Overview

Dataset statistics

Number of variables42
Number of observations10000
Missing cells86603
Missing cells (%)20.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory367.0 B

Variable types

Text14
Numeric15
Categorical9
Unsupported4

Dataset

DescriptionSample
Author선도소프트&지앤티솔루션
URLhttps://bigdata-geo.kr/user/dataset/view.do?data_sn=217

Alerts

GRND_FLCT has constant value ""Constant
LI_CD has constant value ""Constant
RSC_SGG_CD has constant value ""Constant
BD_DPN_SC is highly imbalanced (64.3%)Imbalance
PLT_SC_CD is highly imbalanced (98.4%)Imbalance
PLT_SC_NM is highly imbalanced (98.4%)Imbalance
FLR_INFO is highly imbalanced (63.3%)Imbalance
RGST_BD_NM has 9037 (90.4%) missing valuesMissing
BD_ENG_NM has 9929 (99.3%) missing valuesMissing
MNTN_YN has 10000 (100.0%) missing valuesMissing
DETL_BD_NM has 8973 (89.7%) missing valuesMissing
SPOT_NM has 8302 (83.0%) missing valuesMissing
SIC_CD has 587 (5.9%) missing valuesMissing
SIC_NM has 587 (5.9%) missing valuesMissing
LNNO_ADRES has 10000 (100.0%) missing valuesMissing
RDNMADR has 10000 (100.0%) missing valuesMissing
DONG_INFO has 9188 (91.9%) missing valuesMissing
HO_INFO has 10000 (100.0%) missing valuesMissing
CM_BSSH_NO has unique valuesUnique
MNTN_YN is an unsupported type, check if it needs cleaning or further analysisUnsupported
LNNO_ADRES is an unsupported type, check if it needs cleaning or further analysisUnsupported
RDNMADR is an unsupported type, check if it needs cleaning or further analysisUnsupported
HO_INFO is an unsupported type, check if it needs cleaning or further analysisUnsupported
EQB_SN has 8718 (87.2%) zerosZeros
UNDR_FLCT has 6555 (65.5%) zerosZeros

Reproduction

Analysis started2023-12-10 13:23:17.300411
Analysis finished2023-12-10 13:23:21.609093
Duration4.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct5767
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-10T22:23:21.989189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length979
Mean length417.6396
Min length218

Characters and Unicode

Total characters4176396
Distinct characters25
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3910 ?
Unique (%)39.1%

Sample

1st rowMultiPolygon (((344851.83449688035761937 265448.23607142321998253, 344840.65067764848936349 265442.3134551178663969, 344838.62077515781857073 265446.02861647860845551, 344850.49930125678656623 265451.05193528486415744, 344851.83449688035761937 265448.23607142321998253)))
2nd rowMultiPolygon (((345288.72253875230671838 267350.24134957185015082, 345283.99989724589977413 267336.05337751761544496, 345272.60161326359957457 267339.91971061588265002, 345277.38547650235705078 267354.14705258898902684, 345288.72253875230671838 267350.24134957185015082)))
3rd rowMultiPolygon (((340392.09129327716073021 272876.46130779560189694, 340397.43559170159278437 272893.18289641721639782, 340398.2451666941633448 272894.43820217082975432, 340399.24814296665135771 272895.54511192929930985, 340400.41762114834273234 272896.47432541864691302, 340401.7226018242072314 272897.20104228577110916, 340403.12838550104061142 272897.70596208464121446, 340404.59757262142375112 272897.97558428533375263, 340406.09106352081289515 272898.00280829862458631, 340407.56905844633001834 272897.78683348529739305, 340437.96963973919628188 272888.05078253475949168, 340432.24335951585089788 272870.34139350242912769, 340430.85276187607087195 272870.69396893039811403, 340429.45844106609001756 272866.29957208334235474, 340427.04284390428801998 272866.98972894693724811, 340426.59226966521237046 272865.55913002672605217, 340392.09129327716073021 272876.46130779560189694)))
4th rowMultiPolygon (((343938.34816322952974588 266772.43113642616663128, 343937.49782559956656769 266771.84247586911078542, 343926.90060510812327266 266774.30957460438366979, 343929.53760967613197863 266784.8222331756260246, 343936.22644278139341623 266783.16343592840712517, 343937.9121746105956845 266778.23271537513937801, 343937.43387734703719616 266775.792837071523536, 343938.5073020679410547 266774.96952926344238222, 343938.95739724417217076 266773.60612694267183542, 343938.34816322952974588 266772.43113642616663128)))
5th rowMultiPolygon (((339847.36962528014555573 269908.0894719313364476, 339847.7991042120847851 269887.13545232743490487, 339837.36812987161101773 269887.01764749316498637, 339836.87420123972697183 269907.9434344929177314, 339847.36962528014555573 269908.0894719313364476)))
ValueCountFrequency (%)
multipolygon 10000
 
6.0%
345044.09537766082212329 218
 
0.1%
268667.68358523189090192 218
 
0.1%
341401.28585784044116735 178
 
0.1%
267047.83413054759148508 178
 
0.1%
268718.29263363068457693 168
 
0.1%
345458.43055567611008883 168
 
0.1%
268657.68126547173596919 109
 
0.1%
268716.72908605623524636 109
 
0.1%
345026.77054723561741412 109
 
0.1%
Other values (75288) 156487
93.2%
2023-12-10T22:23:22.830255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 424096
10.2%
2 409365
9.8%
4 395229
9.5%
6 381390
9.1%
7 364839
8.7%
5 335247
8.0%
9 331010
7.9%
1 329583
7.9%
8 328740
7.9%
0 313317
7.5%
Other values (15) 563580
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3612816
86.5%
Other Punctuation 226913
 
5.4%
Space Separator 157942
 
3.8%
Lowercase Letter 100000
 
2.4%
Open Punctuation 30000
 
0.7%
Close Punctuation 28725
 
0.7%
Uppercase Letter 20000
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 424096
11.7%
2 409365
11.3%
4 395229
10.9%
6 381390
10.6%
7 364839
10.1%
5 335247
9.3%
9 331010
9.2%
1 329583
9.1%
8 328740
9.1%
0 313317
8.7%
Lowercase Letter
ValueCountFrequency (%)
o 20000
20.0%
l 20000
20.0%
u 10000
10.0%
n 10000
10.0%
g 10000
10.0%
y 10000
10.0%
i 10000
10.0%
t 10000
10.0%
Other Punctuation
ValueCountFrequency (%)
. 157942
69.6%
, 68971
30.4%
Uppercase Letter
ValueCountFrequency (%)
P 10000
50.0%
M 10000
50.0%
Space Separator
ValueCountFrequency (%)
157942
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30000
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28725
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4056396
97.1%
Latin 120000
 
2.9%

Most frequent character per script

Common
ValueCountFrequency (%)
3 424096
10.5%
2 409365
10.1%
4 395229
9.7%
6 381390
9.4%
7 364839
9.0%
5 335247
8.3%
9 331010
8.2%
1 329583
8.1%
8 328740
8.1%
0 313317
7.7%
Other values (5) 443580
10.9%
Latin
ValueCountFrequency (%)
o 20000
16.7%
l 20000
16.7%
u 10000
8.3%
n 10000
8.3%
g 10000
8.3%
y 10000
8.3%
P 10000
8.3%
i 10000
8.3%
t 10000
8.3%
M 10000
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4176396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 424096
10.2%
2 409365
9.8%
4 395229
9.5%
6 381390
9.1%
7 364839
8.7%
5 335247
8.0%
9 331010
7.9%
1 329583
7.9%
8 328740
7.9%
0 313317
7.5%
Other values (15) 563580
13.5%

BD_PRPS_CD
Real number (ℝ)

Distinct90
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4130.7209
Minimum1001
Maximum21999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:23.092789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1001
Q11001
median3001
Q34999
95-th percentile13100
Maximum21999
Range20998
Interquartile range (IQR)3998

Descriptive statistics

Standard deviation3683.2896
Coefficient of variation (CV)0.89168203
Kurtosis2.3643532
Mean4130.7209
Median Absolute Deviation (MAD)1998
Skewness1.6524094
Sum41307209
Variance13566622
MonotonicityNot monotonic
2023-12-10T22:23:23.364743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 2623
26.2%
3001 2585
25.9%
4999 1153
11.5%
6999 710
 
7.1%
1003 612
 
6.1%
13100 398
 
4.0%
4001 222
 
2.2%
4402 195
 
1.9%
8999 162
 
1.6%
14001 160
 
1.6%
Other values (80) 1180
11.8%
ValueCountFrequency (%)
1001 2623
26.2%
1002 64
 
0.6%
1003 612
 
6.1%
3001 2585
25.9%
3002 38
 
0.4%
3004 3
 
< 0.1%
3005 106
 
1.1%
3009 1
 
< 0.1%
3013 1
 
< 0.1%
3015 1
 
< 0.1%
ValueCountFrequency (%)
21999 2
 
< 0.1%
20001 1
 
< 0.1%
19006 4
 
< 0.1%
18003 1
 
< 0.1%
17999 7
 
0.1%
17004 1
 
< 0.1%
17003 16
 
0.2%
16999 63
0.6%
16009 1
 
< 0.1%
16007 12
 
0.1%

BSI_INT_SN
Real number (ℝ)

