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104 lines
3.5 KiB
104 lines
3.5 KiB
from dateutil import parser
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import matplotlib.dates as mdates
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import matplotlib.pyplot as plt
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import numpy as numpy
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import pandas as pd
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import datetime
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df_ferrara = pd.read_csv(r'D:\python\pythonProject\pythonProject\main\ferrara_270615.csv')
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df_milano = pd.read_csv(r'D:\python\pythonProject\pythonProject\main\milano_270615.csv')
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df_mantova = pd.read_csv(r'D:\python\pythonProject\pythonProject\main\mantova_270615.csv')
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df_ravenna = pd.read_csv(r'D:\python\pythonProject\pythonProject\main\ravenna_270615.csv')
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df_torino = pd.read_csv(r'D:\python\pythonProject\pythonProject\main\torino_270615.csv')
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df_asti = pd.read_csv(r'D:\python\pythonProject\pythonProject\main\asti_270615.csv')
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df_bologna = pd.read_csv(r'D:\python\pythonProject\pythonProject\main\bologna_270615.csv')
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df_piacenza = pd.read_csv(r'D:\python\pythonProject\pythonProject\main\piacenza_270615.csv')
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df_cesena = pd.read_csv(r'D:\python\pythonProject\pythonProject\main\cesena_270615.csv')
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df_faenza = pd.read_csv(r'D:\python\pythonProject\pythonProject\main\faenza_270615.csv')
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# 读取温度和日期数据
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y1 = df_ravenna['temp']
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x1 = df_ravenna['day']
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y2 = df_faenza['temp']
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x2 = df_faenza['day']
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y3 = df_cesena['temp']
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x3 = df_cesena['day']
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y4 = df_milano['temp']
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x4 = df_milano['day']
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y5 = df_asti['temp']
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x5 = df_asti['day']
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y6 = df_torino['temp']
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x6 = df_torino['day']
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# 把日期从string类型转化为标准的datetime类型
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day_ravenna = [parser.parse(x) for x in x1]
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day_faenza = [parser.parse(x) for x in x2]
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day_cesena = [parser.parse(x) for x in x3]
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day_milano = [parser.parse(x) for x in x4]
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day_asti = [parser.parse(x) for x in x5]
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day_torino = [parser.parse(x) for x in x6]
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'''
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# 调用subplos()函数,重新定义fig,ax变量
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fig, ax = plt.subplots()
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plt.xticks(rotation=70)
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hours = mdates.DateFormatter('%H:%M')
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ax.xaxis.set_major_formatter(hours)
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# 这里需要画出三根线,所以需要三组参数,'g'代表'green'(r-离海近,g-离海远)
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ax.plot(day_ravenna, y1, 'r', day_faenza, y2, 'r', day_cesena, y3, 'r')
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ax.plot(day_milano, y4, 'g', day_asti, y5, 'g', day_torino, y5, 'g')
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'''
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'''
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收集 10 个城市的最高温和最低温，
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用线性图表示气温最值点和离海远近之间的关系。
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'''
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# dis是一个装城市距离海边距离的列表
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dist = [df_ravenna['dist'][0],
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df_cesena['dist'][0],
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df_faenza['dist'][0],
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df_ferrara['dist'][0],
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df_bologna['dist'][0],
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df_mantova['dist'][0],
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df_piacenza['dist'][0],
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df_milano['dist'][0],
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df_asti['dist'][0],
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df_torino['dist'][0]
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]
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# temp_max是一个存放每个城市最高温度的列表
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temp_max = [df_ravenna['temp'].max(),
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df_cesena['temp'].max(),
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df_faenza['temp'].max(),
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df_ferrara['temp'].max(),
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df_bologna['temp'].max(),
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df_mantova['temp'].max(),
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df_piacenza['temp'].max(),
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df_milano['temp'].max(),
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df_asti['temp'].max(),
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df_torino['temp'].max()
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]
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# temp_min存放最低温度的列表
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temp_min = [df_ravenna['temp'].min(),
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df_cesena['temp'].min(),
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df_faenza['temp'].min(),
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df_ferrara['temp'].min(),
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df_bologna['temp'].min(),
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df_mantova['temp'].min(),
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df_piacenza['temp'].min(),
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df_milano['temp'].min(),
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df_asti['temp'].min(),
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df_torino['temp'].min()
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]
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# print(temp_min)
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# 先画出最高温度
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fig, ax = plt.subplots()
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ax.plot(dist, temp_max, 'ro')
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plt.show()
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