We pay attention to the weather forecast every day. We can add or remove clothing and arrange travel according to the future weather. Daily temperature, wind speed and direction, relative humidity, air quality, etc. become the focus of attention. This time, the requests and BeautifulSoup libraries in Python are used to crawl the data of the current day and the next 14 days from the China Weather Network, and save it as a csv file. Then, matplotlib, numpy, and pandas are used to visualize and analyze the data to obtain the temperature and humidity change curve. , air quality maps, wind direction radar maps and other results provide an effective method for obtaining future weather information.
1. Data acquisition
Request website link
First check the URL of the China Weather Network: http://www.weather.com.cn/weather/101280701.shtml Here you can visit the local weather URL. If you want to crawl different regions, just modify the last The area code is 101280701. The weather in front represents the 7-day web page, weather1d represents the current day, and weather15d represents the next 14 days. Here we mainly visit the 7-day and 14-day China Weather Network. Use the requests.get() method to request the web page. If the access is successful, you will get all the string text of the web page. This is the request process.
def getHTMLtext(url):
"""请求获得网页内容"""
try:
r = requests.get(url, timeout = 30)
r.raise_for_status()
r.encoding = r.apparent_encoding
print("成功访问")
return r.text
except:
print("访问错误")
return" "
Extract useful information
Here, the BeautifulSoup library is used to extract data from the string just obtained. First, check the web page and find the tags for which data needs to be obtained:
It can be found that the 7-day data information is in the div tag with id="7d", and the date, weather, temperature, wind level and other information are in the ul and li tags, so we can use BeautifulSoup to search the obtained web page text div Tag id="7d", find all the ul and li tags it contains, and then extract the corresponding data values in the tags and save them in the corresponding list.
One detail to note here is that sometimes there is no maximum temperature on a date, and the situation without data needs to be judged and processed. In addition, some data storage formats must be processed in advance, such as the Celsius symbol behind the temperature, the extraction of date numbers, and the extraction of wind-level text. This requires character search and string slicing processing.
def get_content(html):
"""处理得到有用信息保存数据文件"""
final = [] # 初始化一个列表保存数据
bs = BeautifulSoup(html, "html.parser") # 创建BeautifulSoup对象
body = bs.body
data = body.find('div', {'id': '7d'}) # 找到div标签且id = 7d
The following crawls the data of the day
data2 = body.find_all('div',{'class':'left-div'})
text = data2[2].find('script').string
text = text[text.index('=')+1 :-2] # 移除改var data=将其变为json数据
jd = json.loads(text)
dayone = jd['od']['od2'] # 找到当天的数据
final_day = [] # 存放当天的数据
count = 0
for i in dayone:
temp = []
if count <=23:
temp.append(i['od21']) # 添加时间
temp.append(i['od22']) # 添加当前时刻温度
temp.append(i['od24']) # 添加当前时刻风力方向
temp.append(i['od25']) # 添加当前时刻风级
temp.append(i['od26']) # 添加当前时刻降水量
temp.append(i['od27']) # 添加当前时刻相对湿度
temp.append(i['od28']) # 添加当前时刻控制质量
#print(temp)
final_day.append(temp)
count = count +1
The following crawls 7 days of data
ul = data.find('ul') # 找到所有的ul标签
li = ul.find_all('li') # 找到左右的li标签
i = 0 # 控制爬取的天数
for day in li: # 遍历找到的每一个li
if i < 7 and i > 0:
temp = [] # 临时存放每天的数据
date = day.find('h1').string # 得到日期
date = date[0:date.index('日')] # 取出日期号
temp.append(date)
inf = day.find_all('p') # 找出li下面的p标签,提取第一个p标签的值，即天气
temp.append(inf[0].string)
tem_low = inf[1].find('i').string # 找到最低气温
if inf[1].find('span') is None: # 天气预报可能没有最高气温
tem_high = None
else:
tem_high = inf[1].find('span').string # 找到最高气温
temp.append(tem_low[:-1])
if tem_high[-1] == '℃':
temp.append(tem_high[:-1])
else:
temp.append(tem_high)
wind = inf[2].find_all('span') # 找到风向
for j in wind:
temp.append(j['title'])
wind_scale = inf[2].find('i').string # 找到风级
index1 = wind_scale.index('级')
temp.append(int(wind_scale[index1-1:index1]))
final.append(temp)
i = i + 1
return final_day,final
The same process is done for /weather15d: 15 days of information. After checking here, it is found that there are only 8-14 days in his 15-day webpage. The first 1-7 days are in /weather. Here we visit two of them respectively. The webpage merges the crawled data to obtain the final 14 days of data. - The front is the data crawling process for the next 14 days. For the 24-hour weather information data of the day, after searching, it is found that it is a json data, which can be used through json.loads()
The method obtains the data of the day and then extracts the weather information of the day.
