Class Day 3 - Data Scraping and Data Manipulation
Day# 3 Notes¶
Data Scraping¶
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
##"Magic Command"
%matplotlib inline
In [2]:
x = np.arange(0,20,1)
y = np.random.normal(10,1,20)
z = np.random.normal(10,2,20)
In [3]:
fig = plt.figure()
ax = fig.add_subplot(111)
# (111)
# (1) row, (1) column, (1) chart
ax.plot(x,y)
Out[3]:
[<matplotlib.lines.Line2D at 0x1b75c198>]
In [4]:
fig = plt.figure()
ax1 = fig.add_subplot(121)
# (1) row, (2) column, (1) chart
ax1.plot(x,y)
ax2 = fig.add_subplot(122)
# (1) row, (2) column, (2) chart
ax2.scatter(y,z)
Out[4]:
<matplotlib.collections.PathCollection at 0x1bbcd518>
In [5]:
fig = plt.figure()
ax1 = fig.add_subplot(223)
ax1.plot(x,y)
ax2 = fig.add_subplot(222)
ax2.scatter(y,z)
ax3 = fig.add_subplot(221)
ax4 = fig.add_subplot(224)
ax3.hist(y)
ax4.boxplot([y,z])
Out[5]:
{'boxes': [<matplotlib.lines.Line2D at 0x1c35ca58>,
<matplotlib.lines.Line2D at 0x1c41e898>],
'caps': [<matplotlib.lines.Line2D at 0x1c2ae668>,
<matplotlib.lines.Line2D at 0x1c1b8eb8>,
<matplotlib.lines.Line2D at 0x1c42d940>,
<matplotlib.lines.Line2D at 0x1c42deb8>],
'fliers': [<matplotlib.lines.Line2D at 0x1c41e358>,
<matplotlib.lines.Line2D at 0x1c43a9e8>],
'means': [],
'medians': [<matplotlib.lines.Line2D at 0x1c405da0>,
<matplotlib.lines.Line2D at 0x1c43a470>],
'whiskers': [<matplotlib.lines.Line2D at 0x1c35cb38>,
<matplotlib.lines.Line2D at 0x1c2b9c88>,
<matplotlib.lines.Line2D at 0x1c41ee10>,
<matplotlib.lines.Line2D at 0x1c42d3c8>]}
In [6]:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y, label = 'Thing #1')
ax.plot(x,y, label = 'Thing #2')
ax.legend()
Out[6]:
<matplotlib.legend.Legend at 0x811b0f0>
In [7]:
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax1.plot(x,y)
ax1.set_title("Line")
ax2 = fig.add_subplot(122)
ax2.scatter(y,z)
ax2.set_title("Scatter")
fig.suptitle('Results')
Out[7]:
<matplotlib.text.Text at 0x1c9978d0>
In [8]:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y)
ax.set_ylabel('$$$ Money', fontsize=10)
ax.set_xlabel('Years', fontsize = 20)
# Save Figure to PNG File
fig.savefig('PNGfigure.png')
#Save Figure to JPG File
fig.savefig('JPGfigure.jpg')
In [9]:
# Here is an examples of what a JSON (JavaScript Object Notation) looks like:
json_obj = """
{
"zoo_animal": "Lion",
"food": ["Meat","Veggies","Honey"],
"fur": "Golden",
"clothes": null,
"diet": [{"zoo_animal": "Gazelle", "food": "grass", "fur": "Brown"}]
}
"""
In [10]:
type(json_obj)
Out[10]:
str
In [11]:
import json
# Importing JSON Objection into our data
data = json.loads(json_obj)
type(data)
Out[11]:
dict
In [12]:
data
Out[12]:
{u'clothes': None,
u'diet': [{u'food': u'grass', u'fur': u'Brown', u'zoo_animal': u'Gazelle'}],
u'food': [u'Meat', u'Veggies', u'Honey'],
u'fur': u'Golden',
u'zoo_animal': u'Lion'}In [13]:
# Convert Dictionary back into a JSON Object
json_saved = json.dumps(data)
In [14]:
# Create DataFrames using Pandas
import pandas as pd
dframe = pd.DataFrame(data['diet'])
dframe1 = pd.DataFrame(data['food'])
In [15]:
dframe
Out[15]:
| food | fur | zoo_animal | |
|---|---|---|---|
| 0 | grass | Brown | Gazelle |
In [16]:
dframe1
Out[16]:
| 0 | |
|---|---|
| 0 | Meat |
| 1 | Veggies |
| 2 | Honey |
In [17]:
#Concatinating the data frames. Adding the 2 data frames together
frames = [dframe,dframe1]
result = pd.concat(frames)
result
Out[17]:
| 0 | food | fur | zoo_animal | |
|---|---|---|---|---|
| 0 | NaN | grass | Brown | Gazelle |
| 0 | Meat | NaN | NaN | NaN |
| 1 | Veggies | NaN | NaN | NaN |
| 2 | Honey | NaN | NaN | NaN |
In [18]:
from bs4 import BeautifulSoup
import requests
import pandas as pd
from pandas import Series, DataFrame
In [19]:
#
url = 'http://www.ucop.edu/operating-budget/budgets-and-reports/legislative-reports/2013-14-legislative-session.html'
result = requests.get(url)
result
Out[19]:
<Response [200]>
In [20]:
result.headers['content-type']
Out[20]:
'text/html'
In [21]:
result.encoding
Out[21]:
'ISO-8859-1'
In [22]:
result.content
Out[22]:
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one level of column and row headers" width="100%">\n<tbody>\n<tr>\n<th scope="col"/><th scope="col">Date</th><th scope="col">Report title</th>\n</tr>\n<tr>\n<td scope="row">1</td>\n<td scope="row">08/01/13</td>\n<td scope="row"><a href="../../_files/legreports/1314/2013-14cobcp.pdf">2013-14 (EDU 92495) Proposed Capital Outlay Projects (2013-14 only) (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">2</td>\n<td scope="row">09/01/13</td>\n<td scope="row"><a href="../../_files/legreports/1314/2014-15cobcp.pdf">2014-15  (EDU 92495) Proposed Capital Outlay Projects (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">3</td>\n<td scope="row">11/01/13</td>\n<td scope="row"><a href="../../_files/legreports/1314/utilizationofclassroomsandresearchspace-11-1-13.pdf">Utilization of Classroom and Teaching Laboratories (pdf)</a> (<em>Final Report; biennial</em>)</td>\n</tr>\n<tr>\n<td scope="row">4</td>\n<td scope="row">11/01/13</td>\n<td scope="row"><a 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href="../../_files/legreports/1314/streamlinedcapitaloutlayprojectslegrpt-12-13.pdf">Streamlined Capital Projects Funded from Capital (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">9</td>\n<td scope="row">01/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/annualgeneralobligationbondsaccountabilitylegrep-11-2013.pdf">Annual General Obligation Bonds Accountability (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">10</td>\n<td scope="row">01/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/2012-13sbulegrpt-3-31-14.pdf">Small Business Utilization (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">11</td>\n<td scope="row">01/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/instfinaid-prelimlegreport-2-10-14.pdf">Institutional Financial Aid Programs - Preliminary report (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">12</td>\n<td scope="row">01/10/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/summerenrollmentlegreport-2-18-2014.pdf">Summer Enrollment (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">13</td>\n<td scope="row">01/15/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/contractingoutforservicesatnewlydevelopedfacilitieslegrep-1-8-2014.pdf">Contracting Out for Services at Newly Developed Facilities (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">14</td>\n<td scope="row">03/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/performanceoutcomemeasureslegreport-March-2014.pdf">Performance Measures (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">15</td>\n<td scope="row">03/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/ELWRlegrpt-3-4-14.pdf">Entry Level Writing Requirement (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">16</td>\n<td scope="row">03/31/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/2012-13annualstudentsupportlegreport-4-1-14.pdf">Annual Report on Student Financial Support (pdf)</a><a href="../../_files/legreports/1213/2011-12uc-mexicofacilityresearchandacadPrgms-casa.pdf"></a></td>\n</tr>\n<tr>\n<td scope="row">17</td>\n<td scope="row">04/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/uniquestatewidepupilidentifierlegrpt-03-10-14.pdf">Unique Statewide Pupil Identifier (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">18</td>\n<td scope="row">04/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/progressreportucr-somlegrpt-4-2-14.pdf">Riverside School of Medicine (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">19</td>\n<td scope="row">04/01/14</td>\n<td scope="row">SAPEP Funds and Outcomes - N/A<br/></td>\n</tr>\n<tr>\n<td scope="row">20</td>\n<td scope="row">05/15/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/2012-13ReceiptandUseofLotteryFundsLegReport-5-15-14.pdf">Receipt and Use of Lottery Funds (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">21</td>\n<td scope="row">07/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/5thAmendedProposedSEPListtoState-10-31-13.pdf">Cogeneration and Energy Consv Major Capital Projects (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">\n</td>\n<td scope="row">\n</td>\n<td scope="row">\n</td>\n</tr>\n<tr>\n<td scope="row"><strong> </strong></td>\n<td scope="row"><strong>Future Reports</strong></td>\n<td scope="row">\n</td>\n</tr>\n<tr>\n<td scope="row">24</td>\n<td scope="row">12-<a></a>31-15</td>\n<td scope="row">Breast Cancer Research Fund</td>\n</tr>\n<tr>\n<td scope="row">25</td>\n<td scope="row">12-31-15</td>\n<td scope="row">Cigarette and Tobacco Products Surtax Research Program</td>\n</tr>\n<tr>\n<td scope="row">26</td>\n<td scope="row">01-01-16</td>\n<td scope="row">Best Value Program</td>\n</tr>\n<tr>\n<td scope="row">27</td>\n<td scope="row">01-01-16</td>\n<td scope="row">California Subject Matter Programs</td>\n</tr>\n<tr>\n<td scope="row">28</td>\n<td scope="row">04-01-16</td>\n<td scope="row">COSMOS Program 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tab4">\n<li class=""><a class="" href="../../index.html">Overview</a></li>\n<li class=""><a class="" href="../../staff/index.html">Staff</a></li>\n<li class="active"><a class="" href="../index.html">Budgets & Reports</a></li>\n<li class=""><a class="" href="../../fees-and-enrollments/index.html">Fees & Enrollments</a></li>\n</ul>\n</div>\n</div>\n<!