综述
学生党整理一些关于数据分析的知识：主要整理了一些matplotlib.pyplot库的绘制图像的函数（基础）。包括了绘制折线图、子图操作、柱状图、条形图、盒图及一些细节处理。
代码模块
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
绘制条形图
单条折线图
plt.plot(data_figure1['year'],data_figure1['x1']) #画图
plt.xticks(rotation = 0) #x轴旋转角度
plt.xlabel('Year') #x轴标签
plt.ylabel('x1') #y轴标签
plt.title('data GDP') #标题
plt.show() #显示
)
两条折线图
data_figure2 = data[['year','x2','x3']]
fig = plt.figure()
plt.plot(data_figure2['year'],data_figure2['x2'],c = 'red')
plt.plot(data_figure2['year'],data_figure2['x3'],c = 'blue')
plt.show()
多条折线图
data_figure3 = data[['year','x2','x3','x7','y','x11']]
colors = ['red','blue','green','orange','black']
plt.plot(data_figure3['year'],data_figure3['x2'],c = colors[0],label = 'x2')
plt.plot(data_figure3['year'],data_figure3['x3'],c = colors[1],label = 'x3')
plt.plot(data_figure3['year'],data_figure3['x7'],c = colors[2],label = 'x7')
plt.plot(data_figure3['year'],data_figure3['y'],c = colors[3],label = 'y')
plt.plot(data_figure3['year'],data_figure3['x11'],c = colors[4],label = 'x11')
plt.legend(loc = 'best') #添加图例 loc = 图例位置 best可以放置在最好的位置
plt.show()
子图
#子图操作
fig = plt.figure()
ax1 = fig.add_subplot(2,2,1)
ax2 = fig.add_subplot(2,2,2)
ax3 = fig.add_subplot(2,2,3)
ax4 = fig.add_subplot(2,2,4)
plt.show()
fig = plt.figure(figsize=(3,3)) #(长，宽)
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
ax1.plot(np.random.randint(1,5,5),np.arange(5))
ax2.plot(np.arange(10)*3,np.arange(10))
plt.show()
条形图
条形图分为横向条形图和纵向条形图
#竖向
from numpy import arange
bar_heights = data_figure1
bar_positions = arange(len(bar_heights['x1']))+1
fig, ax = plt.subplots()
ax.bar(bar_positions,bar_heights['x1'].values,0.5) #0.5表示宽度
#x轴显示对应年份
ax.set_xticks(range(1,21))
ax.set_xticklabels(bar_heights['year'].values)
plt.show()
#横向
bar_heights = data_figure1
bar_positions = arange(len(bar_heights['x1']))+1
fig, ax = plt.subplots()
ax.barh(bar_positions,bar_heights['x1'].values,0.5) #0.5表示宽度 横向
#x轴显示对应年份
ax.set_yticks(range(1,21))
ax.set_yticklabels(bar_heights['year'].values)
plt.show()
#横向和纵向的区别为 bar为纵向 barh为横向
散点图
all_county = pd.read_csv('all_county.csv')
fig, ax = plt.subplots()
ax.scatter(all_county['longitude'],all_county['latitude'])
ax.set_xlabel('longitude')
ax.set_xlabel('latitude')
plt.show()
#子图
fig = plt.figure()
ax1 = fig.add_subplot(1,2,1)
ax2 = fig.add_subplot(1,2,2)
ax1.scatter(all_county['longitude'],all_county['latitude'])
ax1.set_xlabel('longitude')
ax1.set_xlabel('latitude')
ax2.scatter(all_county['longitude'],all_county['latitude'])
ax2.set_xlabel('longitude')
ax2.set_xlabel('latitude')
plt.show()
柱状图
bins = 柱的个数，不添加bins参数时系统默认设置合适的数量
fig, ax = plt.subplots()
ax.hist(data_figure1['x1'])
ax.hist(data_figure1['x1'],bins = 20)
plt.show()
盒图
#盒图
fig, ax = plt.subplots()
ax.boxplot(data_figure1['x1'])
ax.set_xticklabels(['x1'])
plt.show()
fig, ax = plt.subplots()
x = ['x1','x2','x3','x4']
ax.boxplot(data[x])
ax.set_xticklabels(x)
plt.show()
细节设置
#rgb设置颜色属性
color_1 = (0/255,107/255,164/255)
#可视化图片可以去掉边框线，刻度等
#在图上做标记
fig, ax = plt.subplots()
ax.plot(data_figure1['year'],data_figure1['x1'],c = color_1) #画图
ax.xlabel('Year') #x轴标签
ax.ylabel('x1') #y轴标签
ax.title('data GDP') #标题
ax.tick_params(bottom = False,top = False,left = False,right = False) #去除标线
ax.text(2000,4453911,'GDP') #在指定位置标记GDP
plt.show() #显示
小结
matplotlib.pyplot绘图较为简单，之后会增加seaborn库绘图。