Now that we've learned about NumPy let's test your knowledge. We'll start off with a few...

     

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Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions. Import NumPy as np In [1]: import numpy as np Create an array of 10 zeros In [3]: np.zeros (10) Out [3]: array([0., e., e., e., e., e., e., e., e., e.]) Create an array of 10 ones In [4]: np.ones (10) Out[4]: array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]) Create an array of 10 fives In [6]: np.full(10,5) Out [6]: array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5]) Create an array of the integers from 10 to 50 In [7]: np.arange (10,51) Out [7]: array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, зе, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5e]) Create an array of all the even integers from 10 to 50 In [8]: np.arange (10,51,2) Out [8]: array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 3e, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50]) Create an array of all the even integers from 10 to 50 In [8]: np.arange(10,51,2) Out [8]: array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50]) Create a 3x3 matrix with values ranging from 0 to 8 In [10]: np.arange (0,9).reshape (3,3) Out[10]: array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) Create a 3x3 identity matrix In [11]: np.eye(3) Out [11]: array([[1., e., e.], [0., 1., е.], [0., e., 1.]]) Use NumPy to generate a random number between 0 and 1 In [12]: np.random.rand (1) Out[12]: array([0.04962707]) Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution In [13]: np.random.randn(25) Out[13]: array ([-0.63282827, -0.18902161, -0.51478523, -0.49530685, -0.61810597, , -0.57429369, 1.03630513, -0.24253243, -0.80656955, 1.552805 -0.13157291, -0.4952518, -0.78311892, 0.00591945, 0.38177906, -0.45719942, 0.43382632, -0.80893512, -0.25909398, 0.93741917, 1.27878852, -0.74325549, 0.14408309, 0.7018985 , 0.71068245]) Create the following matrix: In [39]: np.arange (1,101).reshape (10,10)/100 Out[39]: array ([[0.01, e.e2, 0.03, e.04, e.05, 0.06, 0.07, 0.08, e.09, e.1 ], [ө.11, е.12, е.13, е.14, е.15, в.16, в.17, в.18, е.19, е.2 1, [0.21, 0.22, e.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ], [0.31, 0.32, e.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ], [0.41, 0.42, 0.43, 0.44, e.45, 0.46, 0.47, e.48, 0.49, 0.5 ], [0.51, 0.52, 0.53, 0.54, e.55, 0.56, 0.57, 0.58, 0.59, 0.6 ], [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ], [0.71, 0.72, e.73, e.74, e.75, 0.76, 0.77, 0.78, 0.79, 0.8 ], [0.81, 0.82, e.83, e.84, 0.85, 0.86, 0.87, 0.88, e.89, 0.9 ], [0.91, 0.92, e.93, 0.94, e.95, 0.96, 0.97, 0.98, 0.99, 1. ]]) Create an array of 20 linearly spaced points between 0 and 1: In [16]: np.linspace(0,1,20) Out[16]: array([0. , e.05263158, 0.10526316, e.15789474, 0.21052632, 0.26315789, e.31578947, 0.36842105, e.42105263, e.47368421, 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211, 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ]) Numpy Indexing and Selection Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs: In [18]: mat = np.arange (1,26).reshape (5,5) mat Out[18]: array ([[ 1, 2, 3, 4, 5], 9, 10], [ 6, [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25]]) 7, 8, In [27]: # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T # BE ABLE TO SEE THE OUTPUT ANY MORE mat[2:,1:] Out[27]: array([[12, 13, 14, 15], [17, 18, 19, 20], [22, 23, 24, 25]]) In [28]: # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T # BE ABLE TO SEE THE OUTPUT ANY MORE mat[3][4] Out [28]: 20 In [30]: # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T # BE ABLE TO SEE THE OUTPUT ANY MORE mat[0:3,1].reshape (3,1) Out[30]: array([[ 2], [ 7], [12]]) In [40]: mat[e:3,1:2] Out [40]: array ([[ 2], [ 7], [12]]) In [31]: # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T # BE ABLE TO SEE THE OUTPUT ANY MORE mat[4] Out[31]: array([21, 22, 23, 24, 25]) In [33]: # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON 'T # BE ABLE TO SEE THE OUTPUT ANY MORE mat[3:] Out [33]: array ([[16, 17, 18, 19, 20], [21, 22, 23, 24, 25]]) Now do the following Get the sum of all the values in mat In [35]: np.sum(mat) Out[35]: 325 Get the standard deviation of the values in mat In [36]: np.std(mat) Out[36]: 7.211102550927978 Get the sum of all the columns in mat In [38]: mat.sum(axis=e) Out[38]: array([55, 60, 65, 70, 75]) Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks, and