mirror of
https://github.com/donnemartin/data-science-ipython-notebooks.git
synced 2024-03-22 13:30:56 +08:00
632 lines
53 KiB
Plaintext
632 lines
53 KiB
Plaintext
{
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"metadata": {
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"name": "",
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"signature": "sha256:cb8fc4454a69123dcb745c323968d06c15444cee91494edb720893b06e98c249"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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"worksheets": [
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# NumPy\n",
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"\n",
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"* NumPy Arrays, dtype, and shape\n",
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"* Common Array Operations\n",
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"* Reshaping and In-Place Updating\n",
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"* Combining Arrays\n",
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"* Creating Fake Data and Adding Noise"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"import numpy as np"
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],
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"language": "python",
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"metadata": {},
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"outputs": [],
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"prompt_number": 1
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## NumPy Arrays, dtypes, and shapes"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"a = np.array([1, 2, 3])\n",
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"print(a)\n",
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"print(a.shape)\n",
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"print(a.dtype)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[1 2 3]\n",
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"(3,)\n",
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"int64\n"
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]
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}
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],
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"prompt_number": 2
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"b = np.array([[0, 2, 4], [1, 3, 5]])\n",
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"print(b)\n",
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"print(b.shape)\n",
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"print(b.dtype)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[[0 2 4]\n",
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" [1 3 5]]\n",
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"(2, 3)\n",
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"int64\n"
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]
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}
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],
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"prompt_number": 3
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"np.zeros(5)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 4,
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"text": [
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"array([ 0., 0., 0., 0., 0.])"
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]
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}
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],
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"prompt_number": 4
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"np.ones(shape=(3, 4), dtype=np.int32)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 5,
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"text": [
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"array([[1, 1, 1, 1],\n",
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" [1, 1, 1, 1],\n",
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" [1, 1, 1, 1]], dtype=int32)"
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]
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}
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],
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"prompt_number": 5
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Common Array Operations"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"c = b * 0.5\n",
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"print(c)\n",
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"print(c.shape)\n",
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"print(c.dtype)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[[ 0. 1. 2. ]\n",
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" [ 0.5 1.5 2.5]]\n",
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"(2, 3)\n",
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"float64\n"
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]
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}
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],
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"prompt_number": 6
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"d = a + c\n",
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"print(d)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[[ 1. 3. 5. ]\n",
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" [ 1.5 3.5 5.5]]\n"
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]
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}
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],
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"prompt_number": 7
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"d[0]"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 8,
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"text": [
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"array([ 1., 3., 5.])"
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]
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}
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],
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"prompt_number": 8
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"d[0, 0]"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 9,
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"text": [
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"1.0"
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]
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}
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],
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"prompt_number": 9
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"d[:, 0]"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 10,
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"text": [
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"array([ 1. , 1.5])"
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]
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}
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],
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"prompt_number": 10
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"d.sum()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 11,
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"text": [
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"19.5"
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]
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}
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],
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"prompt_number": 11
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"d.mean()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 12,
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"text": [
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"3.25"
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]
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}
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],
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"prompt_number": 12
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"d.sum(axis=0)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 13,
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"text": [
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"array([ 2.5, 6.5, 10.5])"
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]
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}
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],
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"prompt_number": 13
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"d.mean(axis=1)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 14,
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"text": [
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"array([ 3. , 3.5])"
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]
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}
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],
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"prompt_number": 14
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Reshaping and In-Place Updating"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"e = np.arange(12)\n",
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"print(e)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[ 0 1 2 3 4 5 6 7 8 9 10 11]\n"
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]
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}
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],
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"prompt_number": 15
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# f is a view of contents of e\n",
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"f = e.reshape(3, 4)\n",