Distinct4896
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16833.94
Minimum418
Maximum110645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:23.607248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum418
5-th percentile2559
Q17723
median14120
Q319119.25
95-th percentile35825.25
Maximum110645
Range110227
Interquartile range (IQR)11396.25

Descriptive statistics

Standard deviation16192.456
Coefficient of variation (CV)0.96189342
Kurtosis17.294664
Mean16833.94
Median Absolute Deviation (MAD)6092
Skewness3.5713038
Sum1.683394 × 108
Variance2.6219564 × 108
MonotonicityNot monotonic
2023-12-10T22:23:23.839599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7723 119
 
1.2%
7738 93
 
0.9%
9795 89
 
0.9%
16640 84
 
0.8%
7728 73
 
0.7%
3793 53
 
0.5%
18970 50
 
0.5%
16962 46
 
0.5%
14312 38
 
0.4%
9077 35
 
0.4%
Other values (4886) 9320
93.2%
ValueCountFrequency (%)
418 3
< 0.1%
460 1
 
< 0.1%
462 1
 
< 0.1%
475 1
 
< 0.1%
483 1
 
< 0.1%
495 2
< 0.1%
509 2
< 0.1%
517 1
 
< 0.1%
522 1
 
< 0.1%
534 4
< 0.1%
ValueCountFrequency (%)
110645 1
 
< 0.1%
110478 1
 
< 0.1%
110468 1
 
< 0.1%
110266 2
 
< 0.1%
110265 1
 
< 0.1%
110263 1
 
< 0.1%
110262 1
 
< 0.1%
110065 13
0.1%
110064 3
 
< 0.1%
110062 9
0.1%

BSI_ZON_NO
Real number (ℝ)

Distinct194
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41504.842
Minimum41192
Maximum41728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:24.057287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41192
5-th percentile41413
Q141452
median41505
Q341557
95-th percentile41593
Maximum41728
Range536
Interquartile range (IQR)105

Descriptive statistics

Standard deviation62.461502
Coefficient of variation (CV)0.0015049208
Kurtosis0.37061547
Mean41504.842
Median Absolute Deviation (MAD)52
Skewness0.1206157
Sum4.1504842 × 108
Variance3901.4392
MonotonicityNot monotonic
2023-12-10T22:23:24.392571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41544 309
 
3.1%
41518 284
 
2.8%
41557 254
 
2.5%
41423 244
 
2.4%
41477 186
 
1.9%
41505 169
 
1.7%
41535 169
 
1.7%
41582 164
 
1.6%
41438 159
 
1.6%
41598 144
 
1.4%
Other values (184) 7918
79.2%
ValueCountFrequency (%)
41192 10
 
0.1%
41200 2
 
< 0.1%
41400 13
 
0.1%
41401 31
0.3%
41402 67
0.7%
41403 13
 
0.1%
41404 18
 
0.2%
41405 34
0.3%
41406 23
 
0.2%
41407 13
 
0.1%
ValueCountFrequency (%)
41728 8
 
0.1%
41727 10
 
0.1%
41716 15
 
0.1%
41715 23
 
0.2%
41714 10
 
0.1%
41710 27
 
0.3%
41702 6
 
0.1%
41701 7
 
0.1%
41599 28
 
0.3%
41598 144
1.4%

RGST_BD_NM
Text

MISSING 

Distinct86
Distinct (%)8.9%
Missing9037
Missing (%)90.4%
Memory size156.2 KiB
2023-12-10T22:23:24.782703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length5.8764278
Min length3

Characters and Unicode

Total characters5659
Distinct characters216
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)3.2%

Sample

1st row농산물도매시장
2nd row대성플라자
3rd row산업용재관
4th row대성플라자
5th row칠곡홈플러스
ValueCountFrequency (%)
산업용재관 119
 
10.9%
전자관 93
 
8.5%
일반의류관 84
 
7.7%
전자상가 73
 
6.7%
농산물도매시장 53
 
4.9%
칠곡홈플러스 50
 
4.6%
동아아울렛 46
 
4.2%
강북점 46
 
4.2%
스펙트럼시티 35
 
3.2%
섬유제품관 28
 
2.6%
Other values (89) 463
42.5%
2023-12-10T22:23:25.399812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
357
 
6.3%
249
 
4.4%
217
 
3.8%
208
 
3.7%
141
 
2.5%
139
 
2.5%
139
 
2.5%
127
 
2.2%
126
 
2.2%
123
 
2.2%
Other values (206) 3833
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5278
93.3%
Uppercase Letter 136
 
2.4%
Space Separator 127
 
2.2%
Lowercase Letter 70
 
1.2%
Decimal Number 48
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
357
 
6.8%
249
 
4.7%
217
 
4.1%
208
 
3.9%
141
 
2.7%
139
 
2.6%
139
 
2.6%
126
 
2.4%
123
 
2.3%
119
 
2.3%
Other values (180) 3460
65.6%
Uppercase Letter
ValueCountFrequency (%)
O 22
16.2%
H 16
11.8%
L 12
8.8%
U 11
8.1%
D 11
8.1%
W 11
8.1%
A 11
8.1%
R 11
8.1%
T 11
8.1%
M 6
 
4.4%
Other values (5) 14
10.3%
Lowercase Letter
ValueCountFrequency (%)
l 10
14.3%
u 10
14.3%
e 10
14.3%
m 10
14.3%
o 10
14.3%
s 10
14.3%
p 10
14.3%
Decimal Number
ValueCountFrequency (%)
2 22
45.8%
1 18
37.5%
3 8
 
16.7%
Space Separator
ValueCountFrequency (%)
127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5278
93.3%
Latin 206
 
3.6%
Common 175
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
357
 
6.8%
249
 
4.7%
217
 
4.1%
208
 
3.9%
141
 
2.7%
139
 
2.6%
139
 
2.6%
126
 
2.4%
123
 
2.3%
119
 
2.3%
Other values (180) 3460
65.6%
Latin
ValueCountFrequency (%)
O 22
 
10.7%
H 16
 
7.8%
L 12
 
5.8%
U 11
 
5.3%
D 11
 
5.3%
W 11
 
5.3%
A 11
 
5.3%
R 11
 
5.3%
T 11
 
5.3%
l 10
 
4.9%
Other values (12) 80
38.8%
Common
ValueCountFrequency (%)
127
72.6%
2 22
 
12.6%
1 18
 
10.3%
3 8
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5278
93.3%
ASCII 381
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
357
 
6.8%
249
 
4.7%
217
 
4.1%
208
 
3.9%
141
 
2.7%
139
 
2.6%
139
 
2.6%
126
 
2.4%
123
 
2.3%
119
 
2.3%
Other values (180) 3460
65.6%
ASCII
ValueCountFrequency (%)
127
33.3%
O 22
 
5.8%
2 22
 
5.8%
1 18
 
4.7%
H 16
 
4.2%
L 12
 
3.1%
U 11
 
2.9%
D 11
 
2.9%
W 11
 
2.9%
A 11
 
2.9%
Other values (16) 120
31.5%

BD_DPN_SC
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
9324 
S
 
676

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 9324
93.2%
S 676
 
6.8%

Length

2023-12-10T22:23:25.670703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:23:25.859865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 9324
93.2%
s 676
 
6.8%

BD_ENG_NM
Text

MISSING 

Distinct43
Distinct (%)60.6%
Missing9929
Missing (%)99.3%
Memory size156.2 KiB
2023-12-10T22:23:26.156325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length24
Mean length19.450704
Min length9

Characters and Unicode

Total characters1381
Distinct characters38
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)36.6%

Sample

1st rowDaeu Bldg
2nd rowDaeyeongkeompyuteo Academy
3rd rowElliteu Academy
4th rowYeomdae Bldg
5th rowAsea Bldg
ValueCountFrequency (%)
academy 25
 
15.4%
bldg 17
 
10.5%
school 9
 
5.6%
clinic 8
 
4.9%
nursery 7
 
4.3%
yuhan 5
 
3.1%
elliteu 5
 
3.1%
yeongjinjeonmundaehakbuseol 4
 
2.5%
kindergarten 4
 
2.5%
hospital 3
 
1.9%
Other values (46) 75
46.3%
2023-12-10T22:23:26.848948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 137
 
9.9%
n 110
 
8.0%
a 106
 
7.7%
o 95
 
6.9%
91
 
6.6%
g 75
 
5.4%
i 69
 
5.0%
l 66
 
4.8%
d 62
 
4.5%
m 51
 
3.7%
Other values (28) 519
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1128
81.7%
Uppercase Letter 162
 
