时针顺序呈现；
wedgeprops：设置饼图内外边界的属性，如边界线的粗细、颜色等；
textprops：设置饼图中文本的属性，如字体大小、颜色等；
center：指定饼图的中心点位置，默认为原点
frame：是否要显示饼图背后的图框，如果设置为True的话，需要同时控制图框x轴、y轴的范围和饼图的中心位置；
```

### 绘制基因矩阵的热图


```python
# 来源于百度经验
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm
from matplotlib import axes


def draw_heatmap(data, xlabels, ylabels):  # 自定义函数，3个参数
    cmap = cm.Blues
    figure = plt.figure(facecolor='w')
    ax = figure.add_subplot(1, 1, 1, position=[0.1, 0.15, 0.8, 0.8])
    ax.set_yticks(range(len(ylabels)))
    ax.set_yticklabels(ylabels)
    ax.set_xticks(range(len(xlabels)))
    ax.set_xticklabels(xlabels)
    vmax = data[0][0]
    vmin = data[0][0]
    for i in data:
        for j in i:
            if j > vmax:
                vmax = j
            if j < vmin:
                vmin = j
    map = ax.imshow(data, interpolation='nearest', cmap=cmap, aspect='auto',
                    vmin=vmin, vmax=vmax)  # interpolation插值，cmap:colormap
    cb = plt.colorbar(mappable=map, cax=None, ax=None, shrink=0.5)  # 绘制颜色条
    plt.show()


a = np.random.rand(10, 10)
xlabels = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
ylabels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
draw_heatmap(a, xlabels, ylabels)
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_48_0.png")
```



```python
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(5, 6))
df
```




<!--html_preserve-->
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
      <th>2</th>
      <th>3</th>
      <th>4</th>
      <th>5</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-0.596967</td>
      <td>0.080979</td>
      <td>-1.890122</td>
      <td>-0.629949</td>
      <td>-1.187113</td>
      <td>-0.002018</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1.264042</td>
      <td>0.044216</td>
      <td>-1.669342</td>
      <td>0.805476</td>
      <td>-0.173634</td>
      <td>-0.051918</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.793684</td>
      <td>1.746997</td>
      <td>0.785712</td>
      <td>-0.157126</td>
      <td>0.072688</td>
      <td>0.310271</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0.988716</td>
      <td>0.404472</td>
      <td>0.967111</td>
      <td>-0.612160</td>
      <td>-0.259917</td>
      <td>-0.067299</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.033911</td>
      <td>1.177149</td>
      <td>-0.914153</td>
      <td>-0.579912</td>
      <td>-0.100110</td>
      <td>0.747296</td>
    </tr>
  </tbody>
</table>
</div>
<!--/html_preserve-->




```python
plt.imshow(df)

plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_50_0.png")
```


读入基因表达数据，绘制某基因的箱线图


```python
%cd F: \mega\hello\python\python课程包\merge
import os
os.getcwd()
```

    F:\mega\hello\python\python课程包\merge





    'F:\\mega\\hello\\python\\python课程包\\merge'




```python
import pandas as pd
df = pd.read_table("hello.xls", sep="\t", index_col=0, header=0)
df.head(5)
```




<!--html_preserve-->
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>MCF10F</th>
      <th>MCF12A</th>
      <th>T47DKBluc</th>
      <th>UACC812</th>
      <th>UACC893</th>
      <th>ZR751</th>
      <th>ZR7530</th>
      <th>ZR75B</th>
    </tr>
    <tr>
      <th>EnsEMBL_Gene_ID</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>ENSG00000000003</th>
      <td>91.574061</td>
      <td>13.040870</td>
      <td>73.387310</td>
      <td>26.579280</td>
      <td>50.676579</td>
      <td>31.812999</td>
      <td>121.459918</td>
      <td>2.293898</td>
    </tr>
    <tr>
      <th>ENSG00000001167</th>
      <td>164.221408</td>
      <td>170.101403</td>
      <td>106.114416</td>
      <td>138.140311</td>
      <td>106.342032</td>
      <td>68.458022</td>
      <td>73.910964</td>
      <td>61.625519</td>
    </tr>
    <tr>
      <th>ENSG00000005471</th>
      <td>0.000000</td>
      <td>0.073680</td>
      <td>0.536226</td>
      <td>0.440649</td>
      <td>0.066213</td>
      <td>0.548865</td>
      <td>0.611036</td>
      <td>0.556531</td>
    </tr>
    <tr>
      <th>ENSG00000066629</th>
      <td>24.630773</td>
      <td>18.627459</td>
      <td>34.619613</td>
      <td>79.187879</td>
      <td>0.058835</td>
      <td>25.493800</td>
      <td>25.917323</td>
      <td>32.528112</td>
    </tr>
    <tr>
      <th>ENSG00000154258</th>
      <td>0.896612</td>
      <td>0.214261</td>
      <td>0.000000</td>
      <td>0.040907</td>
      <td>0.021120</td>
      <td>0.000000</td>
      <td>0.033571</td>
      <td>0.177808</td>
    </tr>
  </tbody>
</table>
</div>
<!--/html_preserve-->




```python
df.shape
```




    (36953, 8)