save csv file
The crawled data was added to the list earlier. The csv library is introduced here, and the f_csv.writerow(header) and f_csv.writerows(data) methods are used to write the header and the data of each row respectively. Here, 1 day and the next 14 The data of days are stored separately and saved as weather1.csv and weather14.csv respectively. The following is the table they saved:
2. Visual analysis
Temperature change graph of the day
Use the plt.plot() method in matplotlib to draw the temperature change curve for 24 hours a day, and use the plt.text() method to point out the highest and lowest temperatures, and draw the average temperature line. The following picture is the temperature change curve: (code See Appendix)
The analysis can find that the highest temperature on this day is 33°C, the lowest temperature is 28°C, and the average temperature is around 20.4°C. Through time analysis, it is found that the temperature difference between day and night is 5°C, with low temperatures distributed in the early morning and high temperatures distributed from noon to afternoon. .
Relative humidity change curve chart of the day
Use the plt.plot() method in matplotlib to draw the humidity change curve for 24 hours a day, and draw the average relative humidity line. The following figure is the humidity change curve: (see the appendix for the code)
The analysis can find that the highest relative humidity on this day is 86%, the lowest relative humidity is 58°C, and the average relative humidity is around 75%. Through time analysis, the humidity in the early morning is relatively high, while the humidity in the afternoon to dusk is relatively low.
Temperature and humidity correlation analysis chart
Through the analysis of the previous two figures, we can feel that there is a relationship between temperature and humidity. In order to feel this relationship more clearly and intuitively, use the plt.scatter() method to set the temperature as the abscissa and the humidity as the ordinate. The points at each moment are clicked on the graph, and the correlation coefficient is calculated. The following figure is the result:
Analysis can find that there is a strong correlation between the temperature and humidity of a day. They are negatively correlated, which means that they are negatively correlated with time. Further analysis, when the temperature is lower, there is more moisture in the air, and the humidity is naturally higher. , and when the temperature is higher, the water evaporates, the air is drier and the humidity is lower, which is consistent with normal climate phenomena.
Air quality index bar chart
Air quality index AQI is an index that quantitatively describes air quality conditions. The larger the value, the more serious the air pollution is and the greater the harm to human health. The air quality index is generally divided into 6 levels. The higher the level, the more serious the pollution. The following uses the plt.bar method to draw a histogram of the air quality for 24 hours a day, and according to the six levels, the corresponding histogram The color also goes from light to dark, which also indicates that the pollution is gradually increasing, showing the pollution situation more intuitively. The highest and lowest air quality index are also marked, and the average air quality index is drawn with a dotted line. The figure below is the result of the drawing. :
The above picture is the control quality chart of Zhuhai in the south. It can be seen that the maximum air quality index is also in the healthy range, indicating that the air in Zhuhai is very good. Analysis can find that the highest air quality index reached 35 on this day, the lowest was only 14, and the average was Around 25, we can also find through time that the air is basically the best in the early morning (4-9 o'clock), and the most serious air pollution is in the afternoon, so you can usually go outside to breathe fresh air in the early morning. Minimal pollution.
The air quality map below is from a city in the north. You can see that the environment here is far inferior to Zhuhai.
Wind direction and level radar chart
To count the wind force and wind direction for a day, since the wind force and wind direction are better displayed using polar coordinates, the polar coordinates method is used to display the wind force and wind direction chart for the day. The circle is divided into 8 parts, each part represents a wind direction and radius. It represents the average wind force, and as the wind level increases, the blue color deepens, and the final result is as follows:
The analysis can find that the southwest wind is the strongest on this day, with the average wind level reaching 1.75. The northeast wind also has a small amount of 1.0 level, and there is no wind in the other blank directions.
