Python 学习笔记 095

```python

#import pandas as pd

import matplotlib.pypblot

```

```python

import numpy as np

from matplotlib import pyplot as plt #plt为变量名

x = np.arange(1,120)

y = 2 * x* x + 5

plt.title("Matplotlib demo") #定义图表标题

plt.xlabel("x axis caption") #横轴标签

plt.ylabel("y axis caption") # 纵轴标签

plt.plot(x,y)

#plt.plot(y,x) #反函数的绘制

plt.show()

```

![png](output_1_0.png)

```python

a=sorted([f.name for f in matplotlib.font_manager.fontManager.ttflist])

for i in a:

print(i)

#打印支持的字体列表

```

AR BERKLEY

AR BLANCA

AR BONNIE

AR CARTER

AR CENA

AR CHRISTY

AR DARLING

AR DECODE

AR DELANEY

AR DESTINE

AR ESSENCE

AR HERMANN

AR JULIAN

Aharoni

Aldhabi

Algerian

Andalus

Angsana New

AngsanaUPC

Aparajita

Arabic Typesetting

Arial

Baskerville Old Face

Bauhaus 93

Bell MT

Berlin Sans FB

Berlin Sans FB Demi

Bernard MT Condensed

Bodoni MT

Book Antiqua

Bookman Old Style

Bookshelf Symbol 7

Britannic Bold

Broadway

Browallia New

BrowalliaUPC

Brush Script MT

Calibri

Californian FB

Cambria

Candara

Centaur

Century

Century Gothic

Chiller

Colonna MT

Comic Sans MS

Consolas

Constantia

Cooper Black

Corbel

Cordia New

CordiaUPC

Courier New

DFKai-SB

DaunPenh

David

DejaVu Sans

DejaVu Sans Display

DejaVu Sans Mono

DejaVu Serif

DejaVu Serif Display

DengXian

DilleniaUPC

DokChampa

Ebrima

Estrangelo Edessa

EucrosiaUPC

Euphemia

FZShuTi

FZYaoTi

FangSong

Footlight MT Light

FrankRuehl

Franklin Gothic Medium

FreesiaUPC

Freestyle Script

Gabriola

Gadugi

Garamond

Gautami

Georgia

Gisha

Harlow Solid Italic

Harrington

High Tower Text

Impact

Informal Roman

IrisUPC

Iskoola Pota

JasmineUPC

Javanese Text

Jokerman

Juice ITC

KaiTi

Kalinga

Kartika

Khmer UI

KodchiangUPC

Kokila

Kristen ITC

Kunstler Script

Lao UI

Latha

Leelawadee

Leelawadee UI

Levenim MT

LiSu

LilyUPC

Lucida Bright

Lucida Calligraphy

Lucida Console

Lucida Fax

Lucida Handwriting

Lucida Sans Unicode

MS Reference Sans Serif

MS Reference Specialty

MT Extra

MV Boli

Magneto

Malgun Gothic

Mangal

Marlett

Matura MT Script Capitals

Microsoft Himalaya

Microsoft New Tai Lue

Microsoft PhagsPa

Microsoft Sans Serif

Microsoft Tai Le

Microsoft Uighur

Microsoft Yi Baiti

Miriam

Miriam Fixed

Mistral

Modern No. 20

Mongolian Baiti

Monotype Corsiva

MoolBoran

Myanmar Text

Narkisim

Niagara Engraved

Niagara Solid

Nirmala UI

Nyala

Old English Text MT

Onyx

Palatino Linotype

Parchment

Plantagenet Cherokee

Playbill

Poor Richard

Raavi

Ravie

Rod

STCaiyun

STFangsong

STHupo

STIXGeneral

STIXNonUnicode

STIXSizeFiveSym

STIXSizeFourSym

STIXSizeOneSym

STIXSizeThreeSym

STIXSizeTwoSym

STKaiti

STLiti

STSong

STXihei

STXingkai

STXinwei

STZhongsong

Sakkal Majalla

Segoe Print

Segoe Script

Segoe UI

Segoe UI Emoji

Segoe UI Symbol

Shonar Bangla

Showcard Gothic

Shruti

SimHei

SimSun-ExtB

Simplified Arabic

Simplified Arabic Fixed

Snap ITC

Stencil

Sylfaen

Symbol

Tahoma

TeamViewer15

Tempus Sans ITC

Times New Roman

Traditional Arabic

Trebuchet MS

Tunga

Urdu Typesetting

Utsaah

Vani

Verdana

Vijaya

Viner Hand ITC

Vivaldi

Vladimir Script

Vrinda

Webdings

Wide Latin

Wingdings

Wingdings 2

Wingdings 3

YouYuan

Yu Gothic

Yu Mincho

cmb10

cmex10

cmmi10

cmr10

cmss10

cmsy10

cmtt10

Matplotlib 默认情况不支持中文

我们可以使用系统的字体：

```python

import numpy as np

from matplotlib import pyplot as plt

import matplotlib

# fname 为你下载的字体库路径，注意 SimHei.ttf 字体的路径

zhfont1 = matplotlib.font_manager.FontProperties(fname="C:\Windows\Fonts\msyh.ttc") #微软雅黑

x = np.arange(1,11)

y = 2 * x + 5

plt.title("菜鸟教程 - 测试", fontproperties=zhfont1)

# fontproperties 设置中文显示，fontsize 设置字体大小

plt.xlabel("x 轴", fontproperties=zhfont1,)

plt.ylabel("y 轴", fontproperties=zhfont1)

plt.plot(x,y)

plt.show()

