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Tutorial in iPython
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# An Tutorial for Data Science in Python\n",
"\n",
"Python is an amazing language and here we show a comprehensive tutorial in it for usage in Data Science.\n",
"\n",
"\n",
"### Markdown Tip within Jupyter\n",
"I can also write this text within Jupyter by changing Cell type to Markdown in dropdown. That's what I just did.\n",
"For markdown changing size of font is easy by prefixing by #, or ## , or ### (more the number of # smaller the size of font) while for a non numbered list prefix by a -\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation \n",
"Installation is done using pip or easy_install(from setup tools) . Here we show how to install Pandas package from the Jupyter Notebook itself. I use the --upgrade flag to upgrade it, and I install Bokeh using easy_tools. Pandas is the Python library for Data Analysis and Bokeh helps make interactive data analysis available. Note the ! sign before the sudo command- it helps me use the Terminal without leaving the comfort of my Jupyter Notebook\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mThe directory '/home/ajay/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\u001b[0m\n",
"\u001b[33mYou are using pip version 7.1.0, however version 7.1.2 is available.\n",
"You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
"\u001b[33mThe directory '/home/ajay/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\u001b[0m\n",
"Collecting pandas\n",
" Downloading pandas-0.17.1.tar.gz (6.7MB)\n",
"\u001b[K 100% |████████████████████████████████| 6.7MB 40kB/s \n",
"\u001b[?25hCollecting python-dateutil (from pandas)\n",
" Downloading python_dateutil-2.4.2-py2.py3-none-any.whl (188kB)\n",
"\u001b[K 100% |████████████████████████████████| 192kB 1.3MB/s \n",
"\u001b[?25hCollecting pytz>=2011k (from pandas)\n",
" Downloading pytz-2015.7-py2.py3-none-any.whl (476kB)\n",
"\u001b[K 100% |████████████████████████████████| 479kB 92kB/s \n",
"\u001b[?25hCollecting numpy>=1.7.0 (from pandas)\n",
" Downloading numpy-1.10.1.tar.gz (4.0MB)\n",
"\u001b[K 100% |████████████████████████████████| 4.1MB 75kB/s \n",
"\u001b[?25hCollecting six>=1.5 (from python-dateutil->pandas)\n",
" Downloading six-1.10.0-py2.py3-none-any.whl\n",
"Installing collected packages: six, python-dateutil, pytz, numpy, pandas\n",
" Found existing installation: six 1.9.0\n",
" Uninstalling six-1.9.0:\n",
" Successfully uninstalled six-1.9.0\n",
" Found existing installation: python-dateutil 1.5\n",
" Uninstalling python-dateutil-1.5:\n",
" Successfully uninstalled python-dateutil-1.5\n",
" Found existing installation: pytz 2015.2\n",
" Uninstalling pytz-2015.2:\n",
" Successfully uninstalled pytz-2015.2\n",
" Found existing installation: numpy 1.9.2\n",
" Uninstalling numpy-1.9.2:\n",
" Successfully uninstalled numpy-1.9.2\n",
" Running setup.py install for numpy\n",
" Found existing installation: pandas 0.16.0\n",
" Uninstalling pandas-0.16.0:\n",
" Successfully uninstalled pandas-0.16.0\n",
" Running setup.py install for pandas\n",
"Successfully installed numpy-1.10.1 pandas-0.17.1 python-dateutil-2.4.2 pytz-2015.7 six-1.10.0\n"
]
}
],
"source": [
"! sudo pip install pandas --upgrade"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Searching for bokeh\n",
"Reading https://pypi.python.org/simple/bokeh/\n",
"Best match: bokeh 0.10.0\n",
"Downloading https://pypi.python.org/packages/source/b/bokeh/bokeh-0.10.0.zip#md5=1432ed7d3034ce0c16c9f3c6388ad10d\n",
"Processing bokeh-0.10.0.zip\n",
"Writing /tmp/easy_install-CSs4Vk/bokeh-0.10.0/setup.cfg\n",
"Running bokeh-0.10.0/setup.py -q bdist_egg --dist-dir /tmp/easy_install-CSs4Vk/bokeh-0.10.0/egg-dist-tmp-45CSzc\n",
"\n",
"\n",
"package init file 'bokeh/models/tests/__init__.py' not found (or not a regular file)\n",
"package init file 'bokeh/charts/builder/tests/__init__.py' not found (or not a regular file)\n",
"package init file 'bokeh/charts/tests/__init__.py' not found (or not a regular file)\n",
"package init file 'bokeh/_legacy_charts/tests/__init__.py' not found (or not a regular file)\n",
"package init file 'bokeh/server/tests/__init__.py' not found (or not a regular file)\n",
"package init file 'bokeh/tests/__init__.py' not found (or not a regular file)\n",
"package init file 'bokeh/util/tests/__init__.py' not found (or not a regular file)\n",
"creating /usr/local/lib/python2.7/dist-packages/bokeh-0.10.0-py2.7.egg\n",
"Extracting bokeh-0.10.0-py2.7.egg to /usr/local/lib/python2.7/dist-packages\n",
"Adding bokeh 0.10.0 to easy-install.pth file\n",
"Installing bokeh-server script to /usr/local/bin\n",
"Installing websocket_worker.py script to /usr/local/bin\n",
"\n",
"Installed /usr/local/lib/python2.7/dist-packages/bokeh-0.10.0-py2.7.egg\n",
"Processing dependencies for bokeh\n",
"Finished processing dependencies for bokeh\n"
]
}
],
"source": [
"! sudo easy_install bokeh"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Loading a Python Package\n",
"You can load a Python Package using the following ways\n",
" - import PACKAGE\n",
" - import PACKAGE as PK\n",
" - from PACKAGE import FUN\n",
" \n",
" You can then invoke the function using\n",
" \n",
" PACKAGE.FUN , PK.FUN and FUN respectively\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2015, 12, 2, 22, 30, 1, 850119)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datetime import datetime\n",
"Starttime =datetime.now()\n",
"Starttime"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Data\n",
"Let's import some datasets. We will use Datasets bundled with R language from https://vincentarelbundock.github.io/Rdatasets/datasets.html"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"diamonds =pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/diamonds.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Inspection"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Unnamed: 0', 'carat', 'cut', 'color', 'clarity', 'depth', 'table',\n",
" 'price', 'x', 'y', 'z'],\n",
" dtype='object')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds.columns #Single Line Comment starts with # \n",
"# name of variables is given by columns. In R we would use the command names(object)\n",
"# Note also R uses the FUNCTION(OBJECTNAME) syntax while Python uses OBJECTNAME.FUNCTION"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"53940"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(diamonds) #gives the number of rows"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5.394"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0.0001*len(diamonds)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"round(0.0001*len(diamonds))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 53940 entries, 0 to 53939\n",
"Data columns (total 11 columns):\n",
"Unnamed: 0 53940 non-null int64\n",
"carat 53940 non-null float64\n",
"cut 53940 non-null object\n",
"color 53940 non-null object\n",
"clarity 53940 non-null object\n",
"depth 53940 non-null float64\n",
