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rajagurunath/capstone_project_for_ploting.py

Last active August 22, 2019 13:44
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will create plots using matplotlib for stock prices
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capstone_project_for_ploting.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',plot_type=None):
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]:
#print("col",four_columns[col_count])
if plot_type=='boxplot':
ax[i][j].boxplot(df[four_columns[col_count]])
elif plot_type=='line':
ax[i][j].plot(df.index,df[four_columns[col_count]],color=colors[col_count])
elif plot_type=='hist':
ax[i][j].hist(df[four_columns[col_count]],color=colors[col_count])
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
fig.suptitle('{}-{}'.format(name.upper(),sub_name), fontsize=35)
fig.tight_layout()
fig.savefig('{}_{}.png'.format(name,sub_name), bbox_inches='tight')
def total_range(df,name,plot_type):
make_chart(df,name,"total_range",plot_type)
def monthly(df,name,plot_type):
df2=df.resample('M').sum()
make_chart(df2,name,"montly",plot_type)
def quaterly(df,name,plot_type):
df2=df.resample('Q').sum()
make_chart(df2,name,"Quaterly",plot_type)
def weekly(df,name,plot_type):
df2=df.resample('w').sum()
make_chart(df2,name,'weekly',plot_type)
def custom_dates(df,from_date,to_date,name,plot_type):
df2=df[(df.index>=from_date)&(df.index<to_date)]
make_chart(df2,name,"from {} to {}".format(from_date,to_date),plot_type)
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('--plot_type', type=str, help='choose the plot type : boxpot, line')
parser.add_argument('--fromdate', type=int,
help="custom start date")
parser.add_argument('--todate', type=int,
help='custom to date')
args = parser.parse_args()
dataset=args.DATASET
fromdate=args.fromdate
todate=args.todate
plot_type=args.plot_type
df=read_data(dataset)
total_range(df,dataset,plot_type)
weekly(df,dataset,plot_type)
monthly(df,dataset,plot_type)
quaterly(df,dataset,plot_type)
# custom_dates(df,fromdate,todate,dataset)
custom_dates(df,"2018-05","2018-11",dataset,plot_type)
custom_dates(df,"2017-01","2018-01",dataset,plot_type)
@rajagurunath
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rajagurunath commented Aug 21, 2019

infosys_from 2018-05 to 2018-11
infosys_montly
infosys_Quaterly
infosys_total_range
infosys_weekly
sbi_total_range
sbi_weekly
tcs_from 2018-05 to 2018-11
tcs_montly
tcs_Quaterly
tcs_total_range
tcs_weekly
sbi_from 2018-05 to 2018-11
sbi_montly
sbi_Quaterly

@rajagurunath
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rajagurunath commented Aug 22, 2019 •
edited
Loading

For most of Dataset (companies ) the stock price was showing increasing trend for the period of 2017-2018 (especially for tcs)

infosys_from 2018-05 to 2018-11
infosys_Quaterly
infosys_montly
infosys_weekly
infosys_total_range
tcs_from 2017-01 to 2018-01
tcs_from 2018-05 to 2018-11
tcs_Quaterly
tcs_montly
tcs_weekly
tcs_total_range
sbi_Quaterly
sbi_montly
sbi_weekly
sbi_total_range
infosys_from 2017-01 to 2018-01
sbi_from 2018-05 to 2018-11
sbi_from 2017-01 to 2018-01

@rajagurunath
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rajagurunath commented Aug 22, 2019

`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',plot_type=None):
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]:
        
        #print("col",four_columns[col_count])
        if plot_type=='boxplot':
            ax[i][j].boxplot(df[four_columns[col_count]])
        elif plot_type=='line':
            ax[i][j].plot(df.index,df[four_columns[col_count]],color=colors[col_count])
        elif plot_type=='hist':
            ax[i][j].hist(df[four_columns[col_count]],color=colors[col_count])
        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
fig.suptitle('{}-{}'.format(name.upper(),sub_name), fontsize=35)
fig.tight_layout()

fig.savefig('{}_{}.png'.format(name,sub_name), bbox_inches='tight')

def total_range(df,name,plot_type):
make_chart(df,name,"total_range",plot_type)

def monthly(df,name,plot_type):
df2=df.resample('M').sum()
make_chart(df2,name,"montly",plot_type)

def quaterly(df,name,plot_type):
df2=df.resample('Q').sum()
make_chart(df2,name,"Quaterly",plot_type)

def weekly(df,name,plot_type):
df2=df.resample('w').sum()
make_chart(df2,name,'weekly',plot_type)

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

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('--plot_type', type=str, help='choose the plot type : boxpot, line')

parser.add_argument('--fromdate', type=int,
                help="custom start date")
parser.add_argument('--todate', type=int,
            help='custom to date')

args = parser.parse_args()
dataset=args.DATASET
fromdate=args.fromdate
todate=args.todate
plot_type=args.plot_type

df=read_data(dataset)
total_range(df,dataset,plot_type)
weekly(df,dataset,plot_type)
monthly(df,dataset,plot_type)
quaterly(df,dataset,plot_type)
# custom_dates(df,fromdate,todate,dataset)
custom_dates(df,"2018-05","2018-11",dataset,plot_type)
custom_dates(df,"2017-01","2018-01",dataset,plot_type)

`

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