import pandas as pd
import numpy as np
import os
pd.options.plotting.backend = 'plotly'

Lecture 8 – Unfaithful Data, Hypothesis Testing

DSC 80, Winter 2023

Announcements

• Lab 2's reflection form is due for extra credit tomorrow at 11:59PM.
• Lab 3 is due on Monday, January 30th at 11:59PM.
• Please fill out the anonymous Week 3 Feedback Survey to let us know how the course has been going so far!

• Project 2 will come out tomorrow 😱.

Agenda

• Messy data.
• Unfaithful data.
• Hypothesis testing.

Messy data

More data type ambiguities

• 1649043031 looks like a number, but is probably a date.

  • As we saw earlier, Unix timestamps count the number of seconds since January 1st, 1970.

• "USD 1,000,000" looks like a string, but is actually a number and a unit.

• 92093 looks like a number, but is really a zip code (and isn't equal to 92,093).

• Sometimes, False appears in a column of country codes. Why might this be? 🤔

Example: The Norway problem 🇳🇴

import yaml

player = '''
name: Magnus Carlsen
age: 32
country: NO
'''

yaml.safe_load(player)

{'name': 'Magnus Carlsen', 'age': 32, 'country': False}

Unfaithful data

Is the data "faithful" to the DGP?

• In other words, how well does the data represent reality?

• Does the data contain unrealistic or "incorrect" values?

  • Dates in the future for events in the past.
  • Locations that don't exist.
  • Negative counts.
  • Misspellings of names.
  • Large outliers.

Is the data "faithful" to the DGP?

• Does the data violate obvious dependencies?
  • Age and birthday don't match.
• Was the data entered by hand?
  • Spelling errors.
  • Fields shifted.
  • Did the form require fields or provide default values?
• Are there obvious signs of data falsification (also known as "curbstoning")?
  • Repeated names.
  • Fake looking email addresses.
  • Repeated use of uncommon names or fields.

Example: Police vehicle stops 🚔

The dataset we're working with contains all of the vehicle stops that the San Diego Police Department made in 2016.

stops = pd.read_csv(os.path.join('data', 'vehicle_stops_2016_datasd.csv'))
stops.head()

	stop_id	stop_cause	service_area	subject_race	subject_sex	subject_age	timestamp	stop_date	stop_time	sd_resident	arrested	searched	obtained_consent	contraband_found	property_seized
0	1308198	Equipment Violation	530	W	M	28	2016-01-01 00:06:00	2016-01-01	0:06	Y	N	N	N	N	N
1	1308172	Moving Violation	520	B	M	25	2016-01-01 00:10:00	2016-01-01	0:10	N	N	N	NaN	NaN	NaN
2	1308171	Moving Violation	110	H	F	31	2016-01-01 00:14:00	2016-01-01	0:14	NaN	NaN	NaN	NaN	NaN	NaN
3	1308170	Moving Violation	Unknown	W	F	29	2016-01-01 00:16:00	2016-01-01	0:16	N	N	N	NaN	NaN	NaN
4	1308197	Moving Violation	230	W	M	52	2016-01-01 00:30:00	2016-01-01	0:30	N	N	N	NaN	NaN	NaN

Data types

Are the data types correct? If not, are they easily fixable?

stops.head(1)

	stop_id	stop_cause	service_area	subject_race	subject_sex	subject_age	timestamp	stop_date	stop_time	sd_resident	arrested	searched	obtained_consent	contraband_found	property_seized
0	1308198	Equipment Violation	530	W	M	28	2016-01-01 00:06:00	2016-01-01	0:06	Y	N	N	N	N	N

stops.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 103051 entries, 0 to 103050
Data columns (total 15 columns):
 #   Column            Non-Null Count   Dtype 
---  ------            --------------   ----- 
 0   stop_id           103051 non-null  int64 
 1   stop_cause        103044 non-null  object
 2   service_area      103051 non-null  object
 3   subject_race      102920 non-null  object
 4   subject_sex       102865 non-null  object
 5   subject_age       100284 non-null  object
 6   timestamp         102879 non-null  object
 7   stop_date         103051 non-null  object
 8   stop_time         103051 non-null  object
 9   sd_resident       83689 non-null   object
 10  arrested          84400 non-null   object
 11  searched          83330 non-null   object
 12  obtained_consent  4791 non-null    object
 13  contraband_found  4969 non-null    object
 14  property_seized   4924 non-null    object
dtypes: int64(1), object(14)
memory usage: 11.8+ MB

Unfaithfulness

• Are there suspicious values?
• If a value is suspicious, can we trust the observation?
• For example, consider 'subject_age' – some are too high to be true, some are too low to be true.

stops['subject_age'].unique()

array(['28', '25', '31', '29', '52', '24', '20', '50', '23', '54', '53',
       '35', '38', '18', '48', '68', '45', '63', '49', '42', '27', '19',
       '55', '32', '47', '33', '41', '59', '60', '58', '26', '36', '40',
       '39', '21', '64', '30', '43', '17', '51', '34', '56', '44', '22',
       '69', '46', '16', '57', '37', '65', '72', '67', '66', '70', '62',
       '73', '74', '0', '77', nan, '89', '79', '61', '78', '99', '75',
       '85', '82', '71', '15', '80', '81', '93', '84', '76', '2', '4',
       '86', '91', '83', '88', '98', '87', 'No Age', '9', '100', '14',
       '95', '96', '92', '119', '1', '90', '163', '5', '114', '94', '10',
       '212', '220', '6', '145', '97', '120'], dtype=object)

ages = pd.to_numeric(stops['subject_age'], errors='coerce')
ages.describe()

count    99648.000000
mean        37.277697
std         14.456934
min          0.000000
25%         25.000000
50%         34.000000
75%         47.000000
max        220.000000
Name: subject_age, dtype: float64

