项目2 可视化数据（第16章 下载数据）

import csv

filename = ‘sitka_weather_07-2014.csv‘
with open(filename) as f:
    reader = csv.reader(f)
    # 调用reader的next()方法，从而返回文件的下一行
    # 只调用一次next()，因此得到文件的第一行
    header_row = next(reader)
    print(header_row)

[‘AKDT‘, ‘Max TemperatureF‘, ‘Mean TemperatureF‘, ‘Min TemperatureF‘, ‘Max Dew PointF‘, ‘MeanDew PointF‘, ‘Min DewpointF‘, ‘Max Humidity‘, ‘ Mean Humidity‘, ‘ Min Humidity‘, ‘ Max Sea Level PressureIn‘, ‘ Mean Sea Level PressureIn‘, ‘ Min Sea Level PressureIn‘, ‘ Max VisibilityMiles‘, ‘ Mean VisibilityMiles‘, ‘ Min VisibilityMiles‘, ‘ Max Wind SpeedMPH‘, ‘ Mean Wind SpeedMPH‘, ‘ Max Gust SpeedMPH‘, ‘PrecipitationIn‘, ‘ CloudCover‘, ‘ Events‘, ‘ WindDirDegrees‘]

import csv

filename = ‘sitka_weather_07-2014.csv‘
with open(filename) as f:
    reader = csv.reader(f)
    # 调用reader的next()方法，从而返回文件的下一行
    # 只调用一次next()，因此得到文件的第一行
    header_row = next(reader)
    # 对列表调用enumerate()来获取每个元素的索引及其值
    for index,column_header in enumerate(header_row):
        print(index,column_header)

0 AKDT
1 Max TemperatureF
2 Mean TemperatureF
3 Min TemperatureF
4 Max Dew PointF
5 MeanDew PointF
6 Min DewpointF
7 Max Humidity
8  Mean Humidity
9  Min Humidity
10  Max Sea Level PressureIn
11  Mean Sea Level PressureIn
12  Min Sea Level PressureIn
13  Max VisibilityMiles
14  Mean VisibilityMiles
15  Min VisibilityMiles
16  Max Wind SpeedMPH
17  Mean Wind SpeedMPH
18  Max Gust SpeedMPH
19 PrecipitationIn
20  CloudCover
21  Events
22  WindDirDegrees

import csv

# 从文件中获取最高气温
filename = ‘sitka_weather_07-2014.csv‘
with open(filename) as f:
    reader = csv.reader(f)
    # 调用reader的next()方法，从而返回文件的下一行
    # 只调用一次next()，因此得到文件的第一行
    header_row = next(reader)
    highs = []
    for row in reader:
        high = int(row[1])
        highs.append(high)
    print(highs)

[64, 71, 64, 59, 69, 62, 61, 55, 57, 61, 57, 59, 57, 61, 64, 61, 59, 63, 60, 57, 69, 63, 62, 59, 57, 57, 61, 59, 61, 61, 66]

import csv

from matplotlib import pyplot as plt

# 从文件中获取最高气温
filename = ‘sitka_weather_07-2014.csv‘
with open(filename) as f:
    reader = csv.reader(f)
    # 调用reader的next()方法，从而返回文件的下一行
    # 只调用一次next()，因此得到文件的第一行
    header_row = next(reader)
    highs = []
    for row in reader:
        high = int(row[1])
        highs.append(high)
    print(highs)

# 根据数据绘制图形
fig = plt.figure(dpi=128,figsize=(10,6))
plt.plot(highs,c=‘red‘)
# 设置图形的格式
plt.title("Daily high temperatures, July 2014",fontsize=24)
plt.xlabel(‘‘,fontsize=16)
plt.ylabel("Temperature(F)",fontsize=16)
plt.tick_params(axis=‘both‘,which=‘major‘,labelsize=16)

plt.show()

import csv

from matplotlib import pyplot as plt
from datetime import datetime

# 从文件中获取日期和最高气温
filename = ‘sitka_weather_07-2014.csv‘
with open(filename) as f:
    reader = csv.reader(f)
    # 调用reader的next()方法，从而返回文件的下一行
    # 只调用一次next()，因此得到文件的第一行
    header_row = next(reader)

