{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Lab 1. An Introduction to Pandas and Python"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# The %... is an iPython thing, and is not part of the Python language.\n",
"# In this case we're just telling the plotting library to draw things on\n",
"# the notebook, instead of on a separate window.\n",
"%matplotlib inline \n",
"#this line above prepares IPython notebook for working with matplotlib\n",
"\n",
"# See all the \"as ...\" contructs? They're just aliasing the package names.\n",
"# That way we can call methods like plt.plot() instead of matplotlib.pyplot.plot().\n",
"\n",
"import numpy as np # imports a fast numerical programming library\n",
"import scipy as sp #imports stats functions, amongst other things\n",
"import matplotlib as mpl # this actually imports matplotlib\n",
"import matplotlib.cm as cm #allows us easy access to colormaps\n",
"import matplotlib.pyplot as plt #sets up plotting under plt\n",
"import pandas as pd #lets us handle data as dataframes\n",
"#sets up pandas table display\n",
"pd.set_option('display.width', 500)\n",
"pd.set_option('display.max_columns', 100)\n",
"pd.set_option('display.notebook_repr_html', True)\n",
"import seaborn as sns #sets up styles and gives us more plotting options"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Python depends on packages for most of its functionality; these can be either built-in (such as sys), or third-party (like all the packages below). Either way you need to import the packages you need before using them."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##The Notebook\n",
"\n",
"Look up http:/www.google.com Lets eat a burrito. $\\alpha = \\frac{\\beta}{\\gamma}$\n",
"\n",
"Longer:\n",
"\n",
"$$\\alpha = \\frac{\\beta}{\\gamma}$$\n",
"\n",
"1. an item\n",
"2. another item\n",
"3. i like items"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get Cheatsheet:\n",
"\n",
"from https://drive.google.com/folderview?id=0ByIrJAE4KMTtaGhRcXkxNHhmY2M&usp=sharing\n",
"\n",
"\n",
"We read in some data from a CSV file. CSV files can be output by any spreadsheet software, and are plain text, so make a great way to share data. This dataset is from Goodreads: i scraped the highest regarded (according to Goodread's proprietary algorithm) books on that site. Ypu'll see how to do such a scraping in the next lab."
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>rating</th>\n",
" <th>review_count</th>\n",
" <th>isbn</th>\n",
" <th>booktype</th>\n",
" <th>author_url</th>\n",
" <th>year</th>\n",
" <th>genre_urls</th>\n",
" <th>dir</th>\n",
" <th>rating_count</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4.40</td>\n",
" <td>136455</td>\n",
" <td>0439023483</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/153394.S...</td>\n",
" <td>2008</td>\n",
" <td>/genres/young-adult|/genres/science-fiction|/g...</td>\n",
" <td>dir01/2767052-the-hunger-games.html</td>\n",
" <td>2958974</td>\n",
" <td>The Hunger Games (The Hunger Games, #1)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4.41</td>\n",
" <td>16648</td>\n",
" <td>0439358078</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/1077326....</td>\n",
" <td>2003</td>\n",
" <td>/genres/fantasy|/genres/young-adult|/genres/fi...</td>\n",
" <td>dir01/2.Harry_Potter_and_the_Order_of_the_Phoe...</td>\n",
" <td>1284478</td>\n",
" <td>Harry Potter and the Order of the Phoenix (Har...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3.56</td>\n",
" <td>85746</td>\n",
" <td>0316015849</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/941441.S...</td>\n",
" <td>2005</td>\n",
" <td>/genres/young-adult|/genres/fantasy|/genres/ro...</td>\n",
" <td>dir01/41865.Twilight.html</td>\n",
" <td>2579564</td>\n",
" <td>Twilight (Twilight, #1)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4.23</td>\n",
" <td>47906</td>\n",
" <td>0061120081</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/1825.Har...</td>\n",
" <td>1960</td>\n",
" <td>/genres/classics|/genres/fiction|/genres/histo...</td>\n",
" <td>dir01/2657.To_Kill_a_Mockingbird.html</td>\n",
" <td>2078123</td>\n",
" <td>To Kill a Mockingbird</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4.23</td>\n",
" <td>34772</td>\n",
" <td>0679783261</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/1265.Jan...</td>\n",
" <td>1813</td>\n",
" <td>/genres/classics|/genres/fiction|/genres/roman...</td>\n",
" <td>dir01/1885.Pride_and_Prejudice.html</td>\n",
" <td>1388992</td>\n",
" <td>Pride and Prejudice</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rating review_count isbn booktype author_url year genre_urls dir rating_count name\n",
"0 4.40 136455 0439023483 good_reads:book https://www.goodreads.com/author/show/153394.S... 2008 /genres/young-adult|/genres/science-fiction|/g... dir01/2767052-the-hunger-games.html 2958974 The Hunger Games (The Hunger Games, #1)\n",
"1 4.41 16648 0439358078 good_reads:book https://www.goodreads.com/author/show/1077326.... 2003 /genres/fantasy|/genres/young-adult|/genres/fi... dir01/2.Harry_Potter_and_the_Order_of_the_Phoe... 1284478 Harry Potter and the Order of the Phoenix (Har...\n",
"2 3.56 85746 0316015849 good_reads:book https://www.goodreads.com/author/show/941441.S... 2005 /genres/young-adult|/genres/fantasy|/genres/ro... dir01/41865.Twilight.html 2579564 Twilight (Twilight, #1)\n",
"3 4.23 47906 0061120081 good_reads:book https://www.goodreads.com/author/show/1825.Har... 1960 /genres/classics|/genres/fiction|/genres/histo... dir01/2657.To_Kill_a_Mockingbird.html 2078123 To Kill a Mockingbird\n",
"4 4.23 34772 0679783261 good_reads:book https://www.goodreads.com/author/show/1265.Jan... 1813 /genres/classics|/genres/fiction|/genres/roman... dir01/1885.Pride_and_Prejudice.html 1388992 Pride and Prejudice"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.read_csv(\"all.csv\", header=None,\n",
" names=[\"rating\", 'review_count', 'isbn', 'booktype','author_url', 'year', 'genre_urls', 'dir','rating_count', 'name'],\n",
")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice we have a table! A spreadsheet! And it indexed the rows. Pandas (borrowing from R) calls it a DataFrame. Lets see the types of the columns...\n",
"\n",
"`df`, in python parlance, is an **instance** of the `pd.DataFrame` class, created by calling the `pd.read_csv` function, which cllas the DataFrame constructor inside of it. If you dont understand this sentence, dont worry, it will become clearer later. What you need to take away is that `df` is a dataframe object, and it has **methods**, or functions belonging to it, which allow it to do things. For example `df.head()` is a method that shows the first 5 rows of the dataframe.\n",
"\n",
"\n",
"\n",
"###The basics"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"rating float64\n",
"review_count object\n",
"isbn object\n",
"booktype object\n",
"author_url object\n",
"year float64\n",
"genre_urls object\n",
"dir object\n",
"rating_count object\n",
"name object\n",
"dtype: object"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The shape of the object is:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(6000, 10)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"6000 rows times 10 columns. A spredsheet is a table is a matrix. How can we access members of this **tuple** (brackets like so:() )"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(6000, 10)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape[0], df.shape[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are the column names."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index([u'rating', u'review_count', u'isbn', u'booktype', u'author_url', u'year', u'genre_urls', u'dir', u'rating_count', u'name'], dtype='object')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As the diagram above shows, pandas considers a table (dataframe) as a pasting of many \"series\" together, horizontally."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(pandas.core.series.Series, pandas.core.frame.DataFrame)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(df.rating), type(df)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Querying\n",
