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# [Python/Data Analysis] Numpay - Universal Array Function - Day 7import numpy as np arr =np.arange(11)arr array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) np.sqrt(arr) array([ 0. , 1. , 1.41421356, 1.73205081, 2. , 2.23606798, 2.44948974, 2.64575131, 2.82842712, 3. , 3.16227766]) A = np.random.randn(10)A array([-0.66405485, 0.28749254, 0.27305696, 0.22232217, 0.8804781 , 1.01018702, -0.15188718, -0.7..

# [Python/Data Analysis] Numpay - Array Tranposition 사용하기 - Day 6import numpy as np arr = np.arange(50).reshape((10,5)) arr array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14], [15, 16, 17, 18, 19], [20, 21, 22, 23, 24], [25, 26, 27, 28, 29], [30, 31, 32, 33, 34], [35, 36, 37, 38, 39], [40, 41, 42, 43, 44], [45, 46, 47, 48, 49]]) arr.T array([[ 0, 5, 10, 15, 20, 25, 30, 35, 40, 45], ..

# [Python/Data Analysis] Numpy 및 Matplotlib 사용하기 - Day 5 import numpy as np import matplotlib.pyplot as plt # 1 - Pyplot example x = np.arange(0,6,0.1) y = np.sin(x) plt.plot(x,y) plt.show() # 2 - Pyplot example a = np.arange(0, 6, 0.1) b1 = np.sin(a) b2 = np.cos(a) plt.plot(a, b1, label="sin") plt.plot(a, b2, linestyle="--", label="cos") # a axis name plt.xlabel("a") plt.ylabel("b") plt.title("..

# [Python/Data Analysis] Numpy 및 Matplotlib 사용하기 - Day 3import numpy as np arr = np.arange(0,11) arr2 = np.arange(0,11) arr array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])arr[8] 8# Slice arr[1:5] array([1, 2, 3, 4]) arr[0:5] array([0, 1, 2, 3, 4]) arr[0:5] = 100 arr array([100, 100, 100, 100, 100, 5, 6, 7, 8, 9, 10])arr2[3:7] = 100 arr2 array([ 0, 1, 2, 100, 100, 100, 100, 7, 8, 9, 10])arr = np.arang..

# [Python/Data Analysis] Numpy 및 Matplotlib 사용하기 - Day 2 import numpy as np from __future__ import division 5/2 2.5 arr1 = np.array([[1,2,3,4], [8,9,10,11]]) arr1 array([[ 1, 2, 3, 4], [ 8, 9, 10, 11]]) arr1 * arr1 array([[ 1, 4, 9, 16], [ 64, 81, 100, 121]]) arr1 - arr1 array([[0, 0, 0, 0], [0, 0, 0, 0]]) 1 / arr1 array([[ 1. , 0.5 , 0.33333333, 0.25 ], [ 0.125 , 0.11111111, 0.1 , 0.09090909]])..

# [Python/Data Analysis] Numpy 및 Matplotlib 사용하기 - Day 1 # [Python/Data Analysis] Numpy 및 Matplotlib 사용하기 - Day 1 import numpy as np list1 = [1,2,3,4] array1 = np.array(list1) array1 array([1, 2, 3, 4]) list2 = [11,22,33,44] lists = [list1,list2] array2 = np.array(lists) array2 array([[ 1, 2, 3, 4], [11, 22, 33, 44]]) array2.shape (2, 4) array2.shape (2, 4) array2.dtype dtype('int64') np.zeros(5..