11.7%
Space Separator 91
 
6.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 137
12.1%
n 110
 
9.8%
a 106
 
9.4%
o 95
 
8.4%
g 75
 
6.6%
i 69
 
6.1%
l 66
 
5.9%
d 62
 
5.5%
m 51
 
4.5%
y 51
 
4.5%
Other values (12) 306
27.1%
Uppercase Letter
ValueCountFrequency (%)
A 32
19.8%
B 21
13.0%
S 20
12.3%
G 15
9.3%
Y 13
8.0%
C 11
 
6.8%
D 10
 
6.2%
N 8
 
4.9%
H 7
 
4.3%
O 6
 
3.7%
Other values (5) 19
11.7%
Space Separator
ValueCountFrequency (%)
91
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1290
93.4%
Common 91
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 137
 
10.6%
n 110
 
8.5%
a 106
 
8.2%
o 95
 
7.4%
g 75
 
5.8%
i 69
 
5.3%
l 66
 
5.1%
d 62
 
4.8%
m 51
 
4.0%
y 51
 
4.0%
Other values (27) 468
36.3%
Common
ValueCountFrequency (%)
91
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1381
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 137
 
9.9%
n 110
 
8.0%
a 106
 
7.7%
o 95
 
6.9%
91
 
6.6%
g 75
 
5.4%
i 69
 
5.0%
l 66
 
4.8%
d 62
 
4.5%
m 51
 
3.7%
Other values (28) 519
37.6%

BD_SN
Real number (ℝ)

Distinct5762
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22063.796
Minimum2
Maximum55056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:27.199871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2198
Q18771
median19448.5
Q336469.5
95-th percentile50756.45
Maximum55056
Range55054
Interquartile range (IQR)27698.5

Descriptive statistics

Standard deviation15685.74
Coefficient of variation (CV)0.71092662
Kurtosis-0.94054168
Mean22063.796
Median Absolute Deviation (MAD)11073.5
Skewness0.5506858
Sum2.2063796 × 108
Variance2.4604244 × 108
MonotonicityNot monotonic
2023-12-10T22:23:27.617706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8732 109
 
1.1%
8724 93
 
0.9%
11648 89
 
0.9%
20864 84
 
0.8%
8725 73
 
0.7%
3923 53
 
0.5%
24127 50
 
0.5%
21309 46
 
0.5%
10935 35
 
0.4%
16448 31
 
0.3%
Other values (5752) 9337
93.4%
ValueCountFrequency (%)
2 3
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
13 2
< 0.1%
15 2
< 0.1%
16 2
< 0.1%
20 1
 
< 0.1%
21 1
 
< 0.1%
26 2
< 0.1%
29 1
 
< 0.1%
ValueCountFrequency (%)
55056 1
< 0.1%
55054 1
< 0.1%
54974 1
< 0.1%
54894 1
< 0.1%
54880 1
< 0.1%
54736 1
< 0.1%
54676 1
< 0.1%
54614 1
< 0.1%
54474 2
< 0.1%
54457 1
< 0.1%

EMD_CD
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.0936
Minimum101
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:27.949451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1110
median112
Q3124
95-th percentile127
Maximum131
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.0595741
Coefficient of variation (CV)0.070026258
Kurtosis-1.117787
Mean115.0936
Median Absolute Deviation (MAD)6
Skewness0.21963411
Sum1150936
Variance64.956735
MonotonicityNot monotonic
2023-12-10T22:23:28.467506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
111 1842
18.4%
126 945
 
9.4%
106 616
 
6.2%
109 598
 
6.0%
124 578
 
5.8%
118 561
 
5.6%
112 541
 
5.4%
123 468
 
4.7%
102 444
 
4.4%
110 441
 
4.4%
Other values (21) 2966
29.7%
ValueCountFrequency (%)
101 239
 
2.4%
102 444
4.4%
103 2
 
< 0.1%
104 79
 
0.8%
105 138
 
1.4%
106 616
6.2%
107 145
 
1.5%
108 189
 
1.9%
109 598
6.0%
110 441
4.4%
ValueCountFrequency (%)
131 121
 
1.2%
130 159
 
1.6%
129 70
 
0.7%
128 79
 
0.8%
127 205
 
2.1%
126 945
9.4%
125 352
 
3.5%
124 578
5.8%
123 468
4.7%
122 130
 
1.3%

EQB_SN
Real number (ℝ)

ZEROS 

Distinct575
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.0533
Minimum0
Maximum9777
Zeros8718
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:28.682012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2248
Maximum9777
Range9777
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1046.5773
Coefficient of variation (CV)3.620707
Kurtosis30.031851
Mean289.0533
Median Absolute Deviation (MAD)0
Skewness5.0464694
Sum2890533
Variance1095324
MonotonicityNot monotonic
2023-12-10T22:23:28.991371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8718
87.2%
1210 119
 
1.2%
1211 73
 
0.7%
535 53
 
0.5%
2248 36
 
0.4%
2425 17
 
0.2%
4800 16
 
0.2%
5241 15
 
0.1%
101 15
 
0.1%
1213 15
 
0.1%
Other values (565) 923
 
9.2%
ValueCountFrequency (%)
0 8718
87.2%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 3
 
< 0.1%
4 2
 
< 0.1%
7 1
 
< 0.1%
16 4
 
< 0.1%
19 4
 
< 0.1%
22 1
 
< 0.1%
40 1
 
< 0.1%
ValueCountFrequency (%)
9777 1
 
< 0.1%
9678 4
< 0.1%
9577 3
 
< 0.1%
9499 9
0.1%
9497 2
 
< 0.1%
9317 1
 
< 0.1%
9277 1
 
< 0.1%
9257 1
 
< 0.1%
9017 1
 
< 0.1%
8918 2
 
< 0.1%

GRND_FLCT
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-10T22:23:29.192871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:23:29.339534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

LI_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-10T22:23:29.481899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:23:29.629881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

MNTN_YN
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

NTFC_DT
Real number (ℝ)

Distinct324
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20118239
Minimum20110729
Maximum20220526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:29.852651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20110729
5-th percentile20110729
Q120110729
median20110729
Q320110729
95-th percentile20170907
Maximum20220526
Range109797
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20443.046
Coefficient of variation (CV)0.0010161449
Kurtosis6.991545
Mean20118239
Median Absolute Deviation (MAD)0
Skewness2.8106083
Sum2.0118239 × 1011
Variance4.1791812 × 108
MonotonicityNot monotonic
2023-12-10T22:23:30.237104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20110729 8392
83.9%
20111025 37
 
0.4%
20110830 35
 
0.4%
20170406 34
 
0.3%
20140124 23
 
0.2%
20111110 22
 
0.2%
20140520 21
 
0.2%
20140930 19
 
0.2%
20131120 19
 
0.2%
20181015 18
 
0.2%
Other values (314) 1380
 
13.8%
ValueCountFrequency (%)
20110729 8392
83.9%
20110830 35
 
0.4%
20110910 1
 
< 0.1%
20110930 12
 
0.1%
20111025 37
 
0.4%
20111027 7
 
0.1%
20111110 22
 
0.2%
20111121 1
 
< 0.1%
20111130 6
 
0.1%
20111212 13
 
0.1%
ValueCountFrequency (%)
20220526 2
< 0.1%
20220422 1
< 0.1%
20220324 1
< 0.1%
20220323 1
< 0.1%
20220317 1
< 0.1%
20220104 1
< 0.1%
20211214 1
< 0.1%
20211122 1
< 0.1%
20211019 2
< 0.1%
20211013 1
< 0.1%

DETL_BD_NM
Text

MISSING 

Distinct98
Distinct (%)9.5%
Missing8973
Missing (%)89.7%
Memory size156.2 KiB
2023-12-10T22:23:30.821663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length7.4109056
Min length3

Characters and Unicode

Total characters7611
Distinct characters229
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)3.1%

Sample

1st row농수산물도매시장, 관문동 행정복지센터
2nd row대성플라자
3rd row유통단지산업용재관
4th row대성플라자
5th row칠곡홈플러스
ValueCountFrequency (%)
유통단지산업용재관 119
 
9.3%
유통단지전자관 93
 
7.3%
일반의류관 84
 
6.6%
전자상가 73
 
5.7%
행정복지센터 59
 
4.6%
농수산물도매시장 53
 
4.2%
관문동 53
 
4.2%
칠곡홈플러스 50
 
3.9%
동아아울렛 46
 
3.6%
강북점 46
 
3.6%
Other values (107) 598
46.9%
2023-12-10T22:23:31.678926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
410
 
5.4%
276
 
3.6%
255
 
3.4%
247
 
3.2%
240
 
3.2%
219
 
2.9%
213
 
2.8%
212
 
2.8%
211
 
2.8%
174
 
2.3%
Other values (219) 5154
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7041
92.5%
Space Separator 247
 
3.2%
Uppercase Letter 144
 
1.9%
Lowercase Letter 70
 
0.9%
Decimal Number 56
 
0.7%
Other Punctuation 53
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
410
 
