From 4f331eff92a3b5c3d62282c68d06ed30fa0ac5de Mon Sep 17 00:00:00 2001 From: Daniel Tomlinson Date: Mon, 27 Sep 2021 19:02:23 +0100 Subject: [PATCH] Deployed cad6612 with MkDocs version: 1.2.2 --- 404.html | 136 ++- discussion/exploration.html | 548 ++++++++++++ discussion/introduction.html | 553 ++++++++++++ documentation/installation.html | 140 ++- documentation/usage.html | 155 +++- index.html | 140 ++- pandas-profiling/report.html | 1439 +++++++++++++++++++++++++++++++ search/search_index.json | 2 +- sitemap.xml | 10 + sitemap.xml.gz | Bin 195 -> 198 bytes 10 files changed, 3111 insertions(+), 12 deletions(-) create mode 100644 discussion/exploration.html create mode 100644 discussion/introduction.html create mode 100644 pandas-profiling/report.html diff --git a/404.html b/404.html index 65f92e8..d68a870 100644 --- a/404.html +++ b/404.html @@ -158,6 +158,59 @@ + + +
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- + diff --git a/discussion/exploration.html b/discussion/exploration.html new file mode 100644 index 0000000..0472cb8 --- /dev/null +++ b/discussion/exploration.html @@ -0,0 +1,548 @@ + + + + + + + + + + + + + + + + + Data Exploration Report - The Street Group Technical Test + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Data Exploration Report

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A brief exploration was done on the full dataset using the module pandas-profiling. The module uses pandas to load a dataset and automatically produce quantile/descriptive statistics, common values, extreme values, skew, kurtosis etc.

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The script used to generate this report is located in ./exploration/report.py.

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The report can be viewed by clicking the Data Exploration Report tab at the top of the page.

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Introduction

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This section will go through some discussion of the test including:

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  • Data exploration
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  • Cleaning the data
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  • Interpreting the results
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  • Deploying on GCP DataFlow
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  • Improvements
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pip install poetry

From the root of the repo install the dependencies with:

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poetry install --nodev
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poetry install --no-dev
 

                 
               
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   --output OUTPUT  Full path to the output file without extension.

The default value for input is ./data/input/pp-2020.csv and the default value for output is ./data/output/pp-2020.

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If passing in values for input/output these should be full paths to the files. The test will parse these inputs as a str() and pass this to beam.io.ReadFromText().

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If passing in values for input/output these should be full paths to the files. The test will parse these inputs as a str() and pass this to beam.io.ReadFromText().

Run the pipeline

To run the pipeline and complete the task run:

analyse-properties --runner DirectRunner
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Introduction

 
This documentation accompanies the technical test for the Street Group.

 
The following pages will guide the user through installing the requirements, and running the task to complete the test. In addition, there is some discussion around the approach, and any improvements that could be made.

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Navigate the pages in order by using the section links in the left menu, or by using bar at the bottom of the page. The table of contents in the right menu can be used to navigate sections on each page.

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Navigate sections using the tabs at the top of the page. Pages in this section can be viewed in order by using the section links in the left menu, or by using bar at the bottom of the page. The table of contents in the right menu can be used to navigate sections on each page.

 

 
Note

 
All paths in this documentation, e.g ./analyse_properties/data/output refer to the location of the directory/file from the root of the repo.

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diff --git a/pandas-profiling/report.html b/pandas-profiling/report.html
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+Price Paid Data
Overview
Dataset statistics
Number of variables16
Number of observations26321785
Missing cells32579197
Missing cells (%)7.7%
Total size in memory3.1 GiB
Average record size in memory128.0 B
Variable types
Categorical15
Numeric1
Warnings
record_status has constant value "A" Constant
transaction_id has a high cardinality: 26321785 distinct values High cardinality
date_of_transfer has a high cardinality: 9698 distinct values High cardinality
postcode has a high cardinality: 1274429 distinct values High cardinality
paon has a high cardinality: 508466 distinct values High cardinality
saon has a high cardinality: 58202 distinct values High cardinality
street has a high cardinality: 320310 distinct values High cardinality
locality has a high cardinality: 23716 distinct values High cardinality
town_city has a high cardinality: 1171 distinct values High cardinality
district has a high cardinality: 463 distinct values High cardinality
county has a high cardinality: 130 distinct values High cardinality
saon has 23252525 (88.3%) missing values Missing
street has 411893 (1.6%) missing values Missing
locality has 8868564 (33.7%) missing values Missing
price is highly skewed (γ1 = 212.5121542) Skewed
transaction_id has unique values Unique
Reproduction
Analysis started2021-09-24 16:09:53.212210
Analysis finished2021-09-24 16:15:35.611974
Duration5 minutes and 42.4 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json
Variables
transaction_id
Categorical
HIGH CARDINALITY
UNIQUE
Distinct26321785
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size200.8 MiB
 {A2EF370F-229F-405B-A6EC-3D501915AD67} 
   