"table 53940 non-null float64\n",
"price 53940 non-null int64\n",
"x 53940 non-null float64\n",
"y 53940 non-null float64\n",
"z 53940 non-null float64\n",
"dtypes: float64(6), int64(2), object(3)\n",
"memory usage: 4.3+ MB\n"
]
}
],
"source": [
"'''Lets get some information on the object.\n",
"In R we would get this by str command (for structure). \n",
"In Python str turns the object to string\n",
"This was a multiple line comment using three single quote marks\n",
"'''\n",
"diamonds.info() \n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>carat</th>\n",
" <th>cut</th>\n",
" <th>color</th>\n",
" <th>clarity</th>\n",
" <th>depth</th>\n",
" <th>table</th>\n",
" <th>price</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>z</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0.23</td>\n",
" <td>Ideal</td>\n",
" <td>E</td>\n",
" <td>SI2</td>\n",
" <td>61.5</td>\n",
" <td>55</td>\n",
" <td>326</td>\n",
" <td>3.95</td>\n",
" <td>3.98</td>\n",
" <td>2.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>0.21</td>\n",
" <td>Premium</td>\n",
" <td>E</td>\n",
" <td>SI1</td>\n",
" <td>59.8</td>\n",
" <td>61</td>\n",
" <td>326</td>\n",
" <td>3.89</td>\n",
" <td>3.84</td>\n",
" <td>2.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>0.23</td>\n",
" <td>Good</td>\n",
" <td>E</td>\n",
" <td>VS1</td>\n",
" <td>56.9</td>\n",
" <td>65</td>\n",
" <td>327</td>\n",
" <td>4.05</td>\n",
" <td>4.07</td>\n",
" <td>2.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>0.29</td>\n",
" <td>Premium</td>\n",
" <td>I</td>\n",
" <td>VS2</td>\n",
" <td>62.4</td>\n",
" <td>58</td>\n",
" <td>334</td>\n",
" <td>4.20</td>\n",
" <td>4.23</td>\n",
" <td>2.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0.31</td>\n",
" <td>Good</td>\n",
" <td>J</td>\n",
" <td>SI2</td>\n",
" <td>63.3</td>\n",
" <td>58</td>\n",
" <td>335</td>\n",
" <td>4.34</td>\n",
" <td>4.35</td>\n",
" <td>2.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>0.24</td>\n",
" <td>Very Good</td>\n",
" <td>J</td>\n",
" <td>VVS2</td>\n",
" <td>62.8</td>\n",
" <td>57</td>\n",
" <td>336</td>\n",
" <td>3.94</td>\n",
" <td>3.96</td>\n",
" <td>2.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>0.24</td>\n",
" <td>Very Good</td>\n",
" <td>I</td>\n",
" <td>VVS1</td>\n",
" <td>62.3</td>\n",
" <td>57</td>\n",
" <td>336</td>\n",
" <td>3.95</td>\n",
" <td>3.98</td>\n",
" <td>2.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>0.26</td>\n",
" <td>Very Good</td>\n",
" <td>H</td>\n",
" <td>SI1</td>\n",
" <td>61.9</td>\n",
" <td>55</td>\n",
" <td>337</td>\n",
" <td>4.07</td>\n",
" <td>4.11</td>\n",
" <td>2.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>0.22</td>\n",
" <td>Fair</td>\n",
" <td>E</td>\n",
" <td>VS2</td>\n",
" <td>65.1</td>\n",
" <td>61</td>\n",
" <td>337</td>\n",
" <td>3.87</td>\n",
" <td>3.78</td>\n",
" <td>2.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>0.23</td>\n",
" <td>Very Good</td>\n",
" <td>H</td>\n",
" <td>VS1</td>\n",
" <td>59.4</td>\n",
" <td>61</td>\n",
" <td>338</td>\n",
" <td>4.00</td>\n",
" <td>4.05</td>\n",
" <td>2.39</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 carat cut color clarity depth table price x \\\n",
"0 1 0.23 Ideal E SI2 61.5 55 326 3.95 \n",
"1 2 0.21 Premium E SI1 59.8 61 326 3.89 \n",
"2 3 0.23 Good E VS1 56.9 65 327 4.05 \n",
"3 4 0.29 Premium I VS2 62.4 58 334 4.20 \n",
"4 5 0.31 Good J SI2 63.3 58 335 4.34 \n",
"5 6 0.24 Very Good J VVS2 62.8 57 336 3.94 \n",
"6 7 0.24 Very Good I VVS1 62.3 57 336 3.95 \n",
"7 8 0.26 Very Good H SI1 61.9 55 337 4.07 \n",
"8 9 0.22 Fair E VS2 65.1 61 337 3.87 \n",
"9 10 0.23 Very Good H VS1 59.4 61 338 4.00 \n",
"\n",
" y z \n",
"0 3.98 2.43 \n",
"1 3.84 2.31 \n",
"2 4.07 2.31 \n",
"3 4.23 2.63 \n",
"4 4.35 2.75 \n",
"5 3.96 2.48 \n",
"6 3.98 2.47 \n",
"7 4.11 2.53 \n",
"8 3.78 2.49 \n",
"9 4.05 2.39 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds.head(10) #we check the first 10 rows in the dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- to refer to particular row in Python I can use index. \n",
"- In R I refer to the object in i th row and jth column by OBJECTNAME[i,j]\n",
"- In R I refer to the column name by OBJECTNAME$ColumnName\n",
"- Note in Python Index starts with 0 while in R it starts with 1.\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>carat</th>\n",
" <th>cut</th>\n",
" <th>color</th>\n",
" <th>clarity</th>\n",
" <th>depth</th>\n",
" <th>table</th>\n",
" <th>price</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
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" <tbody>\n",
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" <th>20</th>\n",
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" <td>SI2</td>\n",
" <td>63.3</td>\n",
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" <td>351</td>\n",
" <td>4.26</td>\n",
" <td>4.30</td>\n",
" <td>2.71</td>\n",
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" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>0.23</td>\n",
" <td>Very Good</td>\n",
" <td>E</td>\n",
" <td>VS2</td>\n",
" <td>63.8</td>\n",
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" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
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" <td>61.0</td>\n",
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" <td>3.96</td>\n",
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" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>0.31</td>\n",
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" <td>59.4</td>\n",
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" <td>353</td>\n",
" <td>4.39</td>\n",
" <td>4.43</td>\n",
" <td>2.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>0.31</td>\n",
" <td>Very Good</td>\n",
" <td>J</td>\n",
" <td>SI1</td>\n",
" <td>58.1</td>\n",
" <td>62</td>\n",
" <td>353</td>\n",
" <td>4.44</td>\n",
" <td>4.47</td>\n",
" <td>2.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>0.23</td>\n",
" <td>Very Good</td>\n",
" <td>G</td>\n",
" <td>VVS2</td>\n",
" <td>60.4</td>\n",
" <td>58</td>\n",
" <td>354</td>\n",
" <td>3.97</td>\n",
" <td>4.01</td>\n",
" <td>2.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>0.24</td>\n",
" <td>Premium</td>\n",
" <td>I</td>\n",
" <td>VS1</td>\n",
" <td>62.5</td>\n",
" <td>57</td>\n",
" <td>355</td>\n",
" <td>3.97</td>\n",
" <td>3.94</td>\n",
" <td>2.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>0.30</td>\n",
" <td>Very Good</td>\n",
" <td>J</td>\n",
" <td>VS2</td>\n",
" <td>62.2</td>\n",
" <td>57</td>\n",
" <td>357</td>\n",
" <td>4.28</td>\n",
" <td>4.30</td>\n",
" <td>2.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>0.23</td>\n",
" <td>Very Good</td>\n",
" <td>D</td>\n",
" <td>VS2</td>\n",
" <td>60.5</td>\n",
" <td>61</td>\n",
" <td>357</td>\n",
" <td>3.96</td>\n",
" <td>3.97</td>\n",
" <td>2.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>0.23</td>\n",
" <td>Very Good</td>\n",
" <td>F</td>\n",
" <td>VS1</td>\n",
" <td>60.9</td>\n",
" <td>57</td>\n",
" <td>357</td>\n",
" <td>3.96</td>\n",
" <td>3.99</td>\n",
