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Longer version of pandas lightning tutorial
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pandas_tutorial_longer_version.ipynb
{
"metadata": {
"name": "Pandas Lightning Tutorial"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"About me"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Ghinwa Choueiter, pronounced (Rinwa Shweyter).\n",
"* Data Scientist and Machine Learning Engineer at DataXu.\n",
"* Studied speech science at MIT.\n",
"* When not working or studying, I love reading, biking, hiking, swimming, cooking. "
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"What are Pandas\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Pandas is a python library with rich and easy-to-use tools and data structures for data analysis, manipulation, and modeling.\n",
"* Pandas was developed by Wes McKinney starting in 2008.\n",
"* Pandas derives from Panel Data, a common term to denote multi-dimensional data often observed in finance and econometrics.\n",
"* Great reference: Python for Data Analysis by Wes McKinney.\n",
"* The two most common data structures used in Pandas are:\n",
" * Series: a 1-dimensional labeled data array. \n",
" * DataFrames: a 2-dimensional labeled data structure with columns of potentially different types. \n",
"* In this talk, we focus on DataFrames and we use the MovieLens 1M Data Set and US Baby Names 1880-2010 to illustrate some examples."
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"5 Cool Things to Know About Pandas"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"1. Easy to load tabular data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"from pandas import DataFrame, Series\n",
"\n",
"# Create DataFrames from dicts\n",
"data = {'calories': [100,150,120],\n",
" 'fruit': ['apple','orange','banana']}\n",
"\n",
"df = DataFrame(data)\n",
"print(df)\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" calories fruit\n",
"0 100 apple\n",
"1 150 orange\n",
"2 120 banana\n"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create DataFrames from Series\n",
"data = {'calories': Series([100,150,120,140], index=['a','b','c','d']),\n",
" 'fruit': Series(['apple','orange','banana'],index=['a','b','c'])}\n",
"df = DataFrame(data)\n",
"\n",
"print(df)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" calories fruit\n",
"a 100 apple\n",
"b 150 orange\n",
"c 120 banana\n",
"d 140 NaN\n"
]
}
],
"prompt_number": 70
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.index"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 71,
"text": [
"Index([a, b, c, d], dtype=object)"
]
}
],
"prompt_number": 71
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 72,
"text": [
"Index([calories, fruit], dtype=object)"
]
}
],
"prompt_number": 72
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# read from file\n",
"unames = ['user_id', 'gender', 'age', 'occupation', 'zip'] \n",
"users = pd.read_table('/Users/gchoueiter/Documents/Classes/panda/pydata-book/ch02/movielens/users.dat', \n",
" sep='::', header=None,names=unames)\n",
"\n",
"rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
"ratings = pd.read_table('/Users/gchoueiter/Documents/Classes/panda/pydata-book/ch02/movielens/ratings.dat', \n",
" sep='::', header=None,names=rnames)\n",
"\n",
"mnames = ['movie_id', 'title', 'genres']\n",
"movies = pd.read_table('/Users/gchoueiter/Documents/Classes/panda/pydata-book/ch02/movielens/movies.dat', \n",
" sep='::', header=None,names=mnames)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 73
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"users[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>occupation</th>\n",
" <th>zip</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> F</td>\n",
" <td> 1</td>\n",
" <td> 10</td>\n",
" <td> 48067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> M</td>\n",
" <td> 56</td>\n",
" <td> 16</td>\n",
" <td> 70072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> M</td>\n",
" <td> 25</td>\n",
" <td> 15</td>\n",
" <td> 55117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> M</td>\n",
" <td> 45</td>\n",
" <td> 7</td>\n",
" <td> 02460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> M</td>\n",
" <td> 25</td>\n",
" <td> 20</td>\n",
" <td> 55455</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 74,
"text": [
" user_id gender age occupation zip\n",
