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jminas/intro_pandas.ipynb

Created October 27, 2016 18:14
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intro_pandas.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set up"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"Use conda to install seaborn by running in a terminal\n",
"\n",
"conda install seaborn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# imports a library 'pandas', names it as 'pd'\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# enables inline plots, without it plots don't show up in the notebook\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####What problem does pandas solve?\n",
"\n",
"Python has long been great for data munging and preparation, but less so for data analysis and modeling. pandas helps fill this gap, enabling you to carry out your entire data analysis workflow in Python without having to switch to a more domain specific language like R.\n",
"\n",
"Combined with the excellent IPython toolkit and other libraries, the environment for doing data analysis in Python excels in performance, productivity, and the ability to collaborate.\n",
"\n",
"pandas does not implement significant modeling functionality outside of linear and panel regression; for this, look to statsmodels and scikit-learn. More work is still needed to make Python a first class statistical modeling environment, but we are well on our way toward that goal.\n",
"\n",
"http://pandas.pydata.org/"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# various options in pandas\n",
"\n",
"pd.set_option('display.max_columns', None)\n",
"pd.set_option('display.max_rows', 20)\n",
"pd.set_option('display.precision', 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load a data set"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"Census Income\" dataset\n",
"\n",
"http://archive.ics.uci.edu/ml/"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# download the data and name the columns\n",
"cols = ['age', 'workclass', 'fnlwgt', 'education', 'education_num',\n",
" 'marital_status', 'occupation', 'relationship', 'ethnicity',\n",
" 'gender', 'capital_gain', 'capital_loss', 'hours_per_week',\n",
" 'country_of_origin', 'income']\n",
"\n",
"df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data',\n",
" names = cols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"pandas can load a lot more than csvs, this tutorial shows how pandas can read excel, sql,\n",
"and even copy and paste...\n",
"\n",
"http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## View"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 32561 entries, 0 to 32560\n",
"Data columns (total 15 columns):\n",
"age 32561 non-null int64\n",
"workclass 32561 non-null object\n",
"fnlwgt 32561 non-null int64\n",
"education 32561 non-null object\n",
"education_num 32561 non-null int64\n",
"marital_status 32561 non-null object\n",
"occupation 32561 non-null object\n",
"relationship 32561 non-null object\n",
"ethnicity 32561 non-null object\n",
"gender 32561 non-null object\n",
"capital_gain 32561 non-null int64\n",
"capital_loss 32561 non-null int64\n",
"hours_per_week 32561 non-null int64\n",
"country_of_origin 32561 non-null object\n",
"income 32561 non-null object\n",
"dtypes: int64(6), object(9)\n",
"memory usage: 4.0+ MB\n"
]
}
],
"source": [
"# we can see there are no null values\n",
"# columns with numberical values are type int64, no need to set data type\n",
"\n",
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>ethnicity</th>\n",
" <th>gender</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" <th>country_of_origin</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 39</td>\n",
" <td> State-gov</td>\n",
" <td> 77516</td>\n",
" <td> Bachelors</td>\n",
" <td> 13</td>\n",
" <td> Never-married</td>\n",
" <td> Adm-clerical</td>\n",
" <td> Not-in-family</td>\n",
" <td> White</td>\n",
" <td> Male</td>\n",
" <td> 2174</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 50</td>\n",
" <td> Self-emp-not-inc</td>\n",
" <td> 83311</td>\n",
" <td> Bachelors</td>\n",
" <td> 13</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Exec-managerial</td>\n",
" <td> Husband</td>\n",
" <td> White</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 13</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 38</td>\n",
" <td> Private</td>\n",
" <td> 215646</td>\n",
" <td> HS-grad</td>\n",
" <td> 9</td>\n",
" <td> Divorced</td>\n",
" <td> Handlers-cleaners</td>\n",
" <td> Not-in-family</td>\n",
" <td> White</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 53</td>\n",
" <td> Private</td>\n",
" <td> 234721</td>\n",
" <td> 11th</td>\n",
" <td> 7</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Handlers-cleaners</td>\n",
" <td> Husband</td>\n",
" <td> Black</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 28</td>\n",
" <td> Private</td>\n",
" <td> 338409</td>\n",
" <td> Bachelors</td>\n",
" <td> 13</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Prof-specialty</td>\n",
" <td> Wife</td>\n",
" <td> Black</td>\n",
" <td> Female</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> Cuba</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education education_num \\\n",
"0 39 State-gov 77516 Bachelors 13 \n",
"1 50 Self-emp-not-inc 83311 Bachelors 13 \n",
"2 38 Private 215646 HS-grad 9 \n",
"3 53 Private 234721 11th 7 \n",
"4 28 Private 338409 Bachelors 13 \n",
"\n",
" marital_status occupation relationship ethnicity gender \\\n",
"0 Never-married Adm-clerical Not-in-family White Male \n",
"1 Married-civ-spouse Exec-managerial Husband White Male \n",
"2 Divorced Handlers-cleaners Not-in-family White Male \n",
"3 Married-civ-spouse Handlers-cleaners Husband Black Male \n",
"4 Married-civ-spouse Prof-specialty Wife Black Female \n",
"\n",
" capital_gain capital_loss hours_per_week country_of_origin income \n",
"0 2174 0 40 United-States <=50K \n",
"1 0 0 13 United-States <=50K \n",
"2 0 0 40 United-States <=50K \n",
"3 0 0 40 United-States <=50K \n",
"4 0 0 40 Cuba <=50K "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# to view the first 5 or specify with ex: .head(10)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([' Bachelors', ' HS-grad', ' 11th', ' Masters', ' 9th',\n",
" ' Some-college', ' Assoc-acdm', ' Assoc-voc', ' 7th-8th',\n",
" ' Doctorate', ' Prof-school', ' 5th-6th', ' 10th', ' 1st-4th',\n",