" <td>2.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>31</td>\n",
" <td>0.23</td>\n",
" <td>Very Good</td>\n",
" <td>F</td>\n",
" <td>VS1</td>\n",
" <td>60.0</td>\n",
" <td>57</td>\n",
" <td>402</td>\n",
" <td>4.00</td>\n",
" <td>4.03</td>\n",
" <td>2.41</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 carat cut color clarity depth table price x \\\n",
"20 21 0.30 Good I SI2 63.3 56 351 4.26 \n",
"21 22 0.23 Very Good E VS2 63.8 55 352 3.85 \n",
"22 23 0.23 Very Good H VS1 61.0 57 353 3.94 \n",
"23 24 0.31 Very Good J SI1 59.4 62 353 4.39 \n",
"24 25 0.31 Very Good J SI1 58.1 62 353 4.44 \n",
"25 26 0.23 Very Good G VVS2 60.4 58 354 3.97 \n",
"26 27 0.24 Premium I VS1 62.5 57 355 3.97 \n",
"27 28 0.30 Very Good J VS2 62.2 57 357 4.28 \n",
"28 29 0.23 Very Good D VS2 60.5 61 357 3.96 \n",
"29 30 0.23 Very Good F VS1 60.9 57 357 3.96 \n",
"30 31 0.23 Very Good F VS1 60.0 57 402 4.00 \n",
"\n",
" y z \n",
"20 4.30 2.71 \n",
"21 3.92 2.48 \n",
"22 3.96 2.41 \n",
"23 4.43 2.62 \n",
"24 4.47 2.59 \n",
"25 4.01 2.41 \n",
"26 3.94 2.47 \n",
"27 4.30 2.67 \n",
"28 3.97 2.40 \n",
"29 3.99 2.42 \n",
"30 4.03 2.41 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds.ix[20:30]"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"20 Good\n",
"21 Very Good\n",
"22 Very Good\n",
"23 Very Good\n",
"24 Very Good\n",
"25 Very Good\n",
"Name: cut, dtype: object"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#To refer to a particular column I use it's name\n",
"# I can also chain the commands\n",
"diamonds.ix[20:25].cut"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"20 I\n",
"21 E\n",
"22 H\n",
"23 J\n",
"24 J\n",
"25 G\n",
"Name: color, dtype: object"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds.ix[20:25][\"color\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Random Sample"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[42122 21399 40554 36399 50336]\n"
]
}
],
"source": [
"rows = np.random.choice(diamonds.index.values, round(0.0001*len(diamonds)))\n",
"print(rows)\n"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>carat</th>\n",
" <th>cut</th>\n",
" <th>color</th>\n",
" <th>clarity</th>\n",
" <th>depth</th>\n",
" <th>table</th>\n",
" <th>price</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>z</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>42122</th>\n",
" <td>42123</td>\n",
" <td>0.58</td>\n",
" <td>Ideal</td>\n",
" <td>I</td>\n",
" <td>VS2</td>\n",
" <td>62.0</td>\n",
" <td>54</td>\n",
" <td>1279</td>\n",
" <td>5.35</td>\n",
" <td>5.39</td>\n",
" <td>3.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21399</th>\n",
" <td>21400</td>\n",
" <td>1.51</td>\n",
" <td>Ideal</td>\n",
" <td>E</td>\n",
" <td>SI2</td>\n",
" <td>62.9</td>\n",
" <td>57</td>\n",
" <td>9513</td>\n",
" <td>7.29</td>\n",
" <td>7.23</td>\n",
" <td>4.57</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40554</th>\n",
" <td>40555</td>\n",
" <td>0.41</td>\n",
" <td>Ideal</td>\n",
" <td>G</td>\n",
" <td>VVS1</td>\n",
" <td>61.7</td>\n",
" <td>55</td>\n",
" <td>1151</td>\n",
" <td>4.77</td>\n",
" <td>4.79</td>\n",
" <td>2.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36399</th>\n",
" <td>36400</td>\n",
" <td>0.31</td>\n",
" <td>Ideal</td>\n",
" <td>E</td>\n",
" <td>VS1</td>\n",
" <td>61.8</td>\n",
" <td>56</td>\n",
" <td>942</td>\n",
" <td>4.37</td>\n",
" <td>4.34</td>\n",
" <td>2.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50336</th>\n",
" <td>50337</td>\n",
" <td>0.70</td>\n",
" <td>Good</td>\n",
" <td>D</td>\n",
" <td>SI2</td>\n",
" <td>58.3</td>\n",
" <td>60</td>\n",
" <td>2242</td>\n",
" <td>5.81</td>\n",
" <td>5.89</td>\n",
" <td>3.41</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 carat cut color clarity depth table price x \\\n",
"42122 42123 0.58 Ideal I VS2 62.0 54 1279 5.35 \n",
"21399 21400 1.51 Ideal E SI2 62.9 57 9513 7.29 \n",
"40554 40555 0.41 Ideal G VVS1 61.7 55 1151 4.77 \n",
"36399 36400 0.31 Ideal E VS1 61.8 56 942 4.37 \n",
"50336 50337 0.70 Good D SI2 58.3 60 2242 5.81 \n",
"\n",
" y z \n",
"42122 5.39 3.33 \n",
"21399 7.23 4.57 \n",
"40554 4.79 2.95 \n",
"36399 4.34 2.69 \n",
"50336 5.89 3.41 "
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds.ix[rows]"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"##Mising Values \n",
"\n",
"diamonds= diamonds.dropna(how='any') "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summaries\n",
"We now do summaries for numerical and categorical data."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>Unnamed: 0</th>\n",
" <th>carat</th>\n",
" <th>depth</th>\n",
" <th>table</th>\n",
" <th>price</th>\n",
" <th>x</th>\n",
" <th>y</th>\n",
" <th>z</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>53940.000000</td>\n",
" <td>53940.000000</td>\n",
" <td>53940.000000</td>\n",
" <td>53940.000000</td>\n",
" <td>53940.000000</td>\n",
" <td>53940.000000</td>\n",
" <td>53940.000000</td>\n",
" <td>53940.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>26970.500000</td>\n",
" <td>0.797940</td>\n",
" <td>61.749405</td>\n",
" <td>57.457184</td>\n",
" <td>3932.799722</td>\n",
" <td>5.731157</td>\n",
" <td>5.734526</td>\n",
" <td>3.538734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>15571.281097</td>\n",
" <td>0.474011</td>\n",
" <td>1.432621</td>\n",
" <td>2.234491</td>\n",
" <td>3989.439738</td>\n",
" <td>1.121761</td>\n",
" <td>1.142135</td>\n",
" <td>0.705699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.200000</td>\n",
" <td>43.000000</td>\n",
" <td>43.000000</td>\n",
" <td>326.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>13485.750000</td>\n",
" <td>0.400000</td>\n",
" <td>61.000000</td>\n",
" <td>56.000000</td>\n",
" <td>950.000000</td>\n",
" <td>4.710000</td>\n",
" <td>4.720000</td>\n",
" <td>2.910000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>26970.500000</td>\n",
" <td>0.700000</td>\n",
" <td>61.800000</td>\n",
" <td>57.000000</td>\n",
" <td>2401.000000</td>\n",
" <td>5.700000</td>\n",
" <td>5.710000</td>\n",
" <td>3.530000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>40455.250000</td>\n",
" <td>1.040000</td>\n",
" <td>62.500000</td>\n",
" <td>59.000000</td>\n",
" <td>5324.250000</td>\n",
" <td>6.540000</td>\n",
" <td>6.540000</td>\n",
" <td>4.040000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>53940.000000</td>\n",
" <td>5.010000</td>\n",
" <td>79.000000</td>\n",
" <td>95.000000</td>\n",
" <td>18823.000000</td>\n",
" <td>10.740000</td>\n",
" <td>58.900000</td>\n",
" <td>31.800000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 carat depth table price \\\n",
"count 53940.000000 53940.000000 53940.000000 53940.000000 53940.000000 \n",
"mean 26970.500000 0.797940 61.749405 57.457184 3932.799722 \n",
"std 15571.281097 0.474011 1.432621 2.234491 3989.439738 \n",
"min 1.000000 0.200000 43.000000 43.000000 326.000000 \n",
"25% 13485.750000 0.400000 61.000000 56.000000 950.000000 \n",
"50% 26970.500000 0.700000 61.800000 57.000000 2401.000000 \n",
"75% 40455.250000 1.040000 62.500000 59.000000 5324.250000 \n",
"max 53940.000000 5.010000 79.000000 95.000000 18823.000000 \n",