 1 
 {5F9A7C5C-E476-4986-8722-CCD108EB8B6E} 
   
 1 
 {E31EE92A-128A-4AA3-81B2-E7A8B3A4C90D} 
   
 1 
 {919FEC05-D788-9A90-E053-6C04A8C0A300} 
   
 1 
 {6FFBE023-6AE3-451E-ADA4-8D66F231D8F8} 
   
 1 
 Other values (26321780) 
 26321780  
Unique
Unique26321785 ?
Unique (%)100.0%
Sample
1st row{F887F88E-7D15-4415-804E-52EAC2F10958}
2nd row{40FD4DF2-5362-407C-92BC-566E2CCE89E9}
3rd row{7A99F89E-7D81-4E45-ABD5-566E49A045EA}
4th row{28225260-E61C-4E57-8B56-566E5285B1C1}
5th row{444D34D7-9BA6-43A7-B695-4F48980E0176}
Common Values
ValueCountFrequency (%)
{A2EF370F-229F-405B-A6EC-3D501915AD67}1
   
< 0.1% 
{5F9A7C5C-E476-4986-8722-CCD108EB8B6E}1
   
< 0.1% 
{E31EE92A-128A-4AA3-81B2-E7A8B3A4C90D}1
   
< 0.1% 
{919FEC05-D788-9A90-E053-6C04A8C0A300}1
   
< 0.1% 
{6FFBE023-6AE3-451E-ADA4-8D66F231D8F8}1
   
< 0.1% 
{174380A1-DCA2-4DB5-A7C3-661C6C5D9798}1
   
< 0.1% 
{1D30B696-41C4-4D78-A474-EE409899F476}1
   
< 0.1% 
{C18420AC-7FE9-4E93-89AC-53697390DE27}1
   
< 0.1% 
{62E23376-BBCE-435C-AE3B-A2CA076580F4}1
   
< 0.1% 
{BC6C63E2-BDA1-498E-9E99-3B4A64C67381}1
   
< 0.1% 
Other values (26321775)26321775
 > 99.9% 
price
Real number (ℝ≥0)
SKEWED
Distinct218688
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205226.2064
Minimum1
Maximum630000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.8 MiB
2021-09-24T17:15:38.084341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Quantile statistics
Minimum1
5-th percentile36000
Q180500
median142500
Q3235000
95-th percentile500000
Maximum630000000
Range629999999
Interquartile range (IQR)154500
Descriptive statistics
Standard deviation813350.2288
Coefficient of variation (CV)3.963188927
Kurtosis86409.10552
Mean205226.2064
Median Absolute Deviation (MAD)71290
Skewness212.5121542
Sum5.401920082 × 1012
Variance6.615385948 × 1011
MonotonicityNot monotonic
2021-09-24T17:15:38.218791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50) 
ValueCountFrequency (%)
250000283789
   
 1.1% 
125000246148
   
 0.9% 
120000216854
   
 0.8% 
150000205955
   
 0.8% 
110000192318
   
 0.7% 
175000185637
   
 0.7% 
115000182009
   
 0.7% 
135000181500
   
 0.7% 
60000180568
   
 0.7% 
130000178803
   
 0.7% 
Other values (218678)24268204
 92.2% 
ValueCountFrequency (%)
1101
< 0.1% 
52
   
< 0.1% 
108
   
< 0.1% 
113
   
< 0.1% 
154
   
< 0.1% 
ValueCountFrequency (%)
6300000001
< 0.1% 
5943000001
< 0.1% 
5692000001
< 0.1% 
4485000001
< 0.1% 
4483009791
< 0.1% 
date_of_transfer
Categorical
HIGH CARDINALITY
Distinct9698
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.8 MiB
 2016-03-31 00:00 
   