5.8%
276
 
3.9%
255
 
3.6%
240
 
3.4%
219
 
3.1%
213
 
3.0%
212
 
3.0%
211
 
3.0%
174
 
2.5%
157
 
2.2%
Other values (191) 4674
66.4%
Uppercase Letter
ValueCountFrequency (%)
O 22
15.3%
H 16
11.1%
T 15
10.4%
L 12
8.3%
A 11
7.6%
U 11
7.6%
D 11
7.6%
W 11
7.6%
R 11
7.6%
K 8
 
5.6%
Other values (5) 16
11.1%
Lowercase Letter
ValueCountFrequency (%)
s 10
14.3%
e 10
14.3%
p 10
14.3%
u 10
14.3%
l 10
14.3%
m 10
14.3%
o 10
14.3%
Decimal Number
ValueCountFrequency (%)
2 29
51.8%
1 18
32.1%
3 8
 
14.3%
5 1
 
1.8%
Space Separator
ValueCountFrequency (%)
247
100.0%
Other Punctuation
ValueCountFrequency (%)
, 53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7041
92.5%
Common 356
 
4.7%
Latin 214
 
2.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
410
 
5.8%
276
 
3.9%
255
 
3.6%
240
 
3.4%
219
 
3.1%
213
 
3.0%
212
 
3.0%
211
 
3.0%
174
 
2.5%
157
 
2.2%
Other values (191) 4674
66.4%
Latin
ValueCountFrequency (%)
O 22
 
10.3%
H 16
 
7.5%
T 15
 
7.0%
L 12
 
5.6%
A 11
 
5.1%
U 11
 
5.1%
D 11
 
5.1%
W 11
 
5.1%
R 11
 
5.1%
s 10
 
4.7%
Other values (12) 84
39.3%
Common
ValueCountFrequency (%)
247
69.4%
, 53
 
14.9%
2 29
 
8.1%
1 18
 
5.1%
3 8
 
2.2%
5 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7041
92.5%
ASCII 570
 
7.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
410
 
5.8%
276
 
3.9%
255
 
3.6%
240
 
3.4%
219
 
3.1%
213
 
3.0%
212
 
3.0%
211
 
3.0%
174
 
2.5%
157
 
2.2%
Other values (191) 4674
66.4%
ASCII
ValueCountFrequency (%)
247
43.3%
, 53
 
9.3%
2 29
 
5.1%
O 22
 
3.9%
1 18
 
3.2%
H 16
 
2.8%
T 15
 
2.6%
L 12
 
2.1%
A 11
 
1.9%
U 11
 
1.9%
Other values (18) 136
23.9%

RSC_SN
Real number (ℝ)

Distinct1202
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1241.323
Minimum3
Maximum21095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:31.952958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile113
Q1318
median518
Q3731.25
95-th percentile3188
Maximum21095
Range21092
Interquartile range (IQR)413.25

Descriptive statistics

Standard deviation2830.698
Coefficient of variation (CV)2.2803879
Kurtosis38.428397
Mean1241.323
Median Absolute Deviation (MAD)205
Skewness6.0066621
Sum12413230
Variance8012851.1
MonotonicityNot monotonic
2023-12-10T22:23:32.370390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
644 473
 
4.7%
318 359
 
3.6%
3122 241
 
2.4%
371 202
 
2.0%
366 176
 
1.8%
361 174
 
1.7%
213 138
 
1.4%
368 137
 
1.4%
180 130
 
1.3%
534 119
 
1.2%
Other values (1192) 7851
78.5%
ValueCountFrequency (%)
3 4
 
< 0.1%
4 4
 
< 0.1%
5 2
 
< 0.1%
6 6
 
0.1%
7 1
 
< 0.1%
8 21
0.2%
9 1
 
< 0.1%
11 1
 
< 0.1%
12 29
0.3%
13 21
0.2%
ValueCountFrequency (%)
21095 1
 
< 0.1%
21055 2
 
< 0.1%
21013 5
 
0.1%
20993 25
0.2%
20957 3
 
< 0.1%
20956 2
 
< 0.1%
20955 1
 
< 0.1%
20954 1
 
< 0.1%
20951 1
 
< 0.1%
20950 4
 
< 0.1%

RSC_SGG_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
27230
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row27230
2nd row27230
3rd row27230
4th row27230
5th row27230

Common Values

ValueCountFrequency (%)
27230 10000
100.0%

Length

2023-12-10T22:23:32.759875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:23:32.918221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
27230 10000
100.0%

UNDR_FLCT
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4335
Minimum0
Maximum6
Zeros6555
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:33.093137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.75407865
Coefficient of variation (CV)1.7395125
Kurtosis14.120227
Mean0.4335
Median Absolute Deviation (MAD)0
Skewness3.0279951
Sum4335
Variance0.56863461
MonotonicityNot monotonic
2023-12-10T22:23:33.391657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 6555
65.5%
1 2941
29.4%
2 339
 
3.4%
5 82
 
0.8%
3 50
 
0.5%
4 21
 
0.2%
6 12
 
0.1%
ValueCountFrequency (%)
0 6555
65.5%
1 2941
29.4%
2 339
 
3.4%
3 50
 
0.5%
4 21
 
0.2%
5 82
 
0.8%
6 12
 
0.1%
ValueCountFrequency (%)
6 12
 
0.1%
5 82
 
0.8%
4 21
 
0.2%
3 50
 
0.5%
2 339
 
3.4%
1 2941
29.4%
0 6555
65.5%

CM_BSSH_NO
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19920679
Minimum2891243
Maximum28522889
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:33.652596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2891243
5-th percentile12154077
Q116606356
median19914012
Q324334758
95-th percentile26268938
Maximum28522889
Range25631646
Interquartile range (IQR)7728402

Descriptive statistics

Standard deviation4515672.8
Coefficient of variation (CV)0.22668267
Kurtosis-0.71964895
Mean19920679
Median Absolute Deviation (MAD)3468562.5
Skewness-0.17705728
Sum1.9920679 × 1011
Variance2.0391301 × 1013
MonotonicityNot monotonic
2023-12-10T22:23:34.030080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17517868 1
 
< 0.1%
16495678 1
 
< 0.1%
24881288 1
 
< 0.1%
25639566 1
 
< 0.1%
22961617 1
 
< 0.1%
17130810 1
 
< 0.1%
20470508 1
 
< 0.1%
18137400 1
 
< 0.1%
12024616 1
 
< 0.1%
18140239 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2891243 1
< 0.1%
3051358 1
< 0.1%
3055168 1
< 0.1%
3112057 1
< 0.1%
3253496 1
< 0.1%
3373597 1
< 0.1%
3376226 1
< 0.1%
3421155 1
< 0.1%
3552650 1
< 0.1%
3758082 1
< 0.1%
ValueCountFrequency (%)
28522889 1
< 0.1%
28522597 1
< 0.1%
28521825 1
< 0.1%
28517125 1
< 0.1%
28514449 1
< 0.1%
28514142 1
< 0.1%
28513749 1
< 0.1%
28512505 1
< 0.1%
28509497 1
< 0.1%
28509080 1
< 0.1%

CM_NM
Text

Distinct9034
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-10T22:23:34.827611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length21
Mean length5.4662
Min length1

Characters and Unicode

Total characters54662
Distinct characters1057
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8487 ?
Unique (%)84.9%

Sample

1st row서울
2nd row빨간지붕
3rd row
4th row도미노피자
5th row우강축산유통
ValueCountFrequency (%)
cu 37
 
0.4%
gs25 37
 
0.4%
세븐일레븐 30
 
0.3%
카페 27
 
0.3%
학원 26
 
0.3%
이마트 15
 
0.1%
11
 
0.1%
이마트24 10
 
0.1%
교촌치킨 10
 
0.1%
크린토피아 9
 
0.1%
Other values (9026) 9790
97.9%
2023-12-10T22:23:35.713327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1297
 
2.4%
1290
 
2.4%
798
 
1.5%
771
 
1.4%
750
 
1.4%
678
 
1.2%
596
 
1.1%
578
 
1.1%
556
 
1.0%
540
 
1.0%
Other values (1047) 46808
85.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52605
96.2%
Uppercase Letter 1104
 
2.0%
Decimal Number 573
 
1.0%
Lowercase Letter 193
 
0.4%
Other Punctuation 159
 
0.3%
Dash Punctuation 16
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Connector Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1297
 
2.5%
1290
 
2.5%
798
 
1.5%
771
 
1.5%
750
 
1.4%
678
 
1.3%
596
 
1.1%
578
 
1.1%
556
 
1.1%
540
 
1.0%
Other values (975) 44751
85.1%
Uppercase Letter
ValueCountFrequency (%)
C 148
13.4%
S 110
 
10.0%
G 81
 
7.3%
O 66
 
6.0%
A 62
 
5.6%
T 56
 
5.1%
P 56
 
5.1%
B 55
 
5.0%
E 51
 
4.6%
U 51
 
4.6%
Other values (16) 368
33.3%
Lowercase Letter
ValueCountFrequency (%)
e 34
17.6%
o 16
 