High and low temperature change curve chart for the next 14 days
Count the high and low temperature changes in the next 14 days, and draw their change curves. Use dotted lines to draw their average temperature lines. The final results are as follows:
The analysis can find that the average high temperature in the next 14 days is 30.5°C. The temperature is still relatively high, but there will be a cooling on the 8th day in the future. You need to be prepared for cooling. The low temperature is on a stable trend before it starts to decrease on the 8th day. Along with the high temperature. The overall temperature dropped, with the average low temperature around 27°C.
Radar map of wind direction and level for the next 14 days
Calculate the wind direction and average wind force in the next 14 days, and use polar coordinates as before. Divide the circle into 8 parts, representing 8 directions. The darker the color, the higher the wind level. The final result is as follows:
The analysis can find that the main wind directions of southeasterly and southwesterly winds in the next 14 days, with the highest wind level reaching level 5, and the lowest average westerly wind level of level 3.
Pie chart of climate distribution in the next 14 days
Count the climate in the next 14 days, find the total number of days for each climate, and finally draw a pie chart for each climate. The results are as follows:
The analysis can find that the climate in the next 14 days will basically be "rain", "overcast to rain" and "showers", with more rainy days. Combined with the previous temperature distribution chart, it can be seen that the temperature will drop on the 8th and 9th days, which can be It was speculated that it rained that day, causing the temperature to drop.
3. Conclusion
1. First, based on the analysis of the crawled temperature and humidity data, the temperature ranges from low in the morning to high at noon and then low at night. The trends of humidity and temperature are opposite. Through the correlation coefficient, it is found that temperature and humidity have a strong negative correlation. After consulting the data It was found that as the temperature increases, the evaporation of water vapor increases, and the moisture in the air decreases and the humidity decreases. Of course, humidity is affected by both air pressure and rain, and the humidity will increase significantly when it rains.
2. After reviewing the data, air quality is not only related to smoke and exhaust gas emitted by factories, cars, etc., but more importantly, it is related to meteorological factors. Due to the obvious changes in the temperature difference between day and night, when the ground temperature is higher than the high-altitude temperature, the air rises, and pollutants are easily carried to high altitudes for diffusion; when the ground temperature is lower than a certain height, an inversion layer is formed in the sky, which is like a big cover Pressure above the ground makes it difficult for various pollutants in the surface air to diffuse. Generally, the impact is greater in the evening and early morning. When the sun comes out, the ground heats up rapidly, and the inversion layer gradually dissipates, so the polluted air spreads.
3. Wind is caused by the uneven distribution of air pressure in the horizontal direction. Wind is comprehensively affected by different factors such as atmospheric circulation, topography, and water bodies, and takes various forms, such as monsoons, local sea and land breezes, valley winds, etc. The wind direction also changes in different ways throughout the day. According to the wind direction radar chart for the next 14 days It can be found that all wind directions in the future are basically involved, and there is no particular wind direction. The reason may be that there has been no precipitation recently and the climate has not changed much, resulting in the wind direction not changing much.
4. Weather refers to the specific state of the atmosphere close to the surface in a certain area in a short period of time. It is a comprehensive expression of the distribution of various meteorological elements in the atmosphere at a certain instant. Based on the weather and temperature changes in the next 14 days, the climate at a certain time can be roughly inferred. There is also a connection between weather and temperature.