" ' Preschool', ' 12th'], dtype=object)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# there's a space before each string in this data\n",
"df.education.unique()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([' State-gov', ' Self-emp-not-inc', ' Private', ' Federal-gov',\n",
" ' Local-gov', ' ?', ' Self-emp-inc', ' Without-pay', ' Never-worked'], dtype=object)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# looks like it's in every object column\n",
"df.workclass.unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Strip spaces in columns"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# loop through each column and strip all the spaces\n",
"\n",
"for col in df:\n",
" if df[col].dtype == 'O':\n",
" df[col] = df[col].map(lambda x: x.strip(' '))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Here's a break down of what that for loop is doing"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"age\n",
"workclass\n",
"fnlwgt\n",
"education\n",
"education_num\n",
"marital_status\n",
"occupation\n",
"relationship\n",
"ethnicity\n",
"gender\n",
"capital_gain\n",
"capital_loss\n",
"hours_per_week\n",
"country_of_origin\n",
"income\n"
]
}
],
"source": [
"# loops through df and gets the column names\n",
"for col in df:\n",
" print col"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dtype('O')"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# gets the column type\n",
"df.education.dtype"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# if True then applys the map function\n",
"df.education.dtype == object"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'string'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# strip function\n",
"x = ' string'\n",
"x.strip(' ')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'string'"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# lambda creates a 'throw away' or 'anonymous' function\n",
"strip_string = lambda x: x.strip(' ')\n",
"strip_string(' string')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# same as this\n",
"def strip_string2(x):\n",
" x = x.strip(' ')\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'string'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"strip_string2(' string')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 State-gov\n",
"1 Self-emp-not-inc\n",
"2 Private\n",
"3 Private\n",
"4 Private\n",
"5 Private\n",
"6 Private\n",
"7 Self-emp-not-inc\n",
"...\n",
"32553 Private\n",
"32554 Private\n",
"32555 Private\n",
"32556 Private\n",
"32557 Private\n",
"32558 Private\n",
"32559 Private\n",
"32560 Self-emp-inc\n",
"Name: workclass, Length: 32561, dtype: object"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# map applies the function to each item in the data frame column so\n",
"\n",
"df[col].map(lambda x: x.strip(' '))\n",
"\n",
"# does the same thing as\n",
"\n",
"df['workclass'].map(strip_string2)\n",
"\n",
"# but in the first case we don't have to define and name a function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"for more info on lambda and map\n",
"\n",
"http://www.python-course.eu/lambda.php"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Descriptive "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"HS-grad 10501\n",
"Some-college 7291\n",
"Bachelors 5355\n",
"Masters 1723\n",
"Assoc-voc 1382\n",
"11th 1175\n",
"Assoc-acdm 1067\n",
"10th 933\n",
"7th-8th 646\n",
"Prof-school 576\n",
"9th 514\n",
"12th 433\n",
"Doctorate 413\n",
"5th-6th 333\n",
"1st-4th 168\n",
"Preschool 51\n",
"dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.education.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"40.437455852092995"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.hours_per_week.mean()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/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>age</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 32561.00</td>\n",
" <td> 32561.00</td>\n",
" <td> 32561.00</td>\n",
" <td> 32561.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 38.58</td>\n",
" <td> 1077.65</td>\n",
" <td> 87.30</td>\n",
" <td> 40.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 13.64</td>\n",
" <td> 7385.29</td>\n",
" <td> 402.96</td>\n",
" <td> 12.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 17.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 1.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 28.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 40.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 37.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 40.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 48.00</td>\n",
" <td> 0.00</td>\n",
" <td> 0.00</td>\n",
" <td> 45.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 90.00</td>\n",
" <td> 99999.00</td>\n",
" <td> 4356.00</td>\n",
" <td> 99.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age capital_gain capital_loss hours_per_week\n",
"count 32561.00 32561.00 32561.00 32561.00\n",
"mean 38.58 1077.65 87.30 40.44\n",
"std 13.64 7385.29 402.96 12.35\n",
"min 17.00 0.00 0.00 1.00\n",
"25% 28.00 0.00 0.00 40.00\n",
"50% 37.00 0.00 0.00 40.00\n",
"75% 48.00 0.00 0.00 45.00\n",
"max 90.00 99999.00 4356.00 99.00"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['age', 'capital_gain', 'capital_loss', 'hours_per_week']].describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find nulls"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>ethnicity</th>\n",
" <th>gender</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" <th>country_of_origin</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [age, workclass, fnlwgt, education, education_num, marital_status, occupation, relationship, ethnicity, gender, capital_gain, capital_loss, hours_per_week, country_of_origin, income]\n",
"Index: []"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# as we saw with df.info() there are no nulls... \n",
"# but if there were this would find the rows where age is null\n",
"df[df.age.isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# you could drop all those rows with\n",
"df_no_nulls = df[df.age.notnull()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fill nulls"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"null_df = pd.DataFrame([1,2,4,np.nan], columns = ['column1'])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>column1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" column1\n",
"0 1\n",
"1 2\n",
"2 4\n",
"3 NaN"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"null_df"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 2\n",