"0 1 F 1 10 48067\n",
"1 2 M 56 16 70072\n",
"2 3 M 25 15 55117\n",
"3 4 M 45 7 02460\n",
"4 5 M 25 20 55455"
]
}
],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ratings[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>movie_id</th>\n",
" <th>rating</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> 1193</td>\n",
" <td> 5</td>\n",
" <td> 978300760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 1</td>\n",
" <td> 661</td>\n",
" <td> 3</td>\n",
" <td> 978302109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 1</td>\n",
" <td> 914</td>\n",
" <td> 3</td>\n",
" <td> 978301968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 1</td>\n",
" <td> 3408</td>\n",
" <td> 4</td>\n",
" <td> 978300275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1</td>\n",
" <td> 2355</td>\n",
" <td> 5</td>\n",
" <td> 978824291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 75,
"text": [
" user_id movie_id rating timestamp\n",
"0 1 1193 5 978300760\n",
"1 1 661 3 978302109\n",
"2 1 914 3 978301968\n",
"3 1 3408 4 978300275\n",
"4 1 2355 5 978824291"
]
}
],
"prompt_number": 75
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"movies[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>movie_id</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> Jumanji (1995)</td>\n",
" <td> Adventure|Children's|Fantasy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> Grumpier Old Men (1995)</td>\n",
" <td> Comedy|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> Waiting to Exhale (1995)</td>\n",
" <td> Comedy|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> Father of the Bride Part II (1995)</td>\n",
" <td> Comedy</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 76,
"text": [
" movie_id title genres\n",
"0 1 Toy Story (1995) Animation|Children's|Comedy\n",
"1 2 Jumanji (1995) Adventure|Children's|Fantasy\n",
"2 3 Grumpier Old Men (1995) Comedy|Romance\n",
"3 4 Waiting to Exhale (1995) Comedy|Drama\n",
"4 5 Father of the Bride Part II (1995) Comedy"
]
}
],
"prompt_number": 76
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Reading csv is also easy.\n",
"names_cols = ['name','sex','births']\n",
"names = pd.read_csv('/Users/gchoueiter/Documents/Classes/panda/pydata-book/ch02/names/yob1880.txt',names=names_cols)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 77
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"names[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>sex</th>\n",
" <th>births</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Mary</td>\n",
" <td> F</td>\n",
" <td> 7065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> Anna</td>\n",
" <td> F</td>\n",
" <td> 2604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Emma</td>\n",
" <td> F</td>\n",
" <td> 2003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Elizabeth</td>\n",
" <td> F</td>\n",
" <td> 1939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Minnie</td>\n",
" <td> F</td>\n",
" <td> 1746</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 78,
"text": [
" name sex births\n",
"0 Mary F 7065\n",
"1 Anna F 2604\n",
"2 Emma F 2003\n",
"3 Elizabeth F 1939\n",
"4 Minnie F 1746"
]
}
],
"prompt_number": 78
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"2. Easy to Access, Add, and Delete Data"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"A. Data Access"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Access by column(s)\n",
"users['user_id'][:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 79,
"text": [
"0 1\n",
"1 2\n",
"2 3\n",
"3 4\n",
"4 5\n",
"Name: user_id"
]
}
],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Columns can be accessed via the dot notation as well\n",
"users.user_id[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 80,
"text": [
"0 1\n",
"1 2\n",
"2 3\n",
"3 4\n",
"4 5\n",
"Name: user_id"
]
}
],
"prompt_number": 80
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"users[['user_id','gender']][:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 81,
"text": [
" user_id gender\n",
"0 1 F\n",
"1 2 M\n",
"2 3 M\n",
"3 4 M\n",
"4 5 M"
]
}
],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Access by row(s)\n",
"users.ix[2:3]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>occupation</th>\n",
" <th>zip</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> M</td>\n",
" <td> 25</td>\n",
" <td> 15</td>\n",
" <td> 55117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> M</td>\n",
