Python数据可视化之交易收盘与天气变化走势图

下面的案例是绘出交易收盘走势图

简单说明：整体代码分为5个函数，最后给出了最后两个函数运行的效果图。前面每个函数都为下面的函数做准备。

函数btc_close_2017()、btc_close_2017_01()、btc_close_2017_02()函数为了显示数据

函数btc_close_2017_03()、btc_close_2017_04()可视化数据

绘图用到的函数以及参数说明未单独列出都在程序里注释说明。

整体代码如下：

#交易收盘走势图：JSON格式
from __future__ import (absolute_import,division,print_function,unicode_literals)
import requests

def btc_close_2017():
    #实现兼容python 2.x和3.x的版本，要用到urlopen函数
    try:
        from urllib2 import urlopen
    except ImportError:
        from urllib.request import urlopen

    导入json模块
    import json

    json_url = "https://raw.githubusercontent.com/muxuezi/btc/master/btc_close_2017.json"
    #调用requests.get()方法向github服务器发送请求，返回结果存储在req中
    req = requests.get(json_url)
    # 将数据写入文件
    with open('btc_close_2017_request.json','w') as f:
        #req.text属性可以直接读取文件数据，返回格式为字符串
        f.write(req.text)
    #直接调用req.json就可将btc_close_2017.json文件转换成python列表file_request
    file_request = req.json
    """
    response = urlopen(json_url)
    req = response.read()

    with open("btc_close_2017_urllib.json",'wb') as f:
        f.write(req)
    file_urllib = json.loads(req)
    print(file_urllib)
    """
    print(req.text)

import json

def btc_close_2017_01():
    filename = "btc_close_2017.json"
    with open(filename) as f:
        btc_data = json.load(f)
    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))

# 将字符转化为数字
def btc_close_2017_02():
    filename = "btc_close_2017.json"
    with open(filename) as f:
        btc_data = json.load(f)
    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))

import pygal
# 绘制收盘价折线图
def btc_close_2017_03():
    filename = "btc_close_2017.json"
    with open(filename) as f:
        btc_data = json.load(f)
    dates = []
    month = []
    week = []
    weekday = []
    close = []
    for btc_dict in btc_data:
        dates.append(btc_dict['date'])
        month.append(int(btc_dict['month']))
        week.append(int(btc_dict['week']))
        weekday.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
    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')


import math
#导入python标准库中模块itertools的函数groupby
from itertools import groupby
#计算收盘价均值
def draw_line(x_data,y_data,title,y_legend):
    xy_map = []
    #将x轴,y轴数据合并排序再用groupby分组，分组之后求出均值
    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)])
    #将xy_map中x轴,y轴数据分离
    x_unique,y_mean = [*zip(*xy_map)]
    #实例化折线图对象并画图
    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

def btc_close_2017_04():
    filename = "btc_close_2017.json"
    with open(filename) as f:
        btc_data = json.load(f)
    dates = []
    month = []
    week = []
    weekday = []
    close = []
    for btc_dict in btc_data:
        dates.append(btc_dict['date'])
        month.append(int(btc_dict['month']))
        week.append(int(btc_dict['week']))
        weekday.append(btc_dict['weekday'])
        close.append(int(float(btc_dict['close'])))

    #x轴日期标签旋转20度，不用显示x轴所有标签
    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
    #x轴每隔20天显示一次
    N = 20
    line_chart.x_labels_major = dates[::N]
    #利用对数变换剔除非线性趋势之后，整体上涨更接近线性增长
    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')

    # 收盘价月日均图
    idx_month = dates.index('2017-12-01')
    line_chart_month = draw_line(month[:idx_month],close[:idx_month],'收盘价月日均图','月日均值')


btc_close_2017_04()
"""
btc_close_2017_03()
btc_close_2017_02()
btc_close_2017_01()
"""

收盘价折线图如下：

收盘价对数变换折线图如下：

收盘价月日均图如下：

天气变化折线图程序如下（程序所用函数以及参数含义在注释中已说明）：

import csv
from matplotlib import pyplot as plt
from datetime import datetime

# filename = 'sitka_weather_07-2014.csv'
#要读取的文件
filename = 'sitka_weather_2014.csv'

#打开文件并将文件结果对象存储到f中
with open(filename) as f:
    #创建一个与该文件相关联的阅读器(reader)对象，reader处理文件中以逗号分隔每一行的数据
    reader = csv.reader(f)
    #模块csv包含函数next()，将阅读器对象传递给它将返回文件的下一行
    headr_row = next(reader)

    dates,highs,lows = [],[],[]
    for row in reader:
        #datetime.strptime()设置时间日期
        current_date = datetime.strptime(row[0],"%Y-%m-%d")
        dates.append(current_date)

        high = int(row[1])
        highs.append(high)

        low = int(row[3])
        lows.append(low)

    # 数据可视化
    fig = plt.figure(dpi=128,figsize=(10,6))
    #红色显示最高气温，蓝色显示最低气温
    plt.plot(dates,highs,c='red')
    plt.plot(dates,lows,c = 'blue')
    #向fill_between()函数传递一个x值：dates 两个y值：highs,lows 中间填充色:blue 透明度:0.5
    plt.fill_between(dates,highs,lows,facecolor = 'blue',alpha=0.5)

    #设置图形的格式
    plt.title("Daily hight temperatures,July 2014",fontsize=10)
    plt.xlabel('',fontsize=5)
    #来绘制斜的日期标签
    fig.autofmt_xdate()
    plt.ylabel("Temperature(F)",fontsize = 10)
    plt.tick_params(axis='both',which='major',labelsize = 10)
    plt.show()

效果图如下：