"2 4\n",
"3 1000\n",
"Name: column1, dtype: float64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# you can also fill nulls with a value or string\n",
"null_df.column1.fillna(1000)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 2\n",
"2 4\n",
"3 2\n",
"Name: column1, dtype: float64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"null_df.column1.fillna(null_df.column1.median())"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 2\n",
"2 4\n",
"3 string\n",
"Name: column1, dtype: object"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"null_df.column1.fillna('string')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Selecting rows and columns "
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age 39\n",
"workclass State-gov\n",
"fnlwgt 77516\n",
"education Bachelors\n",
"education_num 13\n",
"marital_status Never-married\n",
"occupation Adm-clerical\n",
"relationship Not-in-family\n",
"ethnicity White\n",
"gender Male\n",
"capital_gain 2174\n",
"capital_loss 0\n",
"hours_per_week 40\n",
"country_of_origin United-States\n",
"income <=50K\n",
"Name: 0, dtype: object"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select a row\n",
"df.iloc[0]"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>ethnicity</th>\n",
" <th>gender</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" <th>country_of_origin</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 37</td>\n",
" <td> Private</td>\n",
" <td> 280464</td>\n",
" <td> Some-college</td>\n",
" <td> 10</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Exec-managerial</td>\n",
" <td> Husband</td>\n",
" <td> Black</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 80</td>\n",
" <td> United-States</td>\n",
" <td> &gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 30</td>\n",
" <td> State-gov</td>\n",
" <td> 141297</td>\n",
" <td> Bachelors</td>\n",
" <td> 13</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Prof-specialty</td>\n",
" <td> Husband</td>\n",
" <td> Asian-Pac-Islander</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> India</td>\n",
" <td> &gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 23</td>\n",
" <td> Private</td>\n",
" <td> 122272</td>\n",
" <td> Bachelors</td>\n",
" <td> 13</td>\n",
" <td> Never-married</td>\n",
" <td> Adm-clerical</td>\n",
" <td> Own-child</td>\n",
" <td> White</td>\n",
" <td> Female</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 30</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 32</td>\n",
" <td> Private</td>\n",
" <td> 205019</td>\n",
" <td> Assoc-acdm</td>\n",
" <td> 12</td>\n",
" <td> Never-married</td>\n",
" <td> Sales</td>\n",
" <td> Not-in-family</td>\n",
" <td> Black</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 50</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 40</td>\n",
" <td> Private</td>\n",
" <td> 121772</td>\n",
" <td> Assoc-voc</td>\n",
" <td> 11</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Craft-repair</td>\n",
" <td> Husband</td>\n",
" <td> Asian-Pac-Islander</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> ?</td>\n",
" <td> &gt;50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education education_num marital_status \\\n",
"10 37 Private 280464 Some-college 10 Married-civ-spouse \n",
"11 30 State-gov 141297 Bachelors 13 Married-civ-spouse \n",
"12 23 Private 122272 Bachelors 13 Never-married \n",
"13 32 Private 205019 Assoc-acdm 12 Never-married \n",
"14 40 Private 121772 Assoc-voc 11 Married-civ-spouse \n",
"\n",
" occupation relationship ethnicity gender capital_gain \\\n",
"10 Exec-managerial Husband Black Male 0 \n",
"11 Prof-specialty Husband Asian-Pac-Islander Male 0 \n",
"12 Adm-clerical Own-child White Female 0 \n",
"13 Sales Not-in-family Black Male 0 \n",
"14 Craft-repair Husband Asian-Pac-Islander Male 0 \n",
"\n",
" capital_loss hours_per_week country_of_origin income \n",
"10 0 80 United-States >50K \n",
"11 0 40 India >50K \n",
"12 0 30 United-States <=50K \n",
"13 0 50 United-States <=50K \n",
"14 0 40 ? >50K "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select a range of rows\n",
"df.iloc[10:15]"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>ethnicity</th>\n",
" <th>gender</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" <th>country_of_origin</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>32559</th>\n",
" <td> 22</td>\n",
" <td> Private</td>\n",
" <td> 201490</td>\n",
" <td> HS-grad</td>\n",
" <td> 9</td>\n",
" <td> Never-married</td>\n",
" <td> Adm-clerical</td>\n",
" <td> Own-child</td>\n",
" <td> White</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 20</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32560</th>\n",
" <td> 52</td>\n",
" <td> Self-emp-inc</td>\n",
" <td> 287927</td>\n",
" <td> HS-grad</td>\n",
" <td> 9</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Exec-managerial</td>\n",
" <td> Wife</td>\n",
" <td> White</td>\n",
" <td> Female</td>\n",
" <td> 15024</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> United-States</td>\n",
" <td> &gt;50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education education_num marital_status \\\n",
"32559 22 Private 201490 HS-grad 9 Never-married \n",
"32560 52 Self-emp-inc 287927 HS-grad 9 Married-civ-spouse \n",
"\n",
" occupation relationship ethnicity gender capital_gain \\\n",
"32559 Adm-clerical Own-child White Male 0 \n",
"32560 Exec-managerial Wife White Female 15024 \n",
"\n",
" capital_loss hours_per_week country_of_origin income \n",
"32559 0 20 United-States <=50K \n",
"32560 0 40 United-States >50K "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# last 2 rows\n",
"df.iloc[-2:]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education_num</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 77516</td>\n",
" <td> Bachelors</td>\n",
" <td> 13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 215646</td>\n",
" <td> HS-grad</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 338409</td>\n",
" <td> Bachelors</td>\n",
" <td> 13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> 160187</td>\n",
" <td> 9th</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td> 45781</td>\n",
" <td> Masters</td>\n",
" <td> 14</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" fnlwgt education education_num\n",
"0 77516 Bachelors 13\n",
"2 215646 HS-grad 9\n",
"4 338409 Bachelors 13\n",
"6 160187 9th 5\n",
"8 45781 Masters 14"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# selecting every other row in columns 3-5\n",