" <td> 45</td>\n",
" <td> 7</td>\n",
" <td> 02460</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 82,
"text": [
" user_id gender age occupation zip\n",
"2 3 M 25 15 55117\n",
"3 4 M 45 7 02460"
]
}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Filtering on rows and columns\n",
"users.ix[2:4,['user_id','gender']]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 83,
"text": [
" user_id gender\n",
"2 3 M\n",
"3 4 M\n",
"4 5 M"
]
}
],
"prompt_number": 83
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Rows can be accessed by names and not just integer indices\n",
"df.ix['a':'c']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>calories</th>\n",
" <th>fruit</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td> 100</td>\n",
" <td> apple</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td> 150</td>\n",
" <td> orange</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td> 120</td>\n",
" <td> banana</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 84,
"text": [
" calories fruit\n",
"a 100 apple\n",
"b 150 orange\n",
"c 120 banana"
]
}
],
"prompt_number": 84
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# ix can also be used with a filter\n",
"df.ix[df.calories > 100]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>calories</th>\n",
" <th>fruit</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>b</th>\n",
" <td> 150</td>\n",
" <td> orange</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td> 120</td>\n",
" <td> banana</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td> 140</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 85,
"text": [
" calories fruit\n",
"b 150 orange\n",
"c 120 banana\n",
"d 140 NaN"
]
}
],
"prompt_number": 85
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"B. Add Data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Adding column\n",
"df['color'] = ['red','orange','yellow','pink']\n",
"\n",
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>calories</th>\n",
" <th>fruit</th>\n",
" <th>color</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>a</th>\n",
" <td> 100</td>\n",
" <td> apple</td>\n",
" <td> red</td>\n",
" </tr>\n",
" <tr>\n",
" <th>b</th>\n",
" <td> 150</td>\n",
" <td> orange</td>\n",
" <td> orange</td>\n",
" </tr>\n",
" <tr>\n",
" <th>c</th>\n",
" <td> 120</td>\n",
" <td> banana</td>\n",
" <td> yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>d</th>\n",
" <td> 140</td>\n",
" <td> NaN</td>\n",
" <td> pink</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 86,
"text": [
" calories fruit color\n",
"a 100 apple red\n",
"b 150 orange orange\n",
"c 120 banana yellow\n",
"d 140 NaN pink"
]
}
],
"prompt_number": 86
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Adding rows, requires a bit more work\n",
"df = df.append({'calories':'100','fruit':'plum','color':'violet'},ignore_index=True)\n",
"df\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>calories</th>\n",
" <th>fruit</th>\n",
" <th>color</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 100</td>\n",
" <td> apple</td>\n",
" <td> red</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 150</td>\n",
" <td> orange</td>\n",
" <td> orange</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 120</td>\n",
" <td> banana</td>\n",
" <td> yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 140</td>\n",
" <td> NaN</td>\n",
" <td> pink</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 100</td>\n",
" <td> plum</td>\n",
" <td> violet</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 87,
"text": [
" calories fruit color\n",
"0 100 apple red\n",
"1 150 orange orange\n",
"2 120 banana yellow\n",
"3 140 NaN pink\n",
"4 100 plum violet"
]
}
],
"prompt_number": 87
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"C. Delete Data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['random_info'] = 'bla'\n",
"df\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>calories</th>\n",
" <th>fruit</th>\n",
" <th>color</th>\n",
" <th>random_info</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 100</td>\n",
" <td> apple</td>\n",
" <td> red</td>\n",
" <td> bla</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 150</td>\n",
" <td> orange</td>\n",
" <td> orange</td>\n",
" <td> bla</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 120</td>\n",
" <td> banana</td>\n",
" <td> yellow</td>\n",