    dates,highs = [],[]
    for row in reader:
        current_data = datetime.strptime(row[0],"%Y-%m-%d")
        dates.append(current_data)
        high = int(row[1])
        highs.append(high)
 # 根据数据绘制图形
fig = plt.figure(dpi=128,figsize=(10,6))
plt.plot(dates,highs,c=‘red‘)
# 设置图形的格式
plt.title("Daily high temperatures, July 2014",fontsize=24)
plt.xlabel(‘‘,fontsize=16)
#绘制写的日期标签
fig.autofmt_xdate()
plt.ylabel("Temperature(F)",fontsize=16)
plt.tick_params(axis=‘both‘,which=‘major‘,labelsize=16)

plt.show()

import csv

from matplotlib import pyplot as plt
from datetime import datetime

# 从文件中获取日期和最高气温
filename = ‘sitka_weather_2014.csv‘
with open(filename) as f:
    reader = csv.reader(f)
    # 调用reader的next()方法，从而返回文件的下一行
    # 只调用一次next()，因此得到文件的第一行
    header_row = next(reader)

    dates,highs = [],[]
    for row in reader:
        current_data = datetime.strptime(row[0],"%Y-%m-%d")
        dates.append(current_data)
        high = int(row[1])
        highs.append(high)

# 根据数据绘制图形
fig = plt.figure(dpi=128,figsize=(10,6))
plt.plot(dates,highs,c=‘red‘)
# 设置图形的格式
plt.title("Daily high temperatures - 2014",fontsize=24)
plt.xlabel(‘‘,fontsize=16)
fig.autofmt_xdate()
plt.ylabel("Temperature(F)",fontsize=16)
plt.tick_params(axis=‘both‘,which=‘major‘,labelsize=16)

plt.show()

import csv

from matplotlib import pyplot as plt
from datetime import datetime

# 从文件中获取日期、最高气温和最低气温
filename = ‘sitka_weather_2014.csv‘
with open(filename) as f:
    reader = csv.reader(f)
    # 调用reader的next()方法，从而返回文件的下一行
    # 只调用一次next()，因此得到文件的第一行
    header_row = next(reader)

    dates,highs,lows = [],[],[]
    for row in reader:
        current_data = datetime.strptime(row[0],"%Y-%m-%d")
        dates.append(current_data)
        high = int(row[1])
        highs.append(high)
        low = int(row[3])
        lows.append(low)

# 根据数据绘制图形
fig = plt.figure(dpi=128,figsize=(10,6))
plt.plot(dates,highs,c=‘red‘)
plt.plot(dates,lows,c=‘blue‘)
# 设置图形的格式
plt.title("Daily high and low temperatures - 2014",fontsize=24)
plt.xlabel(‘‘,fontsize=16)
fig.autofmt_xdate()
plt.ylabel("Temperature(F)",fontsize=16)
plt.tick_params(axis=‘both‘,which=‘major‘,labelsize=16)

plt.show()

import csv

from matplotlib import pyplot as plt
from datetime import datetime

# 从文件中获取日期、最高气温和最低气温
filename = ‘sitka_weather_2014.csv‘
with open(filename) as f:
    reader = csv.reader(f)
    # 调用reader的next()方法，从而返回文件的下一行
    # 只调用一次next()，因此得到文件的第一行
    header_row = next(reader)

    dates,highs,lows = [],[],[]
    for row in reader:
        current_data = datetime.strptime(row[0],"%Y-%m-%d")
        dates.append(current_data)
        high = int(row[1])
        highs.append(high)
        low = int(row[3])
        lows.append(low)

# 根据数据绘制图形
fig = plt.figure(dpi=128,figsize=(10,6))
plt.plot(dates,highs,c=‘red‘,alpha=0.5)
plt.plot(dates,lows,c=‘blue‘,alpha=0.5)
# fill_between接受一个x值系列和两个y值系列，并填充两个y值系列之间的空间
# alpha指定颜色的透明度。0为完全透明
plt.fill_between(dates,highs,lows,facecolor=‘blue‘,alpha=0.1)
# 设置图形的格式
plt.title("Daily high and low temperatures - 2014",fontsize=24)
plt.xlabel(‘‘,fontsize=16)
fig.autofmt_xdate()
plt.ylabel("Temperature(F)",fontsize=16)
plt.tick_params(axis=‘both‘,which=‘major‘,labelsize=16)

plt.show()

Traceback (most recent call last):
  File "D:/data_visualization/high_lows.py", line 18, in <module>
    high = int(row[1])
ValueError: invalid literal for int() with base 10: ‘‘

import csv

from matplotlib import pyplot as plt
from datetime import datetime

# 从文件中获取日期、最高气温和最低气温
filename = ‘death_valley_2014.csv‘
with open(filename) as f:
    reader = csv.reader(f)
    # 调用reader的next()方法，从而返回文件的下一行
    # 只调用一次next()，因此得到文件的第一行
    header_row = next(reader)

    dates,highs,lows = [],[],[]
    for row in reader:
        try:
            current_data = datetime.strptime(row[0],"%Y-%m-%d")
            high = int(row[1])
            low = int(row[3])
        except ValueError:
            print(current_data,‘missing data‘)
        else:
            dates.append(current_data)
            highs.append(high)
            lows.append(low)

