Array Introduction
Array :
numpy.array , numpy.array.shape, numpy.reshape, numpy.concatenate
import numpy as np
L = [1,3,5,7,"apple",2.4]
L
[1, 3, 5, 7, 'apple', 2.4]
L[4]
'apple'
L + L
[1, 3, 5, 7, 1, 3, 5, 7]
A = np.array([1,2,3,4])
array([1, 2, 3, 4])
A = np.array([[2,3,5],[3,5,7],[6,8,9]])
np.zeros([3,4])
array([[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]])
np.ones([3,3])
array([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]])
X = np.random.rand(5,5)
X
array([[0.24328245, 0.97738463, 0.18213799, 0.83052358, 0.3653109 ],
[0.63924704, 0.32634262, 0.93738712, 0.04190707, 0.16856443],
[0.02439421, 0.64339841, 0.25104598, 0.07552312, 0.33859142],
[0.24346396, 0.09796353, 0.3732192 , 0.2614549 , 0.13293965],
[0.13669362, 0.3105436 , 0.51570164, 0.04806873, 0.88910613]])
X = np.array([[1,3,4],[2,4,7],[3,7,9]])
X
array([[1, 3, 4],
[2, 4, 7],
[3, 7, 9]])
LL = [[1,3,4],[2,4,7],[3,7,9]]
LL[1][2]
7
X[1,2]
7
Array Indexing
X = np.random.rand(5,5)
X
array([[0.53015554, 0.34082372, 0.17875984, 0.64703264, 0.2235947 ],
[0.34290729, 0.66984187, 0.40666649, 0.82657358, 0.66325498],
[0.22065645, 0.93000416, 0.57442424, 0.70437704, 0.55194144],
[0.06551909, 0.43306581, 0.40549847, 0.31129663, 0.99645275],
[0.26043963, 0.73481174, 0.05637365, 0.2926374 , 0.82444519]])
X[1]
array([0.34290729, 0.66984187, 0.40666649, 0.82657358, 0.66325498])
X[2,3]
0.7043770359239762
X[2:]
array([[0.22065645, 0.93000416, 0.57442424, 0.70437704, 0.55194144],
[0.06551909, 0.43306581, 0.40549847, 0.31129663, 0.99645275],
[0.26043963, 0.73481174, 0.05637365, 0.2926374 , 0.82444519]])
X[:,1]
array([0.34082372, 0.66984187, 0.93000416, 0.43306581, 0.73481174])
X[:,3]
array([0.64703264, 0.82657358, 0.70437704, 0.31129663, 0.2926374 ])
X[1:3,2:4]
array([[0.55330373, 0.06741463],
[0.97854604, 0.18927008]])
for i in range(5):
for j in range(5):
if i== j:
print(X[i,j])
0.530155537186793
0.6698418707020536
0.5744242448498665
0.31129663385625317
0.8244451854101277
np.diag(X)
array([0.53015554, 0.66984187, 0.57442424, 0.31129663, 0.82444519])
Visualization of an array
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
X = np.random.rand(10,10)
plt.figure(figsize = [15,10])
sns.heatmap(X, annot=True,cmap="YlGnBu")
<matplotlib.axes._subplots.AxesSubplot at 0x1a174a4240>
Subarray :
Splitting to 4 subarrays
plt.figure(figsize = [15,10])
plt.subplot(2,2,1)
sns.heatmap(X[0:5,0:5], annot=True)
plt.subplot(2,2,2)
sns.heatmap(X[5:10,0:5], annot=True)
plt.subplot(2,2,3)
sns.heatmap(X[0:5,5:10], annot=True)
plt.subplot(2,2,4)
sns.heatmap(X[5:10,5:10], annot=True)
plt.show()
Shape:
Change shape of array: from [10 by 10] to [20 by 5]
- Find Array shape
X.shape
(10, 10)
- Modify shape
X.shape = (20,5)
X
array([[0.93852928, 0.5629153 , 0.40031156, 0.05806378, 0.97983286],
[0.94297538, 0.10800161, 0.11069516, 0.90720548, 0.19857628],
[0.58431666, 0.82412861, 0.92679995, 0.77921264, 0.5673404 ],
[0.03117379, 0.36952646, 0.44022574, 0.33607312, 0.4144617 ],
