148 lines
3.4 KiB
Python
148 lines
3.4 KiB
Python
import matplotlib.pyplot as plt
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#from random import randint as rdt
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import csv
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import numpy as np
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import urllib
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import matplotlib.dates as mdates
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import sys
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print(sys.version)
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x=[1,2,3]
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y=[5,7,4]
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x2=[1,2,3]
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y2=[10,14,12]
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plt.plot(x,y,label="First Line")
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plt.plot(x2,y2,label="Second Line")
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plt.xlabel("Plot Number")
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plt.ylabel("Important var")
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plt.title("Interesting graph\nCheck it out!")
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plt.legend()
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plt.show()
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x=[2,4,6,8,10]
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y=[6,7,8,9,10]
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x2=[1,3,5,6,0]
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y2=[6,3,8,3,10]
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population_ages=[20+rdt(0,50) for i in range(20)]
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ids=range(len(population_ages))
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bins=[10*x for x in ids]
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plt.bar(ids,population_ages)
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plt.bar(x,y,label="Bars1",color="blue")
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plt.bar(x2,y2,label="Bars2",color="c")
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plt.hist(population_ages,bins,histtype="bar",rwidth=0.8)
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x=[2,4,6,8,10]
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y=[6,7,8,9,10]
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days=[1,2,3,4,5]
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sleeping=[7,6,6,11,7]
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eating=[2,3,4,3,2]
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working=[5,3,5,6,2]
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playing=[6,4,7,3,9]
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#plt.scatter(x,y,label="skitcat",color="k",marker="x",s=100)
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plt.plot([],[],color="m",label="Sleeping")
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plt.plot([],[],color="c",label="Eating")
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plt.plot([],[],color="r",label="Working")
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plt.plot([],[],color="k",label="Playing")
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plt.stackplot(days,sleeping,eating,working,playing,colors=["m","c","r","k"])
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slices=[7,4,2,13]
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activities=["sleeping","eating","working","playing"]
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cols=["c","m","r","k"]
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plt.pie(slices,labels=activities,colors=cols,startangle=90,shadow=True,explode=[0,0,0.1,0],autopct="%1.1f%%")
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#Part 1
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x=[]
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y=[]
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with open("text.txt","r") as csvfile:
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plots=csv.reader(csvfile,delimiter=",")
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for row in plots:
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x.append(int(row[0]))
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y.append(int(row[1]))
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plt.plot(x,y,label="loaded from file")
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#Part 2
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x,y=np.loadtxt("text.txt",delimiter=",",unpack=True)
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plt.plot(x,y,label="loaded from file")
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#
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def bytespdate2num(fmt,encoding="utf-8"):
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strconverter=mdates.strpdate2num(fmt)
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def bytesconverter(b):
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s=b.decode(encoding)
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return strconverter(s)
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return bytesconverter
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def graph_data(stock):
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#fig=plt.figure()
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#ax1=plt.subplot2grid((1,1),(0,0))
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stock_price_url = 'https://pythonprogramming.net/yahoo_finance_replacement'
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source_code=urllib.request.urlopen(stock_price_url).read().decode()
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#print(source_code)
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stock_data=[]
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split_source=source_code.split('\n')
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for line in split_source:
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split_line=line.split(",")
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if len(split_line)==6:
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if 'values' not in line and 'labels' not in line:
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stock_data.append(line)
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date,closep,highp,lowp,openp,volume=np.loadtxt(stock_data,
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delimiter=",",
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unpack=True,
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#%Y=full year
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#%y=partial year
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#%m=number month
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#%d=day
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#%H=hours
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#%M=minutes
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#%S=seconds
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#%m-%d-%Y
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#converters={0: bytespdate2num('%Y%m%d')})
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converters={0: lambda s : mdates.date2num(datetime.strptime(s.decode("utf-8"),'%Y%m%d'))})
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plt.plot_date(date,closep,"-")
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plt.xlabel("date")
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plt.ylabel("price")
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plt.title("Interesting graph\nCheck it out!")
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plt.legend()
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plt.show()
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graph_data("TSLA")
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