"\n",
" x y z \n",
"count 53940.000000 53940.000000 53940.000000 \n",
"mean 5.731157 5.734526 3.538734 \n",
"std 1.121761 1.142135 0.705699 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 4.710000 4.720000 2.910000 \n",
"50% 5.700000 5.710000 3.530000 \n",
"75% 6.540000 6.540000 4.040000 \n",
"max 10.740000 58.900000 31.800000 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds.describe()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 53940.000000\n",
"mean 3932.799722\n",
"std 3989.439738\n",
"min 326.000000\n",
"25% 950.000000\n",
"50% 2401.000000\n",
"75% 5324.250000\n",
"max 18823.000000\n",
"Name: price, dtype: float64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds.price.describe()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th>carat</th>\n",
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" <td>0.183760</td>\n",
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" <td>-0.405440</td>\n",
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" <td>0.974701</td>\n",
" <td>0.970772</td>\n",
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" <tr>\n",
" <th>y</th>\n",
" <td>-0.395843</td>\n",
" <td>0.951722</td>\n",
" <td>-0.029341</td>\n",
" <td>0.183760</td>\n",
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" <td>0.952006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>z</th>\n",
" <td>-0.399208</td>\n",
" <td>0.953387</td>\n",
" <td>0.094924</td>\n",
" <td>0.150929</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 carat depth table price x \\\n",
"Unnamed: 0 1.000000 -0.377983 -0.034800 -0.100830 -0.306873 -0.405440 \n",
"carat -0.377983 1.000000 0.028224 0.181618 0.921591 0.975094 \n",
"depth -0.034800 0.028224 1.000000 -0.295779 -0.010647 -0.025289 \n",
"table -0.100830 0.181618 -0.295779 1.000000 0.127134 0.195344 \n",
"price -0.306873 0.921591 -0.010647 0.127134 1.000000 0.884435 \n",
"x -0.405440 0.975094 -0.025289 0.195344 0.884435 1.000000 \n",
"y -0.395843 0.951722 -0.029341 0.183760 0.865421 0.974701 \n",
"z -0.399208 0.953387 0.094924 0.150929 0.861249 0.970772 \n",
"\n",
" y z \n",
"Unnamed: 0 -0.395843 -0.399208 \n",
"carat 0.951722 0.953387 \n",
"depth -0.029341 0.094924 \n",
"table 0.183760 0.150929 \n",
"price 0.865421 0.861249 \n",
"x 0.974701 0.970772 \n",
"y 1.000000 0.952006 \n",
"z 0.952006 1.000000 "
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds.corr() #Numerical Corelations"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" <th>carat</th>\n",
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" <tr>\n",
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" <td>False</td>\n",
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" <tr>\n",
" <th>depth</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>table</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>price</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>x</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>y</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>z</th>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 carat depth table price x y z\n",
"Unnamed: 0 True False False False False False False False\n",
"carat False True False False True True True True\n",
"depth False False True False False False False False\n",
"table False False False True False False False False\n",
"price False True False False True True True True\n",
"x False True False False True True True True\n",
"y False True False False True True True True\n",
"z False True False False True True True True"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds.corr()>0.5"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Ideal', 'Premium', 'Good', 'Very Good', 'Fair'], dtype=object)"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds['cut'].unique() #To get unique values\n"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['SI2', 'SI1', 'VS1', 'VS2', 'VVS2', 'VVS1', 'I1', 'IF'], dtype=object)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds['clarity'].unique()\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Ideal 21551\n",
"Premium 13791\n",
"Very Good 12082\n",
"Good 4906\n",
"Fair 1610\n",
"dtype: int64"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.value_counts(diamonds.cut)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"G 11292\n",
"E 9797\n",
"F 9542\n",
"H 8304\n",
"D 6775\n",
"I 5422\n",
"J 2808\n",
"dtype: int64"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.value_counts(diamonds.color)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>color</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" <th>F</th>\n",
" <th>G</th>\n",
" <th>H</th>\n",
" <th>I</th>\n",
" <th>J</th>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>Fair</th>\n",
" <td>163</td>\n",
" <td>224</td>\n",
" <td>312</td>\n",
" <td>314</td>\n",
" <td>303</td>\n",
" <td>175</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Good</th>\n",
" <td>662</td>\n",
" <td>933</td>\n",
" <td>909</td>\n",
" <td>871</td>\n",
" <td>702</td>\n",
" <td>522</td>\n",
" <td>307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ideal</th>\n",
" <td>2834</td>\n",
" <td>3903</td>\n",
" <td>3826</td>\n",
" <td>4884</td>\n",
" <td>3115</td>\n",
" <td>2093</td>\n",
" <td>896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Premium</th>\n",
" <td>1603</td>\n",
" <td>2337</td>\n",
" <td>2331</td>\n",
" <td>2924</td>\n",
" <td>2360</td>\n",
" <td>1428</td>\n",
" <td>808</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Very Good</th>\n",
" <td>1513</td>\n",
" <td>2400</td>\n",
" <td>2164</td>\n",
" <td>2299</td>\n",
" <td>1824</td>\n",
" <td>1204</td>\n",
" <td>678</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"color D E F G H I J\n",
"cut \n",
"Fair 163 224 312 314 303 175 119\n",
"Good 662 933 909 871 702 522 307\n",
"Ideal 2834 3903 3826 4884 3115 2093 896\n",
"Premium 1603 2337 2331 2924 2360 1428 808\n",
"Very Good 1513 2400 2164 2299 1824 1204 678"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(diamonds.cut,diamonds.color)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <tr>\n",
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" <td>4884</td>\n",
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" <td>2093</td>\n",
" <td>896</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>Premium</th>\n",
" <td>1603</td>\n",
" <td>2337</td>\n",
" <td>2331</td>\n",
" <td>2924</td>\n",
" <td>2360</td>\n",
" <td>1428</td>\n",
" <td>808</td>\n",
" <td>13791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Very Good</th>\n",
" <td>1513</td>\n",
" <td>2400</td>\n",
" <td>2164</td>\n",
" <td>2299</td>\n",
" <td>1824</td>\n",
" <td>1204</td>\n",
" <td>678</td>\n",
" <td>12082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>6775</td>\n",
" <td>9797</td>\n",
" <td>9542</td>\n",
" <td>11292</td>\n",
" <td>8304</td>\n",
" <td>5422</td>\n",
" <td>2808</td>\n",
" <td>53940</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"color D E F G H I J All\n",
"cut \n",
"Fair 163 224 312 314 303 175 119 1610\n",