then you'll be asked some more complicated questions. Import NumPy as np In [1]: import numpy as np Create an array of 10 zeros In [3]: np.zeros (10) Out [3]: array([0., e., e., e., e., e., e., e., e., e.]) Create an array of 10 ones In [4]: np.ones (10) Out[4]: array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]) Create an array of 10 fives In [6]: np.full(10,5) Out [6]: array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5]) Create an array of the integers from 10 to 50 In [7]: np.arange (10,51) Out [7]: array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, зе, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5e]) Create an array of all the even integers from 10 to 50 In [8]: np.arange (10,51,2) Out [8]: array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 3e, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50]) Create an array of all the even integers from 10 to 50 In [8]: np.arange(10,51,2) Out [8]: array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50]) Create a 3x3 matrix with values ranging from 0 to 8 In [10]: np.arange (0,9).reshape (3,3) Out[10]: array([[0, 1, 2], [3, 4, 5], [6, 7, 8]]) Create a 3x3 identity matrix In [11]: np.eye(3) Out [11]: array([[1., e., e.], [0., 1., е.], [0., e., 1.]]) Use NumPy to generate a random number between 0 and 1 In [12]: np.random.rand (1) Out[12]: array([0.04962707]) Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution In [13]: np.random.randn(25) Out[13]: array ([-0.63282827, -0.18902161, -0.51478523, -0.49530685, -0.61810597, , -0.57429369, 1.03630513, -0.24253243, -0.80656955, 1.552805 -0.13157291, -0.4952518, -0.78311892, 0.00591945, 0.38177906, -0.45719942, 0.43382632, -0.80893512, -0.25909398, 0.93741917, 1.27878852, -0.74325549, 0.14408309, 0.7018985 , 0.71068245]) Create the following matrix: In [39]: np.arange (1,101).reshape (10,10)/100 Out[39]: array ([[0.01, e.e2, 0.03, e.04, e.05, 0.06, 0.07, 0.08, e.09, e.1 ], [ө.11, е.12, е.13, е.14, е.15, в.16, в.17, в.18, е.19, е.2 1, [0.21, 0.22, e.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ], [0.31, 0.32, e.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ], [0.41, 0.42, 0.43, 0.44, e.45, 0.46, 0.47, e.48, 0.49, 0.5 ], [0.51, 0.52, 0.53, 0.54, e.55, 0.56, 0.57, 0.58, 0.59, 0.6 ], [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ], [0.71, 0.72, e.73, e.74, e.75, 0.76, 0.77, 0.78, 0.79, 0.8 ], [0.81, 0.82, e.83, e.84, 0.85, 0.86, 0.87, 0.88, e.89, 0.9 ], [0.91, 0.92, e.93, 0.94, e.95, 0.96, 0.97, 0.98, 0.99, 1. ]]) Create an array of 20 linearly spaced points between 0 and 1: In [16]: np.linspace(0,1,20) Out[16]: array([0. , e.05263158, 0.10526316, e.15789474, 0.21052632, 0.26315789, e.31578947, 0.36842105, e.42105263, e.47368421, 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211, 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ]) Numpy Indexing and Selection Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs: In [18]: mat = np.arange (1,26).reshape (5,5) mat Out[18]: array ([[ 1, 2, 3, 4, 5], 9, 10], [ 6, [11, 12, 13, 14, 15], [16, 17, 18, 19, 20], [21, 22, 23, 24, 25]]) 7, 8, In [27]: # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T # BE ABLE TO SEE THE OUTPUT ANY MORE mat[2:,1:] Out[27]: array([[12, 13, 14, 15], [17, 18, 19, 20], [22, 23, 24, 25]]) In [28]: # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T # BE ABLE TO SEE THE OUTPUT ANY MORE mat[3][4] Out [28]: 20 In [30]: # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T # BE ABLE TO SEE THE OUTPUT ANY MORE mat[0:3,1].reshape (3,1) Out[30]: array([[ 2], [ 7], [12]]) In [40]: mat[e:3,1:2] Out [40]: array ([[ 2], [ 7], [12]]) In [31]: # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T # BE ABLE TO SEE THE OUTPUT ANY MORE mat[4] Out[31]: array([21, 22, 23, 24, 25]) In [33]: # WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW # BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON 'T # BE ABLE TO SEE THE OUTPUT ANY MORE mat[3:] Out [33]: array ([[16, 17, 18, 19, 20], [21, 22, 23, 24, 25]]) Now do the following Get the sum of all the values in mat In [35]: np.sum(mat) Out[35]: 325 Get the standard deviation of the values in mat In [36]: np.std(mat) Out[36]: 7.211102550927978 Get the sum of all the columns in mat In [38]: mat.sum(axis=e) Out[38]: array([55, 60, 65, 70, 75])

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Important Numpy as np In 1 import numpy as np create ... View the full answer