16.1 CSV文件格式
将数据作为一系列以逗号分隔的值写入文件，这样的文件称为CSV文件。
16.1.1 分析CSV文件头
查看文件（文件已经导入项目文件夹）的第一行：
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']
注意，文件头的格式并非总是一致的，空格和单位可能出现在奇怪的地方。
16.1.2 打印文件头及其位置
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)
输出如下，其中指出了每个文件头的索引：
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
16.1.3 提取并读取数据
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]
16.1.4 绘制气温图表
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()
16.1.5 模块datetime
下面再图表中添加日期，在天气数据文件中，第一个日期在第二行。
读取该数据时，获得的是一个字符串，因为我们需要想办法将字符串‘2014-7-1’转换为一个表示相应日期的对象，可使用模块datetime中的方法strptime()。
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()
16.1.7 涵盖更长的时间
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()
16.1.8 再绘制一个数据系列
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()
16.1.9给图表区域着色
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()
16.1.10 错误检查
有些时候数据收集不全，可能会引发异常，如果不妥善处理，还可能导致程序崩溃。
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: ''
该trackback指出，Python无法处理其中一天的最高气温，因为它无法将空字符串（‘ ’）转换为整数。为解决这种问题，我们在从CSV文件读取值时执行错误检查代码，对分析数据集时可能出现的异常进行处理，如下所示：
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 Death 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
p324 动手试一试 16-2 比较锡特卡和死亡谷的气温
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 += " Sitka, 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()
16.2 制作交易收盘价走势图：JSON格式
16.2.1 下载收盘价数据
收盘价数据位于https://raw.githubusercontent.com/muxuezi/btc/master/btc_close_2017.json
也可以用Python2.x标准库中模块urllib2（Python3.x使用的是urllib）的函数urlopen来做，还可以通过Python的第三方模块requests下载数据。
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)
首先导入下载文件使用的模块。这里用try/except语句实现兼容Python2.x好Python3.x代码。
然后导入模块json，以便之后能够正确地加载文件中的数据。
最后用函数json.load()将文件内容转换成Python能够处理的格式，与前面直接下载的文件内容一致。
函数urlopen的代码稍微复杂一些，第三方模块requests封装了许多常用的方法，让数据集下载和读取方式变得非常简单。
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'
原因是：我的文件名起的就是requests.py，改个名字就可以了。
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': '6', 'weekday': 'Saturday', 'close': '6946.09'}, {'date': '2017-02-12', 'month': '02', 'week': '6', 'weekday': 'Sunday', 'close': '6883.9424'}, {'date': '2017-02-13', 'month': '02', 'week': '7', 'weekday': 'Monday', 'close': '6858.5789'}, {'date': '2017-02-14', 'month': '02', 'week': '7', 'weekday': 'Tuesday', 'close': '6930.882'}, {'date': '2017-02-15', 'month': '02', 'week': '7', 'weekday': 'Wednesday', 'close': '6935.3788'}, {'date': '2017-02-16', 'month': '02', 'week': '7', 'weekday': 'Thursday', 'close': '7088.8535'}, {'date': '2017-02-17', 'month': '02', 'week': '7', 'weekday': 'Friday', 'close': '7229.5808'}, {'date': '2017-02-18', 'month': '02', 'week': '7', 'weekday': 'Saturday', 'close': '7267.5468'}, {'date': '2017-02-19', 'month': '02', 'week': '7', 'weekday': 'Sunday', 'close': '7220.5385'}, {'date': '2017-02-20', 'month': '02', 'week': '8', 'weekday': 'Monday', 'close': '7450.2901'}, {'date': '2017-02-21', 'month': '02', 'week': '8', 'weekday': 'Tuesday', 'close': '7732.4979'}, {'date': '2017-02-22', 'month': '02', 'week': '8', 'weekday': 'Wednesday', 'close': '7716.2218'}, {'date': '2017-02-23', 'month': '02', 'week': '8', 'weekday': 'Thursday', 'close': '8092.0221'}, {'date': '2017-02-24', 