data-science-ipython-notebooks/numpy/numpy.ipynb

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{
"metadata": {
"name": "",
"signature": "sha256:cb8fc4454a69123dcb745c323968d06c15444cee91494edb720893b06e98c249"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NumPy\n",
"\n",
"* NumPy Arrays, dtype, and shape\n",
"* Common Array Operations\n",
"* Reshaping and In-Place Updating\n",
"* Combining Arrays\n",
"* Creating Fake Data and Adding Noise"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NumPy Arrays, dtypes, and shapes"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = np.array([1, 2, 3])\n",
"print(a)\n",
"print(a.shape)\n",
"print(a.dtype)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1 2 3]\n",
"(3,)\n",
"int64\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = np.array([[0, 2, 4], [1, 3, 5]])\n",
"print(b)\n",
"print(b.shape)\n",
"print(b.dtype)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[0 2 4]\n",
" [1 3 5]]\n",
"(2, 3)\n",
"int64\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.zeros(5)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"array([ 0., 0., 0., 0., 0.])"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.ones(shape=(3, 4), dtype=np.int32)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"array([[1, 1, 1, 1],\n",
" [1, 1, 1, 1],\n",
" [1, 1, 1, 1]], dtype=int32)"
]
}
],
"prompt_number": 5
}
],
"metadata": {}
}
]
}