"Good 662 933 909 871 702 522 307 4906\n",
"Ideal 2834 3903 3826 4884 3115 2093 896 21551\n",
"Premium 1603 2337 2331 2924 2360 1428 808 13791\n",
"Very Good 1513 2400 2164 2299 1824 1204 678 12082\n",
"All 6775 9797 9542 11292 8304 5422 2808 53940"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(diamonds.cut,diamonds.color,margins='TRUE')"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>color</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" <th>F</th>\n",
" <th>G</th>\n",
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" <tr>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" </tr>\n",
" <tr>\n",
" <th>Good</th>\n",
" <td>662</td>\n",
" <td>933</td>\n",
" <td>909</td>\n",
" <td>871</td>\n",
" <td>702</td>\n",
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" <td>4906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ideal</th>\n",
" <td>2834</td>\n",
" <td>3903</td>\n",
" <td>3826</td>\n",
" <td>4884</td>\n",
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" <td>2093</td>\n",
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" <tr>\n",
" <th>Premium</th>\n",
" <td>1603</td>\n",
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" <td>2331</td>\n",
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" <td>2360</td>\n",
" <td>1428</td>\n",
" <td>808</td>\n",
" <td>13791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Very Good</th>\n",
" <td>1513</td>\n",
" <td>2400</td>\n",
" <td>2164</td>\n",
" <td>2299</td>\n",
" <td>1824</td>\n",
" <td>1204</td>\n",
" <td>678</td>\n",
" <td>12082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>6775</td>\n",
" <td>9797</td>\n",
" <td>9542</td>\n",
" <td>11292</td>\n",
" <td>8304</td>\n",
" <td>5422</td>\n",
" <td>2808</td>\n",
" <td>53940</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"color D E F G H I J All\n",
"cut \n",
"Fair 163 224 312 314 303 175 119 1610\n",
"Good 662 933 909 871 702 522 307 4906\n",
"Ideal 2834 3903 3826 4884 3115 2093 896 21551\n",
"Premium 1603 2337 2331 2924 2360 1428 808 13791\n",
"Very Good 1513 2400 2164 2299 1824 1204 678 12082\n",
"All 6775 9797 9542 11292 8304 5422 2808 53940"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.crosstab(diamonds.cut,diamonds.color,margins='TRUE')"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"cutgroup=pd.groupby(diamonds,diamonds.cut)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<pandas.core.groupby.DataFrameGroupBy object at 0xae00d54c>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cutgroup"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"cut\n",
"Fair 3282.0\n",
"Good 3050.5\n",
"Ideal 1810.0\n",
"Premium 3185.0\n",
"Very Good 2648.0\n",
"Name: price, dtype: float64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cutgroup.price.median()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cut</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Fair</td>\n",
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" </tr>\n",
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" <td>Good</td>\n",
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" <td>Ideal</td>\n",
" <td>1810.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Premium</td>\n",
" <td>3185.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Very Good</td>\n",
" <td>2648.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cut price\n",
"0 Fair 3282.0\n",
"1 Good 3050.5\n",
"2 Ideal 1810.0\n",
"3 Premium 3185.0\n",
"4 Very Good 2648.0"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cutgroup.price.median().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\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",
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" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>cut</th>\n",
" <td>Fair</td>\n",
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" <td>Premium</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>price</th>\n",
" <td>3282</td>\n",
" <td>3050.5</td>\n",
" <td>1810</td>\n",
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" <td>2648</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4\n",
"cut Fair Good Ideal Premium Very Good\n",
"price 3282 3050.5 1810 3185 2648"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d=cutgroup.price.median().reset_index()\n",
"d.transpose()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<pandas.core.groupby.DataFrameGroupBy object at 0xad6845ac>"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds.groupby(['cut', \"color\"])"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
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" <th>color</th>\n",
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" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>Fair</td>\n",
" <td>E</td>\n",
" <td>2956.0</td>\n",
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" <th>2</th>\n",
" <td>Fair</td>\n",
" <td>F</td>\n",
" <td>3035.0</td>\n",
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" <th>3</th>\n",
" <td>Fair</td>\n",
" <td>G</td>\n",
" <td>3057.0</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>Fair</td>\n",
" <td>H</td>\n",
" <td>3816.0</td>\n",
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" <tr>\n",
" <th>5</th>\n",
" <td>Fair</td>\n",
" <td>I</td>\n",
" <td>3246.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Fair</td>\n",
" <td>J</td>\n",
" <td>3302.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Good</td>\n",
" <td>D</td>\n",
" <td>2728.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Good</td>\n",
" <td>E</td>\n",
" <td>2420.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Good</td>\n",
" <td>F</td>\n",
" <td>2647.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Good</td>\n",
" <td>G</td>\n",
" <td>3340.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Good</td>\n",
" <td>H</td>\n",
" <td>3468.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Good</td>\n",
" <td>I</td>\n",
" <td>3639.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Good</td>\n",
" <td>J</td>\n",
" <td>3733.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Ideal</td>\n",
" <td>D</td>\n",
" <td>1576.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Ideal</td>\n",
" <td>E</td>\n",
" <td>1437.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Ideal</td>\n",
" <td>F</td>\n",
" <td>1775.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Ideal</td>\n",
" <td>G</td>\n",
" <td>1857.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Ideal</td>\n",
" <td>H</td>\n",
" <td>2278.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Ideal</td>\n",
" <td>I</td>\n",
" <td>2659.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Ideal</td>\n",
" <td>J</td>\n",
" <td>4096.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Premium</td>\n",
" <td>D</td>\n",
" <td>2009.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Premium</td>\n",
" <td>E</td>\n",
" <td>1928.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Premium</td>\n",
" <td>F</td>\n",
" <td>2841.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Premium</td>\n",
