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@azhar22k
Created Sep 1, 2016
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Numpy Array Practice via introduction
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"cells": [
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"cell_type": "code",
"execution_count": null,
"metadata": {
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"outputs": [],
"source": [
"# Author: Mohd. Azhar Khan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# importing numpy"
]
},
{
"cell_type": "code",
"execution_count": 23,
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"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"height = [1,2,3,4,5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# numpy arrays can be created from python lists\n",
"# numpy array can only contain one type of data"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"weight = [30,40,50,60,55]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"height=np.array(height)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"weight=np.array(weight)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# numpy does element calculation on array, so be careful while playing with them"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"bmi=weight/height**2"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/plain": [
"array([ 30. , 10. , 5.55555556, 3.75 , 2.2 ])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bmi"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 30. 10. 5.55555556 3.75 2.2 ]\n"
]
}
],
"source": [
"print(bmi)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, False, False, False, False], dtype=bool)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bmi>10"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/plain": [
"array([ 30.])"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bmi[bmi>10]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/plain": [
"numpy.ndarray"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(bmi)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
" np2d=np.array([[1.73,1.68,1.71,1.89,1.79],[65.4,59.2,63.6,88.4,68.7]])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.73, 1.68, 1.71, 1.89, 1.79],\n",
" [ 65.4 , 59.2 , 63.6 , 88.4 , 68.7 ]])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np2d"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# numpy supports multi dimentional arrays"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/plain": [
"(2, 5)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np2d.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Subsetting in Numpy Arrays"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.73, 1.68, 1.71, 1.89, 1.79])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np2d[0]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/plain": [
"1.71"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np2d[0][2]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.71"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np2d[0,2]"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.68, 1.71],\n",
" [ 59.2 , 63.6 ]])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np2d[:,1:3]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 65.4, 59.2, 63.6, 88.4, 68.7])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np2d[1,:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
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"nbformat_minor": 0
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