" <td> bla</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 140</td>\n",
" <td> NaN</td>\n",
" <td> pink</td>\n",
" <td> bla</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 100</td>\n",
" <td> plum</td>\n",
" <td> violet</td>\n",
" <td> bla</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 88,
"text": [
" calories fruit color random_info\n",
"0 100 apple red bla\n",
"1 150 orange orange bla\n",
"2 120 banana yellow bla\n",
"3 140 NaN pink bla\n",
"4 100 plum violet bla"
]
}
],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"del df['random_info']\n",
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>calories</th>\n",
" <th>fruit</th>\n",
" <th>color</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 100</td>\n",
" <td> apple</td>\n",
" <td> red</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 150</td>\n",
" <td> orange</td>\n",
" <td> orange</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 120</td>\n",
" <td> banana</td>\n",
" <td> yellow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 140</td>\n",
" <td> NaN</td>\n",
" <td> pink</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 100</td>\n",
" <td> plum</td>\n",
" <td> violet</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 89,
"text": [
" calories fruit color\n",
"0 100 apple red\n",
"1 150 orange orange\n",
"2 120 banana yellow\n",
"3 140 NaN pink\n",
"4 100 plum violet"
]
}
],
"prompt_number": 89
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"3. Easy to Handle Missing Data"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Create a sample df with missing data\n",
"import numpy as np\n",
"from numpy import nan as NA\n",
"df = DataFrame(np.random.randn(7, 4))\n",
"df.ix[:4, 1] = NA\n",
"df.ix[:2, 2] = NA\n",
"df.ix[:,3] = NA\n",
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0.133800</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.930645</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.809217</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.785018</td>\n",
" <td> NaN</td>\n",
" <td>-0.498913</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1.159472</td>\n",
" <td> NaN</td>\n",
" <td> 0.823045</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.529642</td>\n",
" <td>-1.091542</td>\n",
" <td> 1.117933</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> 0.574640</td>\n",
" <td>-0.345692</td>\n",
" <td> 0.584349</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 90,
"text": [
" 0 1 2 3\n",
"0 0.133800 NaN NaN NaN\n",
"1 -1.930645 NaN NaN NaN\n",
"2 0.809217 NaN NaN NaN\n",
"3 -0.785018 NaN -0.498913 NaN\n",
"4 1.159472 NaN 0.823045 NaN\n",
"5 -0.529642 -1.091542 1.117933 NaN\n",
"6 0.574640 -0.345692 0.584349 NaN"
]
}
],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# computed statistics ignore NaN\n",
"df.describe()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 7.000000</td>\n",
" <td> 2.000000</td>\n",
" <td> 4.000000</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-0.081168</td>\n",
" <td>-0.718617</td>\n",
" <td> 0.506604</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 1.074350</td>\n",
" <td> 0.527396</td>\n",
" <td> 0.704974</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-1.930645</td>\n",
" <td>-1.091542</td>\n",
" <td>-0.498913</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.657330</td>\n",
" <td>-0.905080</td>\n",
" <td> 0.313534</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 0.133800</td>\n",
" <td>-0.718617</td>\n",
" <td> 0.703697</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 0.691929</td>\n",
" <td>-0.532154</td>\n",
" <td> 0.896767</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 1.159472</td>\n",
" <td>-0.345692</td>\n",
" <td> 1.117933</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 91,
"text": [
" 0 1 2 3\n",
"count 7.000000 2.000000 4.000000 0\n",
"mean -0.081168 -0.718617 0.506604 NaN\n",
"std 1.074350 0.527396 0.704974 NaN\n",
"min -1.930645 -1.091542 -0.498913 NaN\n",
"25% -0.657330 -0.905080 0.313534 NaN\n",
"50% 0.133800 -0.718617 0.703697 NaN\n",
"75% 0.691929 -0.532154 0.896767 NaN\n",
"max 1.159472 -0.345692 1.117933 NaN"
]
}
],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.dropna()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <tbody>\n",
" <tr>\n",
" <td>Int64Index([], dtype=int64)</td>\n",
" <td>Empty DataFrame</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 92,