" <td>G</td>\n",
" <td>2745.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Premium</td>\n",
" <td>H</td>\n",
" <td>4511.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Premium</td>\n",
" <td>I</td>\n",
" <td>4640.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Premium</td>\n",
" <td>J</td>\n",
" <td>5063.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Very Good</td>\n",
" <td>D</td>\n",
" <td>2310.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Very Good</td>\n",
" <td>E</td>\n",
" <td>1989.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Very Good</td>\n",
" <td>F</td>\n",
" <td>2471.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Very Good</td>\n",
" <td>G</td>\n",
" <td>2437.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Very Good</td>\n",
" <td>H</td>\n",
" <td>3734.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>Very Good</td>\n",
" <td>I</td>\n",
" <td>3888.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Very Good</td>\n",
" <td>J</td>\n",
" <td>4113.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cut color price\n",
"0 Fair D 3730.0\n",
"1 Fair E 2956.0\n",
"2 Fair F 3035.0\n",
"3 Fair G 3057.0\n",
"4 Fair H 3816.0\n",
"5 Fair I 3246.0\n",
"6 Fair J 3302.0\n",
"7 Good D 2728.5\n",
"8 Good E 2420.0\n",
"9 Good F 2647.0\n",
"10 Good G 3340.0\n",
"11 Good H 3468.5\n",
"12 Good I 3639.5\n",
"13 Good J 3733.0\n",
"14 Ideal D 1576.0\n",
"15 Ideal E 1437.0\n",
"16 Ideal F 1775.0\n",
"17 Ideal G 1857.5\n",
"18 Ideal H 2278.0\n",
"19 Ideal I 2659.0\n",
"20 Ideal J 4096.0\n",
"21 Premium D 2009.0\n",
"22 Premium E 1928.0\n",
"23 Premium F 2841.0\n",
"24 Premium G 2745.0\n",
"25 Premium H 4511.0\n",
"26 Premium I 4640.0\n",
"27 Premium J 5063.0\n",
"28 Very Good D 2310.0\n",
"29 Very Good E 1989.5\n",
"30 Very Good F 2471.0\n",
"31 Very Good G 2437.0\n",
"32 Very Good H 3734.0\n",
"33 Very Good I 3888.0\n",
"34 Very Good J 4113.0"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"diamonds.groupby(['cut', \"color\"]).price.median().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>color</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" <th>F</th>\n",
" <th>G</th>\n",
" <th>H</th>\n",
" <th>I</th>\n",
" <th>J</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cut</th>\n",
" <th></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>Fair</th>\n",
" <td>3730.0</td>\n",
" <td>2956.0</td>\n",
" <td>3035</td>\n",
" <td>3057.0</td>\n",
" <td>3816.0</td>\n",
" <td>3246.0</td>\n",
" <td>3302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Good</th>\n",
" <td>2728.5</td>\n",
" <td>2420.0</td>\n",
" <td>2647</td>\n",
" <td>3340.0</td>\n",
" <td>3468.5</td>\n",
" <td>3639.5</td>\n",
" <td>3733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ideal</th>\n",
" <td>1576.0</td>\n",
" <td>1437.0</td>\n",
" <td>1775</td>\n",
" <td>1857.5</td>\n",
" <td>2278.0</td>\n",
" <td>2659.0</td>\n",
" <td>4096</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Premium</th>\n",
" <td>2009.0</td>\n",
" <td>1928.0</td>\n",
" <td>2841</td>\n",
" <td>2745.0</td>\n",
" <td>4511.0</td>\n",
" <td>4640.0</td>\n",
" <td>5063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Very Good</th>\n",
" <td>2310.0</td>\n",
" <td>1989.5</td>\n",
" <td>2471</td>\n",
" <td>2437.0</td>\n",
" <td>3734.0</td>\n",
" <td>3888.0</td>\n",
" <td>4113</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"color D E F G H I J\n",
"cut \n",
"Fair 3730.0 2956.0 3035 3057.0 3816.0 3246.0 3302\n",
"Good 2728.5 2420.0 2647 3340.0 3468.5 3639.5 3733\n",
"Ideal 1576.0 1437.0 1775 1857.5 2278.0 2659.0 4096\n",
"Premium 2009.0 1928.0 2841 2745.0 4511.0 4640.0 5063\n",
"Very Good 2310.0 1989.5 2471 2437.0 3734.0 3888.0 4113"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"e=diamonds.groupby(['cut', \"color\"]).price.median().reset_index()\n",
"e.pivot(index='cut', columns='color', values='price')"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f=e.pivot(index='cut', columns='color', values='price')"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>color</th>\n",
" <th>D</th>\n",
" <th>E</th>\n",
" <th>F</th>\n",
" <th>G</th>\n",
" <th>H</th>\n",
" <th>I</th>\n",
" <th>J</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cut</th>\n",
" <th></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>Fair</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Good</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ideal</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Premium</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Very Good</th>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"color D E F G H I J\n",
"cut \n",
"Fair False False False False False False False\n",
"Good False False False False False False False\n",
"Ideal False False False False False False True\n",
"Premium False False False False True True True\n",
"Very Good False False False False False False True"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f>4000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Visualization"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"pd.options.display.mpl_style = 'default'\n",
"plt.style.use('ggplot')\n"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[33mThe directory '/home/ajay/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\u001b[0m\n",
"\u001b[33mYou are using pip version 7.1.0, however version 7.1.2 is available.\n",
"You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
"\u001b[33mThe directory '/home/ajay/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\u001b[0m\n",
"Collecting seaborn\n",
" Downloading seaborn-0.6.0.tar.gz (145kB)\n",
"\u001b[K 100% |████████████████████████████████| 147kB 123kB/s \n",
"\u001b[?25hInstalling collected packages: seaborn\n",
" Found existing installation: seaborn 0.5.1\n",
" Uninstalling seaborn-0.5.1:\n",
" Successfully uninstalled seaborn-0.5.1\n",
" Running setup.py install for seaborn\n",
"Successfully installed seaborn-0.6.0\n"
]
}
],
"source": [
"!sudo pip install seaborn --upgrade"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0xa75290ec>"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RUmk+Nwn6EQqNghZ/3dGOfvl59EM/h7cOxdPV6eegn/8bjKmJnujyKr7DBaOU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\nyr13p10AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xa742786c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diamonds['price'].plot()"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 25335., 9328., 7393., 3878., 2364., 1745., 1306.,\n",
" 1002., 863., 726.]),\n",
" array([ 326. , 2175.7, 4025.4, 5875.1, 7724.8, 9574.5,\n",
" 11424.2, 13273.9, 15123.6, 16973.3, 18823. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x98f9d20c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(diamonds.price)"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x98f2bcac>"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ArCA4AAAruEoNC0I6l1RLXFYN5AKCgwUhnUuqJS6rBnIBp9QAAFYQHACAFZxSQ0HgtjpA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9907ecec>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure();\n",