'month': '02', 'week': '8', 'weekday': 'Friday', 'close': '8109.1867'}, {'date': '2017-02-25', 'month': '02', 'week': '8', 'weekday': 'Saturday', 'close': '7908.54'}, {'date': '2017-02-26', 'month': '02', 'week': '8', 'weekday': 'Sunday', 'close': '8137.4131'}, {'date': '2017-02-27', 'month': '02', 'week': '9', 'weekday': 'Monday', 'close': '8206.1829'}, {'date': '2017-02-28', 'month': '02', 'week': '9', 'weekday': 'Tuesday', 'close': '8176.3692'}, {'date': '2017-03-01', 'month': '03', 'week': '9', 'weekday': 'Wednesday', 'close': '8464.3549'}, {'date': '2017-03-02', 'month': '03', 'week': '9', 'weekday': 'Thursday', 'close': '8688.7751'}, {'date': '2017-03-03', 'month': '03', 'week': '9', 'weekday': 'Friday', 'close': '8900.4858'}, {'date': '2017-03-04', 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'weekday': 'Tuesday', 'close': '8616.949'}, {'date': '2017-03-15', 'month': '03', 'week': '11', 'weekday': 'Wednesday', 'close': '8711.306'}, {'date': '2017-03-16', 'month': '03', 'week': '11', 'weekday': 'Thursday', 'close': '8091.7411'}, {'date': '2017-03-17', 'month': '03', 'week': '11', 'weekday': 'Friday', 'close': '7379.6562'}, {'date': '2017-03-18', 'month': '03', 'week': '11', 'weekday': 'Saturday', 'close': '6694.36'}, {'date': '2017-03-19', 'month': '03', 'week': '11', 'weekday': 'Sunday', 'close': '7028.0107'}, {'date': '2017-03-20', 'month': '03', 'week': '12', 'weekday': 'Monday', 'close': '7196.3568'}, {'date': '2017-03-21', 'month': '03', 'week': '12', 'weekday': 'Tuesday', 'close': '7680.723'}, {'date': '2017-03-22', 'month': '03', 'week': '12', 'weekday': 'Wednesday', 'close': '7139.7016'}, {'date': '2017-03-23', 'month': '03', 'week': '12', 'weekday': 'Thursday', 'close': '7092.2246'}, {'date': '2017-03-24', 'month': '03', 'week': '12', 'weekday': 'Friday', 'close': 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'month': '09', 'week': '38', 'weekday': 'Sunday', 'close': '24216.5269'}, {'date': '2017-09-25', 'month': '09', 'week': '39', 'weekday': 'Monday', 'close': '26007.1112'}, {'date': '2017-09-26', 'month': '09', 'week': '39', 'weekday': 'Tuesday', 'close': '25869.3194'}, {'date': '2017-09-27', 'month': '09', 'week': '39', 'weekday': 'Wednesday', 'close': '27955.6252'}, {'date': '2017-09-28', 'month': '09', 'week': '39', 'weekday': 'Thursday', 'close': '27882.4195'}, {'date': '2017-09-29', 'month': '09', 'week': '39', 'weekday': 'Friday', 'close': '27711.6948'}, {'date': '2017-09-30', 'month': '09', 'week': '39', 'weekday': 'Saturday', 'close': '28969.0962'}, {'date': '2017-10-01', 'month': '10', 'week': '39', 'weekday': 'Sunday', 'close': '29264.4926'}, {'date': '2017-10-02', 'month': '10', 'week': '40', 'weekday': 'Monday', 'close': '29295.7562'}, {'date': '2017-10-03', 'month': '10', 'week': '40', 'weekday': 'Tuesday', 'close': '28743.0928'}, {'date': '2017-10-04', 'month': '10', 'week': '40', 'weekday': 'Wednesday', 'close': '28120.5656'}, {'date': '2017-10-05', 'month': '10', 'week': '40', 'weekday': 'Thursday', 'close': '28764.0436'}, {'date': '2017-10-06', 'month': '10', 'week': '40', 'weekday': 'Friday', 'close': '29084.1981'}, {'date': '2017-10-07', 'month': '10', 