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more snippits
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‎code_py_template-Copy0.ipynb

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{
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"metadata": {
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"name": "",
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"signature": "sha256:584adad2f76292ba484cbe44d4af7098eb86fd4996c310edde9b6c17ea58f7d4"
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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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"# code_py template\n",
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"\n",
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"This snippit was written by [Chris R. Albon](http://www.chrisralbon.com/) and is part of his collection of [well-documented Python snippits](https://github.com/chrisalbon/code_py). All code is written in Python 3 in iPython notebook and offered under the [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/)."
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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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"### import modules"
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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 pandas as pd"
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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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"### Create dataframe"
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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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"raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'], \n",
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" 'last_name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze'], \n",
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" 'age': [42, 52, 36, 24, 73], \n",
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" 'preTestScore': [4, 24, 31, 2, 3],\n",
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" 'postTestScore': [25, 94, 57, 62, 70]}\n",
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"df = pd.DataFrame(raw_data, columns = ['first_name', 'last_name', 'age', 'preTestScore', 'postTestScore'])\n",
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"df"
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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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"html": [
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"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>first_name</th>\n",
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" <th>last_name</th>\n",
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" <th>age</th>\n",
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" <th>preTestScore</th>\n",
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" <th>postTestScore</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td> Jason</td>\n",
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" <td> Miller</td>\n",
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" <td> 42</td>\n",
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" <td> 4</td>\n",
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" <td> 25</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td> Molly</td>\n",
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" <td> Jacobson</td>\n",
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" <td> 52</td>\n",
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" <td> 24</td>\n",
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" <td> 94</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td> Tina</td>\n",
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" <td> Ali</td>\n",
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" <td> 36</td>\n",
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" <td> 31</td>\n",
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" <td> 57</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td> Jake</td>\n",
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" <td> Milner</td>\n",
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" <td> 24</td>\n",
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" <td> 2</td>\n",
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" <td> 62</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td> Amy</td>\n",
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" <td> Cooze</td>\n",
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" <td> 73</td>\n",
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" <td> 3</td>\n",
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" <td> 70</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows \u00d7 5 columns</p>\n",
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"</div>"
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],
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"metadata": {},
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"output_type": "pyout",
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"prompt_number": 2,
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"text": [
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" first_name last_name age preTestScore postTestScore\n",
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"0 Jason Miller 42 4 25\n",
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"1 Molly Jacobson 52 24 94\n",
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"2 Tina Ali 36 31 57\n",
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"3 Jake Milner 24 2 62\n",
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"4 Amy Cooze 73 3 70\n",
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"\n",
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"[5 rows x 5 columns]"
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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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"language": "python",
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"metadata": {},
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"outputs": []
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}
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],
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"metadata": {}
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}
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]
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}

‎pandas_outliers.ipynb

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{
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"metadata": {
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"name": "",
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"signature": "sha256:d01871b223db933a2260b5817ae9abbbf82e0a6bd98d4414215f47e56db5204f"
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"signature": "sha256:debb2e45e28149e7e079d0a3a969dcdf39a4fd8626249ed712fdcb09699867dc"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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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": "markdown",
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"metadata": {},
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"source": [
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"### Are there any values inthe score_1 above or below 2?"
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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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],
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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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"17 2.465491\n",
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"Name: score_1, dtype: float64"
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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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"language": "python",
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"metadata": {},
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"outputs": []
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}
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],

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