 32378 
 2001-06-29 00:00 
   
 26583 
 2002-05-31 00:00 
   
 26338 
 2002-06-28 00:00 
   
 26320 
 2007-06-29 00:00 
   
 24970 
 Other values (9693) 
 26185196  
Unique
Unique10 ?
Unique (%)< 0.1%
Sample
1st row1995-07-07 00:00
2nd row1995-02-03 00:00
3rd row1995-01-13 00:00
4th row1995-07-28 00:00
5th row1995-06-28 00:00
Common Values
ValueCountFrequency (%)
2016-03-31 00:0032378
   
 0.1% 
2001-06-29 00:0026583
   
 0.1% 
2002-05-31 00:0026338
   
 0.1% 
2002-06-28 00:0026320
   
 0.1% 
2007-06-29 00:0024970
   
 0.1% 
2000-06-30 00:0024927
   
 0.1% 
2003-11-28 00:0024802
   
 0.1% 
1999-05-28 00:0024335
   
 0.1% 
2006-06-30 00:0024308
   
 0.1% 
2000-03-31 00:0023428
   
 0.1% 
Other values (9688)26063396
 99.0% 
postcode
Categorical
HIGH CARDINALITY
Distinct1274429
Distinct (%)4.8%
Missing42019
Missing (%)0.2%
Memory size200.8 MiB
 YO10 3FT 
   
 534 
 LU1 5FT 
   
 523 
 RH10 3HZ 
   
 387 
 L7 3AA 
   
 372 
 TR8 4LX 
   
 355 
 Other values (1274424) 
 26277595  
Unique
Unique88384 ?
Unique (%)0.3%
Sample
1st rowMK15 9HP
2nd rowSR6 0AQ
3rd rowCO6 1SQ
4th rowB90 4TG
5th rowDY5 1SA
Common Values
ValueCountFrequency (%)
YO10 3FT534
   
< 0.1% 
LU1 5FT523
   
< 0.1% 
RH10 3HZ387
   
< 0.1% 
L7 3AA372
   
< 0.1% 
TR8 4LX355
   
< 0.1% 
M1 5GB348
   
< 0.1% 
BS3 3NG322
   
< 0.1% 
L5 3AA315
   
< 0.1% 
L3 8HA312
   
< 0.1% 
CM21 9PF305
   
< 0.1% 
Other values (1274419)26275993
 99.8% 
(Missing)42019
   
 0.2% 
property_type
Categorical
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.8 MiB
 T 
 7940720  
 S 
 7224820  
 D 
 6077417  
 F 
 4730013  
 O 
   
 348815 
Unique
Unique0 ?
Unique (%)0.0%
Sample
1st rowD
2nd rowT
3rd rowT
4th rowT
5th rowS
Common Values
ValueCountFrequency (%)
T7940720
 30.2% 
S7224820
 27.4% 
D6077417
 23.1% 
F4730013
 18.0% 
O348815
   
 1.3% 
old_new
Categorical
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.8 MiB
 N 
 23590329  
 Y 
 2731456  
Unique
Unique0 ?
Unique (%)0.0%
Sample
1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN
Common Values
ValueCountFrequency (%)
N23590329
 89.6% 
Y2731456
   
 10.4% 
duration
Categorical
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.8 MiB
 F 
 20127890  
 L 
 6193361  
 U 
   
 534 
Unique
Unique0 ?
Unique (%)0.0%
Sample
1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowF
Common Values
ValueCountFrequency (%)
F20127890
 76.5% 
L6193361
   
 23.5% 
U534
   
< 0.1% 
paon
Categorical
HIGH CARDINALITY
Distinct508466
Distinct (%)1.9%
Missing4196
Missing (%)< 0.1%
Memory size200.8 MiB
 1 
   
 656135 
 2 
   
 655728 
 3 
   
 649054 
 4 
   
 628033 
 5 
   
 605766 
 Other values (508461) 
 23122873  
Unique
Unique236792 ?
Unique (%)0.9%
Sample
1st row31
2nd row50
3rd row19
4th row37
5th row59
Common Values
ValueCountFrequency (%)
1656135
   
 2.5% 
2655728
   
 2.5% 
3649054
   
 2.5% 
4628033
   
 2.4% 
5605766
   
 2.3% 
6584416
   
 2.2% 
7559000
   
 2.1% 
8547016
   
 2.1% 
9517669
   
 2.0% 
10505557
   
 1.9% 
Other values (508456)20409215
 77.5% 
saon
Categorical
HIGH CARDINALITY
MISSING
Distinct58202
Distinct (%)1.9%
Missing23252525
Missing (%)88.3%
Memory size200.8 MiB
 FLAT 2 
   
