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plenariplenari
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science python
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‎science/jupyter-notebook-base.ipynb

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{
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"cells": [
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"import os\n",
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"import datetime"
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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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"#### 1. 查找函数"
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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": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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"#np.lookfor('Series')"
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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": 40,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"pd.*date*?"
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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": 39,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"np.*load*?"
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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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"#### 2. 测试程序运行时间"
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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": 43,
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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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"The slowest run took 216.45 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
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"1000000 loops, best of 3: 1.14 µs per loop\n"
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]
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}
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],
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"source": [
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"a=np.random.randn(10,10)\n",
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"%timeit np.dot(a,a)"
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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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"#### 3. 记录日志"
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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": 41,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"logstart?"
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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": 42,
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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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"Activating auto-logging. Current session state plus future input saved.\n",
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"Filename : ipython_log.py\n",
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"Mode : rotate\n",
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"Output logging : False\n",
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"Raw input log : False\n",
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"Timestamping : False\n",
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"State : active\n"
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]
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}
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],
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"source": [
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"%logstart"
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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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"collapsed": true
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},
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"source": [
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"## he "
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%pylab\n",
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"# 模拟抛硬币的结果\n",
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"nsteps = 1000\n",
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"drws=random.randint(0,2,size=nsteps)\n",
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"step=where(drws>0,1,-1)#左闭，右开区间\n",
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"walk=step.cumsum()\n",
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"print(walk.min(),walk.max())"
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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": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from pandas import Series,DataFrame"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"obj=Series([4,5,62,6])\n",
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"print(obj.index,obj.values)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"obj.unique()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"DataFrame.append"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"obj.drop([3,1])"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data=DataFrame(np.arange(16).reshape(4,4))\n",
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"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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.drop([1,2],axis=1)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data[[0,1,3]]"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data.ix[1]"
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]
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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",
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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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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.1"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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