Adam Green ADGEfficiency

## data.sh
$ python data.py --nrows 1000

## pd_subset.py
data = pd.read_csv('data.csv', nrows=1000)

## nrows2.py
nrows = 1000
data = pd.read_csv('data.csv', nrows=nrows)

## home.py
import os

home = os.environ['HOME']
path = os.path.join(home, 'adg'))
os.makedirs(path, exist_ok=True)
np.save(path, data)

## cli.py
#  data.py
parser.add_argument('--nrows', nargs='?')
args = parser.parse_args()
data = pd.read_csv('data.csv', nrows=args.nrows)
print(f'loaded {data.shape[0]} rows')

## int_index.py
data = data[:1000]

## normalizer.py
normalized = (data - np.min(data)) / (np.max(data) - np.min(data))

## standardizer.py
standardized = (data - np.mean(data)) / np.std(data)

## standardizer.py
standardized = (data - np.mean(data)) / np.std(data)

## standardizer.py
standardized = (data - np.mean(data)) / np.std(data)
	import os

	home = os.environ['HOME']
	path = os.path.join(home, 'adg'))
	os.makedirs(path, exist_ok=True)
	np.save(path, data)
	# data.py
	parser.add_argument('--nrows', nargs='?')
	args = parser.parse_args()
	data = pd.read_csv('data.csv', nrows=args.nrows)
	print(f'loaded {data.shape[0]} rows')