 165778 
 FLAT 1 
   
 164781 
 FLAT 3 
   
 148061 
 FLAT 4 
   
 122764 
 FLAT 5 
   
 97065 
 Other values (58197) 
 2370811  
Unique
Unique33682 ?
Unique (%)1.1%
Sample
1st row28
2nd rowFLAT 21
3rd rowFLAT 7A
4th rowFLAT 1
5th rowFLAT 8
Common Values
ValueCountFrequency (%)
FLAT 2165778
   
 0.6% 
FLAT 1164781
   
 0.6% 
FLAT 3148061
   
 0.6% 
FLAT 4122764
   
 0.5% 
FLAT 597065
   
 0.4% 
FLAT 682135
   
 0.3% 
279685
   
 0.3% 
178655
   
 0.3% 
FLAT 766209
   
 0.3% 
FLAT 859802
   
 0.2% 
Other values (58192)2004325
   
 7.6% 
(Missing)23252525
 88.3% 
street
Categorical
HIGH CARDINALITY
MISSING
Distinct320310
Distinct (%)1.2%
Missing411893
Missing (%)1.6%
Memory size200.8 MiB
 HIGH STREET 
   
 169595 
 STATION ROAD 
   
 87514 
 LONDON ROAD 
   
 60153 
 CHURCH ROAD 
   
 49684 
 CHURCH STREET 
   
 49125 
 Other values (320305) 
 25493821  
Unique
Unique16555 ?
Unique (%)0.1%
Sample
1st rowALDRICH DRIVE
2nd rowHOWICK PARK
3rd rowBRICK KILN CLOSE
4th rowRAINSBROOK DRIVE
5th rowMERRY HILL
Common Values
ValueCountFrequency (%)
HIGH STREET169595
   
 0.6% 
STATION ROAD87514
   
 0.3% 
LONDON ROAD60153
   
 0.2% 
CHURCH ROAD49684
   
 0.2% 
CHURCH STREET49125
   
 0.2% 
MAIN STREET48089
   
 0.2% 
PARK ROAD40175
   
 0.2% 
VICTORIA ROAD34925
   
 0.1% 
CHURCH LANE32130
   
 0.1% 
MAIN ROAD29955
   
 0.1% 
Other values (320300)25308547
 96.2% 
(Missing)411893
   
 1.6% 
locality
Categorical
HIGH CARDINALITY
MISSING
Distinct23716
Distinct (%)0.1%
Missing8868564
Missing (%)33.7%
Memory size200.8 MiB
 LONDON 
   
 899924 
 BIRMINGHAM 
   
 111310 
 MANCHESTER 
   
 100845 
 LIVERPOOL 
   
 99506 
 LEEDS 
   
 88968 
 Other values (23711) 
 16152668  
Unique
Unique836 ?
Unique (%)< 0.1%
Sample
1st rowWILLEN
2nd rowSUNDERLAND
3rd rowCOGGESHALL
4th rowSHIRLEY
5th rowBRIERLEY HILL
Common Values
ValueCountFrequency (%)
LONDON899924
   
 3.4% 
BIRMINGHAM111310
   
 0.4% 
MANCHESTER100845
   
 0.4% 
LIVERPOOL99506
   
 0.4% 
LEEDS88968
   
 0.3% 
BRISTOL88659
   
 0.3% 
SHEFFIELD76269
   
 0.3% 
BOURNEMOUTH60354
   
 0.2% 
SOUTHAMPTON56612
   
 0.2% 
PLYMOUTH56250
   
 0.2% 
Other values (23706)15814524
 60.1% 
(Missing)8868564
 33.7% 
town_city
Categorical
HIGH CARDINALITY
Distinct1171
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.8 MiB
 LONDON 
   
 2031341 
 MANCHESTER 
   
 431016 
 BRISTOL 
   
 404300 
 BIRMINGHAM 
   
 386956 
 NOTTINGHAM 
   
 344685 
 Other values (1166) 
 22723487  
Unique
Unique2 ?
Unique (%)< 0.1%
Sample
1st rowMILTON KEYNES
2nd rowSUNDERLAND
3rd rowCOLCHESTER
4th rowSOLIHULL
5th rowBRIERLEY HILL
Common Values
ValueCountFrequency (%)
LONDON2031341
   
