006.python科学计算库matplotlib(上)

谁借莪１个温暖的怀抱￠ 2021-10-06 15:18 400阅读 0赞

### 测试数据 [unrate.csv][] ###

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### plot ###

import matplotlib.pyplot as plt # 使用不同的pyplot函数，我们可以创建、定制和显示一个plot。例如，我们可以使用两个函数 plt.plot() plt.show()

![pycharm64_2018-09-16_11-07-21.png][]

![pycharm64_2018-09-16_11-15-17.png][]

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### xticks ###

![pycharm64_2018-09-16_11-20-16.png][]

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### xlabel/ylabel/title ###

import matplotlib.pyplot as plt import pandas as pd # 使用不同的pyplot函数，我们可以创建、定制和显示一个plot。例如，我们可以使用两个函数 unrate = pd.read_csv('unrate.csv') first_twelve = unrate[0:12] plt.plot(first_twelve['DATE'], first_twelve['VALUE']) # 虽然y轴看起来不错，但x轴标记标签太过接近，无法阅读 # 我们可以旋转x轴刻度标签90度，这样它们就不会重叠 # 我们可以使用浮点数或整数值来指定旋转的程度 # rotation 指定x轴标签的角度 plt.xticks(rotation=90) # xlabel(): 接受一个字符串值，该值被设置为x轴标签 plt.xlabel('Month') # ylabel(): 接受一个字符串值，该值被设置为y轴标签 plt.ylabel('Unemployment Rate') # title(): 接受一个字符串值，该值被设置为plot标题 plt.title('Monthly Unemployment Trends, 1948') # 添加test文本 plt.text('1948-06-01', 3.65, "test") plt.show()

![pycharm64_2018-09-17_10-42-08.png][]

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### add\_subplot ###

import matplotlib.pyplot as plt # figure 创建一个新图形 fig = plt.figure() # add_subplot(first,second,index) first表示行数，second表示列数 ax1 = fig.add_subplot(3, 2, 1) ax2 = fig.add_subplot(3, 2, 2) ax2 = fig.add_subplot(3, 2, 6) plt.show()

![mspaint_2018-09-16_12-31-17.png][]

import matplotlib.pyplot as plt import numpy as np # figure 创建一个新图形 # figsize 宽度，高度以英寸为单位。如果没有提供，默认为rc图形 fig = plt.figure(figsize=(3, 3)) # add_subplot(first,second,index) first表示行数，second表示列数 ax1 = fig.add_subplot(2, 1, 1) ax2 = fig.add_subplot(2, 1, 2) # 随机值 ax1.plot(np.random.randint(1, 5, 5), np.arange(5)) ax1.text(2, 2, "ax1") ax2.plot(np.arange(10) * 3, np.arange(10)) ax2.text(10, 5, "ax2") plt.show()

![pycharm64_2018-09-17_10-44-46.png][]

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### figure上添加多个plot ###

import matplotlib.pyplot as plt import pandas as pd unrate = pd.read_csv('unrate.csv') # 将DATE列转为日期格式 unrate['DATE'] = pd.to_datetime(unrate['DATE']) unrate['MONTH'] = unrate['DATE'].dt.month fig = plt.figure(figsize=(6, 3)) # 在figure上添加两条线段 plt.plot(unrate[0:12]['MONTH'], unrate[0:12]['VALUE'], c='red') plt.plot(unrate[12:24]['MONTH'], unrate[12:24]['VALUE'], c='blue') plt.show()

![pycharm64_2018-09-16_12-42-44.png][]

import matplotlib.pyplot as plt import pandas as pd unrate = pd.read_csv('unrate.csv') # 将DATE列转为日期格式 unrate['DATE'] = pd.to_datetime(unrate['DATE']) unrate['MONTH'] = unrate['DATE'].dt.month fig = plt.figure(figsize=(10, 6)) colors = ['red', 'blue', 'green', 'orange', 'black'] # 五年的数据线段对比 for i in range(5): start_index = i * 12 end_index = (i + 1) * 12 subset = unrate[start_index:end_index] plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i]) plt.show()

![pycharm64_2018-09-16_12-46-01.png][]

import matplotlib.pyplot as plt import pandas as pd unrate = pd.read_csv('unrate.csv') # 将DATE列转为日期格式 unrate['DATE'] = pd.to_datetime(unrate['DATE']) unrate['MONTH'] = unrate['DATE'].dt.month fig = plt.figure(figsize=(10, 6)) colors = ['red', 'blue', 'green', 'orange', 'black'] for i in range(5): start_index = i * 12 end_index = (i + 1) * 12 subset = unrate[start_index:end_index] label = str(1948 + i) plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i], label=label) plt.legend(loc='best') plt.show()

![pycharm64_2018-09-16_12-54-24.png][]  
**其中，plt.legend(loc=‘best’),loc参数定义的是标签框的放置位置**  
![pycharm64_2018-09-16_12-55-36.png][]

import matplotlib.pyplot as plt import pandas as pd unrate = pd.read_csv('unrate.csv') # 将DATE列转为日期格式 unrate['DATE'] = pd.to_datetime(unrate['DATE']) unrate['MONTH'] = unrate['DATE'].dt.month fig = plt.figure(figsize=(10, 6)) colors = ['red', 'blue', 'green', 'orange', 'black'] for i in range(5): start_index = i * 12 end_index = (i + 1) * 12 subset = unrate[start_index:end_index] label = str(1948 + i) plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i], label=label) plt.legend(loc='upper left') plt.xlabel('Month, Integer') plt.ylabel('Unemployment Rate, Percent') plt.title('Monthly Unemployment Trends, 1948-1952') plt.show()

![pycharm64_2018-09-16_12-58-01.png][]

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[unrate.csv]: http://upyun.qubianzhong.cn/file/python_matplotlib/unrate.csv
[pycharm64_2018-09-16_11-07-21.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_11-07-21.png
[pycharm64_2018-09-16_11-15-17.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_11-15-17.png
[pycharm64_2018-09-16_11-20-16.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_11-20-16.png
[pycharm64_2018-09-17_10-42-08.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-17_10-42-08.png
[mspaint_2018-09-16_12-31-17.png]: http://upyun.qubianzhong.cn/picture/mspaint_2018-09-16_12-31-17.png
[pycharm64_2018-09-17_10-44-46.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-17_10-44-46.png
[pycharm64_2018-09-16_12-42-44.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_12-42-44.png
[pycharm64_2018-09-16_12-46-01.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_12-46-01.png
[pycharm64_2018-09-16_12-54-24.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_12-54-24.png
[pycharm64_2018-09-16_12-55-36.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_12-55-36.png
[pycharm64_2018-09-16_12-58-01.png]: http://upyun.qubianzhong.cn/picture/pycharm64_2018-09-16_12-58-01.png