python数据可视化（三）读取csv数据绘制折线图

之前介绍了如何读取csv数据绘制饼状图和条形图， 这次接着介绍如何读取数据绘制折线图

读取csv中的年龄并绘制成区间图 只是一个很简单的实例，如果需要扩展丰富，只需要往这个模板往上加就是了

csv文件链接：https://pan.baidu.com/s/1aLlGEcZEkMiTf9Pfsh-9WQ  提取码：jr5t 

用折线图统计csv文件中的上网高峰时段

import csv
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

def hot_Time1():#统计上网高峰时段
    #根据数据使用折线图
    pr = pd.read_csv("hydata_swjl_0.csv")
    pr1 = pd.read_csv("hydata_swjl_1.csv", low_memory=False)
    print('用折线图统计上网高峰时段：')

    on_time=[]
    off_time=[]
    for i in pr['ONLINETIME']:
        on_time.append(i)
    for i in pr1['ONLINETIME']:
        on_time.append(i)
    for i in pr['OFFLINETIME']:
        off_time.append(i)
    for i in pr1['OFFLINETIME']:
        off_time.append(i)
    time1_on = []  # 0~6点
    time2_on = []  # 6~8点
    time3_on = []  # 8~10点
    time4_on = []  # 10~12点
    time5_on = []  # 12~14点
    time6_on = []  # 14~16点
    time7_on = []  # 16~18点
    time8_on = []  # 18~20点
    time9_on = []  # 20~24点
    time_on = [] #上机时间
    time_off=[] #下机时间
    time1_off = []  # 0~6点
    time2_off = []  # 6~8点
    time3_off = []  # 8~10点
    time4_off = []  # 10~12点
    time5_off = []  # 12~14点
    time6_off = []  # 14~16点
    time7_off = []  # 16~18点
    time8_off = []  # 18~20点
    time9_off = []  # 20~24点


    for i  in on_time:
        t=0
        t=(int(i/10000)%100)
        time_on.append(t)

    for i in off_time:
        t=0
        t=(int(i/10000)%100)
        time_off.append(t)

    for i in time_on:#上机时间统计
        if 0 <=i < 6:
            time1_on.append(i)
        elif 6 <=i < 8:
            time2_on.append(i)
        elif 8 <= i < 10:
            time3_on.append(i)
        elif 10 <= i < 12:
            time4_on.append(i)
        elif 12 <= i < 14:
            time5_on.append(i)
        elif 14 <= i < 16:
            time6_on.append(i)
        elif 16 <= i < 18:
            time7_on.append(i)
        elif 18 <= i < 20:
            time8_on.append(i)
        elif 20 <= i < 24:
            time9_on.append(i)
        else:
            time1_on.append('0')


    for i in time_off:#下机时间统计
        if 0 <=i < 6:
            time1_off.append(i)
        elif 6 <=i < 8:
            time2_off.append(i)
        elif 8 <= i < 10:
            time3_off.append(i)
        elif 10 <= i < 12:
            time4_off.append(i)
        elif 12 <= i < 14:
            time5_off.append(i)
        elif 14 <= i < 16:
            time6_off.append(i)
        elif 16 <= i < 18:
            time7_off.append(i)
        elif 18 <= i < 20:
            time8_off.append(i)
        elif 20 <= i < 24:
            time9_off.append(i)
        else:
            time1_off.append('0')

    values1_on = [len(time1_on),len(time2_on),len(time3_on),len(time4_on),len(time5_on),
                  len(time6_on),len(time7_on),len(time8_on),len(time9_on)]


    values1_off = [len(time1_off),len(time2_off),len(time3_off),len(time4_off),
                   len(time5_off),len(time6_off),len(time7_off),len(time8_off),len(time9_off)]


    #绘制上机统计图
    #plt.title('Peak Hours_1',fontsize=15)
    plt.xlabel('Up and down time_1',color='blue')
    plt.ylabel('Number of time periods_1',color='blue')
    #plt.bar(index+bw, values1_on, bw, alpha=0.7)
    x=['0~6','6~8','8~10','10~12','12~14','14~16','16~18','18~20','20~24']
    plt.plot(x,values1_on,label='Onlinetime',color='y',linewidth=3.0)
    plt.plot(x,values1_off,label='Offtime',color='b',linewidth=2.0)
    plt.plot(0,0)
    plt.plot(0, 0)
    plt.plot(0, 0)
    plt.plot(0, 0)
    plt.plot(0, 0)
    plt.plot(0,0)
    plt.plot(0, 0)
    plt.plot(0, 0)
    plt.plot(0, 0)


    plt.grid(alpha=0.3,linestyle=':')
    #plt.xticks()
   # plt.legend(['0~6','6~8','8~10','10~12','12~14','14~16','16~18','18~20','20~24'],
    #           loc=2,edgecolor='b')
    plt.legend(loc='best')
    plt.show()
    print('可以看出，折线图可以更好地反映上网的高峰时期。')
    print('早晨上网人数较少，而到了下午，上网人数陡增')
    print()

if __name__=="__main__":
    hot_Time1()

结果：

注：在这里是表示的两条折线，如只需要一条，那么在

plt.plot()这里添加一条线的数值即可