-- Example row of columns -->\n<div class="row">\n<div class="span8 dotted-top" role="main">\n<h2>Legislative reports</h2>\n<h3 class="subhead">2013-2014</h3>\n<table cellpadding="5" cellspacing="0" class="table-striped" id="report" summary="2009-10 Legislative Reports in a table with one level of column and row headers" width="100%">\n<tbody>\n<tr>\n<th scope="col"></th><th scope="col">Date</th><th scope="col">Report title</th>\n</tr>\n<tr>\n<td scope="row">1</td>\n<td scope="row">08/01/13</td>\n<td scope="row"><a href="../../_files/legreports/1314/2013-14cobcp.pdf">2013-14 (EDU 92495) Proposed Capital Outlay Projects (2013-14 only) (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">2</td>\n<td scope="row">09/01/13</td>\n<td scope="row"><a href="../../_files/legreports/1314/2014-15cobcp.pdf">2014-15\xa0 (EDU 92495) Proposed Capital Outlay Projects (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">3</td>\n<td scope="row">11/01/13</td>\n<td scope="row"><a href="../../_files/legreports/1314/utilizationofclassroomsandresearchspace-11-1-13.pdf">Utilization of Classroom and Teaching Laboratories (pdf)</a>\xa0(<em>Final Report; biennial</em>)</td>\n</tr>\n<tr>\n<td scope="row">4</td>\n<td scope="row">11/01/13</td>\n<td scope="row"><a href="../../_files/legreports/1314/instructionandresearchspacesummary-11-1-2013.pdf">Instruction and Research Space Summary & Analysis (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">5</td>\n<td scope="row">11/15/13</td>\n<td scope="row"><a href="../../_files/legreports/1314/seplegreport-1-21-14.pdf">Statewide Energy Partnership Program (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">6</td>\n<td scope="row">11/30/13</td>\n<td scope="row"><a href="http://ucop.edu/capital-planning/_files/capital/201323/_UC-Capital-Financial-Plan-2013.pdf">2013-23 Capital Financial Plan (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">7</td>\n<td scope="row">11/30/13</td>\n<td scope="row"><a href="../../_files/legreports/1314/projectsavingsprogramlegrpt-11-2013.pdf">Projects Savings Funded from Capital Outlay Bond Funds (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">8</td>\n<td scope="row">12/01/13</td>\n<td scope="row"><a href="../../_files/legreports/1314/streamlinedcapitaloutlayprojectslegrpt-12-13.pdf">Streamlined Capital Projects Funded from Capital (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">9</td>\n<td scope="row">01/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/annualgeneralobligationbondsaccountabilitylegrep-11-2013.pdf">Annual General Obligation Bonds Accountability (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">10</td>\n<td scope="row">01/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/2012-13sbulegrpt-3-31-14.pdf">Small Business Utilization (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">11</td>\n<td scope="row">01/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/instfinaid-prelimlegreport-2-10-14.pdf">Institutional Financial Aid Programs - Preliminary report (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">12</td>\n<td scope="row">01/10/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/summerenrollmentlegreport-2-18-2014.pdf">Summer Enrollment (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">13</td>\n<td scope="row">01/15/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/contractingoutforservicesatnewlydevelopedfacilitieslegrep-1-8-2014.pdf">Contracting Out for Services at Newly Developed Facilities (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">14</td>\n<td scope="row">03/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/performanceoutcomemeasureslegreport-March-2014.pdf">Performance 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scope="row">19</td>\n<td scope="row">04/01/14</td>\n<td scope="row">SAPEP Funds and Outcomes - N/A<br/></td>\n</tr>\n<tr>\n<td scope="row">20</td>\n<td scope="row">05/15/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/2012-13ReceiptandUseofLotteryFundsLegReport-5-15-14.pdf">Receipt and Use of Lottery Funds (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">21</td>\n<td scope="row">07/01/14</td>\n<td scope="row"><a href="../../_files/legreports/1314/5thAmendedProposedSEPListtoState-10-31-13.pdf">Cogeneration and Energy Consv Major Capital Projects (pdf)</a></td>\n</tr>\n<tr>\n<td scope="row">\n</td>\n<td scope="row">\n</td>\n<td scope="row">\n</td>\n</tr>\n<tr>\n<td scope="row"><strong>\xa0</strong></td>\n<td scope="row"><strong>Future Reports</strong></td>\n<td scope="row">\n</td>\n</tr>\n<tr>\n<td scope="row">24</td>\n<td scope="row">12-<a></a>31-15</td>\n<td scope="row">Breast Cancer Research Fund</td>\n</tr>\n<tr>\n<td scope="row">25</td>\n<td scope="row">12-31-15</td>\n<td 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2013-2014
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Report title
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<td scope="row">
1
</td>
<td scope="row">
08/01/13
</td>
<td scope="row">
<a href="../../_files/legreports/1314/2013-14cobcp.pdf">
2013-14 (EDU 92495) Proposed Capital Outlay Projects (2013-14 only) (pdf)
</a>
</td>
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<td scope="row">
2
</td>
<td scope="row">
09/01/13
</td>
<td scope="row">
<a href="../../_files/legreports/1314/2014-15cobcp.pdf">
2014-15 (EDU 92495) Proposed Capital Outlay Projects (pdf)
</a>
</td>
</tr>
<tr>
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3
</td>
<td scope="row">
11/01/13
</td>
<td scope="row">
<a href="../../_files/legreports/1314/utilizationofclassroomsandresearchspace-11-1-13.pdf">
Utilization of Classroom and Teaching Laboratories (pdf)
</a>
(
<em>
Final Report; biennial
</em>
)
</td>
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<td scope="row">
4
</td>
<td scope="row">
11/01/13
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<td scope="row">
<a href="../../_files/legreports/1314/instructionandresearchspacesummary-11-1-2013.pdf">
Instruction and Research Space Summary & Analysis (pdf)
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5
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11/15/13
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<a href="../../_files/legreports/1314/seplegreport-1-21-14.pdf">
Statewide Energy Partnership Program (pdf)
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11/30/13
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2013-23 Capital Financial Plan (pdf)
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11/30/13
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Projects Savings Funded from Capital Outlay Bond Funds (pdf)
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12/01/13
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Streamlined Capital Projects Funded from Capital (pdf)
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01/01/14
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Annual General Obligation Bonds Accountability (pdf)
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01/01/14
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<a href="../../_files/legreports/1314/2012-13sbulegrpt-3-31-14.pdf">
Small Business Utilization (pdf)
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11
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01/01/14
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<a href="../../_files/legreports/1314/instfinaid-prelimlegreport-2-10-14.pdf">
Institutional Financial Aid Programs - Preliminary report (pdf)
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12
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<td scope="row">
01/10/14
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<a href="../../_files/legreports/1314/summerenrollmentlegreport-2-18-2014.pdf">
Summer Enrollment (pdf)
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13
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01/15/14
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<a href="../../_files/legreports/1314/contractingoutforservicesatnewlydevelopedfacilitieslegrep-1-8-2014.pdf">
Contracting Out for Services at Newly Developed Facilities (pdf)
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14
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03/01/14
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<a href="../../_files/legreports/1314/performanceoutcomemeasureslegreport-March-2014.pdf">
Performance Measures (pdf)
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03/01/14
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Entry Level Writing Requirement (pdf)
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03/31/14
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<a href="../../_files/legreports/1314/2012-13annualstudentsupportlegreport-4-1-14.pdf">
Annual Report on Student Financial Support (pdf)
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<a href="../../_files/legreports/1213/2011-12uc-mexicofacilityresearchandacadPrgms-casa.pdf">
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17
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04/01/14
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<a href="../../_files/legreports/1314/uniquestatewidepupilidentifierlegrpt-03-10-14.pdf">
Unique Statewide Pupil Identifier (pdf)
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<td scope="row">
18
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<td scope="row">
04/01/14
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<a href="../../_files/legreports/1314/progressreportucr-somlegrpt-4-2-14.pdf">
Riverside School of Medicine (pdf)
</a>
</td>
</tr>
<tr>
<td scope="row">
19
</td>
<td scope="row">
04/01/14
</td>
<td scope="row">
SAPEP Funds and Outcomes - N/A
<br/>
</td>
</tr>
<tr>
<td scope="row">
20
</td>
<td scope="row">
05/15/14
</td>
<td scope="row">
<a href="../../_files/legreports/1314/2012-13ReceiptandUseofLotteryFundsLegReport-5-15-14.pdf">
Receipt and Use of Lottery Funds (pdf)
</a>
</td>
</tr>
<tr>
<td scope="row">
21
</td>
<td scope="row">
07/01/14
</td>
<td scope="row">
<a href="../../_files/legreports/1314/5thAmendedProposedSEPListtoState-10-31-13.pdf">
Cogeneration and Energy Consv Major Capital Projects (pdf)
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</td>
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</td>
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<strong>
</strong>
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Future Reports
</strong>
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<td scope="row">
24
</td>
<td scope="row">
12-
<a>
</a>
31-15
</td>
<td scope="row">
Breast Cancer Research Fund
</td>
</tr>
<tr>
<td scope="row">
25
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12-31-15
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Cigarette and Tobacco Products Surtax Research Program
</td>
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<td scope="row">
26
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01-01-16
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Best Value Program
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<td scope="row">
27
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01-01-16
</td>
<td scope="row">
California Subject Matter Programs
</td>
</tr>
<tr>
<td scope="row">
28
</td>
<td scope="row">
04-01-16
</td>
<td scope="row">
COSMOS Program Outcomes
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</tr>
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In [26]:
# Finding the Table through Tags
summary = soup.find("div",{'class':'list-land','id':'content'})
In [27]:
tables = summary.find_all('table')
len(tables)
Out[27]:
1
In [28]:
data = []
rows = tables[0].findAll('tr')
In [29]:
for tr in rows:
cols = tr.findAll('td')
# Check to see if the text is in the row
for td in cols:
text = td.find(text=True)
print text
data.append(text)
1 08/01/13 2013-14 (EDU 92495) Proposed Capital Outlay Projects (2013-14 only) (pdf) 2 09/01/13 2014-15 (EDU 92495) Proposed Capital Outlay Projects (pdf) 3 11/01/13 Utilization of Classroom and Teaching Laboratories (pdf) 4 11/01/13 Instruction and Research Space Summary & Analysis (pdf) 5 11/15/13 Statewide Energy Partnership Program (pdf) 6 11/30/13 2013-23 Capital Financial Plan (pdf) 7 11/30/13 Projects Savings Funded from Capital Outlay Bond Funds (pdf) 8 12/01/13 Streamlined Capital Projects Funded from Capital (pdf) 9 01/01/14 Annual General Obligation Bonds Accountability (pdf) 10 01/01/14 Small Business Utilization (pdf) 11 01/01/14 Institutional Financial Aid Programs - Preliminary report (pdf) 12 01/10/14 Summer Enrollment (pdf) 13 01/15/14 Contracting Out for Services at Newly Developed Facilities (pdf) 14 03/01/14 Performance Measures (pdf) 15 03/01/14 Entry Level Writing Requirement (pdf) 16 03/31/14 Annual Report on Student Financial Support (pdf) 17 04/01/14 Unique Statewide Pupil Identifier (pdf) 18 04/01/14 Riverside School of Medicine (pdf) 19 04/01/14 SAPEP Funds and Outcomes - N/A 20 05/15/14 Receipt and Use of Lottery Funds (pdf) 21 07/01/14 Cogeneration and Energy Consv Major Capital Projects (pdf) Future Reports 24 12- Breast Cancer Research Fund 25 12-31-15 Cigarette and Tobacco Products Surtax Research Program 26 01-01-16 Best Value Program 27 01-01-16 California Subject Matter Programs 28 04-01-16 COSMOS Program Outcomes