```

![png](output_4_0.png)

中文字体测试

```python

import numpy as np

from matplotlib import pyplot as plt

plt.rcParams['font.family']=['Arial'] #字体使用Arial字体

x = np.arange(1,11)

y = 2 * x + 5

plt.title("Matplotlib demo")

plt.xlabel("x axis caption")

plt.ylabel("y axis caption")

plt.plot(x,y,"ob") #散点图属性为ob

plt.show()

```

![png](output_6_0.png)

散点图测试

使用系统字体

```python

import numpy as np

import matplotlib.pyplot as plt

# 计算正弦曲线上点的 x 和 y 坐标

x = np.arange(0, 3 * np.pi, 0.1)

y = np.sin(x)

plt.title("sine wave form")

# 使用 matplotlib 来绘制点

plt.plot(x, y)

plt.show()

```

![png](output_8_0.png)

绘制正弦波

```python

import numpy as np

import matplotlib.pyplot as plt

# 计算正弦和余弦曲线上的点的 x 和 y 坐标

x = np.arange(0, 3 * np.pi, 0.1)

y_sin = np.sin(x)

y_cos = np.cos(x)

# 建立 subplot 网格，高为 2，宽为 1

# 激活第一个 subplot

plt.subplot(2, 1, 1)

# 绘制第一个图像

plt.plot(x, y_sin)

plt.title('Sine')

# 将第二个 subplot 激活，并绘制第二个图像

plt.subplot(2, 1, 2)

plt.plot(x, y_cos)

plt.title('Cosine')

# 展示图像

plt.show()

```

![png](output_10_0.png)

绘制两个图形

```python

from matplotlib import pyplot as plt

x = [5,8,10]

y = [12,16,6]

x2 = [6,9,11]

y2 = [6,15,7]

plt.bar(x, y, align = 'center')

plt.bar(x2, y2, color = 'g', align = 'center')

plt.title('Bar graph')

plt.ylabel('Y axis')

plt.xlabel('X axis')

plt.show()

```

![png](output_12_0.png)

绘制条形图

```python

import numpy as np

a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])

np.histogram(a,bins = [0,20,40,60,80,100])

hist,bins = np.histogram(a,bins = [0,20,40,60,80,100])

print (hist)

print (bins)

```

[3 4 5 2 1]

[ 0 20 40 60 80 100]

#### numpy.histogram()

numpy.histogram() 函数是数据的频率分布的图形表示。水平尺寸相等的矩形对应于类间隔，称为 bin，变量 height 对应于频率。

numpy.histogram()函数将输入数组和 bin 作为两个参数。 bin 数组中的连续元素用作每个 bin 的边界。

```python

from matplotlib import pyplot as plt

import numpy as np

a = np.array([22,87,5,43,56,73,55,54,11,20,51,5,79,31,27])

plt.hist(a, bins = [0,20,40,60,80,100])

plt.title("histogram")

plt.show()

```

![png](output_16_0.png)

#### plt()

Matplotlib 可以将直方图的数字表示转换为图形。

pyplot 子模块的 plt() 函数将包含数据和 bin 数组的数组作为参数，并转换为直方图。

### 测试matplotlib & numpy 库

np.arange() 函数创建 x 轴上的值。y 轴上的对应值存储在另一个数组对象 y 中。

这些值使用 matplotlib 软件包的 pyplot 子模块的 plot() 函数绘制。

图形由 show() 函数显示。

```python

import pandas as pd

dataset = pd.read_csv('pythontest.csv')

dataset.head(10)

```

<div>

.dataframe tbody tr th:only-of-type {

vertical-align: middle;

}

.dataframe tbody tr th {

vertical-align: top;

}

.dataframe thead th {

text-align: right;

}

</style>

<thead>

<th>LotID</th>

<th>GlassID</th>

<th>AreaNo</th>

<th>AreaX</th>

<th>AreaY</th>

<th>RMSESet</th>

<th>RMSEFin</th>

<th>Recipe</th>

</tr>

</thead>

<tbody>

<tr>

<td>5CCBDX0197M</td>

</tr>

<tr>

<td>5CCBDX0197M</td>

</tr>

<tr>

<td>5CCBDX0197M</td>

</tr>

<tr>

<td>5CCBDX0197M</td>

</tr>

<tr>

<td>5CCBDX0197M</td>

</tr>

<tr>

<td>5CCBDX0197M</td>

</tr>

<tr>

<td>5CCBDX0197M</td>

</tr>

<tr>

<td>5CCBDX0197M</td>

</tr>

<tr>

<td>5CCBDX0197M</td>

</tr>

<tr>

<td>5CCBDX0197M</td>

</tr>

</tbody>

</table>

</div>

```python

dataset.L2T.head(10)

y = dataset.L2T

print(y.head(10),dataset.L2T.head(10)) #普通数组也有head属性

```

0 1010.52

1 1008.73

2 1044.63

3 1069.88

4 1049.54

5 1042.95

6 1081.89

7 1093.59

8 1037.44

9 1057.37

Name: L2T, dtype: float64 0 1010.52

1 1008.73

2 1044.63

3 1069.88

4 1049.54

5 1042.95

6 1081.89

7 1093.59

8 1037.44

9 1057.37

Name: L2T, dtype: float64

#### pause

#### Closed