```python
df.size
df.ndim
df.values
```




    array([[  9.15740610e+01,   1.30408701e+01,   7.33873101e+01, ...,
              3.18129988e+01,   1.21459918e+02,   2.29389818e+00],
           [  1.64221408e+02,   1.70101403e+02,   1.06114416e+02, ...,
              6.84580224e+01,   7.39109636e+01,   6.16255191e+01],
           [  0.00000000e+00,   7.36800986e-02,   5.36226145e-01, ...,
              5.48864965e-01,   6.11036013e-01,   5.56531115e-01],
           ..., 
           [  2.71701826e+00,   5.55925621e+00,   2.93123839e+00, ...,
              3.71765369e+00,   2.42540639e+00,   4.56656068e+00],
           [  1.74266818e+01,   3.72964028e+00,   3.49013629e+01, ...,
              5.04982802e+01,   3.75784692e+01,   4.79996494e+01],
           [  0.00000000e+00,   0.00000000e+00,   3.52988338e+00, ...,
              8.15259649e+00,   6.08178979e-01,   3.25252710e+00]])




```python
control = []
for i in range(3):
    control.append(df.iloc[0, i])
control
```




    [91.574060981100004, 13.0408701074, 73.387310079800002]




```python
test = []
for i in range(3, 8):
    test.append(df.iloc[0, i])
test
```




    [26.579280359299997,
     50.676578905299998,
     31.812998762600003,
     121.4599177489,
     2.2938981847999997]




```python
gene = [control, test]
```


```python
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.boxplot(gene, patch_artist=True,  # 箱体添加颜色
           labels=['test', 'control'],  # 添加具体的标签名称
           showmeans=True, )
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_59_0.png")
```


热图


```python
df2 = df.head(10)  # 取部分数据
```


```python
df2
```




<!--html_preserve-->
<div>
<style>
    .dataframe thead tr:only-child th {
        text-align: right;
    }

    .dataframe thead th {
        text-align: left;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>MCF10F</th>
      <th>MCF12A</th>
      <th>T47DKBluc</th>
      <th>UACC812</th>
      <th>UACC893</th>
      <th>ZR751</th>
      <th>ZR7530</th>
      <th>ZR75B</th>
    </tr>
    <tr>
      <th>EnsEMBL_Gene_ID</th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>ENSG00000000003</th>
      <td>91.574061</td>
      <td>13.040870</td>
      <td>73.387310</td>
      <td>26.579280</td>
      <td>50.676579</td>
      <td>31.812999</td>
      <td>121.459918</td>
      <td>2.293898</td>
    </tr>
    <tr>
      <th>ENSG00000001167</th>
      <td>164.221408</td>
      <td>170.101403</td>
      <td>106.114416</td>
      <td>138.140311</td>
      <td>106.342032</td>
      <td>68.458022</td>
      <td>73.910964</td>
      <td>61.625519</td>
    </tr>
    <tr>
      <th>ENSG00000005471</th>
      <td>0.000000</td>
      <td>0.073680</td>
      <td>0.536226</td>
      <td>0.440649</td>
      <td>0.066213</td>
      <td>0.548865</td>
      <td>0.611036</td>
      <td>0.556531</td>
    </tr>
    <tr>
      <th>ENSG00000066629</th>
      <td>24.630773</td>
      <td>18.627459</td>
      <td>34.619613</td>
      <td>79.187879</td>
      <td>0.058835</td>
      <td>25.493800</td>
      <td>25.917323</td>
      <td>32.528112</td>
    </tr>
    <tr>
      <th>ENSG00000154258</th>
      <td>0.896612</td>
      <td>0.214261</td>
      <td>0.000000</td>
      <td>0.040907</td>
      <td>0.021120</td>
      <td>0.000000</td>
      <td>0.033571</td>
      <td>0.177808</td>
    </tr>
    <tr>
      <th>ENSG00000154262</th>
      <td>3.431313</td>
      <td>2.481636</td>
      <td>0.000000</td>
      <td>0.383060</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.105758</td>
    </tr>
    <tr>
      <th>ENSG00000154263</th>
      <td>0.859071</td>
      <td>0.936069</td>
      <td>0.589547</td>
      <td>0.259898</td>
      <td>0.083258</td>
      <td>0.476794</td>
      <td>0.526162</td>
      <td>0.221172</td>
    </tr>
    <tr>
      <th>ENSG00000154265</th>
      <td>2.453971</td>
      <td>1.480553</td>
      <td>9.884425</td>
      <td>2.076723</td>
      <td>1.118819</td>
      <td>5.864268</td>
      <td>10.180796</td>
      <td>4.383174</td>
    </tr>
    <tr>
      <th>ENSG00000154269</th>
      <td>0.216739</td>
      <td>0.000000</td>
      <td>0.854111</td>
      <td>0.970053</td>
      <td>0.738803</td>
      <td>0.521094</td>
      <td>4.648410</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>ENSG00000154274</th>
      <td>2.980792</td>
      <td>0.217999</td>
      <td>276.123679</td>
      <td>82.820443</td>
      <td>34.664053</td>
      <td>106.350607</td>
      <td>94.854069</td>
      <td>14.976808</td>
    </tr>
  </tbody>
</table>
</div>
<!--/html_preserve-->




```python
i = plt.imshow(df2)
plt.colorbar(i)
plt.show()
```


```{r}
knitr::include_graphics("03_WXN_matplotlib_files/03_WXN_matplotlib_63_0.png")
```


https://matplotlib.org/api/pyplot_api.html#所有函数的详细参数。