{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
" 
\n",
"___\n",
"*Copyright Pierian Data 2017*\n",
"*For more information, visit us at www.pieriandata.com*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NumPy Exercises\n",
"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.\n",
"\n",
"** IMPORTANT NOTE! Make sure you don't run the cells directly above the example output shown, otherwise you will end up writing over the example output! **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Import NumPy as np"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create an array of 10 zeros "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create an array of 10 ones"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create an array of 10 fives"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create an array of the integers from 10 to 50"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
" 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
" 44, 45, 46, 47, 48, 49, 50])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create an array of all the even integers from 10 to 50"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
" 44, 46, 48, 50])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create a 3x3 matrix with values ranging from 0 to 8"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0, 1, 2],\n",
" [3, 4, 5],\n",
" [6, 7, 8]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create a 3x3 identity matrix"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1., 0., 0.],\n",
" [ 0., 1., 0.],\n",
" [ 0., 0., 1.]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Use NumPy to generate a random number between 0 and 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.42829726])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.32031013, 1.6798602 , -0.42985892, -1.53116655, 0.85753232,\n",
" 0.87339938, 0.35668636, -1.47491157, 0.15349697, 0.99530727,\n",
" -0.94865451, -1.69174783, 1.57525349, -0.70615234, 0.10991879,\n",
" -0.49478947, 1.08279872, 0.76488333, -2.3039931 , 0.35401124,\n",
" -0.45454399, -0.64754649, -0.29391671, 0.02339861, 0.38272124])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create the following matrix:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
" [ 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
" [ 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
" [ 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
" [ 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
" [ 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
" [ 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
" [ 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
" [ 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
" [ 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create an array of 20 linearly spaced points between 0 and 1:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
" 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
" 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
" 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Numpy Indexing and Selection\n",
"\n",
"Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4, 5],\n",
" [ 6, 7, 8, 9, 10],\n",
" [11, 12, 13, 14, 15],\n",
" [16, 17, 18, 19, 20],\n",
" [21, 22, 23, 24, 25]])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# HERE IS THE GIVEN MATRIX CALLED MAT\n",
"# USE IT FOR THE FOLLOWING TASKS\n",
"mat = np.arange(1,26).reshape(5,5)\n",
"mat"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
"# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
"# BE ABLE TO SEE THE OUTPUT ANY MORE"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[12, 13, 14, 15],\n",
" [17, 18, 19, 20],\n",
" [22, 23, 24, 25]])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
"# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
"# BE ABLE TO SEE THE OUTPUT ANY MORE"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"20"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
"# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
"# BE ABLE TO SEE THE OUTPUT ANY MORE"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 2],\n",
" [ 7],\n",
" [12]])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
"# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
"# BE ABLE TO SEE THE OUTPUT ANY MORE"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([21, 22, 23, 24, 25])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
"# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
"# BE ABLE TO SEE THE OUTPUT ANY MORE"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[16, 17, 18, 19, 20],\n",
" [21, 22, 23, 24, 25]])"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Now do the following"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get the sum of all the values in mat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"325"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get the standard deviation of the values in mat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"7.2111025509279782"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Get the sum of all the columns in mat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# CODE HERE"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([55, 60, 65, 70, 75])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bonus Question\n",
"\n",
"We worked a lot with random data with numpy, but is there a way we can insure that we always get the same random numbers? [Click Here for a Hint](https://www.google.com/search?q=numpy+random+seed&rlz=1C1CHBF_enUS747US747&oq=numpy+random+seed&aqs=chrome..69i57j69i60j0l4.2087j0j7&sourceid=chrome&ie=UTF-8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Great Job!"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
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}