"df.iloc[::2, 2:5].head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age</th>\n",
" <th>relationship </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 39</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 50</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 38</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age relationship \n",
"0 39 NaN\n",
"1 50 NaN\n",
"2 38 NaN"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.loc[0:2, ['age', 'relationship ']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Differences between .loc, .iloc, and .ix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://pandas.pydata.org/pandas-docs/stable/indexing.html"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"by label\n",
"\n",
".loc[]\n",
"\n",
"by integer position\n",
"\n",
".iloc[]\n",
"\n",
"for both\n",
"\n",
".ix[]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# pd.DataFrame let's you turn series, arrays, lists, and more into data frame structures\n",
"\n",
"df_index = pd.DataFrame([[1,2,3,4],[2,4,6,8],[3,5,7,9]], [11,13,12], columns = ['A', 'C', 'D', 'B'])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>A</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" <td> 6</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 3</td>\n",
" <td> 5</td>\n",
" <td> 7</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A C D B\n",
"11 1 2 3 4\n",
"13 2 4 6 8\n",
"12 3 5 7 9"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_index"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>A</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A C D B\n",
"11 1 2 3 4"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# iloc indexes by postion, not by the labels in the index\n",
"df_index.iloc[0:1]"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>A</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" <th>B</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" <td> 6</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 3</td>\n",
" <td> 5</td>\n",
" <td> 7</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A C D B\n",
"11 1 2 3 4\n",
"13 2 4 6 8\n",
"12 3 5 7 9"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# with loc both the start and the stop are included\n",
"df_index.loc[11:12]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>A</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A\n",
"11 1\n",
"13 2\n",
"12 3"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# select columns by position\n",
"df_index.iloc[:,0:1]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>A</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 3</td>\n",
" <td> 5</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A C D\n",
"11 1 2 3\n",
"13 2 4 6\n",
"12 3 5 7"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# or by label\n",
"df_index.loc[:,'A':'D']"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>A</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 3</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A C\n",
"11 1 2\n",
"13 2 4\n",
"12 3 5"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ix: primarily label based, but will fall back to integer positional access\n",
"df_index.ix[:,'A':'C']"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>A</th>\n",
" <th>C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 3</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A C\n",
"11 1 2\n",
"13 2 4\n",
"12 3 5"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# ix: primarily label based, but will fall back to integer positional access\n",
"df_index.ix[:,0:2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rename columns"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'age', u'workclass', u'fnlwgt', u'education', u'education_num', u'marital_status', u'occupation', u'relationship', u'ethnicity', u'gender', u'capital_gain', u'capital_loss', u'hours_per_week', u'country_of_origin', u'income'], dtype='object')"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# replace a column name\n",
"df.rename(columns = {'country_of_origin' : 'native_country'}, inplace = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Boolean"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['United-States', 'Cuba', 'Jamaica', 'India', '?', 'Mexico', 'South',\n",
" 'Puerto-Rico', 'Honduras', 'England', 'Canada', 'Germany', 'Iran',\n",
" 'Philippines', 'Italy', 'Poland', 'Columbia', 'Cambodia',\n",
" 'Thailand', 'Ecuador', 'Laos', 'Taiwan', 'Haiti', 'Portugal',\n",
" 'Dominican-Republic', 'El-Salvador', 'France', 'Guatemala', 'China',\n",
" 'Japan', 'Yugoslavia', 'Peru', 'Outlying-US(Guam-USVI-etc)',\n",
" 'Scotland', 'Trinadad&Tobago', 'Greece', 'Nicaragua', 'Vietnam',\n",
" 'Hong', 'Ireland', 'Hungary', 'Holand-Netherlands'], dtype=object)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.native_country.unique()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>ethnicity</th>\n",
" <th>gender</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" <th>native_country</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 39</td>\n",
" <td> State-gov</td>\n",
" <td> 77516</td>\n",
" <td> Bachelors</td>\n",
" <td> 13</td>\n",
" <td> Never-married</td>\n",
" <td> Adm-clerical</td>\n",
" <td> Not-in-family</td>\n",
" <td> White</td>\n",
" <td> Male</td>\n",
" <td> 2174</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 50</td>\n",
" <td> Self-emp-not-inc</td>\n",
" <td> 83311</td>\n",
" <td> Bachelors</td>\n",
" <td> 13</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Exec-managerial</td>\n",
" <td> Husband</td>\n",
" <td> White</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 13</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 38</td>\n",
" <td> Private</td>\n",
" <td> 215646</td>\n",
" <td> HS-grad</td>\n",
" <td> 9</td>\n",
" <td> Divorced</td>\n",
" <td> Handlers-cleaners</td>\n",
" <td> Not-in-family</td>\n",
" <td> White</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 53</td>\n",
" <td> Private</td>\n",
" <td> 234721</td>\n",
" <td> 11th</td>\n",
" <td> 7</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Handlers-cleaners</td>\n",
" <td> Husband</td>\n",
" <td> Black</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> 37</td>\n",
" <td> Private</td>\n",
" <td> 284582</td>\n",
" <td> Masters</td>\n",
" <td> 14</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Exec-managerial</td>\n",
" <td> Wife</td>\n",