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"print(f)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[[ 0 1 2 3]\n",
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" [ 4 5 6 7]\n",
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" [ 8 9 10 11]]\n"
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]
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}
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],
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"prompt_number": 16
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# Set last five values of e to zero\n",
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"e[5:] = 0\n",
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"print(e)"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"stream": "stdout",
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"text": [
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"[0 1 2 3 4 0 0 0 0 0 0 0]\n"
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]
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}
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],
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"prompt_number": 17
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# f is also updated\n",
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"f"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 18,
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"text": [
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"array([[0, 1, 2, 3],\n",
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" [4, 0, 0, 0],\n",
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" [0, 0, 0, 0]])"
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]
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}
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],
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"prompt_number": 18
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"# OWNDATA shows f does not own its data\n",
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"f.flags"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 19,
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"text": [
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" C_CONTIGUOUS : True\n",
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" F_CONTIGUOUS : False\n",
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" OWNDATA : False\n",
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" WRITEABLE : True\n",
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" ALIGNED : True\n",
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" UPDATEIFCOPY : False"
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]
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}
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],
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"prompt_number": 19
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Combining Arrays"
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]
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"a"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 20,
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"text": [
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"array([1, 2, 3])"
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]
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}
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],
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"prompt_number": 20
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"b"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 21,
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"text": [
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"array([[0, 2, 4],\n",
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" [1, 3, 5]])"
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]
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}
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],
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"prompt_number": 21
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"d"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 22,
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"text": [
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"array([[ 1. , 3. , 5. ],\n",
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" [ 1.5, 3.5, 5.5]])"
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]
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}
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],
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"prompt_number": 22
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"np.concatenate([a, a, a])"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 23,
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"text": [
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"array([1, 2, 3, 1, 2, 3, 1, 2, 3])"
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]
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}
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],
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"prompt_number": 23
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
|
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"# Use broadcasting when needed to do this automatically\n",
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"np.vstack([a, b, d])"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "pyout",
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|
"prompt_number": 24,
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"text": [
|
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"array([[ 1. , 2. , 3. ],\n",
|
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" [ 0. , 2. , 4. ],\n",
|
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" [ 1. , 3. , 5. ],\n",
|
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" [ 1. , 3. , 5. ],\n",
|
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" [ 1.5, 3.5, 5.5]])"
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]
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}
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],
|
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"prompt_number": 24
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},
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
|
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"# In machine learning, useful to enrich or \n",
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"# add new/concatenate features with hstack\n",
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"np.hstack([b, d])"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
|
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{
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"metadata": {},
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"output_type": "pyout",
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|
"prompt_number": 25,
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"text": [
|
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"array([[ 0. , 2. , 4. , 1. , 3. , 5. ],\n",
|
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" [ 1. , 3. , 5. , 1.5, 3.5, 5.5]])"
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]
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}
|
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],
|
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"prompt_number": 25
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},
|
|
{
|
|
"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"## Creating Fake Data and Adding Noise"
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]
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},
|
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{
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"cell_type": "code",
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"collapsed": false,
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"input": [
|
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"import pylab as plt\n",
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"import seaborn\n",
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"\n",
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"seaborn.set()\n",
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"\n",
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"x = np.random.uniform(1, 100, 1000)\n",
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"y = np.log(x) + np.random.normal(0, .3, 1000)\n",
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"\n",
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"plt.scatter(x, y)\n",
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"plt.show()"
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],
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"language": "python",
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"metadata": {},
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"outputs": [
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{
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"metadata": {},
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"output_type": "display_data",
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"png": 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xj3cVtaylOMcxg4O3cfvtDURHj2XMmJeVd9Vsbn3zBJgGPIEQvVAVqfStHtXu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\nK5zm9iFwtzKP25zGvxn4jcOHI+2uYlV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u0X0mJOQy0tMrEDKRIOK5E4FcXn/9Q4qKviAl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\n",
|
|
"text": [
|
|
"<matplotlib.figure.Figure at 0x108053c50>"
|
|
]
|
|
}
|
|
],
|
|
"prompt_number": 26
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"collapsed": false,
|
|
"input": [],
|
|
"language": "python",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"prompt_number": 26
|
|
}
|
|
],
|
|
"metadata": {}
|
|
}
|
|
]
|
|
} |