In [30]:
# Extracting the data
# Set up enpty lists
reports = []
date = []
# Set index counter
index = 0
# Go find the PDF cells
for index, item in enumerate(data): # enumerate function will set index counter
if 'pdf' in item:
#Add the date and reports
date.append(data[index-1])
#get rid of /xa0
reports.append(item.replace(u'/xa0',u' '))
In [31]:
print reports
print data
[u'2013-14 (EDU 92495) Proposed Capital Outlay Projects (2013-14 only) (pdf)', u'2014-15\xa0 (EDU 92495) Proposed Capital Outlay Projects (pdf)', u'Utilization of Classroom and Teaching Laboratories (pdf)', u'Instruction and Research Space Summary & Analysis (pdf)', u'Statewide Energy Partnership Program (pdf)', u'2013-23 Capital Financial Plan (pdf)', u'Projects Savings Funded from Capital Outlay Bond Funds (pdf)', u'Streamlined Capital Projects Funded from Capital (pdf)', u'Annual General Obligation Bonds Accountability (pdf)', u'Small Business Utilization (pdf)', u'Institutional Financial Aid Programs - Preliminary report (pdf)', u'Summer Enrollment (pdf)', u'Contracting Out for Services at Newly Developed Facilities (pdf)', u'Performance Measures (pdf)', u'Entry Level Writing Requirement (pdf)', u'Annual Report on Student\xa0Financial Support (pdf)', u'Unique Statewide Pupil Identifier (pdf)', u'Riverside School of Medicine (pdf)', u'Receipt and Use of Lottery Funds (pdf)', u'Cogeneration and Energy Consv Major Capital Projects (pdf)'] [u'1', u'08/01/13', u'2013-14 (EDU 92495) Proposed Capital Outlay Projects (2013-14 only) (pdf)', u'2', u'09/01/13', u'2014-15\xa0 (EDU 92495) Proposed Capital Outlay Projects (pdf)', u'3', u'11/01/13', u'Utilization of Classroom and Teaching Laboratories (pdf)', u'4', u'11/01/13', u'Instruction and Research Space Summary & Analysis (pdf)', u'5', u'11/15/13', u'Statewide Energy Partnership Program (pdf)', u'6', u'11/30/13', u'2013-23 Capital Financial Plan (pdf)', u'7', u'11/30/13', u'Projects Savings Funded from Capital Outlay Bond Funds (pdf)', u'8', u'12/01/13', u'Streamlined Capital Projects Funded from Capital (pdf)', u'9', u'01/01/14', u'Annual General Obligation Bonds Accountability (pdf)', u'10', u'01/01/14', u'Small Business Utilization (pdf)', u'11', u'01/01/14', u'Institutional Financial Aid Programs - Preliminary report (pdf)', u'12', u'01/10/14', u'Summer Enrollment (pdf)', u'13', u'01/15/14', u'Contracting Out for Services at Newly Developed Facilities (pdf)', u'14', u'03/01/14', u'Performance Measures (pdf)', u'15', u'03/01/14', u'Entry Level Writing Requirement (pdf)', u'16', u'03/31/14', u'Annual Report on Student\xa0Financial Support (pdf)', u'17', u'04/01/14', u'Unique Statewide Pupil Identifier (pdf)', u'18', u'04/01/14', u'Riverside School of Medicine (pdf)', u'19', u'04/01/14', u'SAPEP Funds and Outcomes - N/A', u'20', u'05/15/14', u'Receipt and Use of Lottery Funds (pdf)', u'21', u'07/01/14', u'Cogeneration and Energy Consv Major Capital Projects (pdf)', u'\n', u'\n', u'\n', u'\xa0', u'Future Reports', u'\n', u'24', u'12-', u'Breast Cancer Research Fund', u'25', u'12-31-15', u'Cigarette and Tobacco Products Surtax Research Program', u'26', u'01-01-16', u'Best Value Program', u'27', u'01-01-16', u'California Subject Matter Programs', u'28', u'04-01-16', u'COSMOS Program Outcomes']
In [32]:
date = Series(date)
reports = Series(reports)
print date
print reports
0 08/01/13 1 09/01/13 2 11/01/13 3 11/01/13 4 11/15/13 5 11/30/13 6 11/30/13 7 12/01/13 8 01/01/14 9 01/01/14 10 01/01/14 11 01/10/14 12 01/15/14 13 03/01/14 14 03/01/14 15 03/31/14 16 04/01/14 17 04/01/14 18 05/15/14 19 07/01/14 dtype: object 0 2013-14 (EDU 92495) Proposed Capital Outlay Pr... 1 2014-15 (EDU 92495) Proposed Capital Outlay P... 2 Utilization of Classroom and Teaching Laborato... 3 Instruction and Research Space Summary & Analy... 4 Statewide Energy Partnership Program (pdf) 5 2013-23 Capital Financial Plan (pdf) 6 Projects Savings Funded from Capital Outlay Bo... 7 Streamlined Capital Projects Funded from Capit... 8 Annual General Obligation Bonds Accountability... 9 Small Business Utilization (pdf) 10 Institutional Financial Aid Programs - Prelimi... 11 Summer Enrollment (pdf) 12 Contracting Out for Services at Newly Develope... 13 Performance Measures (pdf) 14 Entry Level Writing Requirement (pdf) 15 Annual Report on Student Financial Support (pdf) 16 Unique Statewide Pupil Identifier (pdf) 17 Riverside School of Medicine (pdf) 18 Receipt and Use of Lottery Funds (pdf) 19 Cogeneration and Energy Consv Major Capital Pr... dtype: object
In [33]:
legislative_df = pd.concat([date,reports],axis=1)
legislative_df
Out[33]:
| 0 | 1 | |
|---|---|---|
| 0 | 08/01/13 | 2013-14 (EDU 92495) Proposed Capital Outlay Pr... |
| 1 | 09/01/13 | 2014-15 (EDU 92495) Proposed Capital Outlay P... |
| 2 | 11/01/13 | Utilization of Classroom and Teaching Laborato... |
| 3 | 11/01/13 | Instruction and Research Space Summary & Analy... |
| 4 | 11/15/13 | Statewide Energy Partnership Program (pdf) |
| 5 | 11/30/13 | 2013-23 Capital Financial Plan (pdf) |
| 6 | 11/30/13 | Projects Savings Funded from Capital Outlay Bo... |
| 7 | 12/01/13 | Streamlined Capital Projects Funded from Capit... |
| 8 | 01/01/14 | Annual General Obligation Bonds Accountability... |
| 9 | 01/01/14 | Small Business Utilization (pdf) |
| 10 | 01/01/14 | Institutional Financial Aid Programs - Prelimi... |
| 11 | 01/10/14 | Summer Enrollment (pdf) |
| 12 | 01/15/14 | Contracting Out for Services at Newly Develope... |
| 13 | 03/01/14 | Performance Measures (pdf) |
| 14 | 03/01/14 | Entry Level Writing Requirement (pdf) |
| 15 | 03/31/14 | Annual Report on Student Financial Support (pdf) |
| 16 | 04/01/14 | Unique Statewide Pupil Identifier (pdf) |
| 17 | 04/01/14 | Riverside School of Medicine (pdf) |
| 18 | 05/15/14 | Receipt and Use of Lottery Funds (pdf) |
| 19 | 07/01/14 | Cogeneration and Energy Consv Major Capital Pr... |
In [34]:
legislative_df.columns = ['Date','Reports']
legislative_df
Out[34]:
| Date | Reports | |
|---|---|---|
| 0 | 08/01/13 | 2013-14 (EDU 92495) Proposed Capital Outlay Pr... |
| 1 | 09/01/13 | 2014-15 (EDU 92495) Proposed Capital Outlay P... |
| 2 | 11/01/13 | Utilization of Classroom and Teaching Laborato... |
| 3 | 11/01/13 | Instruction and Research Space Summary & Analy... |
| 4 | 11/15/13 | Statewide Energy Partnership Program (pdf) |
| 5 | 11/30/13 | 2013-23 Capital Financial Plan (pdf) |
| 6 | 11/30/13 | Projects Savings Funded from Capital Outlay Bo... |
| 7 | 12/01/13 | Streamlined Capital Projects Funded from Capit... |
| 8 | 01/01/14 | Annual General Obligation Bonds Accountability... |
| 9 | 01/01/14 | Small Business Utilization (pdf) |
| 10 | 01/01/14 | Institutional Financial Aid Programs - Prelimi... |
| 11 | 01/10/14 | Summer Enrollment (pdf) |
| 12 | 01/15/14 | Contracting Out for Services at Newly Develope... |
| 13 | 03/01/14 | Performance Measures (pdf) |
| 14 | 03/01/14 | Entry Level Writing Requirement (pdf) |
| 15 | 03/31/14 | Annual Report on Student Financial Support (pdf) |
| 16 | 04/01/14 | Unique Statewide Pupil Identifier (pdf) |
| 17 | 04/01/14 | Riverside School of Medicine (pdf) |
| 18 | 05/15/14 | Receipt and Use of Lottery Funds (pdf) |
| 19 | 07/01/14 | Cogeneration and Energy Consv Major Capital Pr... |
Using Pandas to Explore/Manipulate Data¶
In [35]:
import pandas as pd
import numpy as np
sales = pd.read_excel('sales-estimate.xlsx')
sales.head(20)
Out[35]:
| Account | Name | State | Rep | Manager | Current_Price | Quantity | New_Product_Price | |
|---|---|---|---|---|---|---|---|---|
| 0 | 714466 | Trantow-Barrows | MN | Craig Booker | Debra Henley | 500 | 100 | 550 |
| 1 | 737550 | Fritsch, Russel and Anderson | MN | Craig Booker | Debra Henley | 600 | 90 | 725 |
| 2 | 146832 | Kiehn-Spinka | TX | Daniel Hilton | Debra Henley | 225 | 475 | 255 |
| 3 | 218895 | Kulas Inc | TX | Daniel Hilton | Debra Henley | 290 | 375 | 300 |
| 4 | 412290 | Jerde-Hilpert | WI | John Smith | Debra Henley | 375 | 400 | 400 |
| 5 | 740150 | Barton LLC | WI | John Smith | Debra Henley | 550 | 100 | 600 |
| 6 | 141962 | Herman LLC | CA | Cedric Moss | Fred Anderson | 400 | 200 | 425 |
| 7 | 163416 | Purdy-Kunde | CA | Cedric Moss | Fred Anderson | 450 | 150 | 475 |
| 8 | 239344 | Stokes LLC | WA | Cedric Moss | Fred Anderson | 550 | 75 | 610 |
| 9 | 307599 | Kassulke, Ondricka and Metz | NV | Wendy Yule | Fred Anderson | 275 | 450 | 300 |
| 10 | 688981 | Keeling LLC | NV | Wendy Yule | Fred Anderson | 300 | 250 | 350 |
| 11 | 729833 | Koepp Ltd | NV | Wendy Yule | Fred Anderson | 350 | 100 | 375 |
In [36]:
print sales['Current_Price'].mean(), sales['New_Product_Price'].mean()
405.416666667 447.083333333
In [37]:
total_shoes = sales['Quantity'].sum()
print total_shoes
2765
In [38]:
weightedAvg = (sales['Current_Price'] * sales['Quantity']).sum()/float(total_shoes)
print weightedAvg
342.540687161
In [39]:
weightAveNew = (sales['New_Product_Price'] * sales['Quantity']).sum()/float(sales['Quantity'].sum())
print weightAveNew
374.638336347
In [40]:
# GroupBy function allows you to group the data by items within a column
sales.groupby('Manager')[['Current_Price','New_Product_Price']].mean()
Out[40]:
| Current_Price | New_Product_Price | |
|---|---|---|
| Manager | ||
| Debra Henley | 423.333333 | 471.666667 |
| Fred Anderson | 387.500000 | 422.500000 |
In [41]:
def wavg(group,avg_name,weight_name):
d = group[avg_name]
w = group[weight_name]
try:
return (d*w).sum()/float(w.sum())
except ZeroDivisionError:
return np.nan
In [42]:
print wavg(sales,'Current_Price','Quantity'), wavg(sales,'New_Product_Price','Quantity')
342.540687161 374.638336347
In [43]:
print sales.groupby('Manager').apply(wavg,'Current_Price','Quantity')
print ''
print sales.groupby('Rep').apply(wavg,'Current_Price','Quantity')
Manager Debra Henley 340.665584 Fred Anderson 344.897959 dtype: float64 Rep Cedric Moss 444.117647 Craig Booker 547.368421 Daniel Hilton 253.676471 John Smith 410.000000 Wendy Yule 292.187500 dtype: float64
In [44]:
print sales.groupby(['Manager','State']).apply(wavg,'Current_Price','Quantity')
Manager State
Debra Henley MN 547.368421
TX 253.676471
WI 410.000000
Fred Anderson CA 421.428571
NV 292.187500
WA 550.000000
dtype: float64
In [45]:
f = {'New_Product_Price':['mean'],'Current_Price':['median'],'Quantity':['sum','mean']}
