mirror of
https://github.com/donnemartin/data-science-ipython-notebooks.git
synced 2024-03-22 13:30:56 +08:00
667 lines
65 KiB
Plaintext
667 lines
65 KiB
Plaintext
{
|
|
"metadata": {
|
|
"name": "",
|
|
"signature": "sha256:3968461b55764bd352e164c8cf911ac5515f1616c26827bf2eadf78953616464"
|
|
},
|
|
"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",
|
|
"* Reshape and Update In-Place\n",
|
|
"* Combine Arrays\n",
|
|
"* Create Sample Data"
|
|
]
|
|
},
|
|
{
|
|
"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
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Common Array Operations"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"c = b * 0.5\n",
|
|
"print(c)\n",
|
|
"print(c.shape)\n",
|
|
"print(c.dtype)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"[[ 0. 1. 2. ]\n",
|
|
" [ 0.5 1.5 2.5]]\n",
|
|
"(2, 3)\n",
|
|
"float64\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 6
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"d = a + c\n",
|
|
"print(d)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"[[ 1. 3. 5. ]\n",
|
|
" [ 1.5 3.5 5.5]]\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 7
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"d[0]"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 8,
|
|
"text": [
|
|
"array([ 1., 3., 5.])"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 8
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"d[0, 0]"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 9,
|
|
"text": [
|
|
"1.0"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 9
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"d[:, 0]"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 10,
|
|
"text": [
|
|
"array([ 1. , 1.5])"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 10
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"d.sum()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 11,
|
|
"text": [
|
|
"19.5"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 11
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"d.mean()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 12,
|
|
"text": [
|
|
"3.25"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 12
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"d.sum(axis=0)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 13,
|
|
"text": [
|
|
"array([ 2.5, 6.5, 10.5])"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 13
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"d.mean(axis=1)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 14,
|
|
"text": [
|
|
"array([ 3. , 3.5])"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 14
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Reshape and Update In-Place"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"e = np.arange(12)\n",
|
|
"print(e)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"[ 0 1 2 3 4 5 6 7 8 9 10 11]\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 15
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# f is a view of contents of e\n",
|
|
"f = e.reshape(3, 4)\n",
|
|
"print(f)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"[[ 0 1 2 3]\n",
|
|
" [ 4 5 6 7]\n",
|
|
" [ 8 9 10 11]]\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 16
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# Set last five values of e to zero\n",
|
|
"e[5:] = 0\n",
|
|
"print(e)"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"output_type": "stream",
|
|
"stream": "stdout",
|
|
"text": [
|
|
"[0 1 2 3 4 0 0 0 0 0 0 0]\n"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 17
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# f is also updated\n",
|
|
"f"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 18,
|
|
"text": [
|
|
"array([[0, 1, 2, 3],\n",
|
|
" [4, 0, 0, 0],\n",
|
|
" [0, 0, 0, 0]])"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 18
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# OWNDATA shows f does not own its data\n",
|
|
"f.flags"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 19,
|
|
"text": [
|
|
" C_CONTIGUOUS : True\n",
|
|
" F_CONTIGUOUS : False\n",
|
|
" OWNDATA : False\n",
|
|
" WRITEABLE : True\n",
|
|
" ALIGNED : True\n",
|
|
" UPDATEIFCOPY : False"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 19
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Combine Arrays"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"a"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 20,
|
|
"text": [
|
|
"array([1, 2, 3])"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 20
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"b"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 21,
|
|
"text": [
|
|
"array([[0, 2, 4],\n",
|
|
" [1, 3, 5]])"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 21
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"d"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 22,
|
|
"text": [
|
|
"array([[ 1. , 3. , 5. ],\n",
|
|
" [ 1.5, 3.5, 5.5]])"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 22
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"np.concatenate([a, a, a])"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 23,
|
|
"text": [
|
|
"array([1, 2, 3, 1, 2, 3, 1, 2, 3])"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 23
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# Use broadcasting when needed to do this automatically\n",
|
|
"np.vstack([a, b, d])"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 24,
|
|
"text": [
|
|
"array([[ 1. , 2. , 3. ],\n",
|
|
" [ 0. , 2. , 4. ],\n",
|
|
" [ 1. , 3. , 5. ],\n",
|
|
" [ 1. , 3. , 5. ],\n",
|
|
" [ 1.5, 3.5, 5.5]])"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 24
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# In machine learning, useful to enrich or \n",
|
|
"# add new/concatenate features with hstack\n",
|
|
"np.hstack([b, d])"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "pyout",
|
|
"prompt_number": 25,
|
|
"text": [
|
|
"array([[ 0. , 2. , 4. , 1. , 3. , 5. ],\n",
|
|
" [ 1. , 3. , 5. , 1.5, 3.5, 5.5]])"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 25
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Create Sample Data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"import pylab as plt\n",
|
|
"import seaborn\n",
|
|
"\n",
|
|
"seaborn.set()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 3
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# Create evenly spaced numbers over the specified interval\n",
|
|
"x = np.linspace(0, 2, 10)\n",
|
|
"plt.plot(x, 'o-');\n",
|
|
"plt.show()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAeQAAAFXCAYAAABz8D0iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x10bc25110>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 4
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"np.random.normal?"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 8
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [
|
|
"# Create sample data, add some noise\n",
|
|
"x = np.random.uniform(1, 100, 1000)\n",
|
|
"y = np.log(x) + np.random.normal(0, .3, 1000)\n",
|
|
"\n",
|
|
"plt.scatter(x, y)\n",
|
|
"plt.show()"
|
|
],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"metadata": {},
|
|
"output_type": "display_data",
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAekAAAFXCAYAAACGDraSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqrkAAAAASUVORK5CYII=\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x10bd30110>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 6
|
|
}
|
|
],
|
|
"metadata": {}
|
|
}
|
|
]
|
|
} |