"\n",
"A spreadsheet is useless if you cant dice/sort/etc it. Here we look for all books with a rating less than 3. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 False\n",
"1 False\n",
"2 False\n",
"3 False\n",
"4 False\n",
"5 False\n",
"6 False\n",
"7 False\n",
"8 False\n",
"9 False\n",
"10 False\n",
"11 False\n",
"12 False\n",
"13 False\n",
"14 False\n",
"15 False\n",
"16 False\n",
"17 False\n",
"18 False\n",
"19 False\n",
"20 False\n",
"21 False\n",
"22 False\n",
"23 False\n",
"24 False\n",
"25 False\n",
"26 False\n",
"27 False\n",
"28 False\n",
"29 False\n",
" ... \n",
"5970 False\n",
"5971 False\n",
"5972 False\n",
"5973 False\n",
"5974 False\n",
"5975 False\n",
"5976 False\n",
"5977 False\n",
"5978 False\n",
"5979 True\n",
"5980 False\n",
"5981 False\n",
"5982 False\n",
"5983 False\n",
"5984 False\n",
"5985 False\n",
"5986 False\n",
"5987 False\n",
"5988 False\n",
"5989 False\n",
"5990 False\n",
"5991 False\n",
"5992 False\n",
"5993 False\n",
"5994 False\n",
"5995 False\n",
"5996 False\n",
"5997 False\n",
"5998 False\n",
"5999 False\n",
"Name: rating, dtype: bool"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.rating < 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This gives us `True`s and `False`s. Such a series is called a mask. If we count the number of `True`s, and divide by the total, we'll get the fraction of ratings $\\lt$ 3. To do this numerically see this:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sum(df.rating < 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Why did that work?"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 0\n"
]
}
],
"source": [
"print 1*True, 1*False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we ought to be able to do this"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sum(df.rating < 3)/df.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But we get a 0? Why? In Python 2.x division is **integer division** by default. So one can fix by converting the `df.shape[0]` to a float"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.00066666666666666664"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sum(df.rating < 3)/float(df.shape[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that you could just find the average since the `True`s map to 1s."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.00066666666666666664"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(df.rating < 3.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or directly, in Pandas, which works since `df.rating < 3` is a pandas Series."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.00066666666666666664"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(df.rating < 3).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Filtering\n",
"\n",
"Here are two ways to get a filtered dataframe"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>rating</th>\n",
" <th>review_count</th>\n",
" <th>isbn</th>\n",
" <th>booktype</th>\n",
" <th>author_url</th>\n",
" <th>year</th>\n",
" <th>genre_urls</th>\n",
" <th>dir</th>\n",
" <th>rating_count</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>4.58</td>\n",
" <td>1314</td>\n",
" <td>0345538374</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/656983.J...</td>\n",
" <td>1973</td>\n",
" <td>/genres/fantasy|/genres/classics|/genres/scien...</td>\n",
" <td>dir01/30.J_R_R_Tolkien_4_Book_Boxed_Set.html</td>\n",
" <td>68495</td>\n",
" <td>J.R.R. Tolkien 4-Book Boxed Set</td>\n",
" </tr>\n",
" <tr>\n",
" <th>162</th>\n",
" <td>4.55</td>\n",
" <td>15777</td>\n",
" <td>075640407X</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/108424.P...</td>\n",
" <td>2007</td>\n",
" <td>/genres/fantasy|/genres/fiction</td>\n",
" <td>dir02/186074.The_Name_of_the_Wind.html</td>\n",
" <td>210018</td>\n",
" <td>The Name of the Wind (The Kingkiller Chronicle...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>222</th>\n",
" <td>4.53</td>\n",
" <td>15256</td>\n",
" <td>055357342X</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/346732.G...</td>\n",
" <td>2000</td>\n",
" <td>/genres/fantasy|/genres/fiction|/genres/fantas...</td>\n",
" <td>dir03/62291.A_Storm_of_Swords.html</td>\n",
" <td>327992</td>\n",
" <td>A Storm of Swords (A Song of Ice and Fire, #3)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242</th>\n",
" <td>4.53</td>\n",
" <td>5404</td>\n",
" <td>0545265355</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/153394.S...</td>\n",
" <td>2010</td>\n",
" <td>/genres/young-adult|/genres/fiction|/genres/fa...</td>\n",
" <td>dir03/7938275-the-hunger-games-trilogy-boxset....</td>\n",
" <td>102330</td>\n",
" <td>The Hunger Games Trilogy Boxset (The Hunger Ga...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>249</th>\n",
" <td>4.80</td>\n",
" <td>644</td>\n",
" <td>0740748475</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/13778.Bi...</td>\n",
" <td>2005</td>\n",
" <td>/genres/sequential-art|/genres/comics|/genres/...</td>\n",
" <td>dir03/24812.The_Complete_Calvin_and_Hobbes.html</td>\n",
" <td>22674</td>\n",
" <td>The Complete Calvin and Hobbes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>284</th>\n",
" <td>4.58</td>\n",
" <td>15195</td>\n",
" <td>1406321346</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/150038.C...</td>\n",
" <td>2013</td>\n",
" <td>/genres/fantasy|/genres/young-adult|/genres/fa...</td>\n",
" <td>dir03/18335634-clockwork-princess.html</td>\n",
" <td>130161</td>\n",
" <td>Clockwork Princess (The Infernal Devices, #3)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>304</th>\n",
" <td>4.54</td>\n",
" <td>572</td>\n",
" <td>0140259449</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/1265.Jan...</td>\n",
" <td>1933</td>\n",
" <td>/genres/classics|/genres/fiction|/genres/roman...</td>\n",
" <td>dir04/14905.The_Complete_Novels.html</td>\n",
" <td>17539</td>\n",
" <td>The Complete Novels</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386</th>\n",
" <td>4.55</td>\n",
" <td>8820</td>\n",
" <td>0756404738</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/108424.P...</td>\n",
" <td>2011</td>\n",
" <td>/genres/fantasy|/genres/fantasy|/genres/epic-f...</td>\n",
" <td>dir04/1215032.The_Wise_Man_s_Fear.html</td>\n",
" <td>142499</td>\n",
" <td>The Wise Man's Fear (The Kingkiller Chronicle,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>400</th>\n",
" <td>4.53</td>\n",
" <td>9292</td>\n",
" <td>1423140605</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/15872.Ri...</td>\n",
" <td>2012</td>\n",
" <td>/genres/fantasy|/genres/young-adult|/genres/fa...</td>\n",
" <td>dir05/12127750-the-mark-of-athena.html</td>\n",
" <td>128412</td>\n",
" <td>The Mark of Athena (The Heroes of Olympus, #3)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>475</th>\n",
" <td>4.57</td>\n",
" <td>824</td>\n",
" <td>1416997857</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/150038.C...</td>\n",
" <td>2009</td>\n",
" <td>/genres/fantasy|/genres/young-adult|/genres/fa...</td>\n",
" <td>dir05/6485421-the-mortal-instruments-boxed-set...</td>\n",
" <td>39720</td>\n",