'week': '40', 'weekday': 'Saturday', 'close': '29521.3602'}, {'date': '2017-10-08', 'month': '10', 'week': '40', 'weekday': 'Sunday', 'close': '30583.2886'}, {'date': '2017-10-09', 'month': '10', 'week': '41', 'weekday': 'Monday', 'close': '31622.869'}, {'date': '2017-10-10', 'month': '10', 'week': '41', 'weekday': 'Tuesday', 'close': '31243.3645'}, {'date': '2017-10-11', 'month': '10', 'week': '41', 'weekday': 'Wednesday', 'close': '31830.4848'}, {'date': '2017-10-12', 'month': '10', 'week': '41', 'weekday': 'Thursday', 'close': '35833.2539'}, {'date': '2017-10-13', 'month': '10', 'week': '41', 'weekday': 'Friday', 'close': '37106.6814'}, {'date': '2017-10-14', 'month': '10', 'week': '41', 'weekday': 'Saturday', 'close': '38222.2666'}, {'date': '2017-10-15', 'month': '10', 'week': '41', 'weekday': 'Sunday', 'close': '37517.0856'}, {'date': '2017-10-16', 'month': '10', 'week': '42', 'weekday': 'Monday', 'close': '37917.9925'}, {'date': '2017-10-17', 'month': '10', 'week': '42', 'weekday': 'Tuesday', 'close': '37060.3182'}, {'date': '2017-10-18', 'month': '10', 'week': '42', 'weekday': 'Wednesday', 'close': '36928.632'}, {'date': '2017-10-19', 'month': '10', 'week': '42', 'weekday': 'Thursday', 'close': '37704.4579'}, {'date': '2017-10-20', 'month': '10', 'week': '42', 'weekday': 'Friday', 'close': '39634.3994'}, {'date': '2017-10-21', 'month': '10', 'week': '42', 'weekday': 'Saturday', 'close': '39827.4189'}, {'date': '2017-10-22', 'month': '10', 'week': '42', 'weekday': 'Sunday', 'close': '39673.3776'}, {'date': '2017-10-23', 'month': '10', 'week': '43', 'weekday': 'Monday', 'close': '39144.7834'}, {'date': '2017-10-24', 'month': '10', 'week': '43', 'weekday': 'Tuesday', 'close': '36611.829'}, {'date': '2017-10-25', 'month': '10', 'week': '43', 'weekday': 'Wednesday', 'close': '38067.9613'}, {'date': '2017-10-26', 'month': '10', 'week': '43', 'weekday': 'Thursday', 'close': '39108.6475'}, {'date': '2017-10-27', 'month': '10', 'week': '43', 'weekday': 'Friday', 'close': '38353.9173'}, {'date': '2017-10-28', 'month': '10', 'week': '43', 'weekday': 'Saturday', 'close': '38122.1385'}, {'date': '2017-10-29', 'month': '10', 'week': '43', 'weekday': 'Sunday', 'close': '40925.4142'}, {'date': '2017-10-30', 'month': '10', 'week': '44', 'weekday': 'Monday', 'close': '40682.7268'}, {'date': '2017-10-31', 'month': '10', 'week': '44', 'weekday': 'Tuesday', 'close': '42779.3067'}, {'date': '2017-11-01', 'month': '11', 'week': '44', 'weekday': 'Wednesday', 'close': '44572.0627'}, {'date': '2017-11-02', 'month': '11', 'week': '44', 'weekday': 'Thursday', 'close': '46462.0654'}, {'date': '2017-11-03', 'month': '11', 'week': '44', 'weekday': 'Friday', 'close': '47518.0205'}, {'date': '2017-11-04', 'month': '11', 'week': '44', 'weekday': 'Saturday', 'close': '49047.8253'}, {'date': '2017-11-05', 'month': '11', 'week': '44', 'weekday': 'Sunday', 'close': '48907.9843'}, {'date': '2017-11-06', 'month': '11', 'week': '45', 'weekday': 'Monday', 'close': '46159.7307'}, {'date': '2017-11-07', 'month': '11', 'week': '45', 'weekday': 'Tuesday', 'close': '47249.3415'}, {'date': '2017-11-08', 'month': '11', 'week': '45', 'weekday': 