[0.43455739, 0.74942589, 0.51639448, 0.61336095, 0.48473164],
[0.40440422, 0.60614083, 0.57705755, 0.79288636, 0.21180724],
[0.72241987, 0.19907833, 0.80578831, 0.36106624, 0.0778791 ],
[0.11341762, 0.05541729, 0.67124287, 0.59567082, 0.27976907],
[0.24750836, 0.52605463, 0.97923593, 0.1903222 , 0.26829744],
[0.46525502, 0.34985357, 0.44309043, 0.69688965, 0.33400436],
[0.23468938, 0.94940977, 0.89626526, 0.86187936, 0.99052044],
[0.96050669, 0.0065963 , 0.49770304, 0.85285478, 0.65581363],
[0.09482151, 0.57702584, 0.86883705, 0.14260322, 0.10167723],
[0.2856549 , 0.11293518, 0.75423858, 0.12482127, 0.68321163],
[0.34892935, 0.23105718, 0.39435598, 0.2528592 , 0.74207706],
[0.8628569 , 0.08761234, 0.70950303, 0.60906093, 0.15525657],
[0.09659796, 0.40521082, 0.3779406 , 0.03447772, 0.06621547],
[0.76406415, 0.18869095, 0.73287798, 0.98307838, 0.41521145],
[0.30999207, 0.0850545 , 0.79054693, 0.72950549, 0.94548169],
[0.88379333, 0.51067006, 0.08175909, 0.63589428, 0.01402678]])
Reshaping the Array
X = np.arange(35)
X
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])
XX = np.reshape(X, (7, 5))
XX
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]])
X = np.arange(35).reshape(7,5)
X
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]])
Flatten the Array
X = np.random.rand(4,5)
X.shape
(4, 5)
X
array([[0.46425663, 0.26273357, 0.76715497, 0.02803551, 0.06077554],
[0.45085724, 0.20021709, 0.83866571, 0.93010498, 0.82287586],
[0.64015274, 0.40214994, 0.46140888, 0.10875569, 0.90314464],
[0.1996534 , 0.00575243, 0.19888495, 0.80968552, 0.66819322]])
type(X)
numpy.ndarray
Y = X.flatten()
Y.shape
(20,)
Y
array([0.46425663, 0.26273357, 0.76715497, 0.02803551, 0.06077554,
0.45085724, 0.20021709, 0.83866571, 0.93010498, 0.82287586,
0.64015274, 0.40214994, 0.46140888, 0.10875569, 0.90314464,
0.1996534 , 0.00575243, 0.19888495, 0.80968552, 0.66819322])
Concatenate the Array
A = np.random.rand(3,4)
B = np.random.rand(3,4)
Verticle addition
C = np.concatenate((A,B),axis =0)
C
array([[0.72279121, 0.69991245, 0.02998703, 0.12220733],
[0.0016784 , 0.57873713, 0.43938855, 0.20021548],
[0.18972308, 0.44890851, 0.76709578, 0.73554246],
[0.89593371, 0.48033999, 0.45776717, 0.65204457],
[0.53473347, 0.76373758, 0.67908483, 0.74560973],
[0.32444887, 0.838872 , 0.72398484, 0.81445926]])
Horizontal Addition
D = np.concatenate((A,B),axis =1)
D
array([[0.72279121, 0.69991245, 0.02998703, 0.12220733, 0.89593371,
0.48033999, 0.45776717, 0.65204457],
[0.0016784 , 0.57873713, 0.43938855, 0.20021548, 0.53473347,
0.76373758, 0.67908483, 0.74560973],
[0.18972308, 0.44890851, 0.76709578, 0.73554246, 0.32444887,
0.838872 , 0.72398484, 0.81445926]])
Row sum and column sum
X = np.random.rand(8,6)
sum of all elements
X.sum()
24.566949569026413
Row sum
np.sum(X,axis =1)
array([4.20288936, 2.3996539 , 2.46388761, 4.30291271, 2.60671914,
2.23461285, 2.68832592, 3.6679481 ])
Column sum
np.sum(X,axis =0)
array([3.69453055, 3.84555591, 5.87500707, 2.90605569, 4.41441644,
3.83138392])