sales.groupby('Manager').agg(f)
Out[45]:
| Current_Price | New_Product_Price | Quantity | ||
|---|---|---|---|---|
| median | mean | sum | mean | |
| Manager | ||||
| Debra Henley | 437.5 | 471.666667 | 1540 | 256.666667 |
| Fred Anderson | 375.0 | 422.500000 | 1225 | 204.166667 |
In [46]:
data_1 = sales.groupby('Manager').apply(wavg,'New_Product_Price','Quantity')
data_2 = sales.groupby('Manager').apply(wavg,'Current_Price','Quantity')
print data_1
print data_2
Manager Debra Henley 372.646104 Fred Anderson 377.142857 dtype: float64 Manager Debra Henley 340.665584 Fred Anderson 344.897959 dtype: float64
In [47]:
summary = pd.DataFrame(data=dict(s1=data_1,s2=data_2))
summary.head()
Out[47]:
| s1 | s2 | |
|---|---|---|
| Manager | ||
| Debra Henley | 372.646104 | 340.665584 |
| Fred Anderson | 377.142857 | 344.897959 |
In [48]:
print summary.columns
summary.columns = ['New Product Price','Current Product Price']
summary.head()
Index([u's1', u's2'], dtype='object')
Out[48]:
| New Product Price | Current Product Price | |
|---|---|---|
| Manager | ||
| Debra Henley | 372.646104 | 340.665584 |
| Fred Anderson | 377.142857 | 344.897959 |
In [49]:
# Numpy has a built-in weighted average function
np.average(sales['Current_Price'],weights=sales['Quantity'])
Out[49]:
342.54068716094031
In [50]:
sales.groupby('Manager').apply(lambda x: np.average(x['New_Product_Price'], weights = x['Quantity']))
Out[50]:
Manager Debra Henley 372.646104 Fred Anderson 377.142857 dtype: float64
In [51]:
import pandas as pd
df = pd.read_excel('excel-comp-data.xlsx')
df.head()
Out[51]:
| account | name | street | city | state | postal-code | Jan | Feb | Mar | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 211829 | Kerluke, Koepp and Hilpert | 34456 Sean Highway | New Jaycob | Texas | 28752 | 10000 | 62000 | 35000 |
| 1 | 320563 | Walter-Trantow | 1311 Alvis Tunnel | Port Khadijah | NorthCarolina | 38365 | 95000 | 45000 | 35000 |
| 2 | 648336 | Bashirian, Kunde and Price | 62184 Schamberger Underpass Apt. 231 | New Lilianland | Iowa | 76517 | 91000 | 120000 | 35000 |
| 3 | 109996 | D'Amore, Gleichner and Bode | 155 Fadel Crescent Apt. 144 | Hyattburgh | Maine | 46021 | 45000 | 120000 | 10000 |
| 4 | 121213 | Bauch-Goldner | 7274 Marissa Common | Shanahanchester | California | 49681 | 162000 | 120000 | 35000 |
In [52]:
df['Total'] = df['Jan']+df['Feb']+df['Mar']
df.head()
Out[52]:
| account | name | street | city | state | postal-code | Jan | Feb | Mar | Total | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 211829 | Kerluke, Koepp and Hilpert | 34456 Sean Highway | New Jaycob | Texas | 28752 | 10000 | 62000 | 35000 | 107000 |
| 1 | 320563 | Walter-Trantow | 1311 Alvis Tunnel | Port Khadijah | NorthCarolina | 38365 | 95000 | 45000 | 35000 | 175000 |
| 2 | 648336 | Bashirian, Kunde and Price | 62184 Schamberger Underpass Apt. 231 | New Lilianland | Iowa | 76517 | 91000 | 120000 | 35000 | 246000 |
| 3 | 109996 | D'Amore, Gleichner and Bode | 155 Fadel Crescent Apt. 144 | Hyattburgh | Maine | 46021 | 45000 | 120000 | 10000 | 175000 |
| 4 | 121213 | Bauch-Goldner | 7274 Marissa Common | Shanahanchester | California | 49681 | 162000 | 120000 | 35000 | 317000 |
In [53]:
sum_row = df[['Jan','Feb','Mar','Total']].sum()
print sum_row
Jan 1462000 Feb 1507000 Mar 717000 Total 3686000 dtype: int64
In [54]:
df.describe()
Out[54]:
| account | postal-code | Jan | Feb | Mar | Total | |
|---|---|---|---|---|---|---|
| count | 15.000000 | 15.000000 | 15.000000 | 15.000000 | 15.000000 | 15.000000 |
| mean | 236509.466667 | 46400.600000 | 97466.666667 | 100466.666667 | 47800.000000 | 245733.333333 |
| std | 128420.806565 | 17343.118919 | 51159.228558 | 34450.516618 | 34996.326338 | 67072.952465 |
| min | 109996.000000 | 18008.000000 | 10000.000000 | 10000.000000 | 10000.000000 | 107000.000000 |
| 25% | 175142.500000 | 31667.000000 | 58500.000000 | 95000.000000 | 35000.000000 | 205000.000000 |
| 50% | 212303.000000 | 46308.000000 | 91000.000000 | 120000.000000 | 35000.000000 | 246000.000000 |
| 75% | 255561.500000 | 58123.000000 | 150000.000000 | 120000.000000 | 45000.000000 | 311000.000000 |
| max | 648336.000000 | 76517.000000 | 162000.000000 | 120000.000000 | 162000.000000 | 340000.000000 |
In [55]:
df_sum = pd.DataFrame(data=sum_row).T #.T allows you to transpose your matrix
df_sum
Out[55]:
| Jan | Feb | Mar | Total | |
|---|---|---|---|---|
| 0 | 1462000 | 1507000 | 717000 | 3686000 |
In [56]:
df_sum = df_sum.reindex(columns=df.columns)
df_sum
Out[56]:
| account | name | street | city | state | postal-code | Jan | Feb | Mar | Total | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | NaN | NaN | NaN | NaN | NaN | 1462000 | 1507000 | 717000 | 3686000 |
In [57]:
df_final = df.append(df_sum,ignore_index=True)
df_final.tail()
Out[57]:
| account | name | street | city | state | postal-code | Jan | Feb | Mar | Total | |
|---|---|---|---|---|---|---|---|---|---|---|
| 11 | 231907.0 | Hahn-Moore | 18115 Olivine Throughway | Norbertomouth | NorthDakota | 31415.0 | 150000 | 10000 | 162000 | 322000 |
| 12 | 242368.0 | Frami, Anderson and Donnelly | 182 Bertie Road | East Davian | Iowa | 72686.0 | 162000 | 120000 | 35000 | 317000 |
| 13 | 268755.0 | Walsh-Haley | 2624 Beatty Parkways | Goodwinmouth | RhodeIsland | 31919.0 | 55000 | 120000 | 35000 | 210000 |
| 14 | 273274.0 | McDermott PLC | 8917 Bergstrom Meadow | Kathryneborough | Delaware | 27933.0 | 150000 | 120000 | 70000 | 340000 |
| 15 | NaN | NaN | NaN | NaN | NaN | NaN | 1462000 | 1507000 | 717000 | 3686000 |
In [58]:
import pandas as pd
df = pd.read_excel('sample-salesv3.xlsx')
df.head()
Out[58]:
| account number | name | sku | quantity | unit price | ext price | date | |
|---|---|---|---|---|---|---|---|
| 0 | 740150 | Barton LLC | B1-20000 | 39 | 86.69 | 3380.91 | 2014-01-01 07:21:51 |
| 1 | 714466 | Trantow-Barrows | S2-77896 | -1 | 63.16 | -63.16 | 2014-01-01 10:00:47 |
| 2 | 218895 | Kulas Inc | B1-69924 | 23 | 90.70 | 2086.10 | 2014-01-01 13:24:58 |
| 3 | 307599 | Kassulke, Ondricka and Metz | S1-65481 | 41 | 21.05 | 863.05 | 2014-01-01 15:05:22 |
| 4 | 412290 | Jerde-Hilpert | S2-34077 | 6 | 83.21 | 499.26 | 2014-01-01 23:26:55 |
In [59]:
# Find Data Types
df.info()
df.dtypes
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1500 entries, 0 to 1499 Data columns (total 7 columns): account number 1500 non-null int64 name 1500 non-null object sku 1500 non-null object quantity 1500 non-null int64 unit price 1500 non-null float64 ext price 1500 non-null float64 date 1500 non-null object dtypes: float64(2), int64(2), object(3) memory usage: 82.1+ KB
Out[59]:
account number int64 name object sku object quantity int64 unit price float64 ext price float64 date object dtype: object
In [60]:
# Creating DateTime Variables
df['date'] = pd.to_datetime(df['date'])
df.head()
df.dtypes
Out[60]:
account number int64 name object sku object quantity int64 unit price float64 ext price float64 date datetime64[ns] dtype: object
In [61]:
# Filtering the data
df[(df['account number']==307599) & (df['quantity']>20)].head()
Out[61]:
| account number | name | sku | quantity | unit price | ext price | date | |
|---|---|---|---|---|---|---|---|
| 3 | 307599 | Kassulke, Ondricka and Metz | S1-65481 | 41 | 21.05 | 863.05 | 2014-01-01 15:05:22 |
| 34 | 307599 | Kassulke, Ondricka and Metz | S2-78676 | 35 | 33.04 | 1156.40 | 2014-01-10 05:26:31 |
| 58 | 307599 | Kassulke, Ondricka and Metz | B1-20000 | 22 | 37.87 | 833.14 | 2014-01-15 16:22:22 |
| 70 | 307599 | Kassulke, Ondricka and Metz | S2-10342 | 44 | 96.79 | 4258.76 | 2014-01-18 06:32:31 |
| 143 | 307599 | Kassulke, Ondricka and Metz | S1-27722 | 32 | 95.66 | 3061.12 | 2014-02-03 11:27:34 |
In [62]:
# Startswith function allows you to choose specific variables
# Since we are filtering within a single column or "series" we use the .map function to manipulate the data
df[df['sku'].map(lambda x: x.startswith('B1'))].head()
Out[62]:
| account number | name | sku | quantity | unit price | ext price | date | |
|---|---|---|---|---|---|---|---|
| 0 | 740150 | Barton LLC | B1-20000 | 39 | 86.69 | 3380.91 | 2014-01-01 07:21:51 |
| 2 | 218895 | Kulas Inc | B1-69924 | 23 | 90.70 | 2086.10 | 2014-01-01 13:24:58 |
| 6 | 218895 | Kulas Inc | B1-65551 | 2 | 31.10 | 62.20 | 2014-01-02 10:57:23 |
| 14 | 737550 | Fritsch, Russel and Anderson | B1-53102 | 23 | 71.56 | 1645.88 | 2014-01-04 08:57:48 |
| 17 | 239344 | Stokes LLC | B1-50809 | 14 | 16.23 | 227.22 | 2014-01-04 22:14:32 |
In [63]:
df[(df['sku'].map(lambda x: x.startswith('B1'))) & (df['quantity']>22)].head()
# Filtering data for
Out[63]:
| account number | name | sku | quantity | unit price | ext price | date | |
|---|---|---|---|---|---|---|---|
| 0 | 740150 | Barton LLC | B1-20000 | 39 | 86.69 | 3380.91 | 2014-01-01 07:21:51 |
| 2 | 218895 | Kulas Inc | B1-69924 | 23 | 90.70 | 2086.10 | 2014-01-01 13:24:58 |
| 14 | 737550 | Fritsch, Russel and Anderson | B1-53102 | 23 | 71.56 | 1645.88 | 2014-01-04 08:57:48 |
| 26 | 737550 | Fritsch, Russel and Anderson | B1-53636 | 42 | 42.06 | 1766.52 | 2014-01-08 00:02:11 |
| 31 | 714466 | Trantow-Barrows | B1-33087 | 32 | 19.56 | 625.92 | 2014-01-09 10:16:32 |
In [64]:
df[df['account number'].isin([737550,740150])].head()
Out[64]:
| account number | name | sku | quantity | unit price | ext price | date | |
|---|---|---|---|---|---|---|---|
| 0 | 740150 | Barton LLC | B1-20000 | 39 | 86.69 | 3380.91 | 2014-01-01 07:21:51 |
| 9 | 737550 | Fritsch, Russel and Anderson | S2-82423 | 14 | 81.92 | 1146.88 | 2014-01-03 19:07:37 |
| 14 | 737550 | Fritsch, Russel and Anderson | B1-53102 | 23 | 71.56 | 1645.88 | 2014-01-04 08:57:48 |
| 26 | 737550 | Fritsch, Russel and Anderson | B1-53636 | 42 | 42.06 | 1766.52 | 2014-01-08 00:02:11 |
| 32 | 737550 | Fritsch, Russel and Anderson | S1-27722 | 20 | 29.54 | 590.80 | 2014-01-09 13:20:40 |
In [65]:
df[df['name'].str.contains('Inc')].head(10)
Out[65]:
| account number | name | sku | quantity | unit price | ext price | date | |
|---|---|---|---|---|---|---|---|
| 2 | 218895 | Kulas Inc | B1-69924 | 23 | 90.70 | 2086.10 | 2014-01-01 13:24:58 |
| 6 | 218895 | Kulas Inc | B1-65551 | 2 | 31.10 | 62.20 | 2014-01-02 10:57:23 |
| 33 | 218895 | Kulas Inc | S1-06532 | 3 | 22.36 | 67.08 | 2014-01-09 23:58:27 |
| 36 | 218895 | Kulas Inc | S2-34077 | 16 | 73.04 | 1168.64 | 2014-01-10 12:07:30 |
| 43 | 218895 | Kulas Inc | B1-50809 | 43 | 47.21 | 2030.03 | 2014-01-12 01:54:37 |
| 47 | 218895 | Kulas Inc | S1-47412 | 1 | 94.01 | 94.01 | 2014-01-13 08:36:18 |
| 65 | 218895 | Kulas Inc | S1-93683 | 43 | 21.72 | 933.96 | 2014-01-17 16:18:29 |
| 88 | 218895 | Kulas Inc | S2-83881 | 41 | 78.27 | 3209.07 | 2014-01-20 09:37:58 |
| 95 | 218895 | Kulas Inc | S2-16558 | 20 | 57.96 | 1159.20 | 2014-01-21 18:16:45 |
| 102 | 218895 | Kulas Inc | S1-93683 | 21 | 49.59 | 1041.39 | 2014-01-22 23:49:39 |
In [66]:
df.query('name == ["Kulas Inc","Barton LLC"]').head(10)
Out[66]:
| account number | name | sku | quantity | unit price | ext price | date | |