" <td> White</td>\n",
" <td> Female</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> United-States</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education education_num \\\n",
"0 39 State-gov 77516 Bachelors 13 \n",
"1 50 Self-emp-not-inc 83311 Bachelors 13 \n",
"2 38 Private 215646 HS-grad 9 \n",
"3 53 Private 234721 11th 7 \n",
"5 37 Private 284582 Masters 14 \n",
"\n",
" marital_status occupation relationship ethnicity gender \\\n",
"0 Never-married Adm-clerical Not-in-family White Male \n",
"1 Married-civ-spouse Exec-managerial Husband White Male \n",
"2 Divorced Handlers-cleaners Not-in-family White Male \n",
"3 Married-civ-spouse Handlers-cleaners Husband Black Male \n",
"5 Married-civ-spouse Exec-managerial Wife White Female \n",
"\n",
" capital_gain capital_loss hours_per_week native_country income \n",
"0 2174 0 40 United-States <=50K \n",
"1 0 0 13 United-States <=50K \n",
"2 0 0 40 United-States <=50K \n",
"3 0 0 40 United-States <=50K \n",
"5 0 0 40 United-States <=50K "
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.native_country == 'United-States'].head()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education_num</th>\n",
" <th>marital_status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>ethnicity</th>\n",
" <th>gender</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" <th>native_country</th>\n",
" <th>income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 28</td>\n",
" <td> Private</td>\n",
" <td> 338409</td>\n",
" <td> Bachelors</td>\n",
" <td> 13</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Prof-specialty</td>\n",
" <td> Wife</td>\n",
" <td> Black</td>\n",
" <td> Female</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> Cuba</td>\n",
" <td> &lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 30</td>\n",
" <td> State-gov</td>\n",
" <td> 141297</td>\n",
" <td> Bachelors</td>\n",
" <td> 13</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Prof-specialty</td>\n",
" <td> Husband</td>\n",
" <td> Asian-Pac-Islander</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> India</td>\n",
" <td> &gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 40</td>\n",
" <td> Private</td>\n",
" <td> 121772</td>\n",
" <td> Assoc-voc</td>\n",
" <td> 11</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Craft-repair</td>\n",
" <td> Husband</td>\n",
" <td> Asian-Pac-Islander</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 40</td>\n",
" <td> ?</td>\n",
" <td> &gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> 54</td>\n",
" <td> ?</td>\n",
" <td> 180211</td>\n",
" <td> Some-college</td>\n",
" <td> 10</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> ?</td>\n",
" <td> Husband</td>\n",
" <td> Asian-Pac-Islander</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 60</td>\n",
" <td> South</td>\n",
" <td> &gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> 31</td>\n",
" <td> Private</td>\n",
" <td> 84154</td>\n",
" <td> Some-college</td>\n",
" <td> 10</td>\n",
" <td> Married-civ-spouse</td>\n",
" <td> Sales</td>\n",
" <td> Husband</td>\n",
" <td> White</td>\n",
" <td> Male</td>\n",
" <td> 0</td>\n",
" <td> 0</td>\n",
" <td> 38</td>\n",
" <td> ?</td>\n",
" <td> &gt;50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt education education_num marital_status \\\n",
"4 28 Private 338409 Bachelors 13 Married-civ-spouse \n",
"11 30 State-gov 141297 Bachelors 13 Married-civ-spouse \n",
"14 40 Private 121772 Assoc-voc 11 Married-civ-spouse \n",
"27 54 ? 180211 Some-college 10 Married-civ-spouse \n",
"38 31 Private 84154 Some-college 10 Married-civ-spouse \n",
"\n",
" occupation relationship ethnicity gender capital_gain \\\n",
"4 Prof-specialty Wife Black Female 0 \n",
"11 Prof-specialty Husband Asian-Pac-Islander Male 0 \n",
"14 Craft-repair Husband Asian-Pac-Islander Male 0 \n",
"27 ? Husband Asian-Pac-Islander Male 0 \n",
"38 Sales Husband White Male 0 \n",
"\n",
" capital_loss hours_per_week native_country income \n",
"4 0 40 Cuba <=50K \n",
"11 0 40 India >50K \n",
"14 0 40 ? >50K \n",
"27 0 60 South >50K \n",
"38 0 38 ? >50K "
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df.native_country != 'United-States') & (df.education_num > 9)].head()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<=50K 1137\n",
">50K 536\n",
"dtype: int64"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df.native_country != 'United-States') & (df.education_num > 9)].income.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Groupby"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age</th>\n",
" <th>fnlwgt</th>\n",
" <th>education_num</th>\n",
" <th>capital_gain</th>\n",
" <th>capital_loss</th>\n",
" <th>hours_per_week</th>\n",
" </tr>\n",
" <tr>\n",
" <th>relationship</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Husband</th>\n",
" <td> 43.82</td>\n",
" <td> 187074.86</td>\n",
" <td> 10.33</td>\n",
" <td> 1795.06</td>\n",
" <td> 124.16</td>\n",
" <td> 44.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Not-in-family</th>\n",
" <td> 38.35</td>\n",
" <td> 191131.80</td>\n",
" <td> 10.32</td>\n",
" <td> 743.33</td>\n",
" <td> 75.39</td>\n",
" <td> 40.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Other-relative</th>\n",
" <td> 33.16</td>\n",
" <td> 205059.82</td>\n",
" <td> 8.79</td>\n",
" <td> 279.60</td>\n",
" <td> 51.33</td>\n",
" <td> 37.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Own-child</th>\n",
" <td> 24.83</td>\n",
" <td> 193175.41</td>\n",
" <td> 9.49</td>\n",
" <td> 155.66</td>\n",
" <td> 39.51</td>\n",
" <td> 33.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Unmarried</th>\n",
" <td> 40.29</td>\n",
" <td> 191128.41</td>\n",
" <td> 9.64</td>\n",
" <td> 455.03</td>\n",
" <td> 41.46</td>\n",
" <td> 39.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wife</th>\n",
" <td> 39.85</td>\n",
" <td> 181849.51</td>\n",
" <td> 10.46</td>\n",
" <td> 1659.79</td>\n",
" <td> 118.01</td>\n",
" <td> 36.86</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age fnlwgt education_num capital_gain capital_loss \\\n",
"relationship \n",
"Husband 43.82 187074.86 10.33 1795.06 124.16 \n",
"Not-in-family 38.35 191131.80 10.32 743.33 75.39 \n",
"Other-relative 33.16 205059.82 8.79 279.60 51.33 \n",
"Own-child 24.83 193175.41 9.49 155.66 39.51 \n",
"Unmarried 40.29 191128.41 9.64 455.03 41.46 \n",
"Wife 39.85 181849.51 10.46 1659.79 118.01 \n",
"\n",
" hours_per_week \n",
"relationship \n",
"Husband 44.12 \n",
"Not-in-family 40.60 \n",