4. Code framework
The code is mainly divided into weather.py: crawl weather data from China Weather Network and save csv files; data1_analysis.py: visualize the weather information of the day; data14_analysis.py: visualize the weather information of the next 14 days. Here is the structure diagram of the code:
Source code attached
weather.py
# weather.py
import requests
from bs4 import BeautifulSoup
import csv
import json
def getHTMLtext(url):
"""请求获得网页内容"""
try:
r = requests.get(url, timeout = 30)
r.raise_for_status()
r.encoding = r.apparent_encoding
print("成功访问")
return r.text
except:
print("访问错误")
return" "
def get_content(html):
"""处理得到有用信息保存数据文件"""
final = [] # 初始化一个列表保存数据
bs = BeautifulSoup(html, "html.parser") # 创建BeautifulSoup对象
body = bs.body
data = body.find('div', {<!-- -->'id': '7d'}) # 找到div标签且id = 7d
# 下面爬取当天的数据
data2 = body.find_all('div',{<!-- -->'class':'left-div'})
text = data2[2].find('script').string
text = text[text.index('=')+1 :-2] # 移除改var data=将其变为json数据
jd = json.loads(text)
dayone = jd['od']['od2'] # 找到当天的数据
final_day = [] # 存放当天的数据
count = 0
for i in dayone:
temp = []
if count <=23:
temp.append(i['od21']) # 添加时间
temp.append(i['od22']) # 添加当前时刻温度
temp.append(i['od24']) # 添加当前时刻风力方向
temp.append(i['od25']) # 添加当前时刻风级
temp.append(i['od26']) # 添加当前时刻降水量
temp.append(i['od27']) # 添加当前时刻相对湿度
temp.append(i['od28']) # 添加当前时刻控制质量
#print(temp)
final_day.append(temp)
count = count +1
# 下面爬取7天的数据
ul = data.find('ul') # 找到所有的ul标签
li = ul.find_all('li') # 找到左右的li标签
i = 0 # 控制爬取的天数
for day in li: # 遍历找到的每一个li
if i < 7 and i > 0:
temp = [] # 临时存放每天的数据
date = day.find('h1').string # 得到日期
date = date[0:date.index('日')] # 取出日期号
temp.append(date)
inf = day.find_all('p') # 找出li下面的p标签,提取第一个p标签的值，即天气
temp.append(inf[0].string)
tem_low = inf[1].find('i').string # 找到最低气温
if inf[1].find('span') is None: # 天气预报可能没有最高气温
tem_high = None
else:
tem_high = inf[1].find('span').string # 找到最高气温
temp.append(tem_low[:-1])
if tem_high[-1] == '℃':
temp.append(tem_high[:-1])
else:
temp.append(tem_high)
wind = inf[2].find_all('span') # 找到风向
for j in wind:
temp.append(j['title'])
wind_scale = inf[2].find('i').string # 找到风级
index1 = wind_scale.index('级')
temp.append(int(wind_scale[index1-1:index1]))
final.append(temp)
i = i + 1
return final_day,final
#print(final)
def get_content2(html):
"""处理得到有用信息保存数据文件"""
final = [] # 初始化一个列表保存数据
bs = BeautifulSoup(html, "html.parser") # 创建BeautifulSoup对象
body = bs.body
data = body.find('div', {<!-- -->'id': '15d'}) # 找到div标签且id = 15d
ul = data.find('ul') # 找到所有的ul标签
li = ul.find_all('li') # 找到左右的li标签
final = []
i = 0 # 控制爬取的天数
for day in li: # 遍历找到的每一个li
if i < 8:
temp = [] # 临时存放每天的数据
date = day.find('span',{<!-- -->'class':'time'}).string # 得到日期
date = date[date.index('（')+1:-2] # 取出日期号
temp.append(date)
weather = day.find('span',{<!-- -->'class':'wea'}).string # 找到天气
temp.append(weather)
tem = day.find('span',{<!-- -->'class':'tem'}).text # 找到温度
temp.append(tem[tem.index('/')+1:-1]) # 找到最低气温
temp.append(tem[:tem.index('/')-1]) # 找到最高气温
wind = day.find('span',{<!-- -->'class':'wind'}).string # 找到风向
if '转' in wind: # 如果有风向变化
temp.append(wind[:wind.index('转')])