|---|---|---|---|---|---|---|---|
| 0 | 740150 | Barton LLC | B1-20000 | 39 | 86.69 | 3380.91 | 2014-01-01 07:21:51 |
| 2 | 218895 | Kulas Inc | B1-69924 | 23 | 90.70 | 2086.10 | 2014-01-01 13:24:58 |
| 6 | 218895 | Kulas Inc | B1-65551 | 2 | 31.10 | 62.20 | 2014-01-02 10:57:23 |
| 33 | 218895 | Kulas Inc | S1-06532 | 3 | 22.36 | 67.08 | 2014-01-09 23:58:27 |
| 36 | 218895 | Kulas Inc | S2-34077 | 16 | 73.04 | 1168.64 | 2014-01-10 12:07:30 |
| 43 | 218895 | Kulas Inc | B1-50809 | 43 | 47.21 | 2030.03 | 2014-01-12 01:54:37 |
| 47 | 218895 | Kulas Inc | S1-47412 | 1 | 94.01 | 94.01 | 2014-01-13 08:36:18 |
| 65 | 218895 | Kulas Inc | S1-93683 | 43 | 21.72 | 933.96 | 2014-01-17 16:18:29 |
| 85 | 740150 | Barton LLC | B1-50809 | 8 | 19.60 | 156.80 | 2014-01-20 01:48:47 |
| 88 | 218895 | Kulas Inc | S2-83881 | 41 | 78.27 | 3209.07 | 2014-01-20 09:37:58 |
In [67]:
df = df.sort_values('date')
df.head(10)
Out[67]:
| account number | name | sku | quantity | unit price | ext price | date | |
|---|---|---|---|---|---|---|---|
| 0 | 740150 | Barton LLC | B1-20000 | 39 | 86.69 | 3380.91 | 2014-01-01 07:21:51 |
| 1 | 714466 | Trantow-Barrows | S2-77896 | -1 | 63.16 | -63.16 | 2014-01-01 10:00:47 |
| 2 | 218895 | Kulas Inc | B1-69924 | 23 | 90.70 | 2086.10 | 2014-01-01 13:24:58 |
| 3 | 307599 | Kassulke, Ondricka and Metz | S1-65481 | 41 | 21.05 | 863.05 | 2014-01-01 15:05:22 |
| 4 | 412290 | Jerde-Hilpert | S2-34077 | 6 | 83.21 | 499.26 | 2014-01-01 23:26:55 |
| 5 | 714466 | Trantow-Barrows | S2-77896 | 17 | 87.63 | 1489.71 | 2014-01-02 10:07:15 |
| 6 | 218895 | Kulas Inc | B1-65551 | 2 | 31.10 | 62.20 | 2014-01-02 10:57:23 |
| 7 | 729833 | Koepp Ltd | S1-30248 | 8 | 33.25 | 266.00 | 2014-01-03 06:32:11 |
| 8 | 714466 | Trantow-Barrows | S1-50961 | 22 | 84.09 | 1849.98 | 2014-01-03 11:29:02 |
| 9 | 737550 | Fritsch, Russel and Anderson | S2-82423 | 14 | 81.92 | 1146.88 | 2014-01-03 19:07:37 |
In [68]:
df[df['date'] >= '10-10-2014'].head(10)
# Since our dates are in datetime format, you can read in many formats of date and time
Out[68]:
| account number | name | sku | quantity | unit price | ext price | date | |
|---|---|---|---|---|---|---|---|
| 1174 | 257198 | Cronin, Oberbrunner and Spencer | S2-34077 | 13 | 12.24 | 159.12 | 2014-10-10 02:59:06 |
| 1175 | 740150 | Barton LLC | S1-65481 | 28 | 53.00 | 1484.00 | 2014-10-10 15:08:53 |
| 1176 | 146832 | Kiehn-Spinka | S1-27722 | 15 | 64.39 | 965.85 | 2014-10-10 18:24:01 |
| 1177 | 257198 | Cronin, Oberbrunner and Spencer | S2-16558 | 3 | 35.34 | 106.02 | 2014-10-11 01:48:13 |
| 1178 | 737550 | Fritsch, Russel and Anderson | B1-53636 | 10 | 56.95 | 569.50 | 2014-10-11 10:25:53 |
| 1179 | 737550 | Fritsch, Russel and Anderson | S1-06532 | 36 | 40.42 | 1455.12 | 2014-10-11 20:04:09 |
| 1180 | 729833 | Koepp Ltd | B1-53102 | 6 | 39.89 | 239.34 | 2014-10-11 20:43:21 |
| 1181 | 163416 | Purdy-Kunde | S1-06532 | 34 | 79.87 | 2715.58 | 2014-10-12 01:21:27 |
| 1182 | 737550 | Fritsch, Russel and Anderson | B1-33087 | 8 | 66.92 | 535.36 | 2014-10-12 07:35:14 |
| 1183 | 239344 | Stokes LLC | S1-93683 | 3 | 17.43 | 52.29 | 2014-10-12 08:56:05 |
In [69]:
df[(df['date'] >= '2014-04-01') & (df['date'] <= '2014-06-01')].head(10)
Out[69]:
| account number | name | sku | quantity | unit price | ext price | date | |
|---|---|---|---|---|---|---|---|
| 384 | 383080 | Will LLC | S2-34077 | 19 | 52.21 | 991.99 | 2014-04-01 02:27:22 |
| 385 | 714466 | Trantow-Barrows | S2-00301 | 8 | 65.91 | 527.28 | 2014-04-01 17:59:55 |
| 386 | 307599 | Kassulke, Ondricka and Metz | B1-65551 | 48 | 43.44 | 2085.12 | 2014-04-01 20:48:35 |
| 387 | 307599 | Kassulke, Ondricka and Metz | S2-10342 | 37 | 18.44 | 682.28 | 2014-04-01 21:27:10 |
| 388 | 218895 | Kulas Inc | S2-77896 | 5 | 52.86 | 264.30 | 2014-04-01 22:57:04 |
| 389 | 218895 | Kulas Inc | S1-47412 | 42 | 93.63 | 3932.46 | 2014-04-02 02:27:06 |
| 390 | 729833 | Koepp Ltd | S1-30248 | 2 | 68.02 | 136.04 | 2014-04-02 07:32:39 |
| 391 | 604255 | Halvorson, Crona and Champlin | S1-06532 | 24 | 11.21 | 269.04 | 2014-04-02 19:25:34 |
| 392 | 786968 | Frami, Hills and Schmidt | S2-16558 | 10 | 44.95 | 449.50 | 2014-04-03 05:19:42 |
| 393 | 672390 | Kuhn-Gusikowski | S2-11481 | 37 | 33.20 | 1228.40 | 2014-04-03 14:06:35 |
In [70]:
df2 = df.set_index(['date'])
df2.head()
Out[70]:
| account number | name | sku | quantity | unit price | ext price | |
|---|---|---|---|---|---|---|
| date | ||||||
| 2014-01-01 07:21:51 | 740150 | Barton LLC | B1-20000 | 39 | 86.69 | 3380.91 |
| 2014-01-01 10:00:47 | 714466 | Trantow-Barrows | S2-77896 | -1 | 63.16 | -63.16 |
| 2014-01-01 13:24:58 | 218895 | Kulas Inc | B1-69924 | 23 | 90.70 | 2086.10 |
| 2014-01-01 15:05:22 | 307599 | Kassulke, Ondricka and Metz | S1-65481 | 41 | 21.05 | 863.05 |
| 2014-01-01 23:26:55 | 412290 | Jerde-Hilpert | S2-34077 | 6 | 83.21 | 499.26 |
In [71]:
df2['2014-01-01':'2014-02-10'].head(8)
Out[71]:
| account number | name | sku | quantity | unit price | ext price | |
|---|---|---|---|---|---|---|
| date | ||||||
| 2014-01-01 07:21:51 | 740150 | Barton LLC | B1-20000 | 39 | 86.69 | 3380.91 |
| 2014-01-01 10:00:47 | 714466 | Trantow-Barrows | S2-77896 | -1 | 63.16 | -63.16 |
| 2014-01-01 13:24:58 | 218895 | Kulas Inc | B1-69924 | 23 | 90.70 | 2086.10 |
| 2014-01-01 15:05:22 | 307599 | Kassulke, Ondricka and Metz | S1-65481 | 41 | 21.05 | 863.05 |
| 2014-01-01 23:26:55 | 412290 | Jerde-Hilpert | S2-34077 | 6 | 83.21 | 499.26 |
| 2014-01-02 10:07:15 | 714466 | Trantow-Barrows | S2-77896 | 17 | 87.63 | 1489.71 |
| 2014-01-02 10:57:23 | 218895 | Kulas Inc | B1-65551 | 2 | 31.10 | 62.20 |
| 2014-01-03 06:32:11 | 729833 | Koepp Ltd | S1-30248 | 8 | 33.25 | 266.00 |
In [72]:
df['name'].nunique()
df['name'].unique()
Out[72]:
array([u'Barton LLC', u'Trantow-Barrows', u'Kulas Inc',
u'Kassulke, Ondricka and Metz', u'Jerde-Hilpert', u'Koepp Ltd',
u'Fritsch, Russel and Anderson', u'Kiehn-Spinka', u'Keeling LLC',
u'Frami, Hills and Schmidt', u'Stokes LLC', u'Kuhn-Gusikowski',
u'Herman LLC', u'White-Trantow', u'Sanford and Sons',
u'Pollich LLC', u'Will LLC', u'Cronin, Oberbrunner and Spencer',
u'Halvorson, Crona and Champlin', u'Purdy-Kunde'], dtype=object)In [73]:
df['account number'].nunique()
Out[73]:
20
In [74]:
df.drop_duplicates(subset=['account number','name'])
Out[74]:
| account number | name | sku | quantity | unit price | ext price | date | |
|---|---|---|---|---|---|---|---|
| 0 | 740150 | Barton LLC | B1-20000 | 39 | 86.69 | 3380.91 | 2014-01-01 07:21:51 |
| 1 | 714466 | Trantow-Barrows | S2-77896 | -1 | 63.16 | -63.16 | 2014-01-01 10:00:47 |
| 2 | 218895 | Kulas Inc | B1-69924 | 23 | 90.70 | 2086.10 | 2014-01-01 13:24:58 |
| 3 | 307599 | Kassulke, Ondricka and Metz | S1-65481 | 41 | 21.05 | 863.05 | 2014-01-01 15:05:22 |
| 4 | 412290 | Jerde-Hilpert | S2-34077 | 6 | 83.21 | 499.26 | 2014-01-01 23:26:55 |
| 7 | 729833 | Koepp Ltd | S1-30248 | 8 | 33.25 | 266.00 | 2014-01-03 06:32:11 |
| 9 | 737550 | Fritsch, Russel and Anderson | S2-82423 | 14 | 81.92 | 1146.88 | 2014-01-03 19:07:37 |
| 10 | 146832 | Kiehn-Spinka | S2-82423 | 15 | 67.74 | 1016.10 | 2014-01-03 19:39:53 |
| 11 | 688981 | Keeling LLC | S2-00301 | 7 | 20.26 | 141.82 | 2014-01-04 00:02:36 |
| 12 | 786968 | Frami, Hills and Schmidt | S2-23246 | 6 | 61.31 | 367.86 | 2014-01-04 06:51:53 |
| 15 | 239344 | Stokes LLC | S1-06532 | 34 | 71.51 | 2431.34 | 2014-01-04 11:34:58 |
| 16 | 672390 | Kuhn-Gusikowski | S1-30248 | 14 | 72.75 | 1018.50 | 2014-01-04 19:59:02 |
| 18 | 141962 | Herman LLC | S1-82801 | 10 | 94.30 | 943.00 | 2014-01-05 15:12:16 |
| 20 | 424914 | White-Trantow | S2-00301 | 45 | 96.95 | 4362.75 | 2014-01-06 08:20:27 |
| 21 | 527099 | Sanford and Sons | S1-30248 | 24 | 50.29 | 1206.96 | 2014-01-06 14:14:00 |
| 30 | 642753 | Pollich LLC | S1-93683 | 4 | 87.18 | 348.72 | 2014-01-09 00:57:12 |
| 37 | 383080 | Will LLC | S2-23246 | 23 | 59.98 | 1379.54 | 2014-01-10 13:44:08 |
| 51 | 257198 | Cronin, Oberbrunner and Spencer | B1-05914 | 8 | 23.05 | 184.40 | 2014-01-14 01:57:35 |
| 67 | 604255 | Halvorson, Crona and Champlin | B1-65551 | 34 | 62.86 | 2137.24 | 2014-01-17 19:00:16 |
| 106 | 163416 | Purdy-Kunde | S1-47412 | 31 | 54.32 | 1683.92 | 2014-01-24 04:14:33 |
In [75]:
data = pd.read_csv('combine.csv')
data.describe()
Out[75]:
| year | heightfeet | heightinches | heightinchestotal | weight | arms | hands | fortyyd | twentyyd | tenyd | twentyss | threecone | vertical | broad | bench | round | pickround | picktotal | wonderlic | nflgrade | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 | 4947.000000 |
| mean | 2007.132201 | 5.805337 | 4.371437 | 74.035476 | 245.579745 | 7.640243 | 2.252426 | 4.610386 | 0.073734 | 0.129149 | 3.298106 | 1.503002 | 28.741257 | 95.944006 | 15.723873 | 2.435415 | 11.318981 | 71.879523 | 1.144532 | 0.700627 |
| std | 5.029664 | 0.395981 | 3.279223 | 2.614778 | 45.639366 | 13.801035 | 4.070448 | 0.974087 | 0.432330 | 0.436941 | 1.907526 | 2.929683 | 11.596749 | 41.826340 | 10.840896 | 2.476746 | 12.262220 | 79.417514 | 5.524047 | 1.813870 |
| min | 1999.000000 | 5.000000 | 0.000000 | 65.000000 | 155.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 2003.000000 | 6.000000 | 2.000000 | 73.000000 | 208.000000 | 0.000000 | 0.000000 | 4.530000 | 0.000000 | 0.000000 | 3.835000 | 0.000000 | 28.000000 | 101.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 2007.000000 | 6.000000 | 4.000000 | 74.000000 | 237.000000 | 0.000000 | 0.000000 | 4.690000 | 0.000000 | 0.000000 | 4.240000 | 0.000000 | 32.500000 | 112.000000 | 18.000000 | 2.000000 | 7.000000 | 42.000000 | 0.000000 | 0.000000 |
| 75% | 2012.000000 | 6.000000 | 6.000000 | 76.000000 | 289.000000 | 0.000000 | 0.000000 | 4.990000 | 0.000000 | 0.000000 | 4.470000 | 0.000000 | 35.500000 | 119.000000 | 24.000000 | 5.000000 | 21.000000 | 134.000000 | 0.000000 | 0.000000 |
| max | 2015.000000 | 6.000000 | 11.875000 | 82.000000 | 386.000000 | 37.750000 | 11.375000 | 6.050000 | 2.980000 | 1.920000 | 5.560000 | 8.310000 | 46.000000 | 147.000000 | 51.000000 | 8.000000 | 53.000000 | 262.000000 | 48.000000 | 7.500000 |
In [76]:
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 4947 entries, 0 to 4946 Data columns (total 26 columns): year 4947 non-null int64 name 4947 non-null object firstname 4947 non-null object lastname 4947 non-null object position 4947 non-null object heightfeet 4947 non-null int64 heightinches 4947 non-null float64 heightinchestotal 4947 non-null float64 weight 4947 non-null int64 arms 4947 non-null float64 hands 4947 non-null float64 fortyyd 4947 non-null float64 twentyyd 4947 non-null float64 tenyd 4947 non-null float64 twentyss 4947 non-null float64 threecone 4947 non-null float64 vertical 4947 non-null float64 broad 4947 non-null int64 bench 4947 non-null int64 round 4947 non-null int64 college 3477 non-null object pick 3156 non-null object pickround 4947 non-null int64 picktotal 4947 non-null int64 wonderlic 4947 non-null int64 nflgrade 4947 non-null float64 dtypes: float64(11), int64(9), object(6) memory usage: 1004.9+ KB