 7.7% 
MANCHESTER431016
   
 1.6% 
BRISTOL404300
   
 1.5% 
BIRMINGHAM386956
   
 1.5% 
NOTTINGHAM344685
   
 1.3% 
LEEDS296653
   
 1.1% 
LIVERPOOL272491
   
 1.0% 
SHEFFIELD250601
   
 1.0% 
LEICESTER231450
   
 0.9% 
SOUTHAMPTON213759
   
 0.8% 
Other values (1161)21458533
 81.5% 
district
Categorical
HIGH CARDINALITY
Distinct463
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.8 MiB
 BIRMINGHAM 
   
 387632 
 LEEDS 
   
 351319 
 BRADFORD 
   
 230830 
 SHEFFIELD 
   
 213316 
 MANCHESTER 
   
 210509 
 Other values (458) 
 24928179  
Unique
Unique1 ?
Unique (%)< 0.1%
Sample
1st rowMILTON KEYNES
2nd rowSUNDERLAND
3rd rowBRAINTREE
4th rowSOLIHULL
5th rowDUDLEY
Common Values
ValueCountFrequency (%)
BIRMINGHAM387632
   
 1.5% 
LEEDS351319
   
 1.3% 
BRADFORD230830
   
 0.9% 
SHEFFIELD213316
   
 0.8% 
MANCHESTER210509
   
 0.8% 
CITY OF BRISTOL205163
   
 0.8% 
LIVERPOOL183632
   
 0.7% 
KIRKLEES178730
   
 0.7% 
WANDSWORTH177641
   
 0.7% 
EAST RIDING OF YORKSHIRE174522
   
 0.7% 
Other values (453)24008491
 91.2% 
county
Categorical
HIGH CARDINALITY
Distinct130
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.8 MiB
 GREATER LONDON 
 3406054  
 GREATER MANCHESTER 
   
 1165840 
 WEST MIDLANDS 
   
 1005283 
 WEST YORKSHIRE 
   
 1000437 
 KENT 
   
 747469 
 Other values (125) 
 18996702  
Unique
Unique0 ?
Unique (%)0.0%
Sample
1st rowMILTON KEYNES
2nd rowTYNE AND WEAR
3rd rowESSEX
4th rowWEST MIDLANDS
5th rowWEST MIDLANDS
Common Values
ValueCountFrequency (%)
GREATER LONDON3406054
   
 12.9% 
GREATER MANCHESTER1165840
   
 4.4% 
WEST MIDLANDS1005283
   
 3.8% 
WEST YORKSHIRE1000437
   
 3.8% 
KENT747469
   
 2.8% 
ESSEX732466
   
 2.8% 
HAMPSHIRE691504
   
 2.6% 
SURREY593744
   
 2.3% 
LANCASHIRE593351
   
 2.3% 
HERTFORDSHIRE559718
   
 2.1% 
Other values (120)15825919
 60.1% 
ppd_category
Categorical
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.8 MiB
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Unique
Unique0 ?
Unique (%)0.0%
Sample
1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
Common Values
ValueCountFrequency (%)
A25365945
 96.4% 
B955840
   
 3.6% 
record_status
Categorical
CONSTANT
REJECTED
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.8 MiB
 A 
 26321785  
Unique
Unique0 ?
Unique (%)0.0%
Sample
1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
Common Values
ValueCountFrequency (%)
A26321785
 100.0% 

