Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells153
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory683.6 KiB
Average record size in memory70.0 B

Variable types

Text1
Numeric6

Alerts

CRS_AREA_DIMS is highly overall correlated with BULD_CNT_TOTHigh correlation
LA is highly overall correlated with LOHigh correlation
LO is highly overall correlated with LAHigh correlation
BULD_CNT_TOT is highly overall correlated with CRS_AREA_DIMS and 2 other fieldsHigh correlation
SPANUAT_BULD_CNT_TOT is highly overall correlated with BULD_CNT_TOTHigh correlation
AREA_ISE_NMHSH is highly overall correlated with BULD_CNT_TOTHigh correlation
CRS_AREA_DIMS is highly skewed (γ1 = 41.16282147)Skewed
CRS_AREA_CD has unique valuesUnique
CRS_AREA_DIMS has unique valuesUnique
LA has unique valuesUnique
LO has unique valuesUnique
BULD_CNT_TOT has 2090 (20.9%) zerosZeros
SPANUAT_BULD_CNT_TOT has 2880 (28.8%) zerosZeros
AREA_ISE_NMHSH has 5546 (55.5%) zerosZeros

Reproduction

Analysis started2023-12-11 22:32:34.997656
Analysis finished2023-12-11 22:32:41.209428
Duration6.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

CRS_AREA_CD
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T07:32:41.602494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters200000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowG1168010100107650000
2nd rowG2641010300103780001
3rd rowG1120011100111050000
4th rowG1126010200101050033
5th rowG1171010700100290000
ValueCountFrequency (%)
g1168010100107650000 1
 
< 0.1%
g2629010900104860028 1
 
< 0.1%
g1114016200103690118 1
 
< 0.1%
g1159010200101860006 1
 
< 0.1%
g2644010400118750004 1
 
< 0.1%
g1150010400114740000 1
 
< 0.1%
g2641011000103021527 1
 
< 0.1%
g1174011000105750005 1
 
< 0.1%
g1130510100103190005 1
 
< 0.1%
g1168010100107490005 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-12T07:32:41.972584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 79367
39.7%
1 48204
24.1%
2 14210
 
7.1%
G 10000
 
5.0%
6 9373
 
4.7%
3 9361
 
4.7%
4 8325
 
4.2%
5 7311
 
3.7%
7 4928
 
2.5%
8 4601
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190000
95.0%
Uppercase Letter 10000
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 79367
41.8%
1 48204
25.4%
2 14210
 
7.5%
6 9373
 
4.9%
3 9361
 
4.9%
4 8325
 
4.4%
5 7311
 
3.8%
7 4928
 
2.6%
8 4601
 
2.4%
9 4320
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
G 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190000
95.0%
Latin 10000
 
5.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 79367
41.8%
1 48204
25.4%
2 14210
 
7.5%
6 9373
 
4.9%
3 9361
 
4.9%
4 8325
 
4.4%
5 7311
 
3.8%
7 4928
 
2.6%
8 4601
 
2.4%
9 4320
 
2.3%
Latin
ValueCountFrequency (%)
G 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 79367
39.7%
1 48204
24.1%
2 14210
 
7.1%
G 10000
 
5.0%
6 9373
 
4.7%
3 9361
 
4.7%
4 8325
 
4.2%
5 7311
 
3.7%
7 4928
 
2.5%
8 4601
 
2.3%

CRS_AREA_DIMS
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5568.5241
Minimum0.22903138
Maximum1730382.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:42.144526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.22903138
5-th percentile30.435615
Q1649.15909
median1772.9828
Q34019.1591
95-th percentile18522.654
Maximum1730382.1
Range1730381.9
Interquartile range (IQR)3370

Descriptive statistics

Standard deviation29545.893
Coefficient of variation (CV)5.305875
Kurtosis2269.0518
Mean5568.5241
Median Absolute Deviation (MAD)1384.7974
Skewness41.162821
Sum55685241
Variance8.7295977 × 108
MonotonicityNot monotonic
2023-12-12T07:32:42.536996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16308.737853343637 1
 
