capstone_project_for_ploting_v2.py

import pandas as pd 
import path
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import argparse
plt.style.use("seaborn-poster")


DATA_DIR=path.Path(r"D:\experiments\data\Capstone_proj")
dataset_lookup={
                'sbi':'sbi.csv',
                'infosys':'infosys.csv',
                'tcs':'tcs.csv',
                'usd_inr':'USD INR Historical Data - Investing.com India.csv'
                }

four_columns=['Open Price', 'High Price', 'Low Price', 'Close Price']
colors=['r','b','g','black']

def read_data(dataset_name):
    dataset_name=dataset_lookup.get(dataset_name)
    df=pd.read_csv(DATA_DIR+"\\{}".format(dataset_name))
    df.set_index(pd.to_datetime(df['Date']),inplace=True)
    df.sort_index(inplace=True)
    return df


def make_chart(df,name='SBI',sub_name='total_range'):
    years = mdates.YearLocator()   # every year
    months = mdates.MonthLocator()  # every month
    years_fmt = mdates.DateFormatter('%Y-%m')

    #after sorting and set index
    fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(20,15))
    col_count=0
    for i in [0,1]:
        for j in [0,1]:
            ax[i][j].plot(df[0].index,df[0][four_columns[col_count]],label='sbi')
            ax[i][j].plot(df[1].index,df[1][four_columns[col_count]],label='infosys')
            ax[i][j].plot(df[2].index,df[2][four_columns[col_count]],label='tcs')
            ax[i][j].legend()

            ax[i][j].spines['top'].set_visible(False)
            ax[i][j].spines['right'].set_visible(False)

            ax[i][j].xaxis.set_tick_params(top='off', direction='out', width=1)
            ax[i][j].yaxis.set_tick_params(right='off', direction='out', width=1)
            
            ax[i][j].set_title(four_columns[col_count])
            ax[i][j].set_xlabel("DATE RANGE")
            ax[i][j].set_ylabel(four_columns[col_count])
            
            ax[i][j].xaxis.set_major_locator(years)
            ax[i][j].xaxis.set_major_formatter(years_fmt)
            ax[i][j].xaxis.set_minor_locator(months)

            col_count+=1
    plt.legend()
    fig.suptitle('{}-{}'.format(name.upper(),sub_name), fontsize=35)
    fig.tight_layout()
    
    fig.savefig('{}_{}.png'.format(name,sub_name), bbox_inches='tight')

def plot_3(df1,df2,df3,df4=None,name='None',col_name="Perc_change"):
    
    plt.show()
    plt.plot(df1[col_name],label='sbi')
    plt.plot(df2[col_name],label='infosys')
    plt.plot(df3[col_name],label='tcs')
    if col_name=="Perc_change":
        plt.plot(df4[col_name],label='usd-inr')
    plt.xticks(rotation=40)
    plt.legend()
    plt.title(name+"-percentage change")
    plt.tight_layout()
    plt.savefig("{}.png".format(name))


def custom_dates_v1(df_list,from_date,to_date,name,name2):
    df2_list=[]
    for df in df_list:
        df2_list.append(df[(df.index>=from_date)&(df.index<to_date)])
    plot_3(*df2_list,name,name2)

def total_range(df,name):

    plot_3(*df,"total_range")

def monthly(df_list,name):
    df2_list=[]
    for df in df_list:
        df2_list.append(df.resample('M').mean())
    # df2=df.resample('M').mean()
    plot_3(*df2_list,"montly")
    
def quaterly(df_list,name):
    df2_list=[]
    for df in df_list:
        df2_list.append(df.resample('Q').mean())
    plot_3(*df2_list,"Quarterly")

def weekly(df_list,name):
    df2_list=[]
    for df in df_list:
        df2_list.append(df.resample('w').mean())
    plot_3(*df2_list,'weekly')


def custom_dates(df,from_date,to_date,name):
    df2=df[(df.index>=from_date)&(df.index<to_date)]
    make_chart(df2,name,"from {} to {}".format(from_date,to_date))

def percentage(x):
    diff = x['High Price'] - x['Low Price']
    x['Perc_change'] = (diff/x['Low Price']) * 100
    return x

if __name__=="__main__":
    parser = argparse.ArgumentParser(description='helper functions for capstone project')
    # parser.add_argument('--DATASET', type=str, help='choose one of the dataset:  sbi ,infosys,tcs,usd_inr')
    # parser.add_argument('--fromdate', type=str,
    #                  help="custom start date")
    # parser.add_argument('--todate', type=str,
    #              help='custom to date')

    # args = parser.parse_args()
    # # dataset=args.DATASET
    # fromdate=args.fromdate
    # todate=args.todate
    fromdate="2017-01"
    todate="2017-05"
    sbi=read_data("sbi")
    infos=read_data("infosys")
    tcs=read_data("tcs")
    usd_inr=read_data("usd_inr")
    print(usd_inr.dtypes)
    usd_inr["High Price"]=usd_inr["High Price"].astype("float")
    usd_inr["Low Price"]=usd_inr["Low Price"].astype("float")
    
    
    sbi=sbi.apply(percentage,axis=1)
    infos=infos.apply(percentage,axis=1)
    tcs=tcs.apply(percentage,axis=1)
    usd_inr=usd_inr.apply(percentage,axis=1)
    
    df_list=[sbi,infos,tcs,usd_inr]

    plot_3(*df_list,"Low Price","Low Price")
    plot_3(*df_list,"High Price","High Price")
    # plot_3(sbi,infos,tcs)
    custom_dates_v1([sbi,infos,tcs,usd_inr],fromdate,todate,"custom","High Price")
    custom_dates_v1([sbi,infos,tcs,usd_inr],fromdate,todate,"custom","Low Price")
    
    total_range([sbi,infos,tcs,usd_inr],"days")
    
    
    dataset=None
    weekly(df_list,dataset)
    monthly(df_list,dataset)
    quaterly(df_list,dataset)


    # # custom_dates(df,fromdate,todate,dataset)
    # custom_dates(df,"2018-05","2018-11",dataset)
    # custom_dates(df,"2017-01","2018-01",dataset)