\ No newline at end of file
diff --git a/search/search_index.json b/search/search_index.json
index 361e3f6..48fc518 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"index.html","text":"Welcome \u00b6 Introduction \u00b6 This documentation accompanies the technical test for the Street Group. The following pages will guide the user through installing the requirements, and running the task to complete the test. In addition, there is some discussion around the approach, and any improvements that could be made. Navigate the pages in order by using the section links in the left menu, or by using bar at the bottom of the page. The table of contents in the right menu can be used to navigate sections on each page. Note All paths in this documentation, e.g ./analyse_properties/data/output refer to the location of the directory/file from the root of the repo.","title":"Welcome"},{"location":"index.html#welcome","text":"","title":"Welcome"},{"location":"index.html#introduction","text":"This documentation accompanies the technical test for the Street Group. The following pages will guide the user through installing the requirements, and running the task to complete the test. In addition, there is some discussion around the approach, and any improvements that could be made. Navigate the pages in order by using the section links in the left menu, or by using bar at the bottom of the page. The table of contents in the right menu can be used to navigate sections on each page. Note All paths in this documentation, e.g ./analyse_properties/data/output refer to the location of the directory/file from the root of the repo.","title":"Introduction"},{"location":"documentation/installation.html","text":"Installation \u00b6 The task is written in Python 3.7.9 using Apache Beam 2.32.0. Python versions 3.6.14 and 3.8.11 should also be compatible but have not been tested. The task has been tested on MacOS Big Sur and WSL2. The task should run on Windows but this wasn't tested. For Beam 2.32.0 the supported versions of the Python SDK can be found here . Poetry \u00b6 The test uses Poetry for dependency management. Info If you already have Poetry installed globally you can go straight to the poetry install step. In a virtual environment install poetry: pip install poetry From the root of the repo install the dependencies with: poetry install --nodev","title":"Installation"},{"location":"documentation/installation.html#installation","text":"The task is written in Python 3.7.9 using Apache Beam 2.32.0. Python versions 3.6.14 and 3.8.11 should also be compatible but have not been tested. The task has been tested on MacOS Big Sur and WSL2. The task should run on Windows but this wasn't tested. For Beam 2.32.0 the supported versions of the Python SDK can be found here .","title":"Installation"},{"location":"documentation/installation.html#poetry","text":"The test uses Poetry for dependency management. Info If you already have Poetry installed globally you can go straight to the poetry install step. In a virtual environment install poetry: pip install poetry From the root of the repo install the dependencies with: poetry install --nodev","title":"Poetry"},{"location":"documentation/usage.html","text":"Usage \u00b6 This page documents how to run the pipeline locally to complete the task for the dataset for 2020 . The pipeline also runs in GCP using DataFlow and is discussed further on but can be viewed here. We also discuss how to adapt the pipeline so it can run against the full dataset . Download dataset \u00b6 The input data by default should go in ./data/input . For convenience the data is available publicly in a GCP Cloud Storage bucket. Run: wget https://storage.googleapis.com/street-group-technical-test-dmot-euw1/input/pp-2020.csv -P data/input to download the data for 2020 and place in the input directory above. Entrypoint \u00b6 The entrypoint to the pipeline is analyse-properties . Available options \u00b6 Running analyse-properties --help gives the following output: usage: analyse-properties [ -h ] [ --input INPUT ] [ --output OUTPUT ] optional arguments: -h, --help show this help message and exit --input INPUT Full path to the input file. --output OUTPUT Full path to the output file without extension. The default value for input is ./data/input/pp-2020.csv and the default value for output is ./data/output/pp-2020 . If passing in values for input / output these should be full paths to the files. The test will parse these inputs as a str() and pass this to beam.io.ReadFromText() . 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The test will parse these inputs as a str() and pass this to beam.io.ReadFromText() .","title":"Available options"},{"location":"documentation/usage.html#run-the-pipeline","text":"To run the pipeline and complete the task run: analyse-properties --runner DirectRunner The pipeline will use the 2020 dataset located in ./data/input and output the resulting .json to ./data/output .","title":"Run the pipeline"}]}
\ No newline at end of file
+{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"index.html","text":"Welcome \u00b6 Introduction \u00b6 This documentation accompanies the technical test for the Street Group. The following pages will guide the user through installing the requirements, and running the task to complete the test. In addition, there is some discussion around the approach, and any improvements that could be made. Navigate sections using the tabs at the top of the page. Pages in this section can be viewed in order by using the section links in the left menu, or by using bar at the bottom of the page. The table of contents in the right menu can be used to navigate sections on each page. 