" <td>The Mortal Instruments Boxed Set (The Mortal I...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>483</th>\n",
" <td>4.59</td>\n",
" <td>2622</td>\n",
" <td>0312362153</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/4430.She...</td>\n",
" <td>2008</td>\n",
" <td>/genres/romance|/genres/paranormal-romance|/ge...</td>\n",
" <td>dir05/2299110.Acheron.html</td>\n",
" <td>35028</td>\n",
" <td>Acheron (Dark-Hunter, #8)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>554</th>\n",
" <td>4.54</td>\n",
" <td>4809</td>\n",
" <td>0385341679</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/48206.Ka...</td>\n",
" <td>2011</td>\n",
" <td>/genres/fantasy|/genres/urban-fantasy|/genres/...</td>\n",
" <td>dir06/7304203-shadowfever.html</td>\n",
" <td>52812</td>\n",
" <td>Shadowfever (Fever, #5)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>577</th>\n",
" <td>4.60</td>\n",
" <td>5732</td>\n",
" <td>0765326353</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/38550.Br...</td>\n",
" <td>2010</td>\n",
" <td>/genres/science-fiction-fantasy|/genres/fantas...</td>\n",
" <td>dir06/7235533-the-way-of-kings.html</td>\n",
" <td>76551</td>\n",
" <td>The Way of Kings (The Stormlight Archive, #1)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>620</th>\n",
" <td>4.54</td>\n",
" <td>7767</td>\n",
" <td>1423146727</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/15872.Ri...</td>\n",
" <td>2013</td>\n",
" <td>/genres/fantasy|/genres/young-adult|/genres/fa...</td>\n",
" <td>dir07/12127810-the-house-of-hades.html</td>\n",
" <td>72082</td>\n",
" <td>The House of Hades (The Heroes of Olympus, #4)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>840</th>\n",
" <td>4.57</td>\n",
" <td>431</td>\n",
" <td>1423113497</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/15872.Ri...</td>\n",
" <td>2008</td>\n",
" <td>/genres/fantasy|/genres/young-adult|/genres/fa...</td>\n",
" <td>dir09/3165162-percy-jackson-and-the-olympians-...</td>\n",
" <td>22937</td>\n",
" <td>Percy Jackson and the Olympians Boxed Set (Per...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>883</th>\n",
" <td>4.58</td>\n",
" <td>558</td>\n",
" <td>0140286802</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/500.Jorg...</td>\n",
" <td>1998</td>\n",
" <td>/genres/short-stories|/genres/literature|/genr...</td>\n",
" <td>dir09/17961.Collected_Fictions.html</td>\n",
" <td>12596</td>\n",
" <td>Collected Fictions</td>\n",
" </tr>\n",
" <tr>\n",
" <th>911</th>\n",
" <td>4.85</td>\n",
" <td>26</td>\n",
" <td>1491732954</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/8189303....</td>\n",
" <td>2014</td>\n",
" <td>/genres/fiction</td>\n",
" <td>dir10/22242097-honor-and-polygamy.html</td>\n",
" <td>97</td>\n",
" <td>Honor and Polygamy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>935</th>\n",
" <td>4.64</td>\n",
" <td>148</td>\n",
" <td>1595142711</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/137902.R...</td>\n",
" <td>2009</td>\n",
" <td>/genres/paranormal|/genres/vampires|/genres/yo...</td>\n",
" <td>dir10/6339989-vampire-academy-collection.html</td>\n",
" <td>21743</td>\n",
" <td>Vampire Academy Collection (Vampire Academy, #...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>938</th>\n",
" <td>4.51</td>\n",
" <td>11011</td>\n",
" <td>1481426303</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/150038.C...</td>\n",
" <td>2014</td>\n",
" <td>/genres/fantasy|/genres/young-adult|/genres/fa...</td>\n",
" <td>dir10/8755785-city-of-heavenly-fire.html</td>\n",
" <td>69924</td>\n",
" <td>City of Heavenly Fire (The Mortal Instruments,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>953</th>\n",
" <td>4.56</td>\n",
" <td>27</td>\n",
" <td>1477276068</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/6621980....</td>\n",
" <td>2012</td>\n",
" <td>NaN</td>\n",
" <td>dir10/16243767-crossing-the-seas.html</td>\n",
" <td>90</td>\n",
" <td>Crossing the Seas</td>\n",
" </tr>\n",
" <tr>\n",
" <th>958</th>\n",
" <td>4.57</td>\n",
" <td>38199</td>\n",
" <td>0545010225</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/1077326....</td>\n",
" <td>2007</td>\n",
" <td>/genres/fantasy|/genres/young-adult|/genres/fa...</td>\n",
" <td>dir10/136251.Harry_Potter_and_the_Deathly_Hall...</td>\n",
" <td>1245866</td>\n",
" <td>Harry Potter and the Deathly Hallows (Harry Po...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1033</th>\n",
" <td>4.56</td>\n",
" <td>1304</td>\n",
" <td>0007119550</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/346732.G...</td>\n",
" <td>2000</td>\n",
" <td>/genres/fiction|/genres/fantasy|/genres/epic-f...</td>\n",
" <td>dir11/147915.A_Storm_of_Swords.html</td>\n",
" <td>41161</td>\n",
" <td>A Storm of Swords (A Song of Ice and Fire, #3-2)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1109</th>\n",
" <td>4.70</td>\n",
" <td>23</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/7488658....</td>\n",
" <td>2013</td>\n",
" <td>/genres/romance</td>\n",
" <td>dir12/19181419-a-bird-without-wings.html</td>\n",
" <td>56</td>\n",
" <td>A Bird Without Wings</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1127</th>\n",
" <td>4.52</td>\n",
" <td>644</td>\n",
" <td>0141183047</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/7816.Fer...</td>\n",
" <td>1982</td>\n",
" <td>/genres/poetry|/genres/fiction|/genres/philoso...</td>\n",
" <td>dir12/45974.The_Book_of_Disquiet.html</td>\n",
" <td>7463</td>\n",
" <td>The Book of Disquiet</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151</th>\n",
" <td>4.64</td>\n",
" <td>84</td>\n",
" <td>1491877928</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/7271860....</td>\n",
" <td>2013</td>\n",
" <td>/genres/war|/genres/historical-fiction|/genres...</td>\n",
" <td>dir12/18501652-the-guardian-of-secrets-and-her...</td>\n",
" <td>167</td>\n",
" <td>The Guardian of Secrets and Her Deathly Pact</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1186</th>\n",
" <td>4.51</td>\n",
" <td>4853</td>\n",
" <td>1619630621</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/3433047....</td>\n",
" <td>2013</td>\n",
" <td>/genres/fantasy|/genres/young-adult|/genres/ro...</td>\n",
" <td>dir12/17167166-crown-of-midnight.html</td>\n",
" <td>34142</td>\n",
" <td>Crown of Midnight (Throne of Glass, #2)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1202</th>\n",
" <td>4.59</td>\n",
" <td>1260</td>\n",
" <td>0310902711</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/5158478....</td>\n",
" <td>1972</td>\n",
" <td>/genres/religion|/genres/christian|/genres/non...</td>\n",
" <td>dir13/280111.Holy_Bible.html</td>\n",
" <td>25584</td>\n",
" <td>Holy Bible</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1260</th>\n",
" <td>4.60</td>\n",
" <td>1943</td>\n",
" <td>0842377506</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/6492.Fra...</td>\n",
" <td>1993</td>\n",
" <td>/genres/christian-fiction|/genres/historical-f...</td>\n",
" <td>dir13/95617.A_Voice_in_the_Wind.html</td>\n",
" <td>37923</td>\n",
" <td>A Voice in the Wind (Mark of the Lion, #1)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1268</th>\n",
" <td>4.52</td>\n",
" <td>215</td>\n",
" <td>1557091528</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/63859.Ja...</td>\n",
" <td>1787</td>\n",
" <td>/genres/history|/genres/non-fiction|/genres/po...</td>\n",
" <td>dir13/89959.The_Constitution_of_the_United_Sta...</td>\n",
" <td>12894</td>\n",
" <td>The Constitution of the United States of America</td>\n",
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" <tr>\n",
" <th>1300</th>\n",
" <td>4.61</td>\n",
" <td>24</td>\n",
" <td>1499227299</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/7414345....</td>\n",