"text": [
"Empty DataFrame\n",
"Columns: [0, 1, 2, 3]\n",
"Index: []"
]
}
],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.dropna(axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0.133800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.930645</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.809217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.785018</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1.159472</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.529642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> 0.574640</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 93,
"text": [
" 0\n",
"0 0.133800\n",
"1 -1.930645\n",
"2 0.809217\n",
"3 -0.785018\n",
"4 1.159472\n",
"5 -0.529642\n",
"6 0.574640"
]
}
],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.dropna(how='all',axis=0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0.133800</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.930645</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.809217</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.785018</td>\n",
" <td> NaN</td>\n",
" <td>-0.498913</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1.159472</td>\n",
" <td> NaN</td>\n",
" <td> 0.823045</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.529642</td>\n",
" <td>-1.091542</td>\n",
" <td> 1.117933</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> 0.574640</td>\n",
" <td>-0.345692</td>\n",
" <td> 0.584349</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 94,
"text": [
" 0 1 2 3\n",
"0 0.133800 NaN NaN NaN\n",
"1 -1.930645 NaN NaN NaN\n",
"2 0.809217 NaN NaN NaN\n",
"3 -0.785018 NaN -0.498913 NaN\n",
"4 1.159472 NaN 0.823045 NaN\n",
"5 -0.529642 -1.091542 1.117933 NaN\n",
"6 0.574640 -0.345692 0.584349 NaN"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.dropna(how='all',axis=1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0.133800</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.930645</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 0.809217</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.785018</td>\n",
" <td> NaN</td>\n",
" <td>-0.498913</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1.159472</td>\n",
" <td> NaN</td>\n",
" <td> 0.823045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>-0.529642</td>\n",
" <td>-1.091542</td>\n",
" <td> 1.117933</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> 0.574640</td>\n",
" <td>-0.345692</td>\n",
" <td> 0.584349</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 95,
"text": [
" 0 1 2\n",
"0 0.133800 NaN NaN\n",
"1 -1.930645 NaN NaN\n",
"2 0.809217 NaN NaN\n",
"3 -0.785018 NaN -0.498913\n",
"4 1.159472 NaN 0.823045\n",
"5 -0.529642 -1.091542 1.117933\n",
"6 0.574640 -0.345692 0.584349"
]
}
],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.fillna(0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 0.133800</td>\n",
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"# let's merge the user, ratings, and movie dataframes\n",
"movie_data = pd.merge(pd.merge(ratings, users, on ='user_id'), movies,on='movie_id')\n",
"movie_data"
],
"language": "python",
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"user_ratings"
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"language": "python",
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"df2 = DataFrame({'fruit':['orange','apple','banana'],'calories':['100','120','150']}) \n",
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"data"
],
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"outputs": [
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"</table>\n",
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"text": [
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"0 orange orange 100\n",
"1 red apple 120\n",
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"3 yellow banana 150"
]
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"data"
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"text": [
" color fruit calories\n",
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"1 red apple 120\n",
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"# First let's make the fruit column in df1 an index\n",
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"df1"
],
"language": "python",
"metadata": {},
"outputs": [
{
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"text": [
