Python數據可視化之交易收盤與天氣變化走勢圖

下面的案例是繪出交易收盤走勢圖

簡單說明：整體代碼分為5個函數，最后給出了最后兩個函數運行的效果圖。前面每個函數都為下面的函數做准備。

函數btc_close_2017()、btc_close_2017_01()、btc_close_2017_02()函數為了顯示數據

函數btc_close_2017_03()、btc_close_2017_04()可視化數據

繪圖用到的函數以及參數說明未單獨列出都在程序里注釋說明。

整體代碼如下：

#交易收盤走勢圖：JSON格式
from __future__ import (absolute_import,division,print_function,unicode_literals)
import requests

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

    導入json模塊
    import json

    json_url = "https://raw.githubusercontent.com/muxuezi/btc/master/btc_close_2017.json"
    #調用requests.get()方法向github服務器發送請求，返回結果存儲在req中
    req = requests.get(json_url)
    # 將數據寫入文件
    with open('btc_close_2017_request.json','w') as f:
        #req.text屬性可以直接讀取文件數據，返回格式為字符串
        f.write(req.text)
    #直接調用req.json就可將btc_close_2017.json文件轉換成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))

# 將字符轉化為數字
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
# 繪制收盤價折線圖
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 = "收盤價（￥）"
    line_chart.x_labels = dates
    N = 20
    line_chart.x_labels_major = dates[::N]
    line_chart.add("收盤價",close)
    line_chart.render_to_file('收盤價折線圖.svg')


import math
#導入python標准庫中模塊itertools的函數groupby
from itertools import groupby
#計算收盤價均值
def draw_line(x_data,y_data,title,y_legend):
    xy_map = []
    #將x軸,y軸數據合並排序再用groupby分組，分組之后求出均值
    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)])
    #將xy_map中x軸,y軸數據分離
    x_unique,y_mean = [*zip(*xy_map)]
    #實例化折線圖對象並畫圖
    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軸日期標簽旋轉20度，不用顯示x軸所有標簽
    line_chart = pygal.Line(x_label_rotation=20,show_minor_x_labels=False)
    line_chart.title = "收盤價對數變換（￥）"
    line_chart.x_labels = dates
    #x軸每隔20天顯示一次
    N = 20
    line_chart.x_labels_major = dates[::N]
    #利用對數變換剔除非線性趨勢之后，整體上漲更接近線性增長
    close_log = [math.log10(_) for _ in close]
    line_chart.add("log收盤價",close_log)
    line_chart.render_to_file('收盤價對數變換折線圖.svg')

    # 收盤價月日均圖
    idx_month = dates.index('2017-12-01')
    line_chart_month = draw_line(month[:idx_month],close[:idx_month],'收盤價月日均圖','月日均值')


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

收盤價折線圖如下：

收盤價對數變換折線圖如下：

收盤價月日均圖如下：

天氣變化折線圖程序如下（程序所用函數以及參數含義在注釋中已說明）：

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

# filename = 'sitka_weather_07-2014.csv'
#要讀取的文件
filename = 'sitka_weather_2014.csv'

#打開文件並將文件結果對象存儲到f中
with open(filename) as f:
    #創建一個與該文件相關聯的閱讀器(reader)對象，reader處理文件中以逗號分隔每一行的數據
    reader = csv.reader(f)
    #模塊csv包含函數next()，將閱讀器對象傳遞給它將返回文件的下一行
    headr_row = next(reader)

    dates,highs,lows = [],[],[]
    for row in reader:
        #datetime.strptime()設置時間日期
        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)

    # 數據可視化
    fig = plt.figure(dpi=128,figsize=(10,6))
    #紅色顯示最高氣溫，藍色顯示最低氣溫
    plt.plot(dates,highs,c='red')
    plt.plot(dates,lows,c = 'blue')
    #向fill_between()函數傳遞一個x值：dates 兩個y值：highs,lows 中間填充色:blue 透明度:0.5
    plt.fill_between(dates,highs,lows,facecolor = 'blue',alpha=0.5)

    #設置圖形的格式
    plt.title("Daily hight temperatures,July 2014",fontsize=10)
    plt.xlabel('',fontsize=5)
    #來繪制斜的日期標簽
    fig.autofmt_xdate()
    plt.ylabel("Temperature(F)",fontsize = 10)
    plt.tick_params(axis='both',which='major',labelsize = 10)
    plt.show()

效果圖如下：