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Note All paths in this documentation, e.g ./analyse_properties/data/output refer to the location of the directory/file from the root of the repo.","title":"Introduction"},{"location":"discussion/exploration.html","text":"Data Exploration Report \u00b6 A brief exploration was done on the full dataset using the module pandas-profiling . The module uses pandas to load a dataset and automatically produce quantile/descriptive statistics, common values, extreme values, skew, kurtosis etc. The script used to generate this report is located in ./exploration/report.py . The report can be viewed by clicking the Data Exploration Report tab at the top of the page.","title":"Data Exploration Report"},{"location":"discussion/exploration.html#data-exploration-report","text":"A brief exploration was done on the full dataset using the module pandas-profiling . The module uses pandas to load a dataset and automatically produce quantile/descriptive statistics, common values, extreme values, skew, kurtosis etc. The script used to generate this report is located in ./exploration/report.py . The report can be viewed by clicking the Data Exploration Report tab at the top of the page.","title":"Data Exploration Report"},{"location":"discussion/introduction.html","text":"Introduction \u00b6 This section will go through some discussion of the test including: Data exploration Cleaning the data Interpreting the results Deploying on GCP DataFlow Improvements","title":"Introduction"},{"location":"discussion/introduction.html#introduction","text":"This section will go through some discussion of the test including: Data exploration Cleaning the data Interpreting the results Deploying on GCP DataFlow Improvements","title":"Introduction"},{"location":"documentation/installation.html","text":"Installation \u00b6 The task is written in Python 3.7.9 using Apache Beam 2.32.0. Python versions 3.6.14 and 3.8.11 should also be compatible but have not been tested. The task has been tested on MacOS Big Sur and WSL2. The task should run on Windows but this wasn't tested. For Beam 2.32.0 the supported versions of the Python SDK can be found here . Poetry \u00b6 The test uses Poetry for dependency management. Info If you already have Poetry installed globally you can go straight to the poetry install step. In a virtual environment install poetry: pip install poetry From the root of the repo install the dependencies with: poetry install --no-dev","title":"Installation"},{"location":"documentation/installation.html#installation","text":"The task is written in Python 3.7.9 using Apache Beam 2.32.0. Python versions 3.6.14 and 3.8.11 should also be compatible but have not been tested. The task has been tested on MacOS Big Sur and WSL2. The task should run on Windows but this wasn't tested. For Beam 2.32.0 the supported versions of the Python SDK can be found here .","title":"Installation"},{"location":"documentation/installation.html#poetry","text":"The test uses Poetry for dependency management. Info If you already have Poetry installed globally you can go straight to the poetry install step. In a virtual environment install poetry: pip install poetry From the root of the repo install the dependencies with: poetry install --no-dev","title":"Poetry"},{"location":"documentation/usage.html","text":"Usage \u00b6 This page documents how to run the pipeline locally to complete the task for the dataset for 2020 . The pipeline also runs in GCP using DataFlow and is discussed further on but can be viewed here. We also discuss how to adapt the pipeline so it can run against the full dataset . Download dataset \u00b6 The input data by default should go in ./data/input . For convenience the data is available publicly in a GCP Cloud Storage bucket. Run: wget https://storage.googleapis.com/street-group-technical-test-dmot-euw1/input/pp-2020.csv -P data/input to download the data for 2020 and place in the input directory above. Entrypoint \u00b6 The entrypoint to the pipeline is analyse-properties . Available options \u00b6 Running analyse-properties --help gives the following output: usage: analyse-properties [ -h ] [ --input INPUT ] [ --output OUTPUT ] optional arguments: -h, --help show this help message and exit --input INPUT Full path to the input file. --output OUTPUT Full path to the output file without extension. The default value for input is ./data/input/pp-2020.csv and the default value for output is ./data/output/pp-2020 . If passing in values for input / output these should be full paths to the files. The test will parse these inputs as a str() and pass this to beam . io . ReadFromText () . Run the pipeline \u00b6 To run the pipeline and complete the task run: analyse-properties --runner DirectRunner The pipeline will use the 2020 dataset located in ./data/input and output the resulting .json to ./data/output .","title":"Usage"},{"location":"documentation/usage.html#usage","text":"This page documents how to run the pipeline locally to complete the task for the dataset for 2020 . The pipeline also runs in GCP using DataFlow and is discussed further on but can be viewed here. We also discuss how to adapt the pipeline so it can run against the full dataset .","title":"Usage"},{"location":"documentation/usage.html#download-dataset","text":"The input data by default should go in ./data/input . For convenience the data is available publicly in a GCP Cloud Storage bucket. 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ReadFromText () .","title":"Available options"},{"location":"documentation/usage.html#run-the-pipeline","text":"To run the pipeline and complete the task run: analyse-properties --runner DirectRunner The pipeline will use the 2020 dataset located in ./data/input and output the resulting .json to ./data/output .","title":"Run the pipeline"}]}
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index d284f2e..dcbcf6d 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -15,4 +15,14 @@
          2021-09-27
          daily
     
+    
+         None
+         2021-09-27
+         daily
+    
+    
+         None
+         2021-09-27
+         daily
+    
 
\ No newline at end of file
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
index 8e3838fb7b9eabfa5171f635f02f93f6c28939bb..b06f1579a60d64a12018c6353a9a6e3cf50ace17 100644
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