temp.append(wind[wind.index('转')+1:])
else: # 如果没有风向变化，前后风向一致
temp.append(wind)
temp.append(wind)
wind_scale = day.find('span',{<!-- -->'class':'wind1'}).string # 找到风级
index1 = wind_scale.index('级')
temp.append(int(wind_scale[index1-1:index1]))
final.append(temp)
return final
def write_to_csv(file_name, data, day=14):
"""保存为csv文件"""
with open(file_name, 'a', errors='ignore', newline='') as f:
if day == 14:
header = ['日期','天气','最低气温','最高气温','风向1','风向2','风级']
else:
header = ['小时','温度','风力方向','风级','降水量','相对湿度','空气质量']
f_csv = csv.writer(f)
f_csv.writerow(header)
f_csv.writerows(data)
def main():
"""主函数"""
print("Weather test")
# 珠海
url1 = 'http://www.weather.com.cn/weather/101280701.shtml' # 7天天气中国天气网
url2 = 'http://www.weather.com.cn/weather15d/101280701.shtml' # 8-15天天气中国天气网
html1 = getHTMLtext(url1)
data1, data1_7 = get_content(html1) # 获得1-7天和当天的数据
html2 = getHTMLtext(url2)
data8_14 = get_content2(html2) # 获得8-14天数据
data14 = data1_7 + data8_14
#print(data)
write_to_csv('weather14.csv',data14,14) # 保存为csv文件
write_to_csv('weather1.csv',data1,1)
if __name__ == '__main__':
main()
data1_analysis.py:
# data1_analysis.py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import math
def tem_curve(data):
"""温度曲线绘制"""
hour = list(data['小时'])
tem = list(data['温度'])
for i in range(0,24):
if math.isnan(tem[i]) == True:
tem[i] = tem[i-1]
tem_ave = sum(tem)/24 # 求平均温度
tem_max = max(tem)
tem_max_hour = hour[tem.index(tem_max)] # 求最高温度
tem_min = min(tem)
tem_min_hour = hour[tem.index(tem_min)] # 求最低温度
x = []
y = []
for i in range(0, 24):
x.append(i)
y.append(tem[hour.index(i)])
plt.figure(1)
plt.plot(x,y,color='red',label='温度') # 画出温度曲线
plt.scatter(x,y,color='red') # 点出每个时刻的温度点
plt.plot([0, 24], [tem_ave, tem_ave], c='blue', linestyle='--',label='平均温度') # 画出平均温度虚线
plt.text(tem_max_hour+0.15, tem_max+0.15, str(tem_max), ha='center', va='bottom', fontsize=10.5) # 标出最高温度
plt.text(tem_min_hour+0.15, tem_min+0.15, str(tem_min), ha='center', va='bottom', fontsize=10.5) # 标出最低温度
plt.xticks(x)
plt.legend()
plt.title('一天温度变化曲线图')
plt.xlabel('时间/h')
plt.ylabel('摄氏度/℃')
plt.show()
def hum_curve(data):
"""相对湿度曲线绘制"""
hour = list(data['小时'])
hum = list(data['相对湿度'])
for i in range(0,24):
if math.isnan(hum[i]) == True:
hum[i] = hum[i-1]
hum_ave = sum(hum)/24 # 求平均相对湿度
hum_max = max(hum)
hum_max_hour = hour[hum.index(hum_max)] # 求最高相对湿度
hum_min = min(hum)
hum_min_hour = hour[hum.index(hum_min)] # 求最低相对湿度
x = []
y = []
for i in range(0, 24):
x.append(i)
y.append(hum[hour.index(i)])
plt.figure(2)
plt.plot(x,y,color='blue',label='相对湿度') # 画出相对湿度曲线
plt.scatter(x,y,color='blue') # 点出每个时刻的相对湿度
plt.plot([0, 24], [hum_ave, hum_ave], c='red', linestyle='--',label='平均相对湿度') # 画出平均相对湿度虚线
plt.text(hum_max_hour+0.15, hum_max+0.15, str(hum_max), ha='center', va='bottom', fontsize=10.5) # 标出最高相对湿度
plt.text(hum_min_hour+0.15, hum_min+0.15, str(hum_min), ha='center', va='bottom', fontsize=10.5) # 标出最低相对湿度
plt.xticks(x)
plt.legend()
plt.title('一天相对湿度变化曲线图')
plt.xlabel('时间/h')
plt.ylabel('百分比/%')
plt.show()
def air_curve(data):
"""空气质量曲线绘制"""
hour = list(data['小时'])
air = list(data['空气质量'])
print(type(air[0]))
for i in range(0,24):