In [77]:
data.isnull().sum()
# Tells you where you have null values in the dataset
Out[77]:
year 0 name 0 firstname 0 lastname 0 position 0 heightfeet 0 heightinches 0 heightinchestotal 0 weight 0 arms 0 hands 0 fortyyd 0 twentyyd 0 tenyd 0 twentyss 0 threecone 0 vertical 0 broad 0 bench 0 round 0 college 1470 pick 1791 pickround 0 picktotal 0 wonderlic 0 nflgrade 0 dtype: int64
In [78]:
print data['college'].nunique()
print data['college'].unique()
284 ['Nebraska' 'USC' 'Boise St.' 'LSU' 'West Virginia' 'Penn St.' 'Utah' 'Stanford' 'South Carolina' 'Clemson' 'Oregon' 'Auburn' 'Florida' 'N.C. State' 'Oklahoma' 'Southeastern Louisiana' 'Ohio St.' 'Iowa St.' 'Memphis' 'Virginia Tech' 'Duke' 'UNLV' 'Delaware' 'South Alabama' 'Notre Dame' 'Northern Illinois' 'Louisville' 'Texas' 'Rutgers' 'Northwestern' 'East Carolina' 'Navy' 'TCU' 'Western Michigan' 'Miami' 'Michigan' 'Texas A&M' 'Pittsburgh' 'Minnesota' 'Tennessee' 'Indiana' 'Alabama' 'Georgia' 'Washington St.' 'Rice' 'Mississippi St.' 'North Dakota St.' 'Maryland' 'Florida St.' 'Colorado St.' 'Iowa' 'Connecticut' 'Central Michigan' 'Fresno St.' 'Towson' 'Arkansas' 'Tulane' 'Arizona State' 'Michigan St.' 'Kentucky' 'Ball St.' 'Syracuse' 'Nevada' 'Kansas St.' 'BYU' 'Boston College' 'Central Florida' 'Missouri' 'Mississippi' 'Baylor' 'Wisconsin' 'Oregon St.' 'Coastal Carolina' 'Virginia' 'Kansas' 'Harvard' 'UCLA' 'Hawaii' 'Northern Iowa' 'Stephen F. Austin' 'Wake Forest' 'Texas St.' 'Washington' 'Central Arkansas' 'Portland St.' 'Tennessee-Chattanooga' 'Prairie View A&M' 'Harding' 'Toledo' 'Louisiana-Monroe' 'James Madison' 'Hobart & William Smith' 'Houston' 'William & Mary' 'Eastern Washington' 'Tennessee St.' 'UAB' 'Southern Miss' 'Wyoming' 'San Diego St.' 'Southern Illinois' 'Newberry' 'Miami (OH)' 'Purdue' 'Lafayette' 'Massachusetts' 'Northwestern St. (LA)' 'Georgia Tech' 'Florida Atlantic' 'Samford' 'Texas-El Paso' 'Norfolk St.' 'Yale' 'Montana' 'South Dakota St.' 'Utah St.' 'Texas Tech' 'Western Kentucky' 'Kent St.' 'Grand Valley St.' 'San Jose St.' 'North Carolina' 'Vanderbilt' 'Pittsburg St. (KS)' 'Illinois' 'Arizona' 'Arkansas State' 'Ohio' 'California' 'Alabama St.' 'Lindenwood' 'Northwest Missouri St.' 'Furman' 'Dixie St.' 'Louisiana Tech' 'Bloomsburg' 'Sam Houston St.' 'New Mexico St.' 'Eastern Illinois' 'Oklahoma St.' 'McNeese St.' 'Maine' 'Saginaw Valley St.' 'Shepherd' 'Missouri Western' 'South Florida' 'Buffalo' 'Cornell' 'Georgia Southern' 'Concordia (MN)' 'Colorado State-Pueblo' 'Murray St.' 'Princeton' 'Colorado' 'South Dakota' 'West Texas A&M' 'Georgia State' 'Richmond' nan 'North Carolina State' 'Arkansas-Pine Bluff' 'Mississippi State' 'Michigan State' 'Jacksonville State' 'Kansas State' 'East Central Oklahoma' 'Florida International' 'Utah State' 'Florida State' 'San Diego State' 'Ohio State' 'Chadron State' 'Florida A&M' 'Penn State' 'Tennessee-Martin' 'Southern Methodist' 'Miami (FL)' 'Grand Valley State' 'Iowa State' 'Valdosta State' 'Appalachian State' 'Elon' 'UCF' 'Oregon State' 'San Jose State' 'Oklahoma State' 'Southern Utah' 'Boise State' 'Fresno State' 'Missouri Southern State' 'Washington State' 'Kent State' 'Louisiana-Lafayette' 'Presbyterian' 'Chattanooga' 'Marshall' 'Regina' 'Cincinnati' 'North Alabama' 'Troy' 'Temple' 'Midwestern State' 'South Carolina State' 'Citadel' 'Missouri State' 'Tulsa' 'Hampton' 'Idaho' 'Slippery Rock' 'Abilene Christian' 'New Mexico State' 'Mount Union' 'Portland State' 'New Mexico' 'Murray State' 'Indiana (PA)' 'Brigham Young' 'Fordham' 'Colorado State' 'Cal Poly' 'Sam Houston State' 'Ball State' 'Norfolk State' 'Liberty' 'McNeese State' 'Nicholls State' 'Tennessee State' 'Northwestern State' 'Army' 'UTEP' 'Weber State' 'Jackson State' 'San Diego' 'Eastern Michigan' 'Bowling Green' 'Eastern Kentucky' 'Northwest Missouri State' 'Whitworth' 'Akron' 'Western Oregon' 'Alabama State' 'Brown' 'Lane' 'Illinois State' 'Howard' 'Hofstra' 'Northern Colorado' 'Grambling State' 'North Dakota' 'Central Missouri State' 'Michigan Tech' 'Pearl River (JC)' 'Bethune-Cookman' 'North Carolina A&T' 'North Dakota State' 'Tennessee Tech' 'Idaho State' 'Sacramento State' 'Tusculum' 'Northern Arizona' 'North Texas' 'Montana State' 'Mars Hill' 'Middle Tennessee State' 'Tuskegee' 'Gustavus Adolphus' 'Morgan State' 'Clarion' 'Texas State University' 'Wisc. Stout' 'UC Davis' 'Villanova' 'Nebraska-Omaha' 'Texas A&M-Kingsville' 'Western Illinois' 'Kentucky State' 'Texas A&M-Commerce' 'New Hampshire' 'Tarleton State' 'Pennsylvania' 'Air Force' 'South Dakota State' 'Saginaw Valley' 'Southern University' 'Fort Valley State']
In [79]:
data[data.college == 'UCLA']
Out[79]:
| year | name | firstname | lastname | position | heightfeet | heightinches | heightinchestotal | weight | arms | ... | vertical | broad | bench | round | college | pick | pickround | picktotal | wonderlic | nflgrade | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 161 | 2015 | Brett Hundley | Brett | Hundley | QB | 6 | 3.00 | 75.00 | 226 | 0.00 | ... | 36.0 | 120 | 0 | 0 | UCLA | NaN | 0 | 0 | 0 | 5.5 |
| 169 | 2015 | Anthony Jefferson | Anthony | Jefferson | CB | 6 | 1.00 | 73.00 | 198 | 0.00 | ... | 33.5 | 116 | 15 | 0 | UCLA | NaN | 0 | 0 | 0 | 4.9 |
| 179 | 2015 | Eric Kendricks | Eric | Kendricks | ILB | 6 | 0.00 | 72.00 | 232 | 0.00 | ... | 38.0 | 124 | 19 | 0 | UCLA | NaN | 0 | 0 | 0 | 5.8 |
| 209 | 2015 | Ellis McCarthy | Ellis | McCarthy | DT | 6 | 5.00 | 77.00 | 338 | 0.00 | ... | 32.0 | 109 | 24 | 0 | UCLA | NaN | 0 | 0 | 0 | 5.0 |
| 224 | 2015 | Owamagbe Odighizuwa | Owamagbe | Odighizuwa | DE | 6 | 3.00 | 75.00 | 267 | 0.00 | ... | 39.0 | 127 | 25 | 0 | UCLA | NaN | 0 | 0 | 0 | 5.7 |
| 333 | 2014 | Anthony Barr | Anthony | Barr | OLB | 6 | 5.00 | 77.00 | 255 | 0.00 | ... | 34.5 | 119 | 15 | 1 | UCLA | 0 | 9 | 9 | 0 | 6.6 |
| 408 | 2014 | Shaq Evans | Shaq | Evans | WR | 6 | 1.00 | 73.00 | 213 | 0.00 | ... | 34.5 | 122 | 13 | 4 | UCLA | 0 | 19 | 115 | 0 | 5.3 |
| 516 | 2014 | Cassius Marsh | Cassius | Marsh | DE | 6 | 4.00 | 76.00 | 252 | 0.00 | ... | 32.0 | 108 | 14 | 4 | UCLA | 0 | 12 | 108 | 0 | 5.4 |
| 612 | 2014 | Xavier Su'a-Filo | Xavier | Su'a-Filo | OG | 6 | 4.00 | 76.00 | 307 | 0.00 | ... | 25.0 | 102 | 25 | 2 | UCLA | 0 | 1 | 33 | 0 | 5.6 |
| 656 | 2014 | Jordan Zumwalt | Jordan | Zumwalt | ILB | 6 | 4.00 | 76.00 | 235 | 0.00 | ... | 33.0 | 116 | 0 | 6 | UCLA | 0 | 32 | 192 | 0 | 5.3 |
| 667 | 2013 | Jeff Baca | Jeff | Baca | OG | 6 | 3.00 | 75.00 | 302 | 34.25 | ... | 26.5 | 100 | 0 | 6 | UCLA | 28(196) | 28 | 196 | 0 | 0.0 |
| 726 | 2013 | Johnathan Franklin | Johnathan | Franklin | RB | 5 | 10.00 | 70.00 | 205 | 30.00 | ... | 31.5 | 115 | 18 | 4 | UCLA | 28(125) | 28 | 125 | 0 | 0.0 |
| 787 | 2013 | Datone Jones | Datone | Jones | DE | 6 | 4.25 | 76.25 | 283 | 32.75 | ... | 31.5 | 112 | 29 | 1 | UCLA | 26(26) | 26 | 26 | 0 | 0.0 |
| 1268 | 2011 | Akeem Ayers | Akeem | Ayers | OLB | 6 | 3.00 | 75.00 | 254 | 33.00 | ... | 31.0 | 116 | 18 | 2 | UCLA | 7(39) | 7 | 39 | 0 | 0.0 |
| 1441 | 2011 | Rahim Moore | Rahim | Moore | FS | 6 | 1.00 | 73.00 | 202 | 30.25 | ... | 35.0 | 115 | 11 | 2 | UCLA | 13(45) | 13 | 45 | 0 | 0.0 |
| 1765 | 2010 | Brian Price | Brian | Price | DT | 6 | 1.00 | 73.00 | 303 | 32.25 | ... | 28.5 | 96 | 34 | 2 | UCLA | 3(35) | 3 | 35 | 0 | 0.0 |
| 1818 | 2010 | Alterraun Verner | Alterraun | Verner | CB | 5 | 10.00 | 70.00 | 189 | 31.00 | ... | 32.0 | 116 | 11 | 4 | UCLA | 6(104) | 6 | 104 | 0 | 0.0 |
| 2192 | 2008 | Bruce Davis | Bruce | Davis | OLB | 6 | 3.00 | 75.00 | 252 | 0.00 | ... | 29.0 | 109 | 19 | 3 | UCLA | 25(88) | 25 | 88 | 0 | 0.0 |
| 2257 | 2008 | Chris Horton | Chris | Horton | SS | 6 | 1.00 | 73.00 | 212 | 0.00 | ... | 28.5 | 109 | 14 | 7 | UCLA | 42(249) | 42 | 249 | 0 | 0.0 |
| 2814 | 2006 | Marcedes Lewis | Marcedes | Lewis | TE | 6 | 7.00 | 79.00 | 261 | 0.00 | ... | 37.0 | 118 | 23 | 1 | UCLA | 28(28) | 28 | 28 | 0 | 0.0 |
| 2866 | 2006 | Jarrad Page | Jarrad | Page | SS | 6 | 1.00 | 73.00 | 239 | 0.00 | ... | 38.0 | 127 | 0 | 7 | UCLA | 20(228) | 20 | 228 | 0 | 0.0 |
| 2985 | 2005 | Craig Bragg | Craig | Bragg | WR | 6 | 1.00 | 73.00 | 196 | 32.00 | ... | 36.0 | 122 | 0 | 6 | UCLA | 21(195) | 21 | 195 | 0 | 0.0 |
| 3041 | 2005 | Ben Emanuel | Ben | Emanuel | FS | 6 | 3.00 | 75.00 | 212 | 0.00 | ... | 0.0 | 0 | 21 | 5 | UCLA | 35(171) | 35 | 171 | 0 | 0.0 |
| 3165 | 2005 | Tab Perry | Tab | Perry | WR | 6 | 3.00 | 75.00 | 219 | 0.00 | ... | 34.0 | 116 | 0 | 6 | UCLA | 16(190) | 16 | 190 | 0 | 0.0 |
| 3254 | 2004 | Dave Ball | Dave | Ball | DE | 6 | 6.00 | 78.00 | 277 | 0.00 | ... | 0.0 | 0 | 24 | 5 | UCLA | 1(133) | 1 | 133 | 0 | 0.0 |
| 3280 | 2004 | Brandon Chillar | Brandon | Chillar | OLB | 6 | 3.00 | 75.00 | 253 | 0.00 | ... | 33.0 | 115 | 23 | 4 | UCLA | 34(130) | 34 | 130 | 0 | 0.0 |
| 3488 | 2004 | Matt Ware | Matt | Ware | FS | 6 | 3.00 | 75.00 | 209 | 0.00 | ... | 0.0 | 0 | 15 | 3 | UCLA | 26(89) | 26 | 89 | 0 | 0.0 |
| 3715 | 2003 | Mike Seidman | Mike | Seidman | TE | 6 | 5.00 | 77.00 | 261 | 0.00 | ... | 0.0 | 0 | 20 | 3 | UCLA | 12(76) | 12 | 76 | 0 | 0.0 |
| 3783 | 2002 | Marques Anderson | Marques | Anderson | SS | 5 | 11.00 | 71.00 | 213 | 0.00 | ... | 39.5 | 127 | 18 | 3 | UCLA | 27(92) | 27 | 92 | 0 | 0.0 |
| 3825 | 2002 | Kenyon Coleman | Kenyon | Coleman | DE | 6 | 5.00 | 77.00 | 284 | 0.00 | ... | 0.0 | 0 | 32 | 5 | UCLA | 12(147) | 12 | 147 | 0 | 0.0 |
| 3863 | 2002 | DeShaun Foster | DeShaun | Foster | RB | 6 | 1.00 | 73.00 | 222 | 0.00 | ... | 35.5 | 119 | 20 | 2 | UCLA | 2(34) | 2 | 34 | 0 | 0.0 |
| 4018 | 2002 | Robert Thomas | Robert | Thomas | OLB | 6 | 1.00 | 73.00 | 229 | 0.00 | ... | 34.0 | 116 | 21 | 1 | UCLA | 31(31) | 31 | 31 | 0 | 0.0 |
| 4206 | 2001 | Freddie Mitchell | Freddie | Mitchell | WR | 6 | 1.00 | 73.00 | 185 | 0.00 | ... | 39.5 | 120 | 0 | 1 | UCLA | 25(25) | 25 | 25 | 0 | 0.0 |
| 4665 | 1999 | Kris Farris | Kris | Farris | OT | 6 | 8.00 | 80.00 | 313 | 0.00 | ... | 24.0 | 95 | 20 | 3 | UCLA | 13(74) | 13 | 74 | 0 | 0.0 |
| 4795 | 1999 | Cade McNown | Cade | McNown | QB | 6 | 1.00 | 73.00 | 213 | 0.00 | ... | 33.5 | 113 | 0 | 1 | UCLA | 12(12) | 12 | 12 | 28 | 0.0 |
35 rows × 26 columns
In [80]:
data.college.fillna('No College',inplace=True)
data.isnull().sum()
Out[80]:
year 0 name 0 firstname 0 lastname 0 position 0 heightfeet 0 heightinches 0 heightinchestotal 0 weight 0 arms 0 hands 0 fortyyd 0 twentyyd 0 tenyd 0 twentyss 0 threecone 0 vertical 0 broad 0 bench 0 round 0 college 0 pick 1791 pickround 0 picktotal 0 wonderlic 0 nflgrade 0 dtype: int64
In [81]:
data.college.value_counts()
Out[81]:
No College 1470
Georgia 81
Florida 80
USC 77
Alabama 75
LSU 74
Oklahoma 66
Notre Dame 64
Florida State 63
Nebraska 62
Texas 60
Clemson 57
Ohio State 57
Tennessee 56
Miami (FL) 56
Virginia Tech 55
Iowa 54
Auburn 54
Wisconsin 54
Oregon 52
Michigan 52
Texas A&M 50
North Carolina 49
South Carolina 48
California 47
Stanford 46
Arkansas 44
Louisville 43
Arizona State 40
Penn State 38
...