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"apple green"
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"cell_type": "code",
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"data"
],
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"# How about 'merging' or concatenating multiple DataFrames\n",
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"years = xrange(1880, 2011)\n",
"pieces = []\n",
"columns = ['name', 'sex', 'births']\n",
"for year in years:\n",
" path = '/Users/gchoueiter/Documents/Classes/panda/pydata-book/ch02/names/yob%d.txt' % year\n",
" frame = pd.read_csv(path, names=columns)\n",
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"names"
],
"language": "python",
"metadata": {},
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"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
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"# A very common way to group data\n",
"# let's go back to the movie example\n",
"mean_ratings = movie_data.groupby(['gender','title'])['rating'].mean()\n",
"mean_ratings[:5]\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 108,
"text": [
"gender title \n",
"F $1,000,000 Duck (1971) 3.375000\n",
" 'Night Mother (1986) 3.388889\n",
" 'Til There Was You (1997) 2.675676\n",
" 'burbs, The (1989) 2.793478\n",
" ...And Justice for All (1979) 3.828571\n",
"Name: rating"
]
}
],
"prompt_number": 108
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# another way to group/aggregate data\n",
"mean_ratings = movie_data.pivot_table('rating',rows='title',cols='gender',aggfunc='mean')\n",
"mean_ratings[:5] \n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>gender</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" </tr>\n",
" <tr>\n",
" <th>title</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>$1,000,000 Duck (1971)</th>\n",
" <td> 3.375000</td>\n",
" <td> 2.761905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'Night Mother (1986)</th>\n",
" <td> 3.388889</td>\n",
" <td> 3.352941</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'Til There Was You (1997)</th>\n",
" <td> 2.675676</td>\n",
" <td> 2.733333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>'burbs, The (1989)</th>\n",
" <td> 2.793478</td>\n",
" <td> 2.962085</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...And Justice for All (1979)</th>\n",
" <td> 3.828571</td>\n",
" <td> 3.689024</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 109,
"text": [
"gender F M\n",
"title \n",
"$1,000,000 Duck (1971) 3.375000 2.761905\n",
"'Night Mother (1986) 3.388889 3.352941\n",
"'Til There Was You (1997) 2.675676 2.733333\n",
"'burbs, The (1989) 2.793478 2.962085\n",
"...And Justice for All (1979) 3.828571 3.689024"
]
}
],
"prompt_number": 109
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mean_ratings.hist()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 110,
"text": [
"array([[Axes(0.125,0.125;0.336957x0.775),\n",
" Axes(0.563043,0.125;0.336957x0.775)]], dtype=object)"
]
},
{
"output_type": "display_data",
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m/AWyct5XrMHMe+Vz3rSKH4SWlhbTP0wdP35c/PGPf/T5BwVfcY4c\nOSLOnz8vhBBi48aN4tZbbx3wdefPnxf33XefWLx48WX7sTImK3GsjOnYsWPik08+EUIIcfz4cTF5\n8mTxv//9z+/xWIljdY76GIYhvvvd717yuD/7zCyWlTGdPn3a+0e/9vZ2kZycLFpbW4Mek52Y8/bn\nvNVYTuW9ijlv2yUQ5s6di3/84x84fvw44uLi8MQTT+Dzzz8HACxYsAAZGRmYNm0abr75ZowZMwYb\nNmwIKM5f//pXVFZWYsiQIbjxxhvx9NNPDxjnrbfewoYNG7yHLQHAk08+idbWVr/GZCWOlTEdPnwY\n999/P86dO4fY2FiUl5fjmmuuQVVVlV/jsRLH6hxdrO+roL/jsRrLypiOHj2Ku+66CwBw1VVXYcmS\nJYiLi5MyJjsw5wcn563GcjLvVct5R24lSEREg0fzO0wREZEZLvRERCGOCz0RUYjjQk9EFOK40BMR\nhTgu9EREIe7/A8gNLhC6cgHuAAAAAElFTkSuQmCC\n"
}
],
"prompt_number": 110
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mean_ratings.describe()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>gender</th>\n",
" <th>F</th>\n",
" <th>M</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 3481.000000</td>\n",
" <td> 3671.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 3.293716</td>\n",
" <td> 3.217015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 0.714311</td>\n",
" <td> 0.686186</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 1.000000</td>\n",
" <td> 1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 2.888889</td>\n",
" <td> 2.794190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 3.381356</td>\n",
" <td> 3.307692</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 3.824176</td>\n",