if math.isnan(air[i]) == True:
air[i] = air[i-1]
air_ave = sum(air)/24 # 求平均空气质量
air_max = max(air)
air_max_hour = hour[air.index(air_max)] # 求最高空气质量
air_min = min(air)
air_min_hour = hour[air.index(air_min)] # 求最低空气质量
x = []
y = []
for i in range(0, 24):
x.append(i)
y.append(air[hour.index(i)])
plt.figure(3)
for i in range(0,24):
if y[i] <= 50:
plt.bar(x[i],y[i],color='lightgreen',width=0.7) # 1等级
elif y[i] <= 100:
plt.bar(x[i],y[i],color='wheat',width=0.7) # 2等级
elif y[i] <= 150:
plt.bar(x[i],y[i],color='orange',width=0.7) # 3等级
elif y[i] <= 200:
plt.bar(x[i],y[i],color='orangered',width=0.7) # 4等级
elif y[i] <= 300:
plt.bar(x[i],y[i],color='darkviolet',width=0.7) # 5等级
elif y[i] > 300:
plt.bar(x[i],y[i],color='maroon',width=0.7) # 6等级
plt.plot([0, 24], [air_ave, air_ave], c='black', linestyle='--') # 画出平均空气质量虚线
plt.text(air_max_hour+0.15, air_max+0.15, str(air_max), ha='center', va='bottom', fontsize=10.5) # 标出最高空气质量
plt.text(air_min_hour+0.15, air_min+0.15, str(air_min), ha='center', va='bottom', fontsize=10.5) # 标出最低空气质量
plt.xticks(x)
plt.title('一天空气质量变化曲线图')
plt.xlabel('时间/h')
plt.ylabel('空气质量指数AQI')
plt.show()
def wind_radar(data):
"""风向雷达图"""
wind = list(data['风力方向'])
wind_speed = list(data['风级'])
for i in range(0,24):
if wind[i] == "北风":
wind[i] = 90
elif wind[i] == "南风":
wind[i] = 270
elif wind[i] == "西风":
wind[i] = 180
elif wind[i] == "东风":
wind[i] = 360
elif wind[i] == "东北风":
wind[i] = 45
elif wind[i] == "西北风":
wind[i] = 135
elif wind[i] == "西南风":
wind[i] = 225
elif wind[i] == "东南风":
wind[i] = 315
degs = np.arange(45,361,45)
temp = []
for deg in degs:
speed = []
# 获取 wind_deg 在指定范围的风速平均值数据
for i in range(0,24):
if wind[i] == deg:
speed.append(wind_speed[i])
if len(speed) == 0:
temp.append(0)
else:
temp.append(sum(speed)/len(speed))
print(temp)
N = 8
theta = np.arange(0.+np.pi/8,2*np.pi+np.pi/8,2*np.pi/8)
# 数据极径
radii = np.array(temp)
# 绘制极区图坐标系
plt.axes(polar=True)
# 定义每个扇区的RGB值（R,G,B），x越大，对应的颜色越接近蓝色
colors = [(1-x/max(temp), 1-x/max(temp),0.6) for x in radii]
plt.bar(theta,radii,width=(2*np.pi/N),bottom=0.0,color=colors)
plt.title('一天风级图',x=0.2,fontsize=20)
plt.show()
def calc_corr(a, b):
"""计算相关系数"""
a_avg = sum(a)/len(a)
b_avg = sum(b)/len(b)
cov_ab = sum([(x - a_avg)*(y - b_avg) for x,y in zip(a, b)])
sq = math.sqrt(sum([(x - a_avg)**2 for x in a])*sum([(x - b_avg)**2 for x in b]))
corr_factor = cov_ab/sq
return corr_factor
def corr_tem_hum(data):
"""温湿度相关性分析"""
tem = data['温度']
hum = data['相对湿度']
plt.scatter(tem,hum,color='blue')
plt.title("温湿度相关性分析图")
plt.xlabel("温度/℃")
plt.ylabel("相对湿度/%")
plt.text(20,40,"相关系数为："+str(calc_corr(tem,hum)),fontdict={<!-- -->'size':'10','color':'red'})
plt.show()
print("相关系数为："+str(calc_corr(tem,hum)))
def main():
plt.rcParams['font.sans-serif']=['SimHei'] # 解决中文显示问题
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
data1 = pd.read_csv('weather1.csv',encoding='gb2312')
print(data1)
tem_curve(data1)
hum_curve(data1)
air_curve(data1)
wind_radar(data1)
corr_tem_hum(data1)
if __name__ == '__main__':
main()
data14_analysis.py:
# data14_analysis.py
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import math
def tem_curve(data):