Kent St. 1
Prairie View A&M 1
Slippery Rock 1
Hobart & William Smith 1
Texas A&M-Kingsville 1
Presbyterian 1
Texas-El Paso 1
Lindenwood 1
Morgan State 1
Northwest Missouri St. 1
South Dakota St. 1
Pittsburg St. (KS) 1
North Texas 1
Tennessee Tech 1
Murray State 1
Fordham 1
Sacramento State 1
Villanova 1
Army 1
Florida A&M 1
Concordia (MN) 1
Liberty 1
Saginaw Valley St. 1
Valdosta State 1
Southern Utah 1
Murray St. 1
Whitworth 1
Shepherd 1
San Diego 1
Western Oregon 1
Name: college, Length: 285, dtype: int64In [82]:
print data.pick.nunique()
len(data['pick'].unique())
1301
Out[82]:
1302
In [83]:
# Drop Null values from a speific columns 'pick'
data_after_drop = data.dropna(subset=['pick'])
In [84]:
data_dropped = data.drop('pick', axis=1)
data_dropped.head()
Out[84]:
| year | name | firstname | lastname | position | heightfeet | heightinches | heightinchestotal | weight | arms | ... | threecone | vertical | broad | bench | round | college | pickround | picktotal | wonderlic | nflgrade | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2015 | Ameer Abdullah | Ameer | Abdullah | RB | 5 | 9.0 | 69.0 | 205 | 0.0 | ... | 6.79 | 42.5 | 130 | 24 | 0 | Nebraska | 0 | 0 | 0 | 5.9 |
| 1 | 2015 | Nelson Agholor | Nelson | Agholor | WR | 6 | 0.0 | 72.0 | 198 | 0.0 | ... | 0.00 | 0.0 | 0 | 12 | 0 | USC | 0 | 0 | 0 | 5.6 |
| 2 | 2015 | Jay Ajayi | Jay | Ajayi | RB | 6 | 0.0 | 72.0 | 221 | 0.0 | ... | 7.10 | 39.0 | 121 | 19 | 0 | Boise St. | 0 | 0 | 0 | 6.0 |
| 3 | 2015 | Kwon Alexander | Kwon | Alexander | OLB | 6 | 1.0 | 73.0 | 227 | 0.0 | ... | 7.14 | 36.0 | 121 | 24 | 0 | LSU | 0 | 0 | 0 | 5.4 |
| 4 | 2015 | Mario Alford | Mario | Alford | WR | 5 | 8.0 | 68.0 | 180 | 0.0 | ... | 6.64 | 34.0 | 121 | 13 | 0 | West Virginia | 0 | 0 | 0 | 5.3 |
5 rows × 25 columns
In [85]:
data['round'].unique()
Out[85]:
array([0, 6, 2, 4, 5, 1, 8, 3, 7], dtype=int64)
In [86]:
print data['position'].nunique()
data['position'].unique()
20
Out[86]:
array(['RB', 'WR', 'OLB', 'FS', 'DE', 'TE', 'ILB', 'DT', 'P', 'QB', 'OG',
'OT', 'K', 'FB', 'SS', 'LS', 'CB', 'C', 'NT', 'OC'], dtype=object)In [87]:
# Replace Values to make them easier to interpret for the coder
data.position.replace(['QB','RB','WR'],['Quarterback','Running Back','Wide Receiver'],inplace=True)
In [88]:
data[data['position'] == 'Quarterback']
Out[88]:
| year | name | firstname | lastname | position | heightfeet | heightinches | heightinchestotal | weight | arms | ... | vertical | broad | bench | round | college | pick | pickround | picktotal | wonderlic | nflgrade | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 19 | 2015 | Bryan Bennett | Bryan | Bennett | Quarterback | 6 | 2.0 | 74.0 | 211 | 0.0 | ... | 37.0 | 125 | 0 | 0 | Southeastern Louisiana | NaN | 0 | 0 | 0 | 0.0 |
| 25 | 2015 | Anthony Boone | Anthony | Boone | Quarterback | 6 | 0.0 | 72.0 | 231 | 0.0 | ... | 26.5 | 100 | 0 | 0 | Duke | NaN | 0 | 0 | 0 | 5.0 |
| 28 | 2015 | Brandon Bridge | Brandon | Bridge | Quarterback | 6 | 4.0 | 76.0 | 229 | 0.0 | ... | 33.0 | 110 | 0 | 0 | South Alabama | NaN | 0 | 0 | 0 | 5.0 |
| 39 | 2015 | Shane Carden | Shane | Carden | Quarterback | 6 | 2.0 | 74.0 | 218 | 0.0 | ... | 29.5 | 104 | 0 | 0 | East Carolina | NaN | 0 | 0 | 0 | 5.1 |
| 101 | 2015 | Cody Fajardo | Cody | Fajardo | Quarterback | 6 | 1.0 | 73.0 | 223 | 0.0 | ... | 32.5 | 118 | 0 | 0 | Nevada | NaN | 0 | 0 | 0 | 5.0 |
| 126 | 2015 | Garrett Grayson | Garrett | Grayson | Quarterback | 6 | 2.0 | 74.0 | 213 | 0.0 | ... | 0.0 | 0 | 0 | 0 | Colorado St. | NaN | 0 | 0 | 0 | 5.4 |
| 138 | 2015 | Connor Halliday | Connor | Halliday | Quarterback | 6 | 3.0 | 75.0 | 196 | 0.0 | ... | 0.0 | 0 | 0 | 0 | Washington St. | NaN | 0 | 0 | 0 | 5.1 |
| 161 | 2015 | Brett Hundley | Brett | Hundley | Quarterback | 6 | 3.0 | 75.0 | 226 | 0.0 | ... | 36.0 | 120 | 0 | 0 | UCLA | NaN | 0 | 0 | 0 | 5.5 |
| 192 | 2015 | Jerry Lovelocke | Jerry | Lovelocke | Quarterback | 6 | 4.0 | 76.0 | 248 | 0.0 | ... | 31.5 | 113 | 0 | 0 | Prairie View A&M | NaN | 0 | 0 | 0 | 0.0 |
| 197 | 2015 | Sean Mannion | Sean | Mannion | Quarterback | 6 | 6.0 | 78.0 | 229 | 0.0 | ... | 31.0 | 105 | 0 | 0 | Oregon St. | NaN | 0 | 0 | 0 | 5.3 |
| 199 | 2015 | Marcus Mariota | Marcus | Mariota | Quarterback | 6 | 4.0 | 76.0 | 222 | 0.0 | ... | 36.0 | 121 | 0 | 0 | Oregon | NaN | 0 | 0 | 0 | 6.4 |
| 202 | 2015 | Nick Marshall | Nick | Marshall | Quarterback | 6 | 1.0 | 73.0 | 207 | 0.0 | ... | 37.5 | 124 | 12 | 0 | Auburn | NaN | 0 | 0 | 0 | 5.2 |
| 236 | 2015 | Bryce Petty | Bryce | Petty | Quarterback | 6 | 3.0 | 75.0 | 230 | 0.0 | ... | 34.0 | 121 | 0 | 0 | Baylor | NaN | 0 | 0 | 0 | 5.3 |
| 273 | 2015 | Blake Sims | Blake | Sims | Quarterback | 5 | 11.0 | 71.0 | 218 | 0.0 | ... | 30.5 | 115 | 0 | 0 | Alabama | NaN | 0 | 0 | 0 | 5.2 |
| 317 | 2015 | Jameis Winston | Jameis | Winston | Quarterback | 6 | 4.0 | 76.0 | 231 | 0.0 | ... | 28.5 | 103 | 0 | 0 | Florida St. | NaN | 0 | 0 | 0 | 6.5 |
| 347 | 2014 | Blake Bortles | Blake | Bortles | Quarterback | 6 | 5.0 | 77.0 | 232 | 0.0 | ... | 32.5 | 115 | 0 | 1 | Central Florida | 0 | 3 | 3 | 0 | 6.4 |
| 351 | 2014 | Tajh Boyd | Tajh | Boyd | Quarterback | 6 | 1.0 | 73.0 | 222 | 0.0 | ... | 30.5 | 106 | 0 | 7 | Clemson | 0 | 21 | 213 | 0 | 5.4 |
| 354 | 2014 | Teddy Bridgewater | Teddy | Bridgewater | Quarterback | 6 | 2.0 | 74.0 | 214 | 0.0 | ... | 30.0 | 113 | 0 | 1 | Louisville | 0 | 32 | 32 | 0 | 6.3 |
| 369 | 2014 | Derek Carr | Derek | Carr | Quarterback | 6 | 2.0 | 74.0 | 214 | 0.0 | ... | 34.5 | 110 | 0 | 2 | Fresno St. | 0 | 4 | 36 | 0 | 5.8 |
| 410 | 2014 | David Fales | David | Fales | Quarterback | 6 | 2.0 | 74.0 | 212 | 0.0 | ... | 28.0 | 103 | 0 | 6 | San Jose St. | 0 | 23 | 183 | 0 | 5.2 |
| 429 | 2014 | Jimmy Garoppolo | Jimmy | Garoppolo | Quarterback | 6 | 2.0 | 74.0 | 226 | 0.0 | ... | 30.5 | 110 | 0 | 2 | Eastern Illinois | 0 | 30 | 62 | 0 | 5.4 |
| 511 | 2014 | Jordan Lynch | Jordan | Lynch | Quarterback | 6 | 0.0 | 72.0 | 217 | 0.0 | ... | 29.5 | 109 | 0 | 0 | Northern Illinois | 0 | 0 | 0 | 0 | 5.0 |
| 515 | 2014 | Johnny Manziel | Johnny | Manziel | Quarterback | 6 | 0.0 | 72.0 | 207 | 0.0 | ... | 31.5 | 113 | 0 | 1 | Texas A&M | 0 | 22 | 22 | 0 | 5.9 |
| 521 | 2014 | Jeff Mathews | Jeff | Mathews | Quarterback | 6 | 4.0 | 76.0 | 223 | 0.0 | ... | 25.5 | 105 | 0 | 0 | Cornell | 0 | 0 | 0 | 0 | 5.2 |
| 525 | 2014 | AJ McCarron | AJ | McCarron | Quarterback | 6 | 3.0 | 75.0 | 220 | 0.0 | ... | 28.0 | 99 | 0 | 6 | Alabama | 0 | 4 | 164 | 0 | 5.6 |
| 530 | 2014 | Zach Mettenberger | Zach | Mettenberger | Quarterback | 6 | 5.0 | 77.0 | 224 | 0.0 | ... | 0.0 | 0 | 0 | 6 | LSU | 0 | 18 | 178 | 0 | 5.3 |
| 538 | 2014 | Stephen Morris | Stephen | Morris | Quarterback | 6 | 2.0 | 74.0 | 213 | 0.0 | ... | 30.0 | 111 | 0 | 0 | Miami | 0 | 0 | 0 | 0 | 5.3 |
| 544 | 2014 | Aaron Murray | Aaron | Murray | Quarterback | 6 | 1.0 | 73.0 | 207 | 0.0 | ... | 0.0 | 0 | 0 | 6 | Georgia | 0 | 3 | 163 | 0 | 5.2 |
| 571 | 2014 | Bryn Renner | Bryn | Renner | Quarterback | 6 | 3.0 | 75.0 | 228 | 0.0 | ... | 25.5 | 108 | 0 | 0 | North Carolina | 0 | 0 | 0 | 0 | 5.1 |
| 587 | 2014 | Tom Savage | Tom | Savage | Quarterback | 6 | 4.0 | 76.0 | 228 | 0.0 | ... | 27.0 | 105 | 0 | 5 | Pittsburgh | 0 | 7 | 135 | 0 | 5.2 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4445 | 2000 | Sean Keenan | Sean | Keenan | Quarterback | 6 | 3.0 | 75.0 | 202 | 0.0 | ... | 25.5 | 95 | 0 | 0 | No College | NaN | 0 | 0 | 32 | 0.0 |
| 4457 | 2000 | Tim Lester | Tim | Lester | Quarterback | 6 | 1.0 | 73.0 | 202 | 0.0 | ... | 27.0 | 97 | 0 | 0 | No College | NaN | 0 | 0 | 26 | 0.0 |
| 4464 | 2000 | Tee Martin | Tee | Martin | Quarterback | 6 | 2.0 | 74.0 | 227 | 0.0 | ... | 34.5 | 113 | 0 | 5 | Tennessee | 34(163) | 34 | 163 | 11 | 0.0 |
| 4494 | 2000 | Chad Pennington | Chad | Pennington | Quarterback | 6 | 4.0 | 76.0 | 229 | 0.0 | ... | 33.5 | 111 | 0 | 1 | Marshall | 18(18) | 18 | 18 | 25 | 0.0 |
| 4500 | 2000 | Tim Rattay | Tim | Rattay | Quarterback | 6 | 1.0 | 73.0 | 215 | 0.0 | ... | 25.5 | 100 | 0 | 7 | Louisiana Tech | 6(212) | 6 | 212 | 27 | 0.0 |
| 4502 | 2000 | Chris Redman | Chris | Redman | Quarterback | 6 | 3.0 | 75.0 | 222 | 0.0 | ... | 26.5 | 98 | 0 | 3 | Louisville | 13(75) | 13 | 75 | 16 | 0.0 |
| 4567 | 2000 | Spergon Wynn | Spergon | Wynn | Quarterback | 6 | 4.0 | 76.0 | 229 | 0.0 | ... | 34.0 | 108 | 0 | 6 | Texas State University | 17(183) | 17 | 183 | 25 | 0.0 |
| 4578 | 1999 | Todd Bandhaur | Todd | Bandhaur | Quarterback | 6 | 3.0 | 75.0 | 236 | 0.0 | ... | 31.0 | 103 | 0 | 0 | No College | NaN | 0 | 0 | 31 | 0.0 |
| 4586 | 1999 | Michael Bishop | Michael | Bishop | Quarterback | 6 | 1.0 | 73.0 | 205 | 0.0 | ... | 33.0 | 113 | 0 | 7 | Kansas State | 21(227) | 21 | 227 | 10 | 0.0 |
| 4594 | 1999 | Aaron Brooks | Aaron | Brooks | Quarterback | 6 | 4.0 | 76.0 | 203 | 0.0 | ... | 35.5 | 120 | 0 | 4 | Virginia | 36(131) | 36 | 131 | 17 | 0.0 |
| 4630 | 1999 | Mike Cook | Mike | Cook | Quarterback | 6 | 1.0 | 73.0 | 207 | 0.0 | ... | 33.5 | 112 | 0 | 0 | No College | NaN | 0 | 0 | 22 | 0.0 |
| 4634 | 1999 | Tim Couch | Tim | Couch | Quarterback | 6 | 4.0 | 76.0 | 225 | 0.0 | ... | 31.0 | 0 | 0 | 1 | Kentucky | 1(1) | 1 | 1 | 22 | 0.0 |
| 4635 | 1999 | Scott Covington | Scott | Covington | Quarterback | 6 | 2.0 | 74.0 | 219 | 0.0 | ... | 34.0 | 109 | 0 | 7 | Miami (FL) | 39(245) | 39 | 245 | 21 | 0.0 |
| 4637 | 1999 | Daunte Culpepper | Daunte | Culpepper | Quarterback | 6 | 4.0 | 76.0 | 255 | 0.0 | ... | 39.0 | 122 | 0 | 1 | UCF | 11(11) | 11 | 11 | 18 | 0.0 |
| 4640 | 1999 | Kevin Daft | Kevin | Daft | Quarterback | 6 | 2.0 | 74.0 | 203 | 0.0 | ... | 32.5 | 114 | 0 | 5 | UC Davis | 18(151) | 18 | 151 | 32 | 0.0 |
| 4645 | 1999 | Oscar Davenport | Oscar | Davenport | Quarterback | 6 | 4.0 | 76.0 | 199 | 0.0 | ... | 31.0 | 109 | 0 | 0 | No College | NaN | 0 | 0 | 6 | 0.0 |
| 4656 | 1999 | Scott Dreisbach | Scott | Dreisbach | Quarterback | 6 | 3.0 | 75.0 | 216 | 0.0 | ... | 33.0 | 112 | 0 | 0 | No College | NaN | 0 | 0 | 20 | 0.0 |
| 4681 | 1999 | Mark Garcia | Mark | Garcia | Quarterback | 6 | 3.0 | 75.0 | 220 | 0.0 | ... | 33.5 | 108 | 0 | 0 | No College | NaN | 0 | 0 | 21 | 0.0 |
| 4686 | 1999 | Joe Germaine | Joe | Germaine | Quarterback | 6 | 1.0 | 73.0 | 211 | 0.0 | ... | 32.0 | 105 | 0 | 4 | Ohio State | 6(101) | 6 | 101 | 25 | 0.0 |
| 4696 | 1999 | Chris Greisen | Chris | Greisen | Quarterback | 6 | 3.0 | 75.0 | 223 | 0.0 | ... | 30.5 | 108 | 0 | 7 | Northwest Missouri State | 33(239) | 33 | 239 | 27 | 0.0 |
| 4718 | 1999 | Brock Huard | Brock | Huard | Quarterback | 6 | 5.0 | 77.0 | 227 | 0.0 | ... | 30.5 | 107 | 0 | 3 | Washington | 16(77) | 16 | 77 | 25 | 0.0 |
| 4759 | 1999 | Shaun King | Shaun | King | Quarterback | 6 | 1.0 | 73.0 | 221 | 0.0 | ... | 33.0 | 113 | 0 | 2 | Tulane | 19(50) | 19 | 50 | 25 | 0.0 |
