class DataframeUtilitiesProject:
""" Returns dataframes either normally indexed or column indexed"""
FILE_PATH = FileUtilities.get_abs_path(
"../../Course_Materials_Part2/Video_Lecture_NBs/") # noqa: E501
@classmethod
def get_dataframe(cls, csv_file):
""" Returns a normally indexed dataframe """
source_file = cls.FILE_PATH + csv_file
return pd.read_csv(source_file)
@classmethod
def get_indexed_dataframe(cls, csv_file, index_column):
""" Returns a dataframe indexed on the specified index_column """
source_file = cls.FILE_PATH + csv_file
return pd.read_csv(source_file, index_col=index_column)
''' Creating population and sample numpy arrays '''
import numpy as np
import config_file # noqa: F401
from utilities.file_utilities import FileUtilities
PATH = FileUtilities.get_abs_path(
"../../Course_Materials_Part1/Statistics/Course_Materials_Statistics/Video_Lectures_NBs/"
) # noqa: E501
population_file = PATH + "SP500_pop.csv"
sample_file = PATH + "sample.csv"
np.set_printoptions(precision=2, suppress=True)
# Population: 2017 Price Return for all 500 Companies
pop = np.loadtxt(population_file, delimiter=",", usecols=1)
# print(pop)
pop = pop * 100 # converting the values in % form as in 47% or -42%
# print(pop)
# print(pop.size)
# Sample: 2017 Price Return for 50 Companies (randomly selected)
sample = np.loadtxt(sample_file, delimiter=",", usecols=1)
sample = sample * 100
# print(sample)
# print(sample.size)