"""温度曲线绘制"""
date = list(data['日期'])
tem_low = list(data['最低气温'])
tem_high = list(data['最高气温'])
for i in range(0,14):
if math.isnan(tem_low[i]) == True:
tem_low[i] = tem_low[i-1]
if math.isnan(tem_high[i]) == True:
tem_high[i] = tem_high[i-1]
tem_high_ave = sum(tem_high)/14 # 求平均高温
tem_low_ave = sum(tem_low)/14 # 求平均低温
tem_max = max(tem_high)
tem_max_date = tem_high.index(tem_max) # 求最高温度
tem_min = min(tem_low)
tem_min_date = tem_low.index(tem_min) # 求最低温度
x = range(1,15)
plt.figure(1)
plt.plot(x,tem_high,color='red',label='高温') # 画出高温度曲线
plt.scatter(x,tem_high,color='red') # 点出每个时刻的温度点
plt.plot(x,tem_low,color='blue',label='低温') # 画出低温度曲线
plt.scatter(x,tem_low,color='blue') # 点出每个时刻的温度点
plt.plot([1, 15], [tem_high_ave, tem_high_ave], c='black', linestyle='--') # 画出平均温度虚线
plt.plot([1, 15], [tem_low_ave, tem_low_ave], c='black', linestyle='--') # 画出平均温度虚线
plt.legend()
plt.text(tem_max_date+0.15, tem_max+0.15, str(tem_max), ha='center', va='bottom', fontsize=10.5) # 标出最高温度
plt.text(tem_min_date+0.15, tem_min+0.15, str(tem_min), ha='center', va='bottom', fontsize=10.5) # 标出最低温度
plt.xticks(x)
plt.title('未来14天高温低温变化曲线图')
plt.xlabel('未来天数/天')
plt.ylabel('摄氏度/℃')
plt.show()
def change_wind(wind):
"""改变风向"""
for i in range(0,14):
if wind[i] == "北风":
wind[i] = 90
elif wind[i] == "南风":
wind[i] = 270
elif wind[i] == "西风":
wind[i] = 180
elif wind[i] == "东风":
wind[i] = 360
elif wind[i] == "东北风":
wind[i] = 45
elif wind[i] == "西北风":
wind[i] = 135
elif wind[i] == "西南风":
wind[i] = 225
elif wind[i] == "东南风":
wind[i] = 315
return wind
def wind_radar(data):
"""风向雷达图"""
wind1 = list(data['风向1'])
wind2 = list(data['风向2'])
wind_speed = list(data['风级'])
wind1 = change_wind(wind1)
wind2 = change_wind(wind2)
degs = np.arange(45,361,45)
temp = []
for deg in degs:
speed = []
# 获取 wind_deg 在指定范围的风速平均值数据
for i in range(0,14):
if wind1[i] == deg:
speed.append(wind_speed[i])
if wind2[i] == deg:
speed.append(wind_speed[i])
if len(speed) == 0:
temp.append(0)
else:
temp.append(sum(speed)/len(speed))
print(temp)
N = 8
theta = np.arange(0.+np.pi/8,2*np.pi+np.pi/8,2*np.pi/8)
# 数据极径
radii = np.array(temp)
# 绘制极区图坐标系
plt.axes(polar=True)
# 定义每个扇区的RGB值（R,G,B），x越大，对应的颜色越接近蓝色
colors = [(1-x/max(temp), 1-x/max(temp),0.6) for x in radii]
plt.bar(theta,radii,width=(2*np.pi/N),bottom=0.0,color=colors)
plt.title('未来14天风级图',x=0.2,fontsize=20)
plt.show()
def weather_pie(data):
"""绘制天气饼图"""
weather = list(data['天气'])
dic_wea = {<!-- --> }
for i in range(0,14):
if weather[i] in dic_wea.keys():
dic_wea[weather[i]] += 1
else:
dic_wea[weather[i]] = 1
print(dic_wea)
explode=[0.01]*len(dic_wea.keys())
color = ['lightskyblue','silver','yellow','salmon','grey','lime','gold','red','green','pink']
plt.pie(dic_wea.values(),explode=explode,labels=dic_wea.keys(),autopct='%1.1f%%',colors=color)
plt.title('未来14天气候分布饼图')
plt.show()
def main():
plt.rcParams['font.sans-serif']=['SimHei'] # 解决中文显示问题
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
data14 = pd.read_csv('weather14.csv',encoding='gb2312')
print(data14)
tem_curve(data14)
wind_radar(data14)
weather_pie(data14)
if __name__ == '__main__':
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