| 4764 | 1999 | Brian Kuklick | Brian | Kuklick | Quarterback | 6 | 2.0 | 74.0 | 204 | 0.0 | ... | 31.0 | 107 | 0 | 0 | No College | NaN | 0 | 0 | 29 | 0.0 |
| 4768 | 1999 | Graham Leigh | Graham | Leigh | Quarterback | 6 | 2.0 | 74.0 | 212 | 0.0 | ... | 32.5 | 116 | 0 | 0 | No College | NaN | 0 | 0 | 22 | 0.0 |
| 4775 | 1999 | Jason Maas | Jason | Maas | Quarterback | 6 | 2.0 | 74.0 | 210 | 0.0 | ... | 31.5 | 103 | 0 | 0 | No College | NaN | 0 | 0 | 43 | 0.0 |
| 4794 | 1999 | Donovan McNabb | Donovan | McNabb | Quarterback | 6 | 2.0 | 74.0 | 223 | 0.0 | ... | 33.0 | 117 | 0 | 1 | Syracuse | 2(2) | 2 | 2 | 14 | 0.0 |
| 4795 | 1999 | Cade McNown | Cade | McNown | Quarterback | 6 | 1.0 | 73.0 | 213 | 0.0 | ... | 33.5 | 113 | 0 | 1 | UCLA | 12(12) | 12 | 12 | 28 | 0.0 |
| 4873 | 1999 | Akili Smith | Akili | Smith | Quarterback | 6 | 3.0 | 75.0 | 227 | 0.0 | ... | 34.0 | 114 | 0 | 1 | Oregon | 3(3) | 3 | 3 | 26 | 0.0 |
| 4927 | 1999 | Ted White | Ted | White | Quarterback | 6 | 3.0 | 75.0 | 226 | 0.0 | ... | 30.5 | 106 | 0 | 0 | No College | NaN | 0 | 0 | 12 | 0.0 |
| 4941 | 1999 | Anthony Wright | Anthony | Wright | Quarterback | 6 | 2.0 | 74.0 | 195 | 0.0 | ... | 31.0 | 116 | 0 | 0 | No College | NaN | 0 | 0 | 16 | 0.0 |
304 rows × 26 columns
In [89]:
data_dropped = data.drop(['arms','wonderlic'],axis=1)
In [90]:
data_dropped.head(10)
Out[90]:
| year | name | firstname | lastname | position | heightfeet | heightinches | heightinchestotal | weight | hands | ... | threecone | vertical | broad | bench | round | college | pick | pickround | picktotal | nflgrade | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2015 | Ameer Abdullah | Ameer | Abdullah | Running Back | 5 | 9.0 | 69.0 | 205 | 0.0 | ... | 6.79 | 42.5 | 130 | 24 | 0 | Nebraska | NaN | 0 | 0 | 5.9 |
| 1 | 2015 | Nelson Agholor | Nelson | Agholor | Wide Receiver | 6 | 0.0 | 72.0 | 198 | 0.0 | ... | 0.00 | 0.0 | 0 | 12 | 0 | USC | NaN | 0 | 0 | 5.6 |
| 2 | 2015 | Jay Ajayi | Jay | Ajayi | Running Back | 6 | 0.0 | 72.0 | 221 | 0.0 | ... | 7.10 | 39.0 | 121 | 19 | 0 | Boise St. | NaN | 0 | 0 | 6.0 |
| 3 | 2015 | Kwon Alexander | Kwon | Alexander | OLB | 6 | 1.0 | 73.0 | 227 | 0.0 | ... | 7.14 | 36.0 | 121 | 24 | 0 | LSU | NaN | 0 | 0 | 5.4 |
| 4 | 2015 | Mario Alford | Mario | Alford | Wide Receiver | 5 | 8.0 | 68.0 | 180 | 0.0 | ... | 6.64 | 34.0 | 121 | 13 | 0 | West Virginia | NaN | 0 | 0 | 5.3 |
| 5 | 2015 | Javorius Allen | Javorius | Allen | Running Back | 6 | 0.0 | 72.0 | 221 | 0.0 | ... | 6.96 | 35.5 | 121 | 11 | 0 | USC | NaN | 0 | 0 | 5.3 |
| 6 | 2015 | Adrian Amos | Adrian | Amos | FS | 6 | 0.0 | 72.0 | 218 | 0.0 | ... | 7.09 | 35.5 | 122 | 0 | 0 | Penn St. | NaN | 0 | 0 | 5.5 |
| 7 | 2015 | Dres Anderson | Dres | Anderson | Wide Receiver | 6 | 1.0 | 73.0 | 187 | 0.0 | ... | 0.00 | 0.0 | 0 | 13 | 0 | Utah | NaN | 0 | 0 | 5.5 |
| 8 | 2015 | Henry Anderson | Henry | Anderson | DE | 6 | 6.0 | 78.0 | 294 | 0.0 | ... | 7.20 | 30.0 | 111 | 0 | 0 | Stanford | NaN | 0 | 0 | 5.6 |
| 9 | 2015 | Rory 'Busta' Anderson | Rory 'Busta' | Anderson | TE | 6 | 5.0 | 77.0 | 244 | 0.0 | ... | 0.00 | 0.0 | 0 | 0 | 0 | South Carolina | NaN | 0 | 0 | 5.5 |
10 rows × 24 columns
In [91]:
# Creating Dummy Variables
# In order to pass values, such as position, into an algorithm we have to pass numerical values into the algorithm
# Therefore, we created dummy variables using pd.get_dummies
data_positions = pd.get_dummies(data_dropped.position, prefix='Pos',drop_first=True)
data_positions
Out[91]:
| Pos_CB | Pos_DE | Pos_DT | Pos_FB | Pos_FS | Pos_ILB | Pos_K | Pos_LS | Pos_NT | Pos_OC | Pos_OG | Pos_OLB | Pos_OT | Pos_P | Pos_Quarterback | Pos_Running Back | Pos_SS | Pos_TE | Pos_Wide Receiver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 6 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 8 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 13 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 20 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 21 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 22 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 24 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 27 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 29 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4917 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 4918 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4919 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4920 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4921 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 4922 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4923 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4924 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4925 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4926 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4927 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 4928 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4929 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4930 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 4931 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4932 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4933 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4934 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4935 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4936 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4937 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4938 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4939 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4940 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4941 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
| 4942 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4943 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 4944 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4945 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4946 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
4947 rows × 19 columns
In [92]:
data_positions.shape
Out[92]:
(4947, 19)
In [93]:
data_dropped.shape
Out[93]:
(4947, 24)
In [94]:
# Now that we have dummy variables, we are going to merge the dummy variables into our dataset
data = pd.merge(data_dropped, data_positions, left_index=True, right_index = True)
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 4947 entries, 0 to 4946 Data columns (total 43 columns): year 4947 non-null int64 name 4947 non-null object firstname 4947 non-null object lastname 4947 non-null object position 4947 non-null object heightfeet 4947 non-null int64 heightinches 4947 non-null float64 heightinchestotal 4947 non-null float64 weight 4947 non-null int64 hands 4947 non-null float64 fortyyd 4947 non-null float64 twentyyd 4947 non-null float64 tenyd 4947 non-null float64 twentyss 4947 non-null float64 threecone 4947 non-null float64 vertical 4947 non-null float64 broad 4947 non-null int64 bench 4947 non-null int64 round 4947 non-null int64 college 4947 non-null object pick 3156 non-null object pickround 4947 non-null int64 picktotal 4947 non-null int64 nflgrade 4947 non-null float64 Pos_CB 4947 non-null uint8 Pos_DE 4947 non-null uint8 Pos_DT 4947 non-null uint8 Pos_FB 4947 non-null uint8 Pos_FS 4947 non-null uint8 Pos_ILB 4947 non-null uint8 Pos_K 4947 non-null uint8 Pos_LS 4947 non-null uint8 Pos_NT 4947 non-null uint8 Pos_OC 4947 non-null uint8 Pos_OG 4947 non-null uint8 Pos_OLB 4947 non-null uint8 Pos_OT 4947 non-null uint8 Pos_P 4947 non-null uint8 Pos_Quarterback 4947 non-null uint8 Pos_Running Back 4947 non-null uint8 Pos_SS 4947 non-null uint8 Pos_TE 4947 non-null uint8 Pos_Wide Receiver 4947 non-null uint8 dtypes: float64(10), int64(8), object(6), uint8(19) memory usage: 1019.4+ KB
In [95]:
data.head(20)
Out[95]:
| year | name | firstname | lastname | position | heightfeet | heightinches | heightinchestotal | weight | hands | ... | Pos_OC | Pos_OG | Pos_OLB | Pos_OT | Pos_P | Pos_Quarterback | Pos_Running Back | Pos_SS | Pos_TE | Pos_Wide Receiver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2015 | Ameer Abdullah | Ameer | Abdullah | Running Back | 5 | 9.0 | 69.0 | 205 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 1 | 2015 | Nelson Agholor | Nelson | Agholor | Wide Receiver | 6 | 0.0 | 72.0 | 198 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 2 | 2015 | Jay Ajayi | Jay | Ajayi | Running Back | 6 | 0.0 | 72.0 | 221 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 3 | 2015 | Kwon Alexander | Kwon | Alexander | OLB | 6 | 1.0 | 73.0 | 227 | 0.0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 4 | 2015 | Mario Alford | Mario | Alford | Wide Receiver | 5 | 8.0 | 68.0 | 180 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 5 | 2015 | Javorius Allen | Javorius | Allen | Running Back | 6 | 0.0 | 72.0 | 221 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 6 | 2015 | Adrian Amos | Adrian | Amos | FS | 6 | 0.0 | 72.0 | 218 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 7 | 2015 | Dres Anderson | Dres | Anderson | Wide Receiver | 6 | 1.0 | 73.0 | 187 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 8 | 2015 | Henry Anderson | Henry | Anderson | DE | 6 | 6.0 | 78.0 | 294 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 9 | 2015 | Rory 'Busta' Anderson | Rory 'Busta' | Anderson | TE | 6 | 5.0 | 77.0 | 244 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 10 | 2015 | Stephone Anthony | Stephone | Anthony | ILB | 6 | 3.0 | 75.0 | 243 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 2015 | Arik Armstead | Arik | Armstead | DT | 6 | 7.0 | 79.0 | 292 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 12 | 2015 | Cameron Artis-Payne | Cameron | Artis-Payne | Running Back | 5 | 10.0 | 70.0 | 212 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 |
| 13 | 2015 | Neiron Ball | Neiron | Ball | OLB | 6 | 2.0 | 74.0 | 236 | 0.0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 14 | 2015 | Tavaris Barnes | Tavaris | Barnes | DE | 6 | 4.0 | 76.0 | 282 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 15 | 2015 | Wil Baumann | Wil | Baumann | P | 6 | 6.0 | 78.0 | 187 | 0.0 | ... | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |
| 16 | 2015 | Vic Beasley | Vic | Beasley | OLB | 6 | 3.0 | 75.0 | 246 | 0.0 | ... | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17 | 2015 | Blake Bell | Blake | Bell | TE | 6 | 6.0 | 78.0 | 252 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| 18 | 2015 | Kenny Bell | Kenny | Bell | Wide Receiver | 6 | 1.0 | 73.0 | 197 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
| 19 | 2015 | Bryan Bennett | Bryan | Bennett | Quarterback | 6 | 2.0 | 74.0 | 211 | 0.0 | ... | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
20 rows × 43 columns
In [ ]: