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np.random.seed(100) | |
alphabets = list('ABCDEFGHIJKLMNOPQRSTUVXYZ') | |
A = np.random.choice(alphabets, 10) | |
B = np.random.choice(alphabets, 20) | |
C = np.random.choice(alphabets, 5) |
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import numpy as np | |
arr = np.genfromtxt("Datasets/stock_price_miss.csv", delimiter="csv", skip_header=1).round(2) | |
arr[:30] |
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import numpy as np | |
np.random.seed(100) | |
X = np.random.random((10, 7)).round(3) |
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arr = np.genfromtxt("Datasets/Class_and_Fare.csv", delimiter=",", skip_header=1) | |
arr |
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import numpy as np | |
np.random.seed(100) | |
arr = np.random.normal(30, 10, 100).round(2) | |
arr |
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# Calc Profit | |
def calc_profit(open, high, low, close): | |
buy = open * 0.999 | |
# daily range | |
if low < buy < high: | |
return (close - buy)/buy | |
else: | |
return 0 | |
# Read Data |
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import numpy as np | |
np.set_printoptions(suppress=True) | |
data = np.genfromtxt("Datasets/Lifecyclesavings.csv", delimiter=",", skip_header=1) | |
data = data.round() | |
data[:10] |
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import numpy as np | |
# turn off scientific (e+02) notations. | |
np.set_printoptions(suppress=True) | |
# Create normal | |
np.random.seed(100) | |
arr = np.random.normal(100, 75, size=300).round(3) | |
arr |
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import numpy as np | |
arr = np. array([112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, | |
126, 141, 135, 125, 149, 170, 170, 158, 133, 114, 140, 145, 150, | |
178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193, | |
181, 183, 218, 230, 242, 209, 191, 172, 194, 196, 196, 236, 235, | |
229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 227, 234, | |
264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315, | |
364, 347, 312, 274, 237, 278, 284, 277, 317, 313, 318, 374, 413, | |
405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467, | |
404, 347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404, |
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import numpy as np | |
data = np.genfromtxt('Datasets/Mall_Customers_Int.csv', | |
delimiter=",", | |
skip_header=1) |