< 0.1%
2854.6844998963143 1
 
< 0.1%
470.0022844113294 1
 
< 0.1%
77292.31035351618 1
 
< 0.1%
1042.6228563213613 1
 
< 0.1%
38.70780647479718 1
 
< 0.1%
1471.87841063393 1
 
< 0.1%
210.60597259329097 1
 
< 0.1%
2328.1762080354706 1
 
< 0.1%
5331.607092959873 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
0.2290313755413465 1
< 0.1%
0.2361778057972345 1
< 0.1%
0.2380810857910331 1
< 0.1%
0.3684492278014744 1
< 0.1%
0.3695113233139952 1
< 0.1%
0.4713636498889594 1
< 0.1%
0.6053744259714501 1
< 0.1%
0.6379450571384185 1
< 0.1%
0.6781792542362659 1
< 0.1%
0.7059860263085205 1
< 0.1%
ValueCountFrequency (%)
1730382.144342454 1
< 0.1%
1700191.9751874544 1
< 0.1%
546695.7861630644 1
< 0.1%
404193.3930710859 1
< 0.1%
369108.92236382246 1
< 0.1%
368497.34197423374 1
< 0.1%
309581.8082353687 1
< 0.1%
277825.4899256274 1
< 0.1%
271614.41915045073 1
< 0.1%
262757.7141918418 1
< 0.1%

LA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.751547
Minimum34.380887
Maximum37.690874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:42.944848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.380887
5-th percentile35.093583
Q135.203235
median37.506302
Q337.57025
95-th percentile37.633305
Maximum37.690874
Range3.3099874
Interquartile range (IQR)2.3670146

Descriptive statistics

Standard deviation1.1315413
Coefficient of variation (CV)0.030788943
Kurtosis-1.4954333
Mean36.751547
Median Absolute Deviation (MAD)0.085460709
Skewness-0.70205035
Sum367515.47
Variance1.2803857
MonotonicityNot monotonic
2023-12-12T07:32:43.193883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.49674618975183 1
 
< 0.1%
37.49569687831988 1
 
< 0.1%
35.09888873527085 1
 
< 0.1%
37.56566291588756 1
 
< 0.1%
35.215831587397645 1
 
< 0.1%
37.56406512201457 1
 
< 0.1%
37.62166193725834 1
 
< 0.1%
35.14347972594845 1
 
< 0.1%
37.4731003481389 1
 
< 0.1%
37.67492386394811 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
34.38088684534138 1
< 0.1%
34.83616169228189 1
< 0.1%
34.98017905576831 1
< 0.1%
34.98090288619798 1
< 0.1%
34.98149466279844 1
< 0.1%
35.00478319677566 1
< 0.1%
35.00565118965179 1
< 0.1%
35.00956831066665 1
< 0.1%
35.011535783807346 1
< 0.1%
35.01176942785997 1
< 0.1%
ValueCountFrequency (%)
37.69087425039329 1
< 0.1%
37.69013726449748 1
< 0.1%
37.68983247483298 1
< 0.1%
37.68852251434603 1
< 0.1%
37.688511145071 1
< 0.1%
37.687831489778944 1
< 0.1%
37.68660126990064 1
< 0.1%
37.68627425272608 1
< 0.1%
37.68604284707776 1
< 0.1%
37.685684887543815 1
< 0.1%

LO
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.67087
Minimum126.19799
Maximum129.25289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:43.415082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.19799
5-th percentile126.85971
Q1126.95731
median127.05167
Q3128.99998
95-th percentile129.09981
Maximum129.25289
Range3.054903
Interquartile range (IQR)2.0426706

Descriptive statistics

Standard deviation0.96602536
Coefficient of variation (CV)0.0075665292
Kurtosis-1.484753
Mean127.67087
Median Absolute Deviation (MAD)0.13011604
Skewness0.69498995
Sum1276708.7
Variance0.93320501
MonotonicityNot monotonic
2023-12-12T07:32:43.557219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.05168685930576 1
 