" <td> 3.721798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 5.000000</td>\n",
" <td> 5.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 111,
"text": [
"gender F M\n",
"count 3481.000000 3671.000000\n",
"mean 3.293716 3.217015\n",
"std 0.714311 0.686186\n",
"min 1.000000 1.000000\n",
"25% 2.888889 2.794190\n",
"50% 3.381356 3.307692\n",
"75% 3.824176 3.721798\n",
"max 5.000000 5.000000"
]
}
],
"prompt_number": 111
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# groupby + apply = data transformation\n",
"\n",
"# function applied to a group\n",
"def zscore(group):\n",
" return (group - group.mean()) / group.std()\n",
"\n",
"# applied to a string\n",
"def movie_title_length(title):\n",
" return len(title.split(' '))\n",
"\n",
"# data transformation examples with apply \n",
"movie_data['rating_zscore_by_gender'] = movie_data.groupby(['gender','title'])['rating'].apply(zscore)\n",
"\n",
"# data transformation examples with map (applies on Series)\n",
"movie_data['rating_mean_by_gender'] = movie_data.groupby(['gender','title'])['rating'].map(mean)\n",
"movie_data['title_length'] = movie_data['title'].map(movie_title_length)\n",
"\n",
"movie_data[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>movie_id</th>\n",
" <th>rating</th>\n",
" <th>timestamp</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>occupation</th>\n",
" <th>zip</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" <th>rating_zscore_by_gender</th>\n",
" <th>rating_mean_by_gender</th>\n",
" <th>title_length</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> 1193</td>\n",
" <td> 5</td>\n",
" <td> 978300760</td>\n",
" <td> F</td>\n",
" <td> 1</td>\n",
" <td> 10</td>\n",
" <td> 48067</td>\n",
" <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td> Drama</td>\n",
" <td> 0.814585</td>\n",
" <td> 5</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> 1193</td>\n",
" <td> 5</td>\n",
" <td> 978298413</td>\n",
" <td> M</td>\n",
" <td> 56</td>\n",
" <td> 16</td>\n",
" <td> 70072</td>\n",
" <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td> Drama</td>\n",
" <td> 0.757889</td>\n",
" <td> 5</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 12</td>\n",
" <td> 1193</td>\n",
" <td> 4</td>\n",
" <td> 978220179</td>\n",
" <td> M</td>\n",
" <td> 25</td>\n",
" <td> 12</td>\n",
" <td> 32793</td>\n",
" <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td> Drama</td>\n",
" <td>-0.545273</td>\n",
" <td> 4</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 15</td>\n",
" <td> 1193</td>\n",
" <td> 4</td>\n",
" <td> 978199279</td>\n",
" <td> M</td>\n",
" <td> 25</td>\n",
" <td> 7</td>\n",
" <td> 22903</td>\n",
" <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td> Drama</td>\n",
" <td>-0.545273</td>\n",
" <td> 4</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 17</td>\n",
" <td> 1193</td>\n",
" <td> 5</td>\n",
" <td> 978158471</td>\n",
" <td> M</td>\n",
" <td> 50</td>\n",
" <td> 1</td>\n",
" <td> 95350</td>\n",
" <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td> Drama</td>\n",
" <td> 0.757889</td>\n",
" <td> 5</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"output_type": "pyout",
"prompt_number": 114,
"text": [
" user_id movie_id rating timestamp gender age occupation zip \\\n",
"0 1 1193 5 978300760 F 1 10 48067 \n",
"1 2 1193 5 978298413 M 56 16 70072 \n",
"2 12 1193 4 978220179 M 25 12 32793 \n",
"3 15 1193 4 978199279 M 25 7 22903 \n",
"4 17 1193 5 978158471 M 50 1 95350 \n",
"\n",
" title genres rating_zscore_by_gender \\\n",
"0 One Flew Over the Cuckoo's Nest (1975) Drama 0.814585 \n",
"1 One Flew Over the Cuckoo's Nest (1975) Drama 0.757889 \n",
"2 One Flew Over the Cuckoo's Nest (1975) Drama -0.545273 \n",
"3 One Flew Over the Cuckoo's Nest (1975) Drama -0.545273 \n",
"4 One Flew Over the Cuckoo's Nest (1975) Drama 0.757889 \n",
"\n",
" rating_mean_by_gender title_length \n",
"0 5 7 \n",
"1 5 7 \n",
"2 4 7 \n",
"3 4 7 \n",
"4 5 7 "
]
}
],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.scatter(movie_data['rating_mean_by_gender'],movie_data['title_length'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 113,
"text": [
"<matplotlib.collections.PathCollection at 0xf127750>"
]
},
{
"output_type": "display_data",
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}
],
"prompt_number": 113
}
],
"metadata": {}
}
]
}
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