"Other-relative 37.01 \n",
"Own-child 33.27 \n",
"Unmarried 39.10 \n",
"Wife 36.86 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# How to groupby column and apply a function like sum, count, or mean\n",
"df.groupby(['relationship']).mean()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>income</th>\n",
" <th>native_country</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"10\" valign=\"top\">&lt;=50K</th>\n",
" <th>?</th>\n",
" <td> 437</td>\n",
" <td> 36.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cambodia</th>\n",
" <td> 12</td>\n",
" <td> 35.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Canada</th>\n",
" <td> 82</td>\n",
" <td> 41.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>China</th>\n",
" <td> 55</td>\n",
" <td> 41.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Columbia</th>\n",
" <td> 57</td>\n",
" <td> 39.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cuba</th>\n",
" <td> 70</td>\n",
" <td> 47.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dominican-Republic</th>\n",
" <td> 68</td>\n",
" <td> 37.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ecuador</th>\n",
" <td> 24</td>\n",
" <td> 34.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>El-Salvador</th>\n",
" <td> 97</td>\n",
" <td> 32.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>England</th>\n",
" <td> 60</td>\n",
" <td> 38.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"10\" valign=\"top\">&gt;50K</th>\n",
" <th>Portugal</th>\n",
" <td> 4</td>\n",
" <td> 40.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Puerto-Rico</th>\n",
" <td> 12</td>\n",
" <td> 46.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Scotland</th>\n",
" <td> 3</td>\n",
" <td> 52.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>South</th>\n",
" <td> 16</td>\n",
" <td> 44.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Taiwan</th>\n",
" <td> 20</td>\n",
" <td> 40.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Thailand</th>\n",
" <td> 3</td>\n",
" <td> 32.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Trinadad&amp;Tobago</th>\n",
" <td> 2</td>\n",
" <td> 42.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>United-States</th>\n",
" <td> 7171</td>\n",
" <td> 44.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vietnam</th>\n",
" <td> 5</td>\n",
" <td> 35.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yugoslavia</th>\n",
" <td> 6</td>\n",
" <td> 40.17</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>82 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" count mean\n",
"income native_country \n",
"<=50K ? 437 36.85\n",
" Cambodia 12 35.67\n",
" Canada 82 41.01\n",
" China 55 41.80\n",
" Columbia 57 39.23\n",
" Cuba 70 47.29\n",
" Dominican-Republic 68 37.94\n",
" Ecuador 24 34.83\n",
" El-Salvador 97 32.65\n",
" England 60 38.97\n",
"... ... ...\n",
">50K Portugal 4 40.00\n",
" Puerto-Rico 12 46.50\n",
" Scotland 3 52.67\n",
" South 16 44.88\n",
" Taiwan 20 40.80\n",
" Thailand 3 32.67\n",
" Trinadad&Tobago 2 42.50\n",
" United-States 7171 44.30\n",
" Vietnam 5 35.40\n",
" Yugoslavia 6 40.17\n",
"\n",
"[82 rows x 2 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# To groupby multiple columns with multiple functions attached\n",
"df.groupby(['income', 'native_country']).age.agg(['count', 'mean'])\n",
"# grouped in order of which column is listed first"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"education\n",
"10th 38.42\n",
"11th 36.33\n",
"12th 40.24\n",
"1st-4th 40.27\n",
"5th-6th 39.69\n",
"7th-8th 40.41\n",
"9th 38.08\n",
"Assoc-acdm 38.74\n",
"Assoc-voc 41.30\n",
"Bachelors 41.84\n",
"Doctorate 45.29\n",
"HS-grad 40.32\n",
"Masters 41.24\n",
"Preschool 40.91\n",
"Prof-school 47.03\n",
"Some-college 38.74\n",
"Name: hours_per_week, dtype: float64"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# combine groupby with boolean\n",
"\n",
"df[df.native_country != 'United-States'].groupby(['education']).hours_per_week.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## plotting with pandas"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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8OcZTt0jaKumfJc0AZgHdJc93AbPz9n35aw0Bw5Iq2g1lZmaNVevK+EFgX0TslPQFYBXw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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39ce1b5250>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.age.hist(bins = 18);"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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S1A7/ooQkSVIBDHWSJEkF6Hj1qzSRzZs3s3r1qgn7HXDAQey1117TMCJJkspl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\nSZJUAEOdJElSAf4/ME5on8RyNH4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39cd5c5210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# split the histograms by another column (with relatively few unique values)\n",
"df.hours_per_week.hist(by = df.income, bins = 25, figsize = (10,5));"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f39cd685490>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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YJTinin6IWv2cXtId3ItyxgTPdUjvSmrrf4b0QX52K485nJP/6yXdVfdYdY97\nI2KXHEEVbihOwnmsXsYxR1Gpei8HDo2I/wdcKelrwLiM8ZRORFwv6RekUT5fY3UC6SF/TfjZpIEN\nv8scB/BcvZrZETG12HRlznjq1K+l0Qv8jfxNrgCnkSp6fhv4WTvqfA3b2j6SJg/0fJsX3FiDpPtJ\nE3HqZSkqVSNpHvDvdSsJvZZUG2bPXDGVmaRXka5ke0l9OF+JiGwlDCQ9AEwmJbP6daFzjvZpe72a\nZkh6ManWVw/wvxHxfznjqSlqf+1GGjK8A2n2/9tbdbxhe+WfM7kPJiJeUvu+RMWlRtUSP0BE/G9x\n9WYNJF1MKqD2StI6qzuTOuqyiYjtch6/H22vVzOYYhb0oaRy3GOAkyV9swQfSJ2kNv43AK8pNv+y\nlccctsl/I1KW4lI/lzSD9J+iNvfg53lDKq1XRsQekn4SEdOKGdqn5AyoiOEkUrv/uyW9j9R88HCu\nmIp6NdsAkyNinlavN5zTO0irZK0CKEbUzAWyJn/SCKTbSXN//rMdTcBlGhZWVaW4uo6I6cBFpOao\nHtJatJ8e+Kcqa3RRlA9JnUX9/u0zx3Qp8H1SyXBIwxevyBYNIOmTpPkrtYJzZ0r6XMaQanobvs9+\n5x0Re0TEiRHxY9JSly3n5J/febkDACiS2e7F1x7AHsUUc1vbBaRJS18D7pH0R1L9/JxGFiVLVgFE\nRG2N2pzeQTqfuorHx5MmxOV0DXC3pAslXUSaa/PtzDE1ast6wm72yaBx4Q1JbyT/whtXkm47v0jq\nwNyTtBj5IRljKqvfRcRdAJJmAeNJHYg5rZS0L6lGzD+RkmzuYZ8jivpatcebsvZAh7aKiPMk/YBU\nOrmXdIebrWmsH9GOgzj551HGhTfGR8Q5dY/vkHRrtmhKSNJLSQvxnC7p86y5Etv5wAszhvchUs2c\nfyQVmruTNM4+p+9ImgO8RNI3SP1I5+YMSNJrSEu5TiD9/Q5SWig968xjSSdGxH8CRMRH23FMJ/88\nyrjwxghJO9dd0e5KSfojSmRT0mLt/0S6I+oAtiWNavlCxriIiMWkVeKQ9LrM5aUBiIivS/ohsAtp\nqOdpudc3Jl1wnUcaUw/lWN8DYAtJb2Z19WGgtXN/nPzzKM3CG3WOAc6TVFts+9dkTmglNIl09bof\ncB9wC2li1bbAdRnjavRl8tbMv7yfp8pwlf3HiLgk4/H7cwCpj6ReL2mBl5Zw8s+jTAtvABAR99CQ\nMCTdC7w8l7K+AAAF10lEQVSi75+opNNJ1WF/J+kQUlu/gOeTRtpkrxFTyH3HNqP4dxqpA/onpIuc\nvUmlC9qumFEP6SLrLOCnRWyQf30PIuKl7T6mk38GZVp4w9bJ0xFRK5/wNuBbxQS9Lkm5ktqUiJgv\naWpE1FZ+ytrWXyueJum4iNiv7qnvFM1AOdSXdRjP2qOOsib/HKvDOflnUMaFN6wpm0gaSRoN9Tbg\nS/BcHZtcw2L/qxg7f6qkEyjasFWso5v5inYLSdOAn5HG0u8MbJ0jkIg4EkDStIi4of45SYfliKlB\n21eHc/LPozQLbzQUv2uUc/RKGX0LuJvU8fs/EXGfpE2Ay0izRHM4BTgI6KTvYbk5k//7SbOOTyd9\nKAWZ7kok7UzqeD62mHVcP1Lrs+Qf69/21eGc/POoLbyxsAR1fTyOv0l1o1cmRMSvim0rJN1G+gDI\nEdO3gW9L2i8iZueIoT8RcY+k9wNbFYu65PQY8HfSXVtn3fYe0tDP3Nq+OtywrepZZpK+BBwLLKvb\n7GYfW2eD3LllLV0u6b3AiUBHRGwv6Xzg7oj47wyxbFJ8UL+AtHpXvd7M5dRrC7YfT5rrs4I0T+OC\niFg24A9uAF/551HGhTds41R/51a7kss92qfmGNK6tP9TPP4saRZ525M/sLhI/PNYvZhLTUuHVDbp\nSuBa4KutTPj1ctf+qKrawhtmGyQiHirKl3eTFiU5vnj8YuCvGUMDWBURK+oeryDfhKq5wP2kyV1X\nkybEvSIiXpRzHY0655P6JBZI+oGkf5HU0lLYvvLP4yBguqTSLLxhG70rSH1JBxSPJ5E6Md+WKyDg\np5KuAl5QjESaRrrwabuIOBigmMQ4ldTxfJGkxcBtEZG1JHdE3E66K6otFPQZ4Bu0cBSZ2/zNhgFJ\nsyNiP0m3RcTexbaf5JhTIumrEXF88f0epMldfwPujIg72h1PI0mbktbLnUL6sJwYES/PHNMY0szx\naaQPp18B10dEy2aO+8o/gzIuvGEbvQ5Jz63mJWl/8jXr1laioljE5ZTaB1IuRe2sqaQiiiNJNXR+\nClwSEUtyxlb4Hemu6HrguIbmspZw8s/jUlJxqROKx7WFN7L+B7GN2ieAS4CdivHhs4EP5w2pVM4B\nxgJXkX43d5Zhnk2d7YB/Js3/WVEbndTKA7rDN48yLrxhGyFJ+xbNO78F3kJanOQPpGTyoqzBlUhE\nvJJUkXUhqWnlx5LmSjpT0gED/3RbTCetelab4XtWq1c985V/HmVceMM2TqcDRxTfH0zqIMxdbO71\nDfMPVPc429yDiHiC9Dv5vqR/BvYHPgJ8ijTTN6faqmdzisfHAXdQlBBpBSf/PMq48IZtnEpXbA7Y\nIdNx+yXpxaQ2/6mkJLuMtFj6qaSx/7m1fdUzJ/8Myrjwhm20SldsrphnUDbfJyX7WcCnI6JrkP3b\nre2rnjn555d14Q3b6JWx2FzpRMSrc8fQF0m1ukLLSDOfJ5FW8voTqRZRy7iTMb+yTMW3jVBEfJ3U\n1v/eiPhAsW0F6Sq3pR2GNiQay0z8mfRBcCBwVksP7Ele7dPXwhuSJpf0NtnM2kzSoaQh4N8HvhwR\nLbv6d/JvI0n3ka7GTiX9gWs1xTvIv/CGmWUiaW/gNOAXwCkR0dJyzuA2/3Yr88IbZtZmxfKNZwJL\ngfdHxP3tOrav/DMo48IbZtZ+xWzs35AmnzXqjYijW3VsX/m3Uf3El2Id33pZF94wsyxq9Zj6Wouh\npVfmTv7tVeaFN8yszXIO9vBQzzYq+cIbZlYhTv55XEFK9rVmntrCG2ZmbeHkn8f4iLiQtKwdEfFd\nUrlZM7O2cPLPo0wLb5hZBbnDNw8vvGFmWflqs4288IaZlYWv/NurjAtvmFkF+cq/vfpceKOoLZ5r\n4Q0zqyBf+bdX6RbeMLNqcvJvLy+8YWal4MJubSZpMjAhIn5Vt+1DwGXF2qtmZi3n5G9mVkHu8DUz\nqyAnfzOzCnLyNzOrICd/M7MKcvI3M6ug/w+FiuEfr7EODAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39cd48e210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# use value_counts() and a bar plot\n",
"df['workclass'].value_counts().plot(kind = 'bar')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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v5INbXYSmKNq6V625Zh9gR+BjZvYsYA5wObCQkNUvjO8rGhhYPcViTs7w8Cjd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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39cd2a5610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.boxplot(['age'], by = 'relationship');"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f39cd00d610>"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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a0ycZ3fr1Zl+lN1YT5XHUHGCzNHHaSQllJJPr00hb3XG4YuVVVQnngbu5hZKy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NqO6LJywxXnU5ifg16vZ/m8Kezb9jdUnnYe//HtJCUl4fH6UGtjsDYeQvB0s3jeHApz0zAaU1g2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\n9NLYeufocQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39cd337b10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot(kind='scatter',\n",
" x = 'age',\n",
" y ='hours_per_week',\n",
" alpha = .25,\n",
" figsize = (10,5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Practice Exercises"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Male 21790\n",
"Female 10771\n",
"dtype: int64"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# how many males and females are in this data set\n",
"df['gender'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set_context(\"talk\")"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f39ccce2210>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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PlcTc7rV8D8AY4KSIGAm0kspC7u1qPGZmZmbWOSfQ87sXaImI3SVdVzwREdsDg8rccyaw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\nASfQZmZmZmYVcAJtZmZmZlaB/w9NPOC8DdbElQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39ccd00490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot the total number of people in each occupation\n",
"#df['occupation'].value_counts().plot(kind='bar')\n",
"plt.figure(figsize=(10, 5))\n",
"sns.countplot(y=\"occupation\", data = df.sort(\"occupation\"))"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>age</th>\n",
" </tr>\n",
" <tr>\n",
" <th>occupation</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Armed-Forces</th>\n",
" <td> 30.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Handlers-cleaners</th>\n",
" <td> 32.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Other-service</th>\n",
" <td> 34.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adm-clerical</th>\n",
" <td> 36.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tech-support</th>\n",
" <td> 37.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sales</th>\n",
" <td> 37.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Machine-op-inspct</th>\n",
" <td> 37.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Protective-serv</th>\n",
" <td> 38.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Craft-repair</th>\n",
" <td> 39.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Transport-moving</th>\n",
" <td> 40.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Prof-specialty</th>\n",
" <td> 40.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>?</th>\n",
" <td> 40.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Farming-fishing</th>\n",
" <td> 41.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Priv-house-serv</th>\n",
" <td> 41.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exec-managerial</th>\n",
" <td> 42.17</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age\n",
"occupation \n",
"Armed-Forces 30.22\n",
"Handlers-cleaners 32.17\n",
"Other-service 34.95\n",
"Adm-clerical 36.96\n",
"Tech-support 37.02\n",
"Sales 37.35\n",
"Machine-op-inspct 37.72\n",
"Protective-serv 38.95\n",
"Craft-repair 39.03\n",
"Transport-moving 40.20\n",
"Prof-specialty 40.52\n",
"? 40.88\n",
"Farming-fishing 41.21\n",
"Priv-house-serv 41.72\n",
"Exec-managerial 42.17"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# what is the lowest average age of an occupation\n",
"avg_age = df.pivot_table(values='age', columns = 'occupation', aggfunc = np.mean)\n",
"pd.DataFrame(avg_age).sort('age')"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>occupation</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Exec-managerial</th>\n",