< 0.1%
127.03572530231024 1
 
< 0.1%
128.91091256206224 1
 
< 0.1%
126.85568561820742 1
 
< 0.1%
129.09713199034235 1
 
< 0.1%
127.1734811968142 1
 
< 0.1%
127.02502041531186 1
 
< 0.1%
129.0677887695764 1
 
< 0.1%
126.93465336793268 1
 
< 0.1%
127.04318850714762 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
126.1979900933286 1
< 0.1%
126.50857736942498 1
< 0.1%
126.75662862116948 1
< 0.1%
126.76734177921698 1
< 0.1%
126.76802879186206 1
< 0.1%
126.77461537517144 1
< 0.1%
126.77703896931688 1
< 0.1%
126.77719766855438 1
< 0.1%
126.7794565169882 1
< 0.1%
126.77955976740569 1
< 0.1%
ValueCountFrequency (%)
129.2528930627633 1
< 0.1%
129.2283402455202 1
< 0.1%
129.20515853973768 1
< 0.1%
129.20488805629304 1
< 0.1%
129.20450926077208 1
< 0.1%
129.20441092685377 1
< 0.1%
129.20427552977932 1
< 0.1%
129.2041576475478 1
< 0.1%
129.2039889867936 1
< 0.1%
129.20266775705323 1
< 0.1%

BULD_CNT_TOT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct97
Distinct (%)1.0%
Missing51
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean9.2362046
Minimum0
Maximum234
Zeros2090
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:43.694860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q313
95-th percentile30
Maximum234
Range234
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.623789
Coefficient of variation (CV)1.2585028
Kurtosis31.870581
Mean9.2362046
Median Absolute Deviation (MAD)5
Skewness3.7476646
Sum91891
Variance135.11248
MonotonicityNot monotonic
2023-12-12T07:32:43.828213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2090
20.9%
1 623
 
6.2%
6 517
 
5.2%
4 495
 
5.0%
8 479
 
4.8%
2 472
 
4.7%
7 470
 
4.7%
3 458
 
4.6%
5 439
 
4.4%
10 376
 
3.8%
Other values (87) 3530
35.3%
ValueCountFrequency (%)
0 2090
20.9%
1 623
 
6.2%
2 472
 
4.7%
3 458
 
4.6%
4 495
 
5.0%
5 439
 
4.4%
6 517
 
5.2%
7 470
 
4.7%
8 479
 
4.8%
9 334
 
3.3%
ValueCountFrequency (%)
234 1
 
< 0.1%
158 1
 
< 0.1%
139 2
< 0.1%
126 1
 
< 0.1%
122 1
 
< 0.1%
119 3
< 0.1%
112 1
 
< 0.1%
109 1
 
< 0.1%
107 1
 
< 0.1%
103 1
 
< 0.1%

SPANUAT_BULD_CNT_TOT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)0.8%
Missing51
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.2538949
Minimum0
Maximum140
Zeros2880
Zeros (%)28.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:43.938505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q39
95-th percentile22
Maximum140
Range140
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.9127402
Coefficient of variation (CV)1.4251503
Kurtosis24.822146
Mean6.2538949
Median Absolute Deviation (MAD)4
Skewness3.6286321
Sum62220
Variance79.436938
MonotonicityNot monotonic
2023-12-12T07:32:44.073081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2880
28.8%
1 788
 
7.9%
2 656
 
6.6%
3 646
 
6.5%
4 595
 
5.9%
5 546
 
5.5%
6 489
 
4.9%
7 440
 
4.4%
8 390
 
3.9%
9 326
 
3.3%
Other values (70) 2193
21.9%
ValueCountFrequency (%)
0 2880
28.8%
1 788
 
7.9%
2 656
 
6.6%
3 646
 
6.5%
4 595
 
5.9%
5 546
 
5.5%
6 489
 
4.9%
7 440
 
4.4%
8 390
 
3.9%
9 326
 
3.3%
ValueCountFrequency (%)
140 1
< 0.1%
123 1
< 0.1%
117 1
< 0.1%
112 1
< 0.1%
95 1
< 0.1%
92 2
< 0.1%
91 1
< 0.1%
86 1
< 0.1%
82 1
< 0.1%
75 1
< 0.1%

AREA_ISE_NMHSH
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct488
Distinct (%)4.9%
Missing51
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean40.965122
Minimum0
Maximum5656
Zeros5546
Zeros (%)55.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:32:44.208385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q323
95-th percentile142
Maximum5656
Range5656
Interquartile range (IQR)23

Descriptive statistics

Standard deviation190.82527
Coefficient of variation (CV)4.6582376
Kurtosis282.39003
Mean40.965122
Median Absolute Deviation (MAD)0
Skewness13.957549
Sum407562
Variance36414.285
MonotonicityNot monotonic
2023-12-12T07:32:44.353150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5546
55.5%
8 186
 