8.3%
l 14
 
7.3%
c 12
 
6.2%
t 12
 
6.2%
i 12
 
6.2%
r 11
 
5.7%
n 11
 
5.7%
h 10
 
5.2%
a 10
 
5.2%
Other values (11) 51
26.4%
Decimal Number
ValueCountFrequency (%)
2 119
20.8%
1 87
15.2%
0 78
13.6%
5 72
12.6%
3 61
10.6%
4 45
 
7.9%
9 37
 
6.5%
7 29
 
5.1%
8 26
 
4.5%
6 19
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 63
39.6%
& 49
30.8%
, 30
18.9%
/ 5
 
3.1%
: 5
 
3.1%
! 2
 
1.3%
* 2
 
1.3%
· 2
 
1.3%
# 1
 
0.6%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52601
96.2%
Latin 1297
 
2.4%
Common 760
 
1.4%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1297
 
2.5%
1290
 
2.5%
798
 
1.5%
771
 
1.5%
750
 
1.4%
678
 
1.3%
596
 
1.1%
578
 
1.1%
556
 
1.1%
540
 
1.0%
Other values (971) 44747
85.1%
Latin
ValueCountFrequency (%)
C 148
 
11.4%
S 110
 
8.5%
G 81
 
6.2%
O 66
 
5.1%
A 62
 
4.8%
T 56
 
4.3%
P 56
 
4.3%
B 55
 
4.2%
E 51
 
3.9%
U 51
 
3.9%
Other values (37) 561
43.3%
Common
ValueCountFrequency (%)
2 119
15.7%
1 87
11.4%
0 78
10.3%
5 72
9.5%
. 63
8.3%
3 61
8.0%
& 49
6.4%
4 45
 
5.9%
9 37
 
4.9%
, 30
 
3.9%
Other values (15) 119
15.7%
Han
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52601
96.2%
ASCII 2055
 
3.8%
CJK 4
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1297
 
2.5%
1290
 
2.5%
798
 
1.5%
771
 
1.5%
750
 
1.4%
678
 
1.3%
596
 
1.1%
578
 
1.1%
556
 
1.1%
540
 
1.0%
Other values (971) 44747
85.1%
ASCII
ValueCountFrequency (%)
C 148
 
7.2%
2 119
 
5.8%
S 110
 
5.4%
1 87
 
4.2%
G 81
 
3.9%
0 78
 
3.8%
5 72
 
3.5%
O 66
 
3.2%
. 63
 
3.1%
A 62
 
3.0%
Other values (61) 1169
56.9%
None
ValueCountFrequency (%)
· 2
100.0%
CJK
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

SPOT_NM
Text

MISSING 

Distinct721
Distinct (%)42.5%
Missing8302
Missing (%)83.0%
Memory size156.2 KiB
2023-12-10T22:23:36.303284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length4.4840989
Min length2

Characters and Unicode

Total characters7614
Distinct characters409
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique588 ?
Unique (%)34.6%

Sample

1st row경대북문점
2nd row개인의취
3rd row침산점
4th row대구북부대리점
5th row경북대점
ValueCountFrequency (%)
칠곡점 167
 
9.8%
침산점 66
 
3.9%
복현점 49
 
2.9%
경북대점 41
 
2.4%
태전점 37
 
2.2%
산격점 29
 
1.7%
동천점 28
 
1.6%
대구칠곡점 26
 
1.5%
서변점 25
 
1.5%
경대점 23
 
1.4%
Other values (711) 1207
71.1%
2023-12-10T22:23:37.036609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1426
 
18.7%
621
 
8.2%
491
 
6.4%
377
 
5.0%
340
 
4.5%
185
 
2.4%
183
 
2.4%
148
 
1.9%
124
 
1.6%
116
 
1.5%
Other values (399) 3603
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7406
97.3%
Decimal Number 120
 
1.6%
Uppercase Letter 79
 
1.0%
Other Punctuation 6
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1426
19.3%
621
 
8.4%
491
 
6.6%
377
 
5.1%
340
 
4.6%
185
 
2.5%
183
 
2.5%
148
 
2.0%
124
 
1.7%
116
 
1.6%
Other values (364) 3395
45.8%
Uppercase Letter
ValueCountFrequency (%)
C 12
15.2%
N 11
13.9%
T 9
11.4%
D 8
10.1%
O 5
 
6.3%
S 4
 
5.1%
I 4
 
5.1%
A 3
 
3.8%
R 3
 
3.8%
H 3
 
3.8%
Other values (11) 17
21.5%
Decimal Number
ValueCountFrequency (%)
3 39
32.5%
2 34
28.3%
1 20
16.7%
4 20
16.7%
5 3
 
2.5%
6 2
 
1.7%
8 2
 
1.7%
Other Punctuation
ValueCountFrequency (%)
! 2
33.3%
& 2
33.3%
. 1
16.7%
, 1
16.7%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7406
97.3%
Common 129
 
1.7%
Latin 79
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1426
19.3%
621
 
8.4%
491
 
6.6%
377
 
5.1%
340
 
4.6%
185
 
2.5%
183
 
2.5%
148
 
2.0%
124
 
1.7%
116
 
1.6%
Other values (364) 3395
45.8%
Latin
ValueCountFrequency (%)
C 12
15.2%
N 11
13.9%
T 9
11.4%
D 8
10.1%
O 5
 
6.3%
S 4
 
5.1%
I 4
 
5.1%
A 3
 
3.8%
R 3
 
3.8%
H 3
 
3.8%
Other values (11) 17
21.5%
Common
ValueCountFrequency (%)
3 39
30.2%
2 34
26.4%
1 20
15.5%
4 20
15.5%
5 3
 
2.3%
6 2
 
1.6%
! 2
 
1.6%
& 2
 
1.6%
8 2
 
1.6%
) 1
 
0.8%
Other values (4) 4
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7406
97.3%
ASCII 208
 
2.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1426
19.3%
621
 
8.4%
491
 
6.6%
377
 
5.1%
340
 
4.6%
185
 
2.5%
183
 
2.5%
148
 
2.0%
124
 
1.7%
116
 
1.6%
Other values (364) 3395
45.8%
ASCII
ValueCountFrequency (%)
3 39
18.8%
2 34
16.3%
1 20
9.6%
4 20
9.6%
C 12
 
5.8%
N 11
 
5.3%
T 9
 
4.3%
D 8
 
3.8%
O 5
 
2.4%
S 4
 
1.9%
Other values (25) 46
22.1%

CMSC_L_CD
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
D
3596 
Q
3569 
F
1604 
R
536 
L
 
272
Other values (3)
423 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ
2nd rowQ
3rd rowF
4th rowQ
5th rowD

Common Values

ValueCountFrequency (%)
D 3596
36.0%
Q 3569
35.7%
F 1604
16.0%
R 536
 
5.4%
L 272
 
2.7%
N 233
 
2.3%
P 134
 
1.3%
O 56
 
0.6%

Length

2023-12-10T22:23:37.284058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:23:37.461008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d 3596
36.0%
q 3569
35.7%
f 1604
16.0%
r 536
 
5.4%
l 272
 
2.7%
n 233
 
2.3%
p 134
 
1.3%
o 56
 
0.6%

CMSC_L_NM
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
소매
3596 
음식
3569 
생활서비스
1604 
학문/교육
536 
부동산
 
272
Other values (3)
423 

Length

Max length8
Median length2
Mean length2.8224
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row음식
2nd row음식
3rd row생활서비스
4th row음식
5th row소매

Common Values

ValueCountFrequency (%)
소매 3596
36.0%
음식 3569
35.7%
생활서비스 1604
16.0%
학문/교육 536
 
5.4%
부동산 272
 
2.7%
관광/여가/오락 233
 
2.3%
스포츠 134
 
1.3%
숙박 56
 
0.6%

Length

2023-12-10T22:23:37.704663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:23:37.929836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소매 3596
36.0%
음식 3569
35.7%
생활서비스 1604
16.0%
학문/교육 536
 
5.4%
부동산 272
 
2.7%
관광/여가/오락 233
 
2.3%
스포츠 134
 
1.3%
숙박 56
 
0.6%
Distinct84
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-10T22:23:38.370970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30000
Distinct characters18
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

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowQ01
2nd rowQ12
3rd rowF01
4th rowQ07
5th rowD01
ValueCountFrequency (%)
q01 1393
 
13.9%
f01 695
 
7.0%
d01 592
 
5.9%
d03 578
 
5.8%
d07 465
 
4.7%
q12 463
 
4.6%
d05 316
 
3.2%
f14 306
 
3.1%
q04 299
 
3.0%
q05 289
 
2.9%
Other values (74) 4604
46.0%
2023-12-10T22:23:38.963981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7897
26.3%
1 5098
17.0%
D 3596
12.0%
Q 3569
11.9%
2 1789
 