'Wednesday', 'close': '49427.7048'}, {'date': '2017-11-09', 'month': '11', 'week': '45', 'weekday': 'Thursday', 'close': '47448.9118'}, {'date': '2017-11-10', 'month': '11', 'week': '45', 'weekday': 'Friday', 'close': '43637.0596'}, {'date': '2017-11-11', 'month': '11', 'week': '45', 'weekday': 'Saturday', 'close': '42085.6019'}, {'date': '2017-11-12', 'month': '11', 'week': '45', 'weekday': 'Sunday', 'close': '38904.304'}, {'date': '2017-11-13', 'month': '11', 'week': '46', 'weekday': 'Monday', 'close': '43279.9771'}, {'date': '2017-11-14', 'month': '11', 'week': '46', 'weekday': 'Tuesday', 'close': '43801.9668'}, {'date': '2017-11-15', 'month': '11', 'week': '46', 'weekday': 'Wednesday', 'close': '48232.4671'}, {'date': '2017-11-16', 'month': '11', 'week': '46', 'weekday': 'Thursday', 'close': '52012.2859'}, {'date': '2017-11-17', 'month': '11', 'week': '46', 'weekday': 'Friday', 'close': '50973.7739'}, {'date': '2017-11-18', 'month': '11', 'week': '46', 'weekday': 'Saturday', 'close': '51542.0595'}, {'date': '2017-11-19', 'month': '11', 'week': '46', 'weekday': 'Sunday', 'close': '53279.4604'}, {'date': '2017-11-20', 'month': '11', 'week': '47', 'weekday': 'Monday', 'close': '54656.3545'}, {'date': '2017-11-21', 'month': '11', 'week': '47', 'weekday': 'Tuesday', 'close': '53673.7877'}, {'date': '2017-11-22', 'month': '11', 'week': '47', 'weekday': 'Wednesday', 'close': '54403.2305'}, {'date': '2017-11-23', 'month': '11', 'week': '47', 'weekday': 'Thursday', 'close': '52676.5845'}, {'date': '2017-11-24', 'month': '11', 'week': '47', 'weekday': 'Friday', 'close': '54136.6166'}, {'date': '2017-11-25', 'month': '11', 'week': '47', 'weekday': 'Saturday', 'close': '57851.0594'}, {'date': '2017-11-26', 'month': '11', 'week': '47', 'weekday': 'Sunday', 'close': '60980.0178'}, {'date': '2017-11-27', 'month': '11', 'week': '48', 'weekday': 'Monday', 'close': '64246.5971'}, {'date': '2017-11-28', 'month': '11', 'week': '48', 'weekday': 'Tuesday', 'close': '65458.8612'}, {'date': '2017-11-29', 'month': '11', 'week': '48', 'weekday': 'Wednesday', 'close': '64890.9642'}, {'date': '2017-11-30', 'month': '11', 'week': '48', 'weekday': 'Thursday', 'close': '65583.2597'}, {'date': '2017-12-01', 'month': '12', 'week': '48', 'weekday': 'Friday', 'close': '71825.6883'}, {'date': '2017-12-02', 'month': '12', 'week': '48', 'weekday': 'Saturday', 'close': '72079.2312'}, {'date': '2017-12-03', 'month': '12', 'week': '48', 'weekday': 'Sunday', 'close': '74007.4136'}, {'date': '2017-12-04', 'month': '12', 'week': '49', 'weekday': 'Monday', 'close': '76852.0129'}, {'date': '2017-12-05', 'month': '12', 'week': '49', 'weekday': 'Tuesday', 'close': '77398.8752'}, {'date': '2017-12-06', 'month': '12', 'week': '49', 'weekday': 'Wednesday', 'close': '90679.5487'}, {'date': '2017-12-07', 'month': '12', 'week': '49', 'weekday': 'Thursday', 'close': '111589.9776'}, {'date': '2017-12-08', 'month': '12', 'week': '49', 'weekday': 'Friday', 'close': '106233.201'}, {'date': '2017-12-09', 'month': '12', 'week': '49', 'weekday': 'Saturday', 'close': '98676.7747'}, {'date': '2017-12-10', 'month': '12', 'week': '49', 'weekday': 'Sunday', 'close': '99525.1027'}, {'date': '2017-12-11', 'month': '12', 'week': '50', 'weekday': 'Monday', 'close': '110642.88'}, {'date': '2017-12-12', 'month': '12', 'week': '50', 'weekday': 'Tuesday', 'close': '113732.6745'}]