1.9%
16 140
 
1.4%
1 139
 
1.4%
12 112
 
1.1%
10 108
 
1.1%
6 106
 
1.1%
4 94
 
0.9%
18 92
 
0.9%
9 89
 
0.9%
Other values (478) 3337
33.4%
ValueCountFrequency (%)
0 5546
55.5%
1 139
 
1.4%
2 79
 
0.8%
3 74
 
0.7%
4 94
 
0.9%
5 45
 
0.4%
6 106
 
1.1%
7 88
 
0.9%
8 186
 
1.9%
9 89
 
0.9%
ValueCountFrequency (%)
5656 1
< 0.1%
5385 1
< 0.1%
4586 1
< 0.1%
4492 1
< 0.1%
4098 1
< 0.1%
4066 1
< 0.1%
2777 1
< 0.1%
2727 1
< 0.1%
2694 1
< 0.1%
2594 1
< 0.1%

Interactions

2023-12-12T07:32:40.171815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:37.528464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.112728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.596187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.173804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.641329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:40.354441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:37.663733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.193903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.719445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.253890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.733937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:40.469100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:37.766661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.272785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.797794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.333373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.819005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:40.610027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:37.872822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.347777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.874129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.413036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.900633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:40.702854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:37.947295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.437195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.957999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.485179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.981578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:40.785825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.032054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:38.518552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.066938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:39.565285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:32:40.069277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:32:44.480164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CRS_AREA_DIMSLALOBULD_CNT_TOTSPANUAT_BULD_CNT_TOTAREA_ISE_NMHSH
CRS_AREA_DIMS1.0000.0660.0100.3990.2480.368
LA0.0661.0000.8830.0000.0000.000
LO0.0100.8831.0000.0170.0600.000
BULD_CNT_TOT0.3990.0000.0171.0000.8600.518
SPANUAT_BULD_CNT_TOT0.2480.0000.0600.8601.0000.198
AREA_ISE_NMHSH0.3680.0000.0000.5180.1981.000
2023-12-12T07:32:44.581557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CRS_AREA_DIMSLALOBULD_CNT_TOTSPANUAT_BULD_CNT_TOTAREA_ISE_NMHSH
CRS_AREA_DIMS1.000-0.006-0.0550.6150.4370.490
LA-0.0061.000-0.5840.1000.1160.153
LO-0.055-0.5841.000-0.126-0.111-0.184
BULD_CNT_TOT0.6150.100-0.1261.0000.8920.592
SPANUAT_BULD_CNT_TOT0.4370.116-0.1110.8921.0000.425
AREA_ISE_NMHSH0.4900.153-0.1840.5920.4251.000

Missing values

2023-12-12T07:32:40.892013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:32:41.009986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T07:32:41.144885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CRS_AREA_CDCRS_AREA_DIMSLALOBULD_CNT_TOTSPANUAT_BULD_CNT_TOTAREA_ISE_NMHSH
51728G116801010010765000016308.73785337.496746127.051687204225
78793G264101030010378000156.14885135.280733129.085052000
8127G11200111001110500004294.89181237.551302127.020881312623
13464G1126010200101050033887.23293437.594129127.089179770
53791G11710107001002900003722.73562237.500723127.12333714657
26756G11380104001028502201299.28682437.626522126.9125435520
6981G1120010700101280172221.39583937.557096127.037785000
63582G26200102001017000022387.63832735.090825129.04113126250
32740G11440125001020003841.98875537.56838126.908075000
8185G11200115001081200001544.41162837.533721127.055246000
CRS_AREA_CDCRS_AREA_DIMSLALOBULD_CNT_TOTSPANUAT_BULD_CNT_TOTAREA_ISE_NMHSH
4825G11170130001021000015398.93154437.540324126.99292921176
86353G26440104001345500012202.62038735.095786128.923399705
64213G26260107001053300013933.80177535.205791129.07906928148
22231G11320108001055600761495.05896637.686043127.0429098518
65809G26230104001119400943924.13944135.149714129.05056119170
48026G11620101001066100203210.66548837.488654126.93332811662
64537G262001070010133000526.38832235.084487129.038778000
43742G11560133001090600081528.03588437.497152126.9052891180
36317G11500103001081100015528.69399337.533782126.8599582111107
35076G11500109001082900012130.30122837.57757126.812164000