6.0%
F 1604
 
5.3%
3 1212
 
4.0%
5 972
 
3.2%
4 838
 
2.8%
7 703
 
2.3%
Other values (8) 2722
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20000
66.7%
Uppercase Letter 10000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7897
39.5%
1 5098
25.5%
2 1789
 
8.9%
3 1212
 
6.1%
5 972
 
4.9%
4 838
 
4.2%
7 703
 
3.5%
8 549
 
2.7%
6 510
 
2.5%
9 432
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
D 3596
36.0%
Q 3569
35.7%
F 1604
16.0%
R 536
 
5.4%
L 272
 
2.7%
N 233
 
2.3%
P 134
 
1.3%
O 56
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 20000
66.7%
Latin 10000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7897
39.5%
1 5098
25.5%
2 1789
 
8.9%
3 1212
 
6.1%
5 972
 
4.9%
4 838
 
4.2%
7 703
 
3.5%
8 549
 
2.7%
6 510
 
2.5%
9 432
 
2.2%
Latin
ValueCountFrequency (%)
D 3596
36.0%
Q 3569
35.7%
F 1604
16.0%
R 536
 
5.4%
L 272
 
2.7%
N 233
 
2.3%
P 134
 
1.3%
O 56
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7897
26.3%
1 5098
17.0%
D 3596
12.0%
Q 3569
11.9%
2 1789
 
6.0%
F 1604
 
5.3%
3 1212
 
4.0%
5 972
 
3.2%
4 838
 
2.8%
7 703
 
2.3%
Other values (8) 2722
 
9.1%
Distinct84
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-10T22:23:39.390169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.9406
Min length2

Characters and Unicode

Total characters59406
Distinct characters185
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row한식
2nd row커피점/카페
3rd row이/미용/건강
4th row패스트푸드
5th row음/식료품소매
ValueCountFrequency (%)
한식 1393
 
13.9%
이/미용/건강 695
 
7.0%
음/식료품소매 592
 
5.9%
종합소매점 578
 
5.8%
가정/주방/인테리어 465
 
4.7%
커피점/카페 463
 
4.6%
의복의류 316
 
3.2%
자동차/이륜차 306
 
3.1%
분식 299
 
3.0%
닭/오리요리 289
 
2.9%
Other values (74) 4604
46.0%
2023-12-10T22:23:40.028623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 7471
 
12.6%
2939
 
4.9%
2098
 
3.5%
2084
 
3.5%
1469
 
2.5%
1417
 
2.4%
1393
 
2.3%
1302
 
2.2%
1194
 
2.0%
1174
 
2.0%
Other values (175) 36865
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51450
86.6%
Other Punctuation 7471
 
12.6%
Dash Punctuation 329
 
0.6%
Uppercase Letter 156
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2939
 
5.7%
2098
 
4.1%
2084
 
4.1%
1469
 
2.9%
1417
 
2.8%
1393
 
2.7%
1302
 
2.5%
1194
 
2.3%
1174
 
2.3%
1114
 
2.2%
Other values (171) 35266
68.5%
Uppercase Letter
ValueCountFrequency (%)
C 78
50.0%
P 78
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 7471
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 329
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 51450
86.6%
Common 7800
 
13.1%
Latin 156
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2939
 
5.7%
2098
 
4.1%
2084
 
4.1%
1469
 
2.9%
1417
 
2.8%
1393
 
2.7%
1302
 
2.5%
1194
 
2.3%
1174
 
2.3%
1114
 
2.2%
Other values (171) 35266
68.5%
Common
ValueCountFrequency (%)
/ 7471
95.8%
- 329
 
4.2%
Latin
ValueCountFrequency (%)
C 78
50.0%
P 78
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 51450
86.6%
ASCII 7956
 
13.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 7471
93.9%
- 329
 
4.1%
C 78
 
1.0%
P 78
 
1.0%
Hangul
ValueCountFrequency (%)
2939
 
5.7%
2098
 
4.1%
2084
 
4.1%
1469
 
2.9%
1417
 
2.8%
1393
 
2.7%
1302
 
2.5%
1194
 
2.3%
1174
 
2.3%
1114
 
2.2%
Other values (171) 35266
68.5%
Distinct405
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-10T22:23:40.675863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters60000
Distinct characters20
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

Unique85 ?
Unique (%)0.9%

Sample

1st rowQ01A01
2nd rowQ12A01
3rd rowF01A01
4th rowQ07A01
5th rowD01A06
ValueCountFrequency (%)
q01a01 815
 
8.2%
q12a01 454
 
4.5%
f01a01 447
 
4.5%
l01a01 266
 
2.7%
f14a01 221
 
2.2%
q05a08 214
 
2.1%
q01a02 193
 
1.9%
d03a01 164
 
1.6%
d07a15 161
 
1.6%
d16a01 142
 
1.4%
Other values (395) 6923
69.2%
2023-12-10T22:23:41.492829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17074
28.5%
1 10951
18.3%
A 9909
16.5%
D 3596
 
6.0%
Q 3569
 
5.9%
2 3134
 
5.2%
3 2075
 
3.5%
F 1604
 
2.7%
4 1592
 
2.7%
5 1512
 
2.5%
Other values (10) 4984
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40000
66.7%
Uppercase Letter 20000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17074
42.7%
1 10951
27.4%
2 3134
 
7.8%
3 2075
 
5.2%
4 1592
 
4.0%
5 1512
 
3.8%
7 1059
 
2.6%
6 966
 
2.4%
8 922
 
2.3%
9 715
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
A 9909
49.5%
D 3596
 
18.0%
Q 3569
 
17.8%
F 1604
 
8.0%
R 536
 
2.7%
L 272
 
1.4%
N 233
 
1.2%
P 134
 
0.7%
B 91
 
0.5%
O 56
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 40000
66.7%
Latin 20000
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17074
42.7%
1 10951
27.4%
2 3134
 
7.8%
3 2075
 
5.2%
4 1592
 
4.0%
5 1512
 
3.8%
7 1059
 
2.6%
6 966
 
2.4%
8 922
 
2.3%
9 715
 
1.8%
Latin
ValueCountFrequency (%)
A 9909
49.5%
D 3596
 
18.0%
Q 3569
 
17.8%
F 1604
 
8.0%
R 536
 
2.7%
L 272
 
1.4%
N 233
 
1.2%
P 134
 
0.7%
B 91
 
0.5%
O 56
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17074
28.5%
1 10951
18.3%
A 9909
16.5%
D 3596
 
6.0%
Q 3569
 
5.9%
2 3134
 
5.2%
3 2075
 
3.5%
F 1604
 
2.7%
4 1592
 
2.7%
5 1512
 
2.5%
Other values (10) 4984
 
8.3%
Distinct405
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-10T22:23:41.973589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length6.429
Min length2

Characters and Unicode

Total characters64290
Distinct characters386
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)0.9%

Sample

1st row한식/백반/한정식
2nd row커피전문점/카페/다방
3rd row여성미용실
4th row피자전문
5th row육류소매
ValueCountFrequency (%)
한식/백반/한정식 815
 
8.1%
커피전문점/카페/다방 454
 
4.5%
여성미용실 447
 
4.5%
부동산중개 266
 
2.7%
자동차정비/카센타 221
 
2.2%
후라이드/양념치킨 214
 
2.1%
갈비/삼겹살 193
 
1.9%
편의점 164
 
1.6%
유리/페인트/철물건축자재 161
 
1.6%
화장품판매점 142
 
1.4%
Other values (396) 6933
69.3%
2023-12-10T22:23:42.740108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 6031
 
9.4%
2503
 
3.9%
1689
 
2.6%
1687
 
2.6%
1602
 
2.5%
1496
 
2.3%
1310
 
2.0%
1231
 
1.9%
1143
 
1.8%
1077
 
1.7%
Other values (376) 44521
69.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 57582
89.6%
Other Punctuation 6031
 
9.4%
Dash Punctuation 452
 
0.7%
Uppercase Letter 97
 
0.2%
Close Punctuation 59
 
0.1%
Open Punctuation 59
 
0.1%
Space Separator 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2503
 
4.3%
1689
 
2.9%
1687
 
2.9%
1602
 
2.8%
1496
 
2.6%
1310
 
2.3%
1231
 
2.1%
1143
 
2.0%
1077
 
1.9%
1039
 
1.8%
Other values (366) 42805
74.3%
Uppercase Letter
ValueCountFrequency (%)
P 45
46.4%
C 41
42.3%
G 5
 
5.2%
L 5
 
5.2%
D 1
 
1.0%
Other Punctuation
ValueCountFrequency (%)
/ 6031
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 452
100.0%
Close Punctuation
ValueCountFrequency (%)
) 59
100.0%
Open Punctuation
ValueCountFrequency (%)
( 59
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 57582
89.6%
Common 6611
 
10.3%
Latin 97
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2503
 
4.3%
1689
 
2.9%
1687
 
2.9%
1602
 
2.8%
1496
 
2.6%
1310
 
2.3%
1231
 
2.1%
1143
 
2.0%
1077
 
1.9%
1039
 
1.8%
Other values (366) 42805
74.3%
Common
ValueCountFrequency (%)
/ 6031
91.2%
- 452
 