True
16.2.2 提取相关的数据
import json # 将数据加载到一个列表中 filename = 'btc_close_2017.json' with open(filename) as f: btc_data = json.load(f) # 打印每天的信息 for btc_dict in btc_data: date = btc_dict['date'] month = btc_dict['month'] week = btc_dict['week'] weekday = btc_dict['weekday'] close = btc_dict['close'] print("{} is month {} week {},{},the close price is {} RMB".format(date,month,week,weekday,close))
2017-01-01 is month 01 week 52,Sunday,the close price is 6928.6492 RMB 2017-01-02 is month 01 week 1,Monday,the close price is 7070.2554 RMB --ship-- 2017-12-11 is month 12 week 50,Monday,the close price is 110642.88 RMB 2017-12-12 is month 12 week 50,Tuesday,the close price is 113732.6745 RMB Process finished with exit code 0
16.2.3 将字符串转换为数值
# 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' # 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.loads(req) # print(file_urllib) import json # 将数据加载到一个列表中 filename = 'btc_close_2017.json' with open(filename) as f: btc_data = json.load(f) # 打印每天的信息 for btc_dict in btc_data: date = btc_dict['date'] month = int(btc_dict['month']) week = int(btc_dict['week']) weekday = btc_dict['weekday'] close = int(float(btc_dict['close'])) print("{} is month {} week {},{},the close price is {} RMB".format(date,month,week,weekday,close))
2017-01-01 is month 1 week 52,Sunday,the close price is 6928 RMB 2017-01-02 is month 1 week 1,Monday,the close price is 7070 RMB--ship-- 2017-12-11 is month 12 week 50,Monday,the close price is 110642 RMB 2017-12-12 is month 12 week 50,Tuesday,the close price is 113732 RMB Process finished with exit code 0
16.2.4 绘制收盘价折线图
import json import pygal # 将数据加载到一个列表中 filename = 'btc_close_2017.json' with open(filename) as f: btc_data = json.load(f) dates = [] months = [] weeks = [] weekdays = [] close = [] # 打印每天的信息 for btc_dict in btc_data: dates.append(btc_dict['date']) months.append(int(btc_dict['month'])) weeks.append(int(btc_dict['week'])) weekdays.append(btc_dict['weekday']) close.append(int(float(btc_dict['close']))) line_chart = pygal.Line(x_label_rotation=20,show_minor_x_labels=False) line_chart.title = "收盘价（¥)" line_chart.x_labels = dates N = 20 #坐标轴每隔20天显示一次 line_chart.x_labels_major = dates[::N] line_chart.add('收盘价',close) line_chart.render_to_file('收盘价折线图（¥）.svg')
由于数据点较多，x轴要显示346个日期，在有限的屏幕上会显得十分拥挤。因此我们需要利用Pygal的配置参数，对图形进行适当的调整。
x_label_rotation=20，让x轴上的日期标签顺时针旋转20°,show_minor_x_labels=False不用显示所有的x标签。
16.2.5 时间序列特征初探
为了验证周期性的假设，需要首先将非线性的趋势消除。对数变换是常用的处理方法之一。这里用以10为低的对数函数math.log10计算收盘价，日期仍然保持不变。这种方式称为对半数变换。代码如下：
import json import pygal import math # 将数据加载到一个列表中 filename = 'btc_close_2017.json' with open(filename) as f: btc_data = json.load(f) dates = [] months = [] weeks = [] weekdays = [] close = [] # 打印每天的信息 for btc_dict in btc_data: dates.append(btc_dict['date']) months.append(int(btc_dict['month'])) weeks.append(int(btc_dict['week'])) weekdays.append(btc_dict['weekday']) close.append(int(float(btc_dict['close']))) line_chart = pygal.Line(x_label_rotation=20,show_minor_x_labels=False) line_chart.title = "收盘价对数变换（¥)" line_chart.x_labels = dates N = 20 #坐标轴每隔20天显示一次 line_chart.x_labels_major = dates[::N] close_log = [math.log10(_) for _ in close] line_chart.add('log收盘价',close_log) line_chart.render_to_file('收盘价对数变换折线图（¥）.svg')
