mirror of
https://github.com/donnemartin/data-science-ipython-notebooks.git
synced 2024-03-22 13:30:56 +08:00
879 lines
55 KiB
Python
879 lines
55 KiB
Python
{
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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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"Credits: Forked from [Parallel Machine Learning with scikit-learn and IPython](https://github.com/ogrisel/parallel_ml_tutorial) by Olivier Grisel\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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"* Reshape and Update In-Place\n",
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"* Combine Arrays\n",
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"* Create Sample Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np"
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]
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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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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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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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"source": [
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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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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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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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"source": [
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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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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 0., 0., 0., 0., 0.])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.zeros(5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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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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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.ones(shape=(3, 4), dtype=np.int32)"
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]
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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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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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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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"source": [
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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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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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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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"source": [
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"d = a + c\n",
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"print(d)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 1., 3., 5.])"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"d[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1.0"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"d[0, 0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 1. , 1.5])"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"d[:, 0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"19.5"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"d.sum()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"3.25"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"d.mean()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 2.5, 6.5, 10.5])"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"d.sum(axis=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 3. , 3.5])"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"d.mean(axis=1)"
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]
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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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"## Reshape and Update In-Place"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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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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"source": [
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"e = np.arange(12)\n",
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"print(e)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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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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"source": [
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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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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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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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"source": [
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"# Set values of e from index 5 onwards to 0\n",
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"e[5:] = 0\n",
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"print(e)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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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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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# f is also updated\n",
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"f"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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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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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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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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},
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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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"## Combine Arrays"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1, 2, 3])"
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]
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},
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"a"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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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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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"b"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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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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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"d"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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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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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.concatenate([a, a, a])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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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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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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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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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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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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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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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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},
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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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"## Create Sample Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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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()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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hDGBa1FkCmUcYA5gSdZZAdhDGACagzhLILsIYwBjHO7qpswSyjDAGIEkaHBrW3jfe0cuH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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x10bac8d50>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create evenly spaced numbers over the specified interval\n",
|
|
"x = np.linspace(0, 2, 10)\n",
|
|
"plt.plot(x, 'o-');\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# 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()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
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|
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|
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|
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|
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|
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|
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|
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" 56.32993189, 37.77787235, 86.15033356, 95.1657446 , 56.1391928 ,\n",
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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" 46.39544481, 78.34537786, 12.09724051, 14.53354392, 82.302103 ,\n",
|
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|
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|
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|
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|
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|
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|
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|
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|
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" 27.79711887, 64.86774917, 74.91027445, 60.12828689, 69.29063747,\n",
|
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|
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" 62.17339483, 60.23323666, 28.43954838, 11.10739197, 55.77612286,\n",
|
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" 12.67580226, 95.40992531, 62.42992154, 56.06482565, 46.12781689,\n",
|
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|
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|
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|
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" 56.06991209, 81.67945331, 97.11608435, 4.92776729, 1.3771565 ,\n",
|
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" 91.31110986, 90.80864324, 15.9037535 , 85.81519919, 79.76466967,\n",
|
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|
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|
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|
|
" 46.70208863, 59.92869295, 21.55388203, 62.98701444, 4.45183257,\n",
|
|
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|
|
" 40.47106611, 82.46440949, 55.99998777, 65.94642036, 17.88883329,\n",
|
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" 84.37839975, 70.25555238, 50.43084756, 37.37166596, 15.05648505,\n",
|
|
" 76.20628745, 70.10005939, 52.17012073, 71.61366057, 67.03226755,\n",
|
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|
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" 77.42658886, 12.59288102, 52.47095802, 20.41637383, 85.87626666,\n",
|
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" 27.87167901, 59.44302695, 18.20788808, 10.44638281, 56.79568224,\n",
|
|
" 37.6338477 , 52.92135026, 68.42349055, 34.1362987 , 52.95759166,\n",
|
|
" 92.21537018, 1.66060537, 88.44675783, 80.98066845, 3.28514697,\n",
|
|
" 54.01542978, 13.65847397, 16.99730425, 7.76964967, 3.88317039,\n",
|
|
" 93.86374579, 36.13225968, 60.96492413, 29.45753097, 88.3003696 ,\n",
|
|
" 97.0733586 , 27.05594121, 24.92214054, 93.61527649, 11.9576937 ,\n",
|
|
" 99.84326102, 1.267823 , 46.33099186, 75.86836483, 70.78338433,\n",
|
|
" 74.47149114, 88.04387566, 7.42541445, 17.97614452, 27.59771801,\n",
|
|
" 23.26554208, 41.1571498 , 59.49604902, 65.47981923, 74.44316543,\n",
|
|
" 20.58415102, 84.83878697, 46.34912541, 42.00300668, 15.54338058,\n",
|
|
" 89.30469477, 94.97632108, 15.11922228, 26.02091974, 50.66762452,\n",
|
|
" 41.08850685, 5.11189478, 91.92108523, 67.04864043, 14.66461341,\n",
|
|
" 97.22322874, 76.70158353, 96.63558981, 52.79773946, 66.26472366,\n",
|
|
" 17.74787387, 19.5352013 , 74.25434184, 12.53154567, 64.76064791])"
|
|
]
|
|
},
|
|
"execution_count": 4,
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|
"metadata": {},
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|
"output_type": "execute_result"
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|
}
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|
],
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"source": [
|
|
"x"
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|
]
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|
},
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{
|
|
"cell_type": "code",
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|
"execution_count": null,
|
|
"metadata": {},
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"outputs": [],
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|
"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "ipython",
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"version": 3
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"name": "python",
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