6.8%
) 59
 
0.9%
( 59
 
0.9%
10
 
0.2%
Latin
ValueCountFrequency (%)
P 45
46.4%
C 41
42.3%
G 5
 
5.2%
L 5
 
5.2%
D 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 57582
89.6%
ASCII 6708
 
10.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 6031
89.9%
- 452
 
6.7%
) 59
 
0.9%
( 59
 
0.9%
P 45
 
0.7%
C 41
 
0.6%
10
 
0.1%
G 5
 
0.1%
L 5
 
0.1%
D 1
 
< 0.1%
Hangul
ValueCountFrequency (%)
2503
 
4.3%
1689
 
2.9%
1687
 
2.9%
1602
 
2.8%
1496
 
2.6%
1310
 
2.3%
1231
 
2.1%
1143
 
2.0%
1077
 
1.9%
1039
 
1.8%
Other values (366) 42805
74.3%

SIC_CD
Text

MISSING 

Distinct161
Distinct (%)1.7%
Missing587
Missing (%)5.9%
Memory size156.2 KiB
2023-12-10T22:23:43.335254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters56478
Distinct characters23
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

Unique16 ?
Unique (%)0.2%

Sample

1st rowI56111
2nd rowI56220
3rd rowS96112
4th rowI56192
5th rowG47212
ValueCountFrequency (%)
i56111 1707
 
18.1%
i56220 463
 
4.9%
s96112 447
 
4.7%
i56194 301
 
3.2%
l68221 266
 
2.8%
i56193 214
 
2.3%
g47219 208
 
2.2%
g47190 199
 
2.1%
g47519 196
 
2.1%
g47416 190
 
2.0%
Other values (151) 5222
55.5%
2023-12-10T22:23:44.125230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13669
24.2%
2 5729
10.1%
5 5616
9.9%
6 5302
 
9.4%
4 4575
 
8.1%
9 4142
 
7.3%
I 3616
 
6.4%
7 3561
 
6.3%
G 3541
 
6.3%
0 1594
 
2.8%
Other values (13) 5133
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47065
83.3%
Uppercase Letter 9413
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 3616
38.4%
G 3541
37.6%
S 889
 
9.4%
P 462
 
4.9%
R 370
 
3.9%
L 290
 
3.1%
C 67
 
0.7%
M 58
 
0.6%
F 45
 
0.5%
Q 40
 
0.4%
Other values (3) 35
 
0.4%
Decimal Number
ValueCountFrequency (%)
1 13669
29.0%
2 5729
12.2%
5 5616
11.9%
6 5302
 
11.3%
4 4575
 
9.7%
9 4142
 
8.8%
7 3561
 
7.6%
0 1594
 
3.4%
3 1530
 
3.3%
8 1347
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 47065
83.3%
Latin 9413
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 3616
38.4%
G 3541
37.6%
S 889
 
9.4%
P 462
 
4.9%
R 370
 
3.9%
L 290
 
3.1%
C 67
 
0.7%
M 58
 
0.6%
F 45
 
0.5%
Q 40
 
0.4%
Other values (3) 35
 
0.4%
Common
ValueCountFrequency (%)
1 13669
29.0%
2 5729
12.2%
5 5616
11.9%
6 5302
 
11.3%
4 4575
 
9.7%
9 4142
 
8.8%
7 3561
 
7.6%
0 1594
 
3.4%
3 1530
 
3.3%
8 1347
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56478
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13669
24.2%
2 5729
10.1%
5 5616
9.9%
6 5302
 
9.4%
4 4575
 
8.1%
9 4142
 
7.3%
I 3616
 
6.4%
7 3561
 
6.3%
G 3541
 
6.3%
0 1594
 
2.8%
Other values (13) 5133
 
9.1%

SIC_NM
Text

MISSING 

Distinct161
Distinct (%)1.7%
Missing587
Missing (%)5.9%
Memory size156.2 KiB
2023-12-10T22:23:44.693887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length9.4160204
Min length3

Characters and Unicode

Total characters88633
Distinct characters214
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.2%

Sample

1st row한식 음식점업
2nd row비알콜 음료점업
3rd row두발미용업
4th row피자, 햄버거, 샌드위치 및 유사 음식점업
5th row육류 소매업
ValueCountFrequency (%)
소매업 3009
 
11.7%
2424
 
9.4%
음식점업 2146
 
8.3%
한식 1707
 
6.6%
기타 1655
 
6.4%
전문점 515
 
2.0%
비알콜 463
 
1.8%
음료점업 463
 
1.8%
두발미용업 447
 
1.7%
그외 446
 
1.7%
Other values (235) 12544
48.6%
2023-12-10T22:23:45.487892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16406
18.5%
8160
 
9.2%
4984
 
5.6%
3719
 
4.2%
3228
 
3.6%
3187
 
3.6%
2678
 
3.0%
2424
 
2.7%
2269
 
2.6%
1740
 
2.0%
Other values (204) 39838
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71601
80.8%
Space Separator 16406
 
18.5%
Other Punctuation 626
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8160
 
11.4%
4984
 
7.0%
3719
 
5.2%
3228
 
4.5%
3187
 
4.5%
2678
 
3.7%
2424
 
3.4%
2269
 
3.2%
1740
 
2.4%
1713
 
2.4%
Other values (201) 37499
52.4%
Other Punctuation
ValueCountFrequency (%)
, 555
88.7%
· 71
 
11.3%
Space Separator
ValueCountFrequency (%)
16406
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71601
80.8%
Common 17032
 
19.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8160
 
11.4%
4984
 
7.0%
3719
 
5.2%
3228
 
4.5%
3187
 
4.5%
2678
 
3.7%
2424
 
3.4%
2269
 
3.2%
1740
 
2.4%
1713
 
2.4%
Other values (201) 37499
52.4%
Common
ValueCountFrequency (%)
16406
96.3%
, 555
 
3.3%
· 71
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71601
80.8%
ASCII 16961
 
19.1%
None 71
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16406
96.7%
, 555
 
3.3%
Hangul
ValueCountFrequency (%)
8160
 
11.4%
4984
 
7.0%
3719
 
5.2%
3228
 
4.5%
3187
 
4.5%
2678
 
3.7%
2424
 
3.4%
2269
 
3.2%
1740
 
2.4%
1713
 
2.4%
Other values (201) 37499
52.4%
None
ValueCountFrequency (%)
· 71
100.0%

PNU
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7228909 × 1018
Minimum2.71401 × 1018
Maximum2.72308 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:45.712739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.71401 × 1018
5-th percentile2.72301 × 1018
Q12.72301 × 1018
median2.72301 × 1018
Q32.72301 × 1018
95-th percentile2.72301 × 1018
Maximum2.72308 × 1018
Range9.07 × 1015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.7434907 × 1014
Coefficient of variation (CV)0.00028438491
Kurtosis55.949658
Mean2.7228909 × 1018
Median Absolute Deviation (MAD)0
Skewness-7.2263313
Sum1.5146372 × 1018
Variance5.9961648 × 1029
MonotonicityNot monotonic
2023-12-10T22:23:45.924864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2723010000000000000 9711
97.1%
2720000000000000000 149
 
1.5%
2717010000000000000 105
 
1.1%
2714010000000000000 12
 
0.1%
2723070000000000000 11
 
0.1%
2723080000000000000 7
 
0.1%
2723050000000000000 2
 
< 0.1%
2723060000000000000 2
 
< 0.1%
2717060000000000000 1
 
< 0.1%
ValueCountFrequency (%)
2714010000000000000 12
 
0.1%
2717010000000000000 105
 
1.1%
2717060000000000000 1
 
< 0.1%
2720000000000000000 149
 
1.5%
2723010000000000000 9711
97.1%
2723050000000000000 2
 
< 0.1%
2723060000000000000 2
 
< 0.1%
2723070000000000000 11
 
0.1%
2723080000000000000 7
 
0.1%
ValueCountFrequency (%)
2723080000000000000 7
 
0.1%
2723070000000000000 11
 
0.1%
2723060000000000000 2
 
< 0.1%
2723050000000000000 2
 
< 0.1%
2723010000000000000 9711
97.1%
2720000000000000000 149
 
1.5%
2717060000000000000 1
 
< 0.1%
2717010000000000000 105
 
1.1%
2714010000000000000 12
 
0.1%

PLT_SC_CD
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9985 
2
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9985
99.9%
2 15
 
0.1%

Length

2023-12-10T22:23:46.199289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:23:46.810934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9985
99.9%
2 15
 
0.1%

PLT_SC_NM
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대지
9985 
 
15

Length

Max length2
Median length2
Mean length1.9985
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대지
2nd row대지
3rd row대지
4th row대지
5th row대지

Common Values

ValueCountFrequency (%)
대지 9985
99.9%
15
 
0.1%

Length

2023-12-10T22:23:46.985117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:23:47.153896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대지 9985
99.9%
15
 
