Matplotlib
Python的绘图库 - Matplotlib
将数据预处理和清洗后，会将数据按照横纵坐标进行绘图，利用人工来检查数据情况
Install Matplotlib
Open terminal to install matplotlib:
pip install matplotlib
Draw curves
import matplotlib 库的 pyplot 模块
1.Draw a simple curve 绘制一个简单曲线:
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import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
# draw a simple curve
plt.plot([1, 3, 5], [4, 8, 10])
plt.show()
graph input:
2.Draw a custom curve 绘制一条 numpy 计算过的自定义曲线
绘制一条 numpy 计算过的自定义曲线, 区间是从正 pi 到负 pi 之间选择100个点
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# 横坐标是x
# x轴的定义是 -3.14 ~ 3.14, 中间间隔100个元素
# y轴是sin(x)
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(-np.pi, np.pi, 100)
plt.plot(x, np.sin(x))
plt.show()
>>>
3.Draw multiple curves
绘制多条曲线:
figure(1， dpi=50 )- 创建一个图表, 精度为50- 绘制图形的详细精度，精度越高，生成的图片体积就越大
- 如果使用默认的“样式”可以不用
plt.figure，如果使用自定义的“样式”需要显式的指定
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# 定义域为： -2pi ~ 2pi
x = np.linspace(-np.pi * 2, np.pi * 2, 100)
# create a graph
plt.figure(1, dpi=50)
# draw four lines
for i in range(1, 5):
plt.plot(x, np.sin(x / i))
plt.show()
Types of Matplotlib graphs
1.hist() - draw histogram
绘制直方图:plt.hist()
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import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
plt.figure(1, dpi=50)
data = [1, 1, 1 , 2, 2, 2, 3, 3, 4, 5, 5, 6, 4]
plt.hist(data)
plt.show()
2.scatter() - draw scatter
绘制散点图: plt.scatter()
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# scattered graph 散点图
x = np.arange(1, 10)
y = x
fig = plt.figure()
# draw the graph
# c='r' means red color
# marker means turning shape into round
plt.scatter(x, y, c = 'r', marker= 'o')
plt.show()
Use pandas to read data and use matplotlib to draw graph
用pandas读取数据然后用matplotlib来绘制图形:
ir.plot(kind='scatter', x='120', y='4')
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# coding=utf-8
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
ir = pd.read_csv('iris')
print(ir.head())
# based on the first two columns to draw a scattered graph
ir.plot(kind='scatter', x='120', y='4')
# no meaning but to show pandas.plot() method in pyCharm
plt.show()
>>>
120 4 setosa versicolor virginica
0 6.4 2.8 5.6 2.2 2
1 5.0 2.3 3.3 1.0 1
2 4.9 2.5 4.5 1.7 2
3 4.9 3.1 1.5 0.1 0
4 5.7 3.8 1.7 0.3 0
Seaborn
Seaborn - 更漂亮的图形库:
Import seaborn
Open terminal:
pip install seaborn
Draw scatter using seaborn
jointplot() - 用seaborn绘制散点图
- x轴是120这一列
- y轴是4这一列
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# coding=utf-8
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
ir = pd.read_csv('iris')
# set style
sns.set(style='white', color_codes=True)
# set drawing format as scattered graph
sns.jointplot(x='120', y='4', data=ir, size=5)
# use distplot() to draw the line
sns.distplot(ir['120'])
plt.show()
Draw colorful graph using seaborn
Coloring based on categories - 根据分类进行颜色划分:
FacetGridmeans 一般绘图函数hue彩色显示分类0/1/2plt.scatter绘制散点图add_legend()显示分类的描述信息
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ir = pd.read_csv('iris')
sns.set(style='white', color_codes=True)
# FacetGrid means 一般绘图函数
# hue 彩色显示分类0/1/2
# plt.scatter 绘制散点图
# add_legend() 显示分类的描述信息
sns.FacetGrid(ir, hue='virginica', size=5).map(plt.scatter, '120', '4').add_legend()
plt.show()