# 根据数据绘制图形
fig = plt.figure(dpi=128,figsize=(10,6))
plt.plot(dates,highs,c=‘red‘,alpha=0.5)
plt.plot(dates,lows,c=‘blue‘,alpha=0.5)
# fill_between接受一个x值系列和两个y值系列，并填充两个y值系列之间的空间
# alpha指定颜色的透明度。0为完全透明
plt.fill_between(dates,highs,lows,facecolor=‘blue‘,alpha=0.1)
# 设置图形的格式
plt.title("Daily high and low temperatures - 2014\nDeath Valley,CA",fontsize=20)
plt.xlabel(‘‘,fontsize=16)
fig.autofmt_xdate()
plt.ylabel("Temperature(F)",fontsize=16)
plt.tick_params(axis=‘both‘,which=‘major‘,labelsize=16)

plt.show()

2014-02-16 00:00:00 missing data

import csv
from datetime import datetime

from matplotlib import pyplot as plt

def get_weather_data(filename, dates, highs, lows):
    """Get the highs and lows from a data file."""
    with open(filename) as f:
        reader = csv.reader(f)
        header_row = next(reader)

        for row in reader:
            try:
                current_date = datetime.strptime(row[0], "%Y-%m-%d")
                high = int(row[1])
                low = int(row[3])
            except ValueError:
                print(current_date, ‘missing data‘)
            else:
                dates.append(current_date)
                highs.append(high)
                lows.append(low)

# 锡特卡的气温
dates, highs, lows = [], [], []
get_weather_data(‘sitka_weather_2014.csv‘, dates, highs, lows)

fig = plt.figure(dpi=128, figsize=(10, 6))
plt.plot(dates, highs, c=‘red‘, alpha=0.6)
plt.plot(dates, lows, c=‘blue‘, alpha=0.6)
plt.fill_between(dates, highs, lows, facecolor=‘blue‘, alpha=0.15)

# 死亡谷的气温
dates, highs, lows = [], [], []
get_weather_data(‘death_valley_2014.csv‘, dates, highs, lows)

plt.plot(dates, highs, c=‘red‘, alpha=0.3)
plt.plot(dates, lows, c=‘blue‘, alpha=0.3)
plt.fill_between(dates, highs, lows, facecolor=‘blue‘, alpha=0.05)

title = "Daily high and low temperatures - 2014"
title += "\nSitka, AK and Death Valley, CA"
plt.title(title, fontsize=20)
plt.xlabel(‘‘, fontsize=16)
fig.autofmt_xdate()
plt.ylabel("Temperature (F)", fontsize=16)
plt.tick_params(axis=‘both‘, which=‘major‘, labelsize=16)
plt.ylim(10, 120)

plt.show()

from __future__ import (absolute_import,division,                        
　　　　　　　　　　print_function,unicode_literals)
try:    
# python2,x版本
    from urllib2 import urlopen
except ImportError:
    from urllib.request import urlopen
import json

json_url = ‘https://raw.githubusercontent.com/muxuezi/btc/master/btc_close_2017.json‘

#下面这行代码执行只有，Python就会向GitHub的服务器发送请求。GitHub服务器响应请求之后，把文件发送给Python。

response = urlopen(json_url)
# 读取数据
req = response.read()
# 将数据写入文件
with open(‘btc_close_2017_urllib.json‘,‘wb‘) as f:
    f.write(req)
# 加载json格式
file_urllib = json.load(req)
print(file_urllib)

import requests
json_url = ‘https://raw.githubusercontent.com/muxuezi/btc/master/btc_close_2017.json‘
req = requests.get(json_url)
# 将数据写入文件
with open(‘btc_close_2017_request.json‘,‘w‘) as f:
    f.write(req.text)

file_requests = req.json()

AttributeError: module ‘requests‘ has no attribute ‘get‘

import requests
import btc_close_2017

json_url = ‘https://raw.githubusercontent.com/muxuezi/btc/master/btc_close_2017.json‘
req = requests.get(json_url)
# 将数据写入文件
with open(‘btc_close_2017_request.json‘,‘w‘) as f:
    f.write(req.text)