"diamonds['price'].plot(kind='hist', stacked=True, bins=20)"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'boxes': [<matplotlib.lines.Line2D at 0xa75082cc>],\n",
" 'caps': [<matplotlib.lines.Line2D at 0xa750a22c>,\n",
" <matplotlib.lines.Line2D at 0xa750abcc>],\n",
" 'fliers': [<matplotlib.lines.Line2D at 0xa750ff2c>],\n",
" 'means': [],\n",
" 'medians': [<matplotlib.lines.Line2D at 0xa750f58c>],\n",
" 'whiskers': [<matplotlib.lines.Line2D at 0xa7508eec>,\n",
" <matplotlib.lines.Line2D at 0xa750986c>]}"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0xa75ad1ec>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.boxplot(diamonds.price)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x961d1a0c>"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEBCAYAAACQbKXWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAIOIPwAYRPwBwCDiDwAGEX8AMOh/2d+UVqv0QuAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9616602c>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure();\n",
"diamonds['price'].plot(kind='box')"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x9552b92c>"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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9A4FKQkmndHV1IZHgzA3DMPFgOb0AKxRlffXVV/Hggw+GcSiGYZimcCTugpMn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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x95a893ac>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diamonds.plot(kind='hexbin', x='price', y='carat', gridsize=8)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from ggplot import *\n"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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rRnTx/xKpKMNOzyRwzwSMQEoSqxQRBTQRkRbEqaqsu6Gmhshr87B374xvCtdU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\nbeP96BeEiIADr0U/4ofmuKT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DSYYskzPTKJ97DPnD2zEUhZWLFNo6bX6RegKU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VOZSZvAOj6f7KvXXWE135v1N67TzuoSFo1xgr46idreTz+cpz3t6CeSaNf3QSDIW1vQ23w8K9\nyGvkUu9Do7ve/x5A7kWjkPvQGGzbvvhG4qKuuwBNNL7Cm68z9N6rWJNZwlTFWbo8z8wCtRPoAsJc\neLhyvr+EecB4HB0tgFNCKfC9YOL+1Bc+ZXdgxD6H//IwGApzeQLt+th3JFi2o2P6UL6/gULfX6JL\nwxihbkJdv4FxgWSx9s5O7J2dF2yWUorQLy6dzzMVQghxnZMATcwr9/ABci8+Rypf9e21HdhAECC5\noNZwzV952lOUDqym+PY2Qr/4XWwjaJ9hAkYMZTWhDAe77XHMZYvhk4vnPJ5hWEQW/941aLkQQoh/\njSRAE/PK3bsHpzo4awM+SWVu2dVYhVmen6apHFv7Cl0MgeHiuSHO7Lqb/j23YUcybJoxu99wOggv\nkmBLCCFE45AATcwrP5msjcE2UAnO4OqsvlSQe+cmDu++D6u7D9+z6Lx5Fb13uOz7pzT9u5qYWnJZ\nKsSxYquBPYALZgKr6a6r0CghhBDio5MATcyrV9bD3S9WPdB+9Z/Tn4ix78A9FEpNcCoox5QZBsOK\nsuELSZJLSpx+3cWKwA1Phon3fBEvvQvDH0GH12OGeq9+I4UQQojLIAGamDda+0zGf4CnwNLA7UCy\negPmvwdtIsrEC/dTmGytedjLw+CHRRZtzNB7e5zF2zP47jiGEUHrbqzEzdf9SikhhBAfXxKgiXlz\norSL274BhiZ4Za1jRumiK38OrSsrLv2JKJnvPcyh8Y2ztoupMyzv+xb5v8jAI3mI+VUpO0KEl/yv\nwPWbZ0gIIcTHmwRoYl5orSl+9essPl6Og+7jqsw3mxxvpTjYCUpx5t2d5CY7iLQOYLkWhu3i5iMU\n082sij5LqDQI2wjmwFW3RRconP1vxNb9x/lvoBBCCDEPJEAT8+LEq//M4qPlOEgB3cx7gFYq2uz9\n4Mv4e0IowAplufFzXyHcPBgUHVcKraN46mbs75VgCGit3w7ty9CmEEKIxlU/s6YQl8k48NNKHLTz\n6jxHPtfEzi8myTVBCVh653PEO/qwnBLK0CjlYxhpKDK1twAAECBJREFUbPNd1KJEsNN+Zldm0sBg\nK0IIIUSjkgBNzItMezkPGcBi5r33TGtItG/Dsk1ytxrs2XIEvW5//Y39PM5D92NsuglVWgr9qSCi\n0wTJcvva4IW70aUL19QUQgghFpIMcYp5sec+6H0vhKkLVyU4U0Y70d4HAPiFT4b45qGDDDQVaZ0A\ne0Z5TuW0YcR6CT/1K9OP5f7v/ejT2cpGLQaYKijILoQQQjQY6UET8yKfj6OLj4Oxsaor7aPTunIY\nFVlPZPn/Mv27kKO4Z90qzkUdDiZg0IZJE0pOO0Z0A07Xr6OM2srr9l0dkCh/HwmbWNtaUMbVyJor\nhBBCXDnpQRPzYuvXP4W54hTceRicy9tX++VgzAe/aNFnRTjebPHjRA//Kf6luvv0WlsY9k5xLr6X\n4Tj0mBvZ7Hzmgs9hbWtFLYriH5nEWBzFXBa/vEYKIYQQ15AEaGJedJwLo37xfYgVLm2Hcg+Zmw9z\nbs/tnHnnk5y85RB7H3tnehP7ImOlN4UeY7MOgjKlLt4bZnZFMLsk95kQQojGJwGamBdmpAj2pU3o\n0uXJ+gd/+BDZkTXkx9vwlU82la7Zbom6eJ2oSwnMhBBCiOuNzEET8+KDe3ejs1W9UzPnoemqf5kQ\nP/v//gMeOygVW/HDLqXVEzxx9wZWqx5SxNluruF3Io9euxMQQgghGoj0oIl5cWbTOVp+2s3qXAic\nAqRGwaayonPXMjDCcGA5k5s3ce+fdqK1Jjek0Rqi7QmUUvw2Dy/gWQghhBCNQQI0MS+6/PvZc+fz\nHDRcPBQDfgvGcDNfWPwQbeYKWFXZNlb+r1KKaLsMUQohhBAzSYAm5sX9LTv5L283cz51iJJvw9ub\n+N8e76LVjC5004QQQojrjgRoYt78T7duBjYHP6xd0KYIIYQQ1zVZJCCEEEII0WAkQBNCCCGEaDAS\noAkhhBBCNBgJ0IQQQgghGowEaEIIIYQQDaZhVnEePnyYF154Aa01W7du5c4771zoJgkhhBBCLIiG\n6EHzfZ/nn3+eL37xi/zO7/wOe/bsYXBwcKGbJYQQQgixIBoiQDt79iwtLS2kUilM02Tjxo0cOHBg\noZslhBBCCLEgGmKIc2Jigqampumfk8kkZ8+eZWJignQ6XbNtPB7Hshqi2R+ZaZrYtr3QzfjIpq6/\n3IeFJfehcci9aAxyHxrD9X79G0VDXEWl6tdj3LVrF6+99lrNYzt37uSee+65Fs0SF5FKpRa6CQK5\nD41E7kVjkPsgPg4aIkBLJBKMj49P/zwxMUEymWTTpk2sXVtbMygejzM6Oorrute6mfMmFApRKBQW\nuhkfmWVZpFIpuQ8LTO5D45B70RjkPjSGqfsgrkxDBGg9PT2MjIwwOjpKIpHgww8/5IknniCZTJJM\nJmdtPzg4SKlUWoCWzg/Lsq7r9k9xXfe6Pg+5D43h43IfQO5Fo5D7ID4OGiJAM02Thx56iK997Wv4\nvs/WrVtpb29f6GYJIYQQQiyIhgjQAFavXs3q1asXuhlCCCGEEAuuIdJsCCGEEEKICgnQhBBCCCEa\njARoQgghhBANRgI0IYQQQogGIwGaEEIIIUSDkQBNCCGEEKLBSIAmhBBCCNFgJEATQgghhGgwEqAJ\nIYQQQjQYCdCEEEIIIRqMBGhCCCGEEA1GAjQhhBBCiAYjAZoQQgghRIORAE0IIYQQosFIgCaEEEII\n0WAkQBNCCCGEaDASoAkhhBBCNBgJ0IQQQgghGowEaEIIIYQQDUYCNCGEEEKIBiMBmhBCCCFEg5EA\nTQghhBCiwUiAJoQQQgjRYCRAE0IIIYRoMBKgCSGEEEI0GAnQhBBCCCEajARoQgghhBANRmmt9UI3\n4nLk83ny+TzXWbNrGIaB7/sL3YyPTCmF4zgUi0W5DwtI7kPjkHvRGOQ+NAalFM3NzQvdjOuetdAN\nuFzhcJjJyUlKpdJCN+Uji0Qi5HK5hW7GR2bbNs3NzWQyGbkPC0juQ+OQe9EY5D40Btu2F7oJHwsy\nxCmEEEII0WAkQBNCCCGEaDASoAkhhBBCNBgJ0IQQQgghGowEaEIIIYQQDUYCNCGEEEKIBiMBmhBC\nCCFEg5EATQghhBCiwUiAJoQQQgjRYCRAE0IIIYRoMBKgCSGEEEI0GAnQhBBCCCEajARoQgghhBAN\nRgI0IYQQQogGIwGaEEIIIUSDkQBNCCGEEKLBSIAmhBBCCNFgJEATQgghhGgwEqAJIYQQQjQYCdCE\nEEIIIRqMBGhCCCGEEA1GAjQhhBBCiAYjAZoQQgghRIORAE0IIYQQosFIgCaEEEII0WAkQBNCCCH+\n//buLjaK6o3j+Hd3gWVpd+nWtqbvNNisBUHSEqLRRoWEAJFoUCIa44VEQoyESyBIAgmowQuIiY1c\nqEmNYgqxEC6EXthCowkkCI28CRL7wpYXya7dbTcUdnu8aLr/Vij/FpeZsf4+V+2ZTPOc8+yZeXpm\nZ0bEYSbZHcDZs2dpaWnh5s2bvPPOOxQVFdkdkoiIiIitbF9BKygo4LXXXqO8vNzuUEREREQcwfYV\ntPz8fLtDEBEREXEU2wu0+4nFYvT29o5oy87OZtIkR4f9f3k8HiZPnmx3GA9saPyVB3spD86hXDiD\n8uAM//bxdwpLRrG+vv6uQgtg0aJFhEKhUfc7efIkR48eHdFWXl7OK6+8QjAYzHicMjaxWIzm5mZq\namqUBxspD86hXDiD8uAMw/MQCATsDudfy5IC7a233nqg/WpqakYUcH/88QeNjY309vYq6Tbq7e3l\n6NGjhEIh5cFGyoNzKBfOoDw4g/KQGY5ehwwEAkquiIiI/OfYXqCdP3+e77//nkQiwddff01hYSFv\nvvmm3WGJiIiI2Mb2Aq2qqoqqqiq7wxARERFxDM/WrVu32h3EWBljmDJlCjNmzMDr9dodzn+W8uAM\nyoNzKBfOoDw4g/KQGS5jjLE7CBERERH5H9svcQ7X3NzMzz//TFZWFjD4GI7KykoAWltbOXXqFC6X\ni6VLl/LYY48B0N3dzYEDB0gmk1RWVrJ06VIAkskkjY2NXL16FZ/Px8qVK8nJybGnYxPMpUuXOHz4\nMMYYqqurefbZZ+0OaULZtWsXXq8Xt9uN2+1mzZo1JBIJ9u/fz59//klOTg4rV67E5/MB458bMroD\nBw5w6dIlsrKyePfddwEyOvY6Lo3NvfKg84P1enp6aGxspK+vDxh8ssJTTz2lOWEV4yDNzc3mxx9/\nvKv9+vXrpq6uziSTSROJRMzu3bvNwMCAMcaYPXv2mK6uLmOMMV999ZW5ePGiMcaY48ePm0OHDhlj\njPnll19MQ0ODRb2Y2FKplNm9e7eJRCImmUyauro6c+PGDbvDmlB27dpl+vr6RrQdOXLEtLa2GmOM\naW1tNU1NTcaYB5sbMrr29nbT3d1tPv3003RbJsdex6WxuVcedH6wXiwWM93d3cYYY27dumU++eQT\nc+PGDc0Ji9j+Ls6x+PXXX5kzZw4ej4dgMEhubi5XrlwhHo9z+/ZtSkpKAHjyySe5cOFCep958+YB\ngzci/P7777bFP5GEw2Fyc3MJBoN4PB6eeOKJ9JjLwzP88/z3z/l454aMrry8nKlTp45oy+TY67g0\nNvfKw2iUh4fH7/dTWFgIgNfrJS8vj1gspjlhEUdd4gQ4fvw4bW1tFBUVsXjxYnw+H/F4PJ1YGHw+\nWjwex+PxjHhO2lA7QDweT2/zeDx4vV4SiQTTpk2ztkMTTCwWY/r06enfA4EA4XDYxogmpvr6elwu