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Usage: --company=<company>
import os
import tarfile
import pandas as pd
from pandas import errors as pd_errors
from functools import reduce
from docopt import docopt
args = docopt(doc=__doc__, argv=None,
help=True, version=None,
years = [2015, 2016, 2017]
company = args['--company']
# Getting the data files list
data_files_list = []
for year in years:
year_directory = 'data/{year}'.format(year=year)
for file in os.listdir(year_directory):
data_files_list.append('{year_directory}/{file}'.format(year_directory=year_directory, file=file))
def parse_data(file_name, company_symbol):
Returns data for the corresponding company
:param file_name: name of the tar file
:param company_symbol: company symbol
:type file_name: str
:type company_symbol: str
:return: dataframe for the corresponding company data
:rtype: pd.DataFrame
tar =
price_report = pd.read_csv(tar.extractfile('prices.csv'))
company_price_data = price_report[price_report['symbol'] == company_symbol]
return company_price_data
except (KeyError, pd_errors.EmptyDataError):
return pd.DataFrame()
# Getting the complete data for a given company
company_data = reduce(lambda df, file_name: df.append(parse_data(file_name, company)),
company_data = company_data.sort_values(by=['date'])
# Create folder for company data if does not exists
if not os.path.exists('data/company_data'):
# Write data to a CSV file
columns=['date', 'open', 'high', 'low', 'close', 'volume', 'adj_close'],
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