现在，用对数变换剔除非线性之后，整体上涨的趋势更接近线性增长。
16.2.6 收盘价均值
绘制2017年前11个月的日均值：
import json import pygal import math from itertools import groupby # 将数据加载到一个列表中 filename = 'btc_close_2017.json' with open(filename) as f: btc_data = json.load(f) dates = [] months = [] weeks = [] weekdays = [] close = [] # 打印每天的信息 for btc_dict in btc_data: dates.append(btc_dict['date']) months.append(int(btc_dict['month'])) weeks.append(int(btc_dict['week'])) weekdays.append(btc_dict['weekday']) close.append(int(float(btc_dict['close']))) def draw_line(x_data,y_data,title,y_legend): xy_map = [] for x,y in groupby(sorted(zip(x_data,y_data)),key=lambda _: _[0]): y_list = [v for _, v in y] xy_map.append([x, sum(y_list) / len(y_list)]) x_unique,y_mean = [*zip(*xy_map)] line_chart = pygal.Line() line_chart.title = title line_chart.x_labels = x_unique line_chart.add(y_legend,y_mean) line_chart.render_to_file(title+'.svg') return line_chart idx_month = dates.index('2017-12-01') line_chart_month = draw_line(months[:idx_month],close[:idx_month],'收盘价月日均值（¥）','月日均值') line_chart_month
Traceback (most recent call last): File "D:/data_visualization/btc_close_2017.py", line 57, in <module> line_chart_month = draw_line(months[:idx_month],close[:idx_month],'收盘价月日均值（¥）','月日均值') File "D:/data_visualization/btc_close_2017.py", line 53, in draw_line line_chart.render_to_file(title+'.svg') File "D:data_visualizationpygalghost.py", line 165, in render_to_file f.write(self.render(is_unicode=True, **kwargs)) File "D:data_visualizationpygalghost.py", line 122, in render .make_instance(overrides=kwargs) File "D:data_visualizationpygalghost.py", line 116, in make_instance self.xml_filters) File "D:data_visualizationpygalgraphline.py", line 33, in __init__ super(Line, self).__init__(*args, **kwargs) File "D:data_visualizationpygalgraphase.py", line 74, in __init__ self._draw() File "D:data_visualizationpygalgraphase.py", line 291, in _draw self._compute_margin() File "D:data_visualizationpygalgraphase.py", line 128, in _compute_margin self.label_font_size) File "D:data_visualizationpygalutil.py", line 229, in get_texts_box max_len = max(map(len, get_text_title(texts))) File "D:data_visualizationpygalutil.py", line 224, in get_text_title for text in texts: File "D:data_visualizationpygalgraphase.py", line 126, in <lambda> map(lambda x: truncate(x, self.truncate_label or 25), File "D:data_visualizationpygalutil.py", line 271, in truncate if len(string) > index and index > 0: TypeError: object of type 'int' has no len()
今天实在找不到哪里错了，这部分先空着。
先过一下知识点，python3中的zip用法。
>>>a = [1,2,3] >>> b = [4,5,6] >>> c = [4,5,6,7,8] >>> zipped = zip(a,b) # 返回一个对象 >>> zipped <zip object at 0x103abc288> >>> list(zipped) # list() 转换为列表 [(1, 4), (2, 5), (3, 6)] >>> list(zip(a,c)) # 元素个数与最短的列表一致 [(1, 4), (2, 5), (3, 6)] >>> a1, a2 = zip(*zip(a,b)) # 与 zip 相反，zip(*) 可理解为解压，返回二维矩阵式 >>> list(a1) [1, 2, 3] >>> list(a2) [4, 5, 6] >>>