\nF/Pnz6empoa+vj6ys7OBwdedDV1yeJC5IeOTybHXcemf0fnBPtFolGvXrlFSUqI5YRHLC7TRXvu0\ncOFC5s+fz3PPPQfADz/8QFNTEy+99JLVIcp9uFwuu0OY8FavXo3f76evr4/6+nry8vJGbFcO7KOx\nt4/OD/bp7++noaGBJUuW3HVXpubEw2N5gTbW1z5VV1ezd+9eYHCZtaenJ70tFosRCATw+/3EYrG7\n2ofvEwgESKVS9Pf3qyLPgNFyIZnj9/sByMrKoqqqinA4TFZWFvF4HL/fTzweT39RejxzY+jvyvhk\nYux1XPrnhlZsQOcHK6VSKRoaGpg7d276maWaE9Zw1HfQhl+CuXDhAgUFBQCEQiHOnDlDMpkkGo0S\niUQoLi7G7/fj9Xq5cuUKxhja2trS7+4MhUK0tbUBcO7cOSoqKqzv0ARUVFREJBIhGo2STCY5c+bM\nfV94L+Nz+/Zt+vv70z9fvnyZgoKCEZ/n06dP8/jjjwPjmxtD+8j4ZGLsdVz653R+sJ4xhoMHD5Kf\nn8/TTz+dbtecsIajnoP23Xffce3aNVwuFzk5OSxfvjz9X9OxY8c4deoUbrf7nrfu3rlzh8rKSpYt\nWwYM3ro79Pd8Ph+vvvoqwWDQtr5NJEOP2RgYGKC6upra2lq7Q5owotEo3377LQADAwPMnTuX2tpa\nEokE+/bto6en567b2sc7N2R0+/fvp729nUQiQXZ2Ni+88AKhUChjY6/j0tj8PQ/PP/887e3tOj9Y\nrKOjgy+//JJHH300fSlz0aJFFBcXa05YwFEFmoiIiIg47BKniIiIiKhAExEREXEcFWgiIiIiDqMC\nTURERMRhVKCJiIiIOIwKNBERERGHUYEmIpZrbW3Vg3NFRO5Dz0ETERERcRitoImIpZLJpN0hiIg4\nngo0EcmIGTNm8NFHHzF79mxyc3N5++236e/vp6WlhZKSEnbu3ElhYSGrV6+mpaWF0tLS9L5dXV2s\nWLGCgoIC8vLyWLduXXrbF198waxZs8jNzWXJkiV0dnba0T0REUupQBORjPnmm29oamri8uXLXLx4\nke3bt+Nyubh+/TrRaJTOzk727NkzYp9UKsWLL75IRUUFHR0dhMNhVq1aBcDBgwf58MMPaWxs5ObN\nm9TW1vL666/b0TUREUupQBORjHC5XLz33nsUFxcTDAbZvHkze/fuBcDtdrNt2zYmT57M1KlTR+x3\n4sQJrl69yscff4zP58Pr9fLMM88A8Nlnn7Fp0yZCoRBut5tNmzZx+vRpurq6LO+fiIiVVKCJSMYM\nv2xZVlZGd3c3APn5+UyZMuWe+3R1dVFeXo7bfffhqKOjg/Xr1xMMBgkGgzzyyCMAhMPhhxC9iIhz\nTLI7ABGZOIZ/P6yzs5OioiJgcHVtNKWlpXR2dpJKpfB4PCO2lZWVsWXLFl3WFJH/HK2giUhGGGOo\nq6sjHA4TiUTYsWNH+rtk97NgwQIKCwvZuHEjiUSCW7du8dNPPwGwdu1aPvjgA86dOwdAT08P+/bt\ne6j9EBFxAhVoIpIRLpeLN954g8WLFzNz5kwqKyt5//33McbccwVtqM3j8XDo0CF+++03ysrKKC0t\npaGhAYCXX36ZDRs2sGrVKqZPn86cOXM4cuSIpf0SEbGDHlQrIhlRUVHB559/zsKFC+0ORUTkX08r\naCIiIiIOowJNRERExGF0iVNERETEYbSCJiIiIuIwKtBEREREHEYFmoiIiIjDqEATERERcRgVaCIi\nIiIO8xc0eJGHmGTBSwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x93e978ac>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<ggplot: (-918646034)>"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = ggplot(aes(x='price', y='carat',color=\"clarity\"), data=diamonds)\n",
"p + geom_point()"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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yZMgQ5syZw7HHHgvAz3/+c2bOnMmwYcMIBALcfffd3HbbbQDcddddrF27lnHj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+MJnKs/Q9i2mEf9/bwWnOKYHuCORc1YSGwL2pskX7kugjOH44cBgi6N8UEmIXWDTf70BMIrj7NM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\nKCSKiIiIiIdCooiIiIh4/H/Fhn3O8l3caQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x9543caec>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<ggplot: (-918646060)>"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p = ggplot(aes(x='price', y='carat',color=\"cut\"), data=diamonds)\n",
"p + geom_point()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Modeling\n",
"Lets do some basic Regression Modeling"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import statsmodels.formula.api as sm"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"result = sm.ols(formula=\"price ~ carat + color\", data=diamonds).fit()"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>price</td> <th> R-squared: </th> <td> 0.864</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.864</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td>4.893e+04</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Wed, 02 Dec 2015</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>23:44:07</td> <th> Log-Likelihood: </th> <td>-4.6998e+05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 53940</td> <th> AIC: </th> <td>9.400e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 53932</td> <th> BIC: </th> <td>9.400e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 7</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td>-2136.2289</td> <td> 20.122</td> <td> -106.162</td> <td> 0.000</td> <td>-2175.669 -2096.789</td>\n",
"</tr>\n",
"<tr>\n",
" <th>color[T.E]</th> <td> -93.7813</td> <td> 23.252</td> <td> -4.033</td> <td> 0.000</td> <td> -139.355 -48.208</td>\n",
"</tr>\n",
"<tr>\n",
" <th>color[T.F]</th> <td> -80.2629</td> <td> 23.405</td> <td> -3.429</td> <td> 0.001</td> <td> -126.136 -34.390</td>\n",
"</tr>\n",
"<tr>\n",
" <th>color[T.G]</th> <td> -85.5363</td> <td> 22.670</td> <td> -3.773</td> <td> 0.000</td> <td> -129.969 -41.103</td>\n",
"</tr>\n",
"<tr>\n",
" <th>color[T.H]</th> <td> -732.2418</td> <td> 24.354</td> <td> -30.067</td> <td> 0.000</td> <td> -779.975 -684.508</td>\n",
"</tr>\n",
"<tr>\n",
" <th>color[T.I]</th> <td>-1055.7319</td> <td> 27.310</td> <td> -38.657</td> <td> 0.000</td> <td>-1109.260 -1002.203</td>\n",
"</tr>\n",
"<tr>\n",
" <th>color[T.J]</th> <td>-1914.4722</td> <td> 33.777</td> <td> -56.679</td> <td> 0.000</td> <td>-1980.676 -1848.268</td>\n",
"</tr>\n",
"<tr>\n",
" <th>carat</th> <td> 8066.6230</td> <td> 14.040</td> <td> 574.558</td> <td> 0.000</td> <td> 8039.105 8094.141</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>12266.990</td> <th> Durbin-Watson: </th> <td> 0.948</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>165317.069</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 0.719</td> <th> Prob(JB): </th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td>11.455</td> <th> Cond. No. </th> <td> 11.0</td> \n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: price R-squared: 0.864\n",
"Model: OLS Adj. R-squared: 0.864\n",
"Method: Least Squares F-statistic: 4.893e+04\n",
"Date: Wed, 02 Dec 2015 Prob (F-statistic): 0.00\n",
"Time: 23:44:07 Log-Likelihood: -4.6998e+05\n",
"No. Observations: 53940 AIC: 9.400e+05\n",
"Df Residuals: 53932 BIC: 9.400e+05\n",
"Df Model: 7 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"Intercept -2136.2289 20.122 -106.162 0.000 -2175.669 -2096.789\n",
"color[T.E] -93.7813 23.252 -4.033 0.000 -139.355 -48.208\n",
"color[T.F] -80.2629 23.405 -3.429 0.001 -126.136 -34.390\n",
"color[T.G] -85.5363 22.670 -3.773 0.000 -129.969 -41.103\n",
"color[T.H] -732.2418 24.354 -30.067 0.000 -779.975 -684.508\n",
"color[T.I] -1055.7319 27.310 -38.657 0.000 -1109.260 -1002.203\n",
"color[T.J] -1914.4722 33.777 -56.679 0.000 -1980.676 -1848.268\n",
"carat 8066.6230 14.040 574.558 0.000 8039.105 8094.141\n",
"==============================================================================\n",
"Omnibus: 12266.990 Durbin-Watson: 0.948\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 165317.069\n",
"Skew: 0.719 Prob(JB): 0.00\n",
"Kurtosis: 11.455 Cond. No. 11.0\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"\"\"\""
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.summary()"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Intercept -2136.228853\n",
"color[T.E] -93.781288\n",
"color[T.F] -80.262858\n",
"color[T.G] -85.536282\n",
"color[T.H] -732.241826\n",
"color[T.I] -1055.731857\n",
"color[T.J] -1914.472203\n",
"carat 8066.623019\n",
"dtype: float64"
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result.params"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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