" <td>2014</td>\n",
" <td>/genres/paranormal|/genres/vampires|/genres/pa...</td>\n",
" <td>dir14/22090082-vampire-princess-rising.html</td>\n",
" <td>128</td>\n",
" <td>Vampire Princess Rising (The Winters Family Sa...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5532</th>\n",
" <td>4.86</td>\n",
" <td>4</td>\n",
" <td>1477504540</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/5989528....</td>\n",
" <td>2013</td>\n",
" <td>NaN</td>\n",
" <td>dir56/17695243-call-of-the-lost-ages.html</td>\n",
" <td>7</td>\n",
" <td>Call Of The Lost Ages</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5549</th>\n",
" <td>4.62</td>\n",
" <td>13</td>\n",
" <td>0882408704</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/947.Will...</td>\n",
" <td>1899</td>\n",
" <td>/genres/classics|/genres/fiction|/genres/poetr...</td>\n",
" <td>dir56/17134346-the-complete-works-of-william-s...</td>\n",
" <td>217</td>\n",
" <td>The Complete Works of William Shakespeare</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5557</th>\n",
" <td>4.61</td>\n",
" <td>14</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/32401.Al...</td>\n",
" <td>2006</td>\n",
" <td>/genres/fantasy|/genres/young-adult</td>\n",
" <td>dir56/13488552-the-books-of-pellinor.html</td>\n",
" <td>394</td>\n",
" <td>The Books of Pellinor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5563</th>\n",
" <td>4.70</td>\n",
" <td>30</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/7153266....</td>\n",
" <td>2014</td>\n",
" <td>/genres/childrens</td>\n",
" <td>dir56/20445451-children-s-book.html</td>\n",
" <td>57</td>\n",
" <td>Children's book</td>\n",
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" <tr>\n",
" <th>5564</th>\n",
" <td>5.00</td>\n",
" <td>9</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/7738947....</td>\n",
" <td>2014</td>\n",
" <td>/genres/romance|/genres/new-adult</td>\n",
" <td>dir56/21902777-untainted.html</td>\n",
" <td>14</td>\n",
" <td>Untainted (Photographer Trilogy, #3)</td>\n",
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" <tr>\n",
" <th>5584</th>\n",
" <td>4.75</td>\n",
" <td>3</td>\n",
" <td>1481959824</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/5100743....</td>\n",
" <td>2013</td>\n",
" <td>NaN</td>\n",
" <td>dir56/17606460-why-not-world.html</td>\n",
" <td>8</td>\n",
" <td>Why Not-World</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5588</th>\n",
" <td>4.66</td>\n",
" <td>190</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/4942228....</td>\n",
" <td>2011</td>\n",
" <td>/genres/romance|/genres/m-m-romance|/genres/sc...</td>\n",
" <td>dir56/11737700-fade.html</td>\n",
" <td>996</td>\n",
" <td>Fade (In the company of shadows, #4)</td>\n",
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" <tr>\n",
" <th>5591</th>\n",
" <td>4.58</td>\n",
" <td>31</td>\n",
" <td>1500118680</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/7738947....</td>\n",
" <td>2014</td>\n",
" <td>/genres/romance|/genres/new-adult</td>\n",
" <td>dir56/22023804-logan-s-story.html</td>\n",
" <td>45</td>\n",
" <td>Logan's Story (Sand & Clay, #0.5)</td>\n",
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" <tr>\n",
" <th>5601</th>\n",
" <td>4.66</td>\n",
" <td>312</td>\n",
" <td>0842384898</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/5158478....</td>\n",
" <td>1902</td>\n",
" <td>/genres/christian|/genres/religion|/genres/non...</td>\n",
" <td>dir57/930470.Holy_Bible.html</td>\n",
" <td>2666</td>\n",
" <td>Holy Bible</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5607</th>\n",
" <td>4.66</td>\n",
" <td>513</td>\n",
" <td>0007444397</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/4659154....</td>\n",
" <td>2011</td>\n",
" <td>/genres/non-fiction|/genres/biography</td>\n",
" <td>dir57/11792612-dare-to-dream.html</td>\n",
" <td>5572</td>\n",
" <td>Dare to Dream (100% Official)</td>\n",
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" <tr>\n",
" <th>5619</th>\n",
" <td>4.52</td>\n",
" <td>462</td>\n",
" <td>0991190920</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/7092218....</td>\n",
" <td>2014</td>\n",
" <td>/genres/fantasy|/genres/paranormal|/genres/fai...</td>\n",
" <td>dir57/18188649-escaping-destiny.html</td>\n",
" <td>3795</td>\n",
" <td>Escaping Destiny (The Fae Chronicles, #3)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5635</th>\n",
" <td>4.54</td>\n",
" <td>958</td>\n",
" <td>0778315703</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/4480131....</td>\n",
" <td>2013</td>\n",
" <td>/genres/erotica|/genres/bdsm|/genres/adult-fic...</td>\n",
" <td>dir57/17251444-the-mistress.html</td>\n",
" <td>4869</td>\n",
" <td>The Mistress (The Original Sinners, #4)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5642</th>\n",
" <td>4.70</td>\n",
" <td>158</td>\n",
" <td>1417642165</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/13778.Bi...</td>\n",
" <td>1992</td>\n",
" <td>/genres/sequential-art|/genres/comics|/genres/...</td>\n",
" <td>dir57/70487.Calvin_and_Hobbes.html</td>\n",
" <td>9224</td>\n",
" <td>Calvin and Hobbes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5657</th>\n",
" <td>4.80</td>\n",
" <td>8</td>\n",
" <td>1469908530</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/4695431....</td>\n",
" <td>2012</td>\n",
" <td>/genres/fantasy</td>\n",
" <td>dir57/15734769-myrtle-mae-and-the-mirror-in-th...</td>\n",
" <td>10</td>\n",
" <td>Myrtle Mae and the Mirror in the Attic (The Ma...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5665</th>\n",
" <td>4.53</td>\n",
" <td>61</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/7738947....</td>\n",
" <td>2014</td>\n",
" <td>/genres/romance|/genres/new-adult|/genres/myst...</td>\n",
" <td>dir57/20975446-tainted-pictures.html</td>\n",
" <td>103</td>\n",
" <td>Tainted Pictures (Photographer Trilogy, #2)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5683</th>\n",
" <td>4.56</td>\n",
" <td>204</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/3097905....</td>\n",
" <td>NaN</td>\n",
" <td>/genres/fantasy|/genres/young-adult|/genres/ro...</td>\n",
" <td>dir57/12474623-tiger-s-dream.html</td>\n",
" <td>895</td>\n",
" <td>Tiger's Dream (The Tiger Saga, #5)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5692</th>\n",
" <td>5.00</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/5989528....</td>\n",
" <td>2012</td>\n",
" <td>NaN</td>\n",
" <td>dir57/14288412-abstraction-in-theory---laws-of...</td>\n",
" <td>6</td>\n",
" <td>Abstraction In Theory - Laws Of Physical Trans...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5716</th>\n",
" <td>4.67</td>\n",
" <td>34</td>\n",
" <td>0810117134</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/205563.M...</td>\n",
" <td>1970</td>\n",
" <td>/genres/classics|/genres/fiction|/genres/histo...</td>\n",
" <td>dir58/1679497.The_Fortress.html</td>\n",
" <td>1335</td>\n",
" <td>The Fortress</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5717</th>\n",
" <td>4.71</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/5838022....</td>\n",
" <td>2012</td>\n",
" <td>NaN</td>\n",
" <td>dir58/13741511-american-amaranth.html</td>\n",
" <td>14</td>\n",
" <td>American Amaranth</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5718</th>\n",
" <td>4.60</td>\n",
" <td>656</td>\n",
" <td>1613725132</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/1122775....</td>\n",
" <td>2012</td>\n",
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" <td>dir58/13246997-armed-dangerous.html</td>\n",
" <td>5268</td>\n",
" <td>Armed & Dangerous (Cut & Run, #5)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5726</th>\n",