" <td> 42.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Priv-house-serv</th>\n",
" <td> 41.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Farming-fishing</th>\n",
" <td> 41.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>?</th>\n",
" <td> 40.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Prof-specialty</th>\n",
" <td> 40.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Transport-moving</th>\n",
" <td> 40.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Craft-repair</th>\n",
" <td> 39.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Protective-serv</th>\n",
" <td> 38.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Machine-op-inspct</th>\n",
" <td> 37.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sales</th>\n",
" <td> 37.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tech-support</th>\n",
" <td> 37.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Adm-clerical</th>\n",
" <td> 36.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Other-service</th>\n",
" <td> 34.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Handlers-cleaners</th>\n",
" <td> 32.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Armed-Forces</th>\n",
" <td> 30.22</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean\n",
"occupation \n",
"Exec-managerial 42.17\n",
"Priv-house-serv 41.72\n",
"Farming-fishing 41.21\n",
"? 40.88\n",
"Prof-specialty 40.52\n",
"Transport-moving 40.20\n",
"Craft-repair 39.03\n",
"Protective-serv 38.95\n",
"Machine-op-inspct 37.72\n",
"Sales 37.35\n",
"Tech-support 37.02\n",
"Adm-clerical 36.96\n",
"Other-service 34.95\n",
"Handlers-cleaners 32.17\n",
"Armed-Forces 30.22"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(['occupation']).age.agg(['mean']).sort('mean', ascending=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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9pVK1zqxVQ2dtlJ298rzoQlVsK2q1QqGAI0fui2xLpz1MT5txT8PDx+zceTIy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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39cc9a3a50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create a boxplot of hours per week by education level\n",
"\n",
"#sns.boxplot(x=\"education_num\", y=\"hours_per_week\", data=df.sort(\"education_num\"))\n",
"df.boxplot(['hours_per_week'], by = 'education_num');"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"12285"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a new column for income where >50K = 1 and <=50K = 0\n",
"# hint... http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.replace.html\n",
"\n",
"df['income1'] = df.apply(lambda row:(1 if (row['income']=='<=50K') else None), axis=1)\n",
"df['income2'] = df.apply(lambda row:(1 if (row['income']=='>50K') else None), axis=1) \n",
" \n",
"df['fnlwgt'].min()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/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></th>\n",
" <th>income2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>income</th>\n",
" <th>native_country</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>&gt;50K</th>\n",
" <th>United-States</th>\n",
" <td> 91.46</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" income2\n",
"income native_country \n",
">50K United-States 91.46"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# find which \"native_country\" has the highest percent of people earning >50K\n",
"#df[df.income == '>50K'].groupby('native_country').income2.agg(['sum']).sort('sum', ascending=False)\n",
"income = df.groupby(['income', 'native_country']).agg({'income2': 'sum'})\n",
"income_pct = income.groupby(level=0).apply(lambda x: 100*x/float(x.sum()))\n",
"\n",
"income_pct.sort('income2',ascending=False).iloc[0:1]"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f39cd62d390>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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X98xuEvbDK0JpQ7YEfBO6VwHantuk0PigvSDyhXNRdKX7mdd5SGL11GM7aSaU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\nkSeiNf360rrPPdW41VVr50VrIt8ru08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dXEDPOh3RyRZGLUgDAJb9qBGFNhWih2PKbR2I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ImSm/VUTy5M9LXC4EnRv/MktaPWQ767+E1rckCxN1XfKAqXhAywm9dsPPazhHR1xJsvIeSRg/79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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39cd0f3f50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"income3 = df.groupby(['income', 'education_num']).agg({'income1': 'sum'})\n",
"#income3_pct = income2.groupby(level=0).apply(lambda x: 100*x/float(x.sum()))\n",
"income3 = income3.reset_index()\n",
"income3 = income3[income3.income == '<=50K'].sort('education_num')\n",
"income3[['education_num', 'income1']].plot(kind='pie', y='education_num', autopct='%.2f', colors = sns.hls_palette(16, l=.4, s=.9))\n",
"plt.legend(bbox_to_anchor=(1, 1), loc=2, borderaxespad=0.)\n",
"plt.ylabel('Percent of people earn >50K')\n",
"plt.xlabel('Level of Education')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Extra practice"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# make a hexbin plot"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(8, 14)"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39cd15c910>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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mfICS07JZXk+Iq8EDeiI7SWzyVZ20LB+s0bSneS6uk15H6ZTKNvV0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+7U+ZG9s7brPtjlL6JuEn6Xukk8Vutpu7rZYu3sNSVH/k3JPlqbFsWRSKlnHFEaWmRpL9zjY8cU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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f39cd1ceb90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15, 5))\n",
"from scipy.stats import kendalltau\n",
"sns.jointplot(df['fnlwgt'], df['education_num'], kind='hex',color=\"#4CB391\")\n",
"plt.ylim(8, 14)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# check out plotting with Seaborn"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df1 = pd.read_csv('/home/jenni/Documents/sat2.csv')\n",
"df1['jenni_is_cool'] = 1\n",
"df1.to_csv('/home/jenni/Documents/jenni_is_cool.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://nbviewer.ipython.org/gist/mwaskom/8224591"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"http://stanford.edu/~mwaskom/software/seaborn/tutorial/plotting_distributions.html"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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