Ages range all over the place, from 0 to 220. Was a 220 year old really pulled over?

stops[ages > 100]

	stop_id	stop_cause	service_area	subject_race	subject_sex	subject_age	timestamp	stop_date	stop_time	sd_resident	arrested	searched	obtained_consent	contraband_found	property_seized
25029	1333254	Equipment Violation	820	W	F	119	2016-03-19 22:20:00	2016-03-19	22:20	N	N	N	NaN	NaN	NaN
34179	1375483	Moving Violation	510	W	M	163	2016-04-18 16:35:00	2016-04-18	16:35	N	N	N	NaN	NaN	NaN
36968	1378369	Moving Violation	240	A	F	114	2016-04-28 11:44:00	2016-04-28	11:44	NaN	NaN	NaN	NaN	NaN	NaN
63570	1405478	Moving Violation	110	V	M	212	2016-08-04 18:10:00	2016-08-04	18:10	Y	N	N	NaN	NaN	NaN
70267	1411694	Moving Violation	310	H	F	220	2016-08-30 18:28:00	2016-08-30	18:28	Y	N	N	NaN	NaN	NaN
77038	1418885	Moving Violation	830	B	M	145	2016-09-22 20:35:00	2016-09-22	20:35	Y	N	Y	N	N	NaN
99449	1440889	Equipment Violation	120	W	F	120	2016-12-15 19:28:00	2016-12-15	19:28	Y	N	N	NaN	NaN	NaN

What about all of the stops that involved people under the legal driving age?

ages[ages < 16].value_counts()

0.0     218
15.0     27
2.0       6
14.0      5
4.0       4
1.0       2
10.0      2
9.0       1
5.0       1
6.0       1
Name: subject_age, dtype: int64

stops[ages < 16]

	stop_id	stop_cause	service_area	subject_race	subject_sex	subject_age	timestamp	stop_date	stop_time	sd_resident	arrested	searched	obtained_consent	contraband_found	property_seized
547	1308628	Moving Violation	120	W	M	0	2016-01-03 16:20:00	2016-01-03	16:20	NaN	NaN	NaN	NaN	NaN	NaN
747	1308820	Moving Violation	Unknown	H	F	0	2016-01-04 22:13:00	2016-01-04	22:13	N	N	N	NaN	NaN	NaN
1686	1309896	Moving Violation	120	W	M	15	2016-01-08 19:30:00	2016-01-08	19:30	N	N	N	NaN	NaN	NaN
1752	1309975	Moving Violation	Unknown	O	M	0	2016-01-08 23:20:00	2016-01-08	23:20	N	N	N	NaN	NaN	NaN
2054	1310377	Equipment Violation	430	H	F	0	2016-01-10 16:42:00	2016-01-10	16:42	Y	N	N	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
100465	1442806	Moving Violation	810	O	M	15	2016-12-20 10:45:00	2016-12-20	10:45	Y	N	N	NaN	NaN	NaN
101495	1443500	Moving Violation	320	W	M	0	2016-12-23 22:30:00	2016-12-23	22:30	NaN	NaN	NaN	NaN	NaN	NaN
101784	1443745	Moving Violation	Unknown	O	M	0	2016-12-26 19:30:00	2016-12-26	19:30	NaN	NaN	NaN	NaN	NaN	NaN
101790	1443747	Moving Violation	320	W	F	0	2016-12-26 20:00:00	2016-12-26	20:00	NaN	NaN	NaN	NaN	NaN	NaN
102848	1444647	Moving Violation	320	W	M	0	2016-12-31 17:45:00	2016-12-31	17:45	NaN	NaN	NaN	NaN	NaN	NaN

267 rows × 15 columns

Unfaithful 'subject_age'

• Ages of 'No Age' and 0 are likely explicit null values.
• What do we do about the exceptionally small and large ages?
  • Do we throw the entire row away, even if the rest of row is well-formed?
• What about the 14 and 15 year olds?
  • Each has more than one occurrence – these could be real entries!

Human-entered data

• Which fields were likely entered by a human?
• Which fields were likely generated by code?
  • What was the original source?

Let's look at all unique stop causes. Notice that there are three different causes related to bicycles, which should probably all fall under the same cause.

stops['stop_cause'].value_counts()

Moving Violation                      75200
Equipment Violation                   26234
Radio Call/Citizen Contact              571
Muni, County, H&S Code                  319
Personal Knowledge/Informant            289
No Cause Specified on a Card            184
Suspect Info (I.S., Bulletin, Log)      181
Personal Observ/Knowledge                45
MUNI, County, H&S Code                   14
Bicycle                                   2
Suspect Info                              2
BICYCLE                                   1
B & P                                     1
Bicycle Bicycle                           1
Name: stop_cause, dtype: int64

Let's plot the distribution of ages, within a reasonable range (15 to 85). What do you notice? How could we address this?

ages[(ages > 15) & (ages <= 85)].plot(kind='hist')