" <td>4.55</td>\n",
" <td>106</td>\n",
" <td>1594170347</td>\n",
" <td>good_reads:book</td>\n",
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" <td>1750</td>\n",
" <td>Holy Bible</td>\n",
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" <tr>\n",
" <th>5729</th>\n",
" <td>4.83</td>\n",
" <td>16</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/7058502....</td>\n",
" <td>2014</td>\n",
" <td>NaN</td>\n",
" <td>dir58/22312293-the-keeper.html</td>\n",
" <td>29</td>\n",
" <td>The Keeper (The Keeper, #5)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5753</th>\n",
" <td>4.61</td>\n",
" <td>811</td>\n",
" <td>1937551865</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/1122775....</td>\n",
" <td>2013</td>\n",
" <td>/genres/romance|/genres/m-m-romance|/genres/ro...</td>\n",
" <td>dir58/16159276-touch-geaux.html</td>\n",
" <td>4212</td>\n",
" <td>Touch & Geaux (Cut & Run, #7)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5764</th>\n",
" <td>4.54</td>\n",
" <td>228</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/2112402....</td>\n",
" <td>2013</td>\n",
" <td>/genres/non-fiction|/genres/self-help|/genres/...</td>\n",
" <td>dir58/18479831-staying-strong.html</td>\n",
" <td>2343</td>\n",
" <td>Staying Strong</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5778</th>\n",
" <td>4.63</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/4808225....</td>\n",
" <td>2010</td>\n",
" <td>NaN</td>\n",
" <td>dir58/11187937-un-spoken.html</td>\n",
" <td>19</td>\n",
" <td>(Un) Spoken</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5806</th>\n",
" <td>4.57</td>\n",
" <td>121</td>\n",
" <td>0679777458</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/8361.Dor...</td>\n",
" <td>1966</td>\n",
" <td>/genres/historical-fiction|/genres/fiction|/ge...</td>\n",
" <td>dir59/351211.The_Disorderly_Knights.html</td>\n",
" <td>2177</td>\n",
" <td>The Disorderly Knights (The Lymond Chronicles,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5873</th>\n",
" <td>4.55</td>\n",
" <td>103</td>\n",
" <td>144247372X</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/2876763....</td>\n",
" <td>2012</td>\n",
" <td>/genres/fantasy|/genres/paranormal|/genres/ang...</td>\n",
" <td>dir59/14367071-the-complete-hush-hush-saga.html</td>\n",
" <td>2869</td>\n",
" <td>The Complete Hush, Hush Saga</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5874</th>\n",
" <td>4.78</td>\n",
" <td>18</td>\n",
" <td>2851944371</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/318835.O...</td>\n",
" <td>1972</td>\n",
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" <td>dir59/2014000.Le_Monogramme.html</td>\n",
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" <tr>\n",
" <th>5880</th>\n",
" <td>4.61</td>\n",
" <td>123</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>5957</th>\n",
" <td>4.72</td>\n",
" <td>104</td>\n",
" <td>178048044X</td>\n",
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"5874 4.78 18 2851944371 good_reads:book https://www.goodreads.com/author/show/318835.O... 1972 /genres/poetry|/genres/fiction|/genres/nobel-p... dir59/2014000.Le_Monogramme.html 565 Le Monogramme\n",
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"5957 4.72 104 178048044X good_reads:book https://www.goodreads.com/author/show/20248.J_... 2010 /genres/romance|/genres/paranormal|/genres/vam... dir60/10780042-j-r-ward-collection.html 1788 J. R. Ward Collection\n",
"\n",
"[224 rows x 10 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.query(\"rating > 4.5\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we create a mask and use it to \"index\" into the dataframe to get the rows we want."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th>isbn</th>\n",
" <th>booktype</th>\n",
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" <th>rating_count</th>\n",
" <th>name</th>\n",
" <th>author</th>\n",
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" <td>493</td>\n",
" <td>0140443339</td>\n",
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" <td>20570</td>\n",
" <td>The Histories</td>\n",
" <td>Herodotus</td>\n",
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" <td>4.30</td>\n",
" <td>131</td>\n",
" <td>0872203492</td>\n",
" <td>book</td>\n",
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" <td>Plato</td>\n",
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" <th>3274</th>\n",
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" <td>411</td>\n",
" <td>0140449493</td>\n",
" <td>book</td>\n",
" <td>https://www.goodreads.com/author/show/2192.Ari...</td>\n",
" <td>-350</td>\n",
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" <th>3757</th>\n",
" <td>3.82</td>\n",
" <td>364</td>\n",
" <td>0872206033</td>\n",
" <td>book</td>\n",
" <td>https://www.goodreads.com/author/show/1011.Ari...</td>\n",
" <td>-411</td>\n",
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" <td>Lysistrata</td>\n",
" <td>Aristophanes</td>\n",
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" <tr>\n",
" <th>4402</th>\n",
" <td>3.99</td>\n",
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" <td>Plato</td>\n",
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" <td>Five Dialogues</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
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" rating review_count isbn booktype author_url year genre_urls dir rating_count name author\n",
"47 3.68 5785 0143039954 book https://www.goodreads.com/author/show/903.Homer -800 /genres/classics|/genres/fiction|/genres/poetr... dir01/1381.The_Odyssey.html 560248 The Odyssey Homer\n",
"246 4.01 365 0147712556 book https://www.goodreads.com/author/show/903.Homer -800 /genres/classics|/genres/fantasy|/genres/mytho... dir03/1375.The_Iliad_The_Odyssey.html 35123 The Iliad/The Odyssey Homer\n",
"455 3.85 1499 0140449140 book https://www.goodreads.com/author/show/879.Plato -380 /genres/philosophy|/genres/classics|/genres/no... dir05/30289.The_Republic.html 82022 The Republic Plato\n",
"596 3.77 1240 0679729526 book https://www.goodreads.com/author/show/919.Virgil -29 /genres/classics|/genres/poetry|/genres/fictio... dir06/12914.The_Aeneid.html 60308 The Aeneid Virgil\n",
"629 3.64 1231 1580495931 book https://www.goodreads.com/author/show/1002.Sop... -429 /genres/classics|/genres/plays|/genres/drama|/... dir07/1554.Oedipus_Rex.html 93192 Oedipus Rex Sophocles\n",
"674 3.92 3559 1590302257 book https://www.goodreads.com/author/show/1771.Sun... -512 /genres/non-fiction|/genres/politics|/genres/c... dir07/10534.The_Art_of_War.html 114619 The Art of War Sun_Tzu\n",
"746 4.06 1087 0140449183 book https://www.goodreads.com/author/show/5158478.... -500 /genres/classics|/genres/spirituality|/genres/... dir08/99944.The_Bhagavad_Gita.html 31634 The Bhagavad Gita Anonymous\n",
"777 3.52 1038 1580493882 book https://www.goodreads.com/author/show/1002.Sop... -442 /genres/drama|/genres/fiction|/genres/classics... dir08/7728.Antigone.html 49084 Antigone Sophocles\n",
"1233 3.94 704 015602764X book https://www.goodreads.com/author/show/1002.Sop... -400 /genres/classics|/genres/plays|/genres/drama|/... dir13/1540.The_Oedipus_Cycle.html 36008 The Oedipus Cycle Sophocles\n",
"1397 4.03 890 0192840509 book https://www.goodreads.com/author/show/12452.Aesop -560 /genres/classics|/genres/childrens|/genres/lit... dir14/21348.Aesop_s_Fables.html 71259 Aesop's Fables Aesop\n",
"1398 3.60 1644 0141026286 book https://www.goodreads.com/author/show/5158478.... -1500 /genres/religion|/genres/literature|/genres/an... dir14/19351.The_Epic_of_Gilgamesh.html 42026 The Epic of Gilgamesh Anonymous\n",
"1428 3.80 539 0486275485 book https://www.goodreads.com/author/show/973.Euri... -431 /genres/classics|/genres/plays|/genres/drama|/... dir15/752900.Medea.html 29858 Medea Euripides\n",
"1815 3.96 493 0140443339 book https://www.goodreads.com/author/show/990.Aesc... -458 /genres/classics|/genres/plays|/genres/drama|/... dir19/1519.The_Oresteia.html 18729 The Oresteia Aeschylus\n",
"1882 4.02 377 0872205541 book https://www.goodreads.com/author/show/879.Plato -400 /genres/philosophy|/genres/classics|/genres/no... dir19/22632.The_Trial_and_Death_of_Socrates.html 18712 The Trial and Death of Socrates Plato\n",
"2078 3.84 399 0140440399 book https://www.goodreads.com/author/show/957.Thuc... -411 /genres/history|/genres/classics|/genres/non-f... dir21/261243.The_History_of_the_Peloponnesian_... 17212 The History of the Peloponnesian War Thucydides\n",
"2527 3.94 506 0140449086 book https://www.goodreads.com/author/show/901.Hero... -440 /genres/history|/genres/classics|/genres/non-f... dir26/1362.The_Histories.html 20570 The Histories Herodotus\n",
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"3274 3.88 411 0140449493 book https://www.goodreads.com/author/show/2192.Ari... -350 /genres/philosophy|/genres/classics|/genres/no... dir33/19068.The_Nicomachean_Ethics.html 16534 The Nicomachean Ethics Aristotle\n",
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"4402 3.99 516 0140449272 book https://www.goodreads.com/author/show/879.Plato -370 /genres/non-fiction|/genres/classics|/genres/p... dir45/81779.The_Symposium.html 18457 The Symposium Plato\n",
"4475 4.11 281 0865163480 book https://www.goodreads.com/author/show/879.Plato -390 /genres/philosophy|/genres/classics|/genres/no... dir45/73945.Apology.html 11478 Apology Plato\n",
"5367 4.07 133 0872206335 book https://www.goodreads.com/author/show/879.Plato -360 /genres/philosophy|/genres/classics|/genres/no... dir54/30292.Five_Dialogues.html 9964 Five Dialogues Plato"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.year < 0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to combine these conditions, use the second form and put '()' brackets around each condition. The query uses a boolean AND. Each condition ceates a mask of trues and falses."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
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{
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" <td>https://www.goodreads.com/author/show/5158478....</td>\n",
" <td>-500</td>\n",
" <td>/genres/classics|/genres/spirituality|/genres/...</td>\n",
" <td>dir08/99944.The_Bhagavad_Gita.html</td>\n",
" <td>31634</td>\n",
" <td>The Bhagavad Gita</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1397</th>\n",
" <td>4.03</td>\n",
" <td>890</td>\n",
" <td>0192840509</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/12452.Aesop</td>\n",
" <td>-560</td>\n",
" <td>/genres/classics|/genres/childrens|/genres/lit...</td>\n",
" <td>dir14/21348.Aesop_s_Fables.html</td>\n",
" <td>71259</td>\n",
" <td>Aesop's Fables</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1882</th>\n",
" <td>4.02</td>\n",
" <td>377</td>\n",
" <td>0872205541</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/879.Plato</td>\n",
" <td>-400</td>\n",
" <td>/genres/philosophy|/genres/classics|/genres/no...</td>\n",
" <td>dir19/22632.The_Trial_and_Death_of_Socrates.html</td>\n",
" <td>18712</td>\n",
" <td>The Trial and Death of Socrates</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3133</th>\n",
" <td>4.30</td>\n",
" <td>131</td>\n",
" <td>0872203492</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/879.Plato</td>\n",
" <td>-400</td>\n",
" <td>/genres/philosophy|/genres/classics|/genres/no...</td>\n",
" <td>dir32/9462.Complete_Works.html</td>\n",
" <td>7454</td>\n",
" <td>Complete Works</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4475</th>\n",
" <td>4.11</td>\n",
" <td>281</td>\n",
" <td>0865163480</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/879.Plato</td>\n",
" <td>-390</td>\n",
" <td>/genres/philosophy|/genres/classics|/genres/no...</td>\n",
" <td>dir45/73945.Apology.html</td>\n",
" <td>11478</td>\n",
" <td>Apology</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5367</th>\n",
" <td>4.07</td>\n",
" <td>133</td>\n",
" <td>0872206335</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/879.Plato</td>\n",
" <td>-360</td>\n",
" <td>/genres/philosophy|/genres/classics|/genres/no...</td>\n",
" <td>dir54/30292.Five_Dialogues.html</td>\n",
" <td>9964</td>\n",
" <td>Five Dialogues</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rating review_count isbn booktype author_url year genre_urls dir rating_count name\n",
"246 4.01 365 0147712556 good_reads:book https://www.goodreads.com/author/show/903.Homer -800 /genres/classics|/genres/fantasy|/genres/mytho... dir03/1375.The_Iliad_The_Odyssey.html 35123 The Iliad/The Odyssey\n",
"746 4.06 1087 0140449183 good_reads:book https://www.goodreads.com/author/show/5158478.... -500 /genres/classics|/genres/spirituality|/genres/... dir08/99944.The_Bhagavad_Gita.html 31634 The Bhagavad Gita\n",
"1397 4.03 890 0192840509 good_reads:book https://www.goodreads.com/author/show/12452.Aesop -560 /genres/classics|/genres/childrens|/genres/lit... dir14/21348.Aesop_s_Fables.html 71259 Aesop's Fables\n",
"1882 4.02 377 0872205541 good_reads:book https://www.goodreads.com/author/show/879.Plato -400 /genres/philosophy|/genres/classics|/genres/no... dir19/22632.The_Trial_and_Death_of_Socrates.html 18712 The Trial and Death of Socrates\n",
"3133 4.30 131 0872203492 good_reads:book https://www.goodreads.com/author/show/879.Plato -400 /genres/philosophy|/genres/classics|/genres/no... dir32/9462.Complete_Works.html 7454 Complete Works\n",
"4475 4.11 281 0865163480 good_reads:book https://www.goodreads.com/author/show/879.Plato -390 /genres/philosophy|/genres/classics|/genres/no... dir45/73945.Apology.html 11478 Apology\n",
"5367 4.07 133 0872206335 good_reads:book https://www.goodreads.com/author/show/879.Plato -360 /genres/philosophy|/genres/classics|/genres/no... dir54/30292.Five_Dialogues.html 9964 Five Dialogues"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[(df.year < 0) & (df.rating > 4)]#there were none greater than 4.5!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Cleaning"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We first check the datatypes. Notice that `review_count`, `rating_count` are of type `object` (which means they are either strings or Pandas couldnt figure what they are), while `year` is a float."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"rating float64\n",
"review_count object\n",
"isbn object\n",
"booktype object\n",
"author_url object\n",
"year float64\n",
"genre_urls object\n",
"dir object\n",
"rating_count object\n",
"name object\n",
"dtype: object"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Suppose we try and fix this"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "invalid literal for long() with base 10: 'None'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-21-8bf38ae9d108>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rating_count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrating_count\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'review_count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreview_count\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, raise_on_error, **kwargs)\u001b[0m\n\u001b[1;32m 2409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2410\u001b[0m mgr = self._data.astype(\n\u001b[0;32m-> 2411\u001b[0;31m dtype=dtype, copy=copy, raise_on_error=raise_on_error, **kwargs)\n\u001b[0m\u001b[1;32m 2412\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmgr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2503\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2504\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'astype'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2506\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, axes, filter, do_integrity_check, **kwargs)\u001b[0m\n\u001b[1;32m 2457\u001b[0m copy=align_copy)\n\u001b[1;32m 2458\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2459\u001b[0;31m \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2460\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2461\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, raise_on_error, values, **kwargs)\u001b[0m\n\u001b[1;32m 371\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_on_error\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 372\u001b[0m return self._astype(dtype, copy=copy, raise_on_error=raise_on_error,\n\u001b[0;32m--> 373\u001b[0;31m values=values, **kwargs)\n\u001b[0m\u001b[1;32m 374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 375\u001b[0m def _astype(self, dtype, copy=False, raise_on_error=True, values=None,\n",
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36m_astype\u001b[0;34m(self, dtype, copy, raise_on_error, values, klass, **kwargs)\u001b[0m\n\u001b[1;32m 401\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 402\u001b[0m \u001b[0;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_astype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 404\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 405\u001b[0m newb = make_block(values,\n",
"\u001b[0;32m//anaconda/lib/python2.7/site-packages/pandas/core/common.pyc\u001b[0m in \u001b[0;36m_astype_nansafe\u001b[0;34m(arr, dtype, copy)\u001b[0m\n\u001b[1;32m 2729\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobject_\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2730\u001b[0m \u001b[0;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2731\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype_intsafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2732\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2733\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/lib.pyx\u001b[0m in \u001b[0;36mpandas.lib.astype_intsafe (pandas/lib.c:14844)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/src/util.pxd\u001b[0m in \u001b[0;36mutil.set_value_at (pandas/lib.c:63086)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: invalid literal for long() with base 10: 'None'"
]
}
],
"source": [
"df['rating_count']=df.rating_count.astype(int)\n",
"df['review_count']=df.review_count.astype(int)\n",
"df['year']=df.year.astype(int)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Oppos we got an error. Something is not right. Its trying to convert some python datatype: `None` into an int. This usually means data was missing. Was it?"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>rating</th>\n",
" <th>review_count</th>\n",
" <th>isbn</th>\n",
" <th>booktype</th>\n",
" <th>author_url</th>\n",
" <th>year</th>\n",
" <th>genre_urls</th>\n",
" <th>dir</th>\n",
" <th>rating_count</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2442</th>\n",
" <td>4.23</td>\n",
" <td>526</td>\n",
" <td></td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/623606.A...</td>\n",
" <td>NaN</td>\n",
" <td>/genres/religion|/genres/islam|/genres/non-fic...</td>\n",
" <td>dir25/1301625.La_Tahzan.html</td>\n",
" <td>4134</td>\n",
" <td>La Tahzan</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2869</th>\n",
" <td>4.61</td>\n",
" <td>2</td>\n",
" <td></td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/8182217....</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>dir29/22031070-my-death-experiences---a-preach...</td>\n",
" <td>23</td>\n",
" <td>My Death Experiences - A Preacherâs 18 Apoca...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3643</th>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>dir37/9658936-harry-potter.html</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5282</th>\n",
" <td>NaN</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td></td>\n",
" <td>dir53/113138.The_Winner.html</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5572</th>\n",
" <td>3.71</td>\n",
" <td>35</td>\n",
" <td>8423336603</td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/285658.E...</td>\n",
" <td>NaN</td>\n",
" <td>/genres/fiction</td>\n",
" <td>dir56/890680._rase_una_vez_el_amor_pero_tuve_q...</td>\n",
" <td>403</td>\n",
" <td>Ãrase una vez el amor pero tuve que matarlo. ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5658</th>\n",
" <td>4.32</td>\n",
" <td>44</td>\n",
" <td></td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/25307.Ro...</td>\n",
" <td>NaN</td>\n",
" <td>/genres/fantasy|/genres/fantasy|/genres/epic-f...</td>\n",
" <td>dir57/5533041-assassin-s-apprentice-royal-assa...</td>\n",
" <td>3850</td>\n",
" <td>Assassin's Apprentice / Royal Assassin (Farsee...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5683</th>\n",
" <td>4.56</td>\n",
" <td>204</td>\n",
" <td></td>\n",
" <td>good_reads:book</td>\n",
" <td>https://www.goodreads.com/author/show/3097905....</td>\n",
" <td>NaN</td>\n",
" <td>/genres/fantasy|/genres/young-adult|/genres/ro...</td>\n",
" <td>dir57/12474623-tiger-s-dream.html</td>\n",
" <td>895</td>\n",
" <td>Tiger's Dream (The Tiger Saga, #5)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rating review_count isbn booktype author_url year genre_urls dir rating_count name\n",
"2442 4.23 526 good_reads:book https://www.goodreads.com/author/show/623606.A... NaN /genres/religion|/genres/islam|/genres/non-fic... dir25/1301625.La_Tahzan.html 4134 La Tahzan\n",
"2869 4.61 2 good_reads:book https://www.goodreads.com/author/show/8182217.... NaN dir29/22031070-my-death-experiences---a-preach... 23 My Death Experiences - A Preacherâs 18 Apoca...\n",
"3643 NaN None None None None NaN dir37/9658936-harry-potter.html None None\n",
"5282 NaN None None None None NaN dir53/113138.The_Winner.html None None\n",
"5572 3.71 35 8423336603 good_reads:book https://www.goodreads.com/author/show/285658.E... NaN /genres/fiction dir56/890680._rase_una_vez_el_amor_pero_tuve_q... 403 Ãrase una vez el amor pero tuve que matarlo. ...\n",
"5658 4.32 44 good_reads:book https://www.goodreads.com/author/show/25307.Ro... NaN /genres/fantasy|/genres/fantasy|/genres/epic-f... dir57/5533041-assassin-s-apprentice-royal-assa... 3850 Assassin's Apprentice / Royal Assassin (Farsee...\n",
"5683 4.56 204 good_reads:book https://www.goodreads.com/author/show/3097905.... NaN /genres/fantasy|/genres/young-adult|/genres/ro... dir57/12474623-tiger-s-dream.html 895 Tiger's Dream (The Tiger Saga, #5)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.year.isnull()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Aha, we had some incomplete data. Lets get rid of it"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5993, 10)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df[df.year.notnull()]\n",
"df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We removed those 7 rows. Lets try the type conversion again"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df['rating_count']=df.rating_count.astype(int)\n",
"df['review_count']=df.review_count.astype(int)\n",
"df['year']=df.year.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"rating float64\n",
"review_count int64\n",
"isbn object\n",
"booktype object\n",
"author_url object\n",
"year int64\n",
"genre_urls object\n",
"dir object\n",
"rating_count int64\n",
"name object\n",
"dtype: object"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Much cleaner now!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Visualizing\n",
"\n",
"Pandas has handy built in visualization."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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u4J2ZeVlE/D5wXWb+aUcPKklqaS6uAO4D/igiNlfPL5uDY0qSWuj4FYAkqTv5SWBJKpQB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\nUqEMAEkq1P8CWOKSaEYjMvAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ad37d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.rating.hist();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can do this in more detail, plotting against a mean, with cutom binsize or number of bins. Note how to label axes and create legends."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.04220073358 4.04220073358 4.05\n"
]
},
{
"data": {
"image/png": 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o8MfGxur222+XdOa8THBwsPbv36+YmBhJUqdOnbR9+3ZZrVZFR0crJCREISEh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\n9uHh4b72goICrV27VpKUn5+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"text/plain": [
"<matplotlib.figure.Figure at 0x105ba5d90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_context(\"notebook\")\n",
"meanrat=df.rating.mean()\n",
"#you can get means and medians in different ways\n",
"print meanrat, np.mean(df.rating), df.rating.median()\n",
"with sns.axes_style(\"whitegrid\"):\n",
" df.rating.hist(bins=30, alpha=0.4);\n",
" plt.axvline(meanrat, 0, 0.75, color='r', label='Mean')\n",
" plt.xlabel(\"average rating of book\")\n",
" plt.ylabel(\"Counts\")\n",
" plt.title(\"Ratings Histogram\")\n",
" plt.legend()\n",
" #sns.despine()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One can see the sparseness of review counts. This will be important when we learn about recommendations: we'll have to *regularize* our models to deal with it."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x105bd4550>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x105bd4390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.review_count.hist(bins=np.arange(0, 40000, 400))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The structure may be easier to see if we rescale the x-axis to be logarithmic."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x102105850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.review_count.hist(bins=100)\n",
"plt.xscale(\"log\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we make a scatterplot in matplotlib of rating against year. By setting the alpha transparency low we can how the density of highly rated books on goodreads has changed."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x109ca9090>"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WFrBm0+0jcL19zkLSOlxbK/ZOsrKI+XPkvxefQGetG+4HZNjm5zHhtBZFOneAqpCTnncZ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\nPxtVvum0I3bQAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b40e550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(df.year, df.rating, lw=0, alpha=.08)\n",
"plt.xlim([1900,2010])\n",
"plt.xlabel(\"Year\")\n",
"plt.ylabel(\"Rating\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Pythons and ducks\n",
"\n",
"Notice that we used the series in the x-list and y-list slots in the `scatter` function in the `plt` module.\n",
"\n",
"In working with python I always remember: a python is a duck.\n",
"\n",
"What I mean is, python has a certain way of doing things. For example lets call one of these ways listiness. Listiness works on lists, dictionaries, files, and a general notion of something called an iterator.\n",
"\n",
"A Pandas series plays like a python list:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"alist=[1,2,3,4,5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can construct another list by using the syntax below, also called a list comprehension."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 4, 9, 16, 25]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"asquaredlist=[i*i for i in alist]\n",
"asquaredlist"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then we can again make a scatterplot"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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4sFqtlk2b5qSra8ubb8Jy/RdgPBDgUaBWq2XBgovT1NSYPXt6S48DwFHgFDQAFDCsI+CB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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109966c90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(alist, asquaredlist);"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<type 'list'>\n"
]
}
],
"source": [
"print type(alist)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In other words, something is a duck if it quacks like a duck. A Pandas series quacks like a python list. They both support something called the iterator protocol, an notion of behaving in a \"listy\" way. And Python functions like `plt.scatter` will accept anything that behaves listy. Indeed here's one more example:"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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lWUokErJtW6FQaMC4bduybVuWZQ1Y1wvLuiBzO5w7Sfn5oWHWNhe5eEdW3pCTd2TlDTlN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\nGIDCBwDAAP8fTbhp017bPN4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a634f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(df.rating_count.values, bins=100, alpha=0.5);"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.series.Series'> <type 'numpy.ndarray'>\n"
]
}
],
"source": [
"print type(df.rating_count), type(df.rating_count.values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Series and numpy lists behave similarly as well.\n",
"\n",
"\n",
"### Vectorization\n",
"\n",
"Numpy arrays are a bit different from regular python lists, and are the bread and butter of data science. Pandas Series are built atop them. "
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4, 5, 1, 2, 3, 4, 5]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alist + alist"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array(alist)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 2, 4, 6, 8, 10])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array(alist)+np.array(alist)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 4, 9, 16, 25])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array(alist)**2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In other words, operations on numpy arrays, and by extension, Pandas Series, are **vectorized**. You can add two numpy lists by just using `+` whereas the result isnt what you might expect for regular python lists. To add regular python lists elementwise, you will need to use a loop:"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[2, 4, 6, 8, 10]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"newlist=[]\n",
"for item in alist:\n",
" newlist.append(item+item)\n",
"newlist"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Vectorization** is a powerful idiom, and we will use it a lot in this class. And, for almost all data intensive computing, we will use numpy arrays rather than python lists, as the python numerical stack is based on it. \n",
"\n",
"You have seen this in idea in spreadsheets where you add an entire column to another one.\n",
"\n",
"Two final examples"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<type 'numpy.ndarray'>\n",
"[ 1 4 9 16 25]\n"
]
}
],
"source": [
"a=np.array([1,2,3,4,5])\n",
"print type(a)\n",
"b=np.array([1,2,3,4,5])\n",
"\n",
"print a*b"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 3, 4, 5, 6])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a+1"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}