# 直接用req.json()就可以将文件的数据转换成Python列表file_requests，与之前的file_urllib相同

file_requests = req.json() print(btc_close_2017.file_urllib == file_requests)

[{‘date‘: ‘2017-01-01‘, ‘month‘: ‘01‘, ‘week‘: ‘52‘, ‘weekday‘: ‘Sunday‘, ‘close‘: ‘6928.6492‘}, {‘date‘: ‘2017-01-02‘, ‘month‘: ‘01‘, ‘week‘: ‘1‘, ‘weekday‘: ‘Monday‘, ‘close‘: ‘7070.2554‘}, {‘date‘: ‘2017-01-03‘, ‘month‘: ‘01‘, ‘week‘: ‘1‘, ‘weekday‘: ‘Tuesday‘, ‘close‘: ‘7175.1082‘}, {‘date‘: ‘2017-01-04‘, ‘month‘: ‘01‘, ‘week‘: ‘1‘, ‘weekday‘: ‘Wednesday‘, ‘close‘: ‘7835.7615‘}, {‘date‘: ‘2017-01-05‘, ‘month‘: ‘01‘, ‘week‘: ‘1‘, ‘weekday‘: ‘Thursday‘, ‘close‘: ‘6928.7578‘}, {‘date‘: ‘2017-01-06‘, ‘month‘: ‘01‘, ‘week‘: ‘1‘, ‘weekday‘: ‘Friday‘, ‘close‘: ‘6196.6928‘}, {‘date‘: ‘2017-01-07‘, ‘month‘: ‘01‘, ‘week‘: ‘1‘, ‘weekday‘: ‘Saturday‘, ‘close‘: ‘6262.1471‘}, {‘date‘: ‘2017-01-08‘, ‘month‘: ‘01‘, ‘week‘: ‘1‘, ‘weekday‘: ‘Sunday‘, ‘close‘: ‘6319.9404‘}, {‘date‘: ‘2017-01-09‘, ‘month‘: ‘01‘, ‘week‘: ‘2‘, ‘weekday‘: ‘Monday‘, ‘close‘: ‘6239.1506‘}, {‘date‘: ‘2017-01-10‘, ‘month‘: ‘01‘, ‘week‘: ‘2‘, ‘weekday‘: ‘Tuesday‘, ‘close‘: ‘6263.1548‘}, {‘date‘: ‘2017-01-11‘, ‘month‘: ‘01‘, ‘week‘: ‘2‘, ‘weekday‘: ‘Wednesday‘, ‘close‘: ‘5383.0598‘}, {‘date‘: ‘2017-01-12‘, ‘month‘: ‘01‘, ‘week‘: ‘2‘, ‘weekday‘: ‘Thursday‘, ‘close‘: ‘5566.7345‘}, {‘date‘: ‘2017-01-13‘, ‘month‘: ‘01‘, ‘week‘: ‘2‘, ‘weekday‘: ‘Friday‘, ‘close‘: ‘5700.0716‘}, {‘date‘: ‘2017-01-14‘, ‘month‘: ‘01‘, ‘week‘: ‘2‘, ‘weekday‘: ‘Saturday‘, ‘close‘: ‘5648.6897‘}, {‘date‘: ‘2017-01-15‘, ‘month‘: ‘01‘, ‘week‘: ‘2‘, ‘weekday‘: ‘Sunday‘, ‘close‘: ‘5674.7977‘}, {‘date‘: ‘2017-01-16‘, ‘month‘: ‘01‘, ‘week‘: ‘3‘, ‘weekday‘: ‘Monday‘, ‘close‘: ‘5730.0658‘}, {‘date‘: ‘2017-01-17‘, ‘month‘: ‘01‘, ‘week‘: ‘3‘, ‘weekday‘: ‘Tuesday‘, ‘close‘: ‘6202.9704‘}, {‘date‘: ‘2017-01-18‘, ‘month‘: ‘01‘, ‘week‘: ‘3‘, ‘weekday‘: ‘Wednesday‘, ‘close‘: ‘6047.6601‘}, {‘date‘: ‘2017-01-19‘, ‘month‘: ‘01‘, ‘week‘: ‘3‘, ‘weekday‘: ‘Thursday‘, ‘close‘: ‘6170.8433‘}, {‘date‘: ‘2017-01-20‘, ‘month‘: ‘01‘, ‘week‘: ‘3‘, ‘weekday‘: ‘Friday‘, ‘close‘: ‘6131.2511‘}, {‘date‘: ‘2017-01-21‘, ‘month‘: ‘01‘, ‘week‘: ‘3‘, ‘weekday‘: ‘Saturday‘, ‘close‘: ‘6326.3657‘}, {‘date‘: ‘2017-01-22‘, ‘month‘: ‘01‘, ‘week‘: ‘3‘, ‘weekday‘: ‘Sunday‘, ‘close‘: ‘6362.9482‘}, {‘date‘: ‘2017-01-23‘, ‘month‘: ‘01‘, ‘week‘: ‘4‘, ‘weekday‘: ‘Monday‘, ‘close‘: ‘6255.5602‘}, {‘date‘: ‘2017-01-24‘, ‘month‘: ‘01‘, ‘week‘: ‘4‘, ‘weekday‘: ‘Tuesday‘, ‘close‘: ‘6074.8333‘}, {‘date‘: ‘2017-01-25‘, ‘month‘: ‘01‘, ‘week‘: ‘4‘, ‘weekday‘: ‘Wednesday‘, ‘close‘: ‘6154.6958‘}, {‘date‘: ‘2017-01-26‘, ‘month‘: ‘01‘, ‘week‘: ‘4‘, ‘weekday‘: ‘Thursday‘, ‘close‘: ‘6295.3388‘}, {‘date‘: ‘2017-01-27‘, ‘month‘: ‘01‘, ‘week‘: ‘4‘, ‘weekday‘: ‘Friday‘, ‘close‘: ‘6320.7206‘}, {‘date‘: ‘2017-01-28‘, ‘month‘: ‘01‘, ‘week‘: ‘4‘, ‘weekday‘: ‘Saturday‘, ‘close‘: ‘6332.5389‘}, {‘date‘: ‘2017-01-29‘, ‘month‘: ‘01‘, ‘week‘: ‘4‘, ‘weekday‘: ‘Sunday‘, ‘close‘: ‘6289.1698‘}, {‘date‘: ‘2017-01-30‘, ‘month‘: ‘01‘, ‘week‘: ‘5‘, ‘weekday‘: ‘Monday‘, ‘close‘: ‘6332.8246‘}, {‘date‘: ‘2017-01-31‘, ‘month‘: ‘01‘, ‘week‘: ‘5‘, ‘weekday‘: ‘Tuesday‘, ‘close‘: ‘6657.8667‘}, {‘date‘: ‘2017-02-01‘, ‘month‘: ‘02‘, ‘week‘: ‘5‘, ‘weekday‘: ‘Wednesday‘, ‘close‘: ‘6793.7077‘}, {‘date‘: ‘2017-02-02‘, ‘month‘: ‘02‘, ‘week‘: ‘5‘, ‘weekday‘: ‘Thursday‘, ‘close‘: ‘6934.3856‘}, {‘date‘: ‘2017-02-03‘, ‘month‘: ‘02‘, ‘week‘: ‘5‘, ‘weekday‘: ‘Friday‘, ‘close‘: ‘6995.2901‘}, {‘date‘: ‘2017-02-04‘, ‘month‘: ‘02‘, ‘week‘: ‘5‘, ‘weekday‘: ‘Saturday‘, ‘close‘: ‘7102.0714‘}, {‘date‘: ‘2017-02-05‘, ‘month‘: ‘02‘, ‘week‘: ‘5‘, ‘weekday‘: ‘Sunday‘, ‘close‘: ‘6965.9773‘}, {‘date‘: ‘2017-02-06‘, ‘month‘: ‘02‘, ‘week‘: ‘6‘, ‘weekday‘: ‘Monday‘, ‘close‘: ‘7034.2211‘}, {‘date‘: ‘2017-02-07‘, ‘month‘: ‘02‘, ‘week‘: ‘6‘, ‘weekday‘: ‘Tuesday‘, ‘close‘: ‘7245.8877‘}, {‘date‘: ‘2017-02-08‘, ‘month‘: ‘02‘, ‘week‘: ‘6‘, ‘weekday‘: ‘Wednesday‘, ‘close‘: ‘7246.6303‘}, {‘date‘: ‘2017-02-09‘, ‘month‘: ‘02‘, ‘week‘: ‘6‘, ‘weekday‘: ‘Thursday‘, ‘close‘: ‘6811.6794‘}, {‘date‘: ‘2017-02-10‘, ‘month‘: ‘02‘, ‘week‘: ‘6‘, ‘weekday‘: ‘Friday‘, ‘close‘: ‘6833.4884‘}, {‘date‘: ‘2017-02-11‘, ‘month‘: ‘02‘, ‘week‘: 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True