0.1%

LNNO_ADRES
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

RD_NM
Text

Distinct507
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-10T22:23:47.546165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length13.8945
Min length12

Characters and Unicode

Total characters138945
Distinct characters108
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)0.6%

Sample

1st row대구광역시 북구 칠성시장로7길
2nd row대구광역시 북구 대학로23길
3rd row대구광역시 북구 동암로12길
4th row대구광역시 북구 침산남로
5th row대구광역시 북구 학정로13길
ValueCountFrequency (%)
대구광역시 10000
33.3%
북구 9882
32.9%
칠곡중앙대로 503
 
1.7%
유통단지로 366
 
1.2%
동북로 263
 
0.9%
학정로 235
 
0.8%
침산로 196
 
0.7%
팔달로 191
 
0.6%
동천로 169
 
0.6%
팔달로33길 152
 
0.5%
Other values (497) 8043
26.8%
2023-12-10T22:23:48.225545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20483
14.7%
20000
14.4%
12221
8.8%
10522
 
7.6%
10180
 
7.3%
10000
 
7.2%
10000
 
7.2%
9858
 
7.1%
4601
 
3.3%
1 1794
 
1.3%
Other values (98) 29286
21.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111190
80.0%
Space Separator 20000
 
14.4%
Decimal Number 7755
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
20483
18.4%
12221
11.0%
10522
9.5%
10180
9.2%
10000
9.0%
10000
9.0%
9858
8.9%
4601
 
4.1%
1522
 
1.4%
1361
 
1.2%
Other values (87) 20442
18.4%
Decimal Number
ValueCountFrequency (%)
1 1794
23.1%
3 1368
17.6%
2 868
11.2%
4 678
 
8.7%
5 661
 
8.5%
7 589
 
7.6%
6 545
 
7.0%
8 481
 
6.2%
9 460
 
5.9%
0 311
 
4.0%
Space Separator
ValueCountFrequency (%)
20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111190
80.0%
Common 27755
 
20.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
20483
18.4%
12221
11.0%
10522
9.5%
10180
9.2%
10000
9.0%
10000
9.0%
9858
8.9%
4601
 
4.1%
1522
 
1.4%
1361
 
1.2%
Other values (87) 20442
18.4%
Common
ValueCountFrequency (%)
20000
72.1%
1 1794
 
6.5%
3 1368
 
4.9%
2 868
 
3.1%
4 678
 
2.4%
5 661
 
2.4%
7 589
 
2.1%
6 545
 
2.0%
8 481
 
1.7%
9 460
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111190
80.0%
ASCII 27755
 
20.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
20483
18.4%
12221
11.0%
10522
9.5%
10180
9.2%
10000
9.0%
10000
9.0%
9858
8.9%
4601
 
4.1%
1522
 
1.4%
1361
 
1.2%
Other values (87) 20442
18.4%
ASCII
ValueCountFrequency (%)
20000
72.1%
1 1794
 
6.5%
3 1368
 
4.9%
2 868
 
3.1%
4 678
 
2.4%
5 661
 
2.4%
7 589
 
2.1%
6 545
 
2.0%
8 481
 
1.7%
9 460
 
1.7%

RDNMADR
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

O_ZIP_INFO
Real number (ℝ)

Distinct145
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean702641.18
Minimum701010
Maximum703851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:48.432330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum701010
5-th percentile702010
Q1702260
median702819
Q3702856
95-th percentile702894
Maximum703851
Range2841
Interquartile range (IQR)596

Descriptive statistics

Standard deviation350.43416
Coefficient of variation (CV)0.00049873842
Kurtosis-0.087962854
Mean702641.18
Median Absolute Deviation (MAD)50
Skewness-0.80332091
Sum7.0264118 × 109
Variance122804.1
MonotonicityNot monotonic
2023-12-10T22:23:48.625015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
702010 749
 
7.5%
702886 453
 
4.5%
702260 442
 
4.4%
702894 334
 
3.3%
702050 329
 
3.3%
702020 298
 
3.0%
702816 273
 
2.7%
702807 244
 
2.4%
702061 227
 
2.3%
702803 208
 
2.1%
Other values (135) 6443
64.4%
ValueCountFrequency (%)
701010 4
 
< 0.1%
701818 1
 
< 0.1%
701821 7
 
0.1%
702010 749
7.5%
702020 298
 
3.0%
702050 329
3.3%
702052 4
 
< 0.1%
702061 227
 
2.3%
702062 10
 
0.1%
702071 2
 
< 0.1%
ValueCountFrequency (%)
703851 15
 
0.1%
703828 10
 
0.1%
703826 8
 
0.1%
703825 7
 
0.1%
703824 4
 
< 0.1%
703821 9
 
0.1%
703819 1
 
< 0.1%
703040 52
 
0.5%
702915 73
0.7%
702912 156
1.6%

N_ZIP_INFO
Real number (ℝ)

Distinct194
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41504.143
Minimum41192
Maximum41728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:48.865422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41192
5-th percentile41413
Q141452
median41505
Q341557
95-th percentile41593
Maximum41728
Range536
Interquartile range (IQR)105

Descriptive statistics

Standard deviation62.005562
Coefficient of variation (CV)0.0014939608
Kurtosis0.44242189
Mean41504.143
Median Absolute Deviation (MAD)52
Skewness0.13461641
Sum4.1504143 × 108
Variance3844.6898
MonotonicityNot monotonic
2023-12-10T22:23:49.102306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41544 309
 
3.1%
41518 284
 
2.8%
41557 254
 
2.5%
41423 244
 
2.4%
41477 186
 
1.9%
41505 169
 
1.7%
41535 169
 
1.7%
41582 164
 
1.6%
41438 159
 
1.6%
41465 139
 
1.4%
Other values (184) 7923
79.2%
ValueCountFrequency (%)
41192 10
 
0.1%
41200 2
 
< 0.1%
41400 13
 
0.1%
41401 31
0.3%
41402 67
0.7%
41403 13
 
0.1%
41404 18
 
0.2%
41405 34
0.3%
41406 23
 
0.2%
41407 13
 
0.1%
ValueCountFrequency (%)
41728 8
 
0.1%
41727 10
 
0.1%
41716 15
 
0.1%
41715 23
 
0.2%
41714 10
 
0.1%
41710 27
 
0.3%
41702 6
 
0.1%
41701 7
 
0.1%
41599 21
 
0.2%
41598 85
0.9%

DONG_INFO
Text

MISSING 

Distinct79
Distinct (%)9.7%
Missing9188
Missing (%)91.9%
Memory size156.2 KiB
2023-12-10T22:23:49.431262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.2179803
Min length1

Characters and Unicode

Total characters989
Distinct characters26
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)4.1%

Sample

1st row2
2nd row3
3rd row2
4th row102
5th row26
ValueCountFrequency (%)
2 211
26.0%
1 133
16.4%
3 90
11.1%
4 43
 
5.3%
38
 
4.7%
a 35
 
4.3%
b 32
 
3.9%
21
 
2.6%
7 16
 
2.0%
5 14
 
1.7%
Other values (69) 179
22.0%
2023-12-10T22:23:49.964950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 261
26.4%
1 218
22.0%
3 106
10.7%
4 56
 
5.7%
0 43
 
4.3%
38
 
3.8%
A 35
 
3.5%
B 33
 
3.3%
5 31
 
3.1%
7 30
 
3.0%
Other values (16) 138
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 802
81.1%
Other Letter 111
 
11.2%
Uppercase Letter 76
 
7.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
38
34.2%
28
25.2%
12
 
10.8%
9
 
8.1%
7
 
6.3%
6
 
5.4%
4
 
3.6%
3
 
2.7%
2
 
1.8%
1
 
0.9%
Decimal Number
ValueCountFrequency (%)
2 261
32.5%
1 218
27.2%
3 106
13.2%
4 56
 
7.0%
0 43
 
5.4%
5 31
 
3.9%
7 30
 
3.7%
6 25
 
3.1%
8 18
 
2.2%
9 14
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
A 35
46.1%
B 33
43.4%
C 5
 
6.6%
D 2
 
2.6%
E 1
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 802
81.1%
Hangul 111
 
11.2%
Latin 76
 
7.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
38
34.2%
28
25.2%
12
 
10.8%
9
 
8.1%
7
 
6.3%
6
 
5.4%
4
 
3.6%
3
 
2.7%
2
 
1.8%
1
 
0.9%
Common
ValueCountFrequency (%)
2 261
32.5%
1 218
27.2%
3 106
13.2%
4 56
 
7.0%
0 43
 
5.4%
5 31
 
3.9%
7 30
 
3.7%
6 25
 
3.1%
8 18
 
2.2%
9 14
 
1.7%
Latin
ValueCountFrequency (%)
A 35
46.1%
B 33
43.4%
C 5
 
6.6%
D 2
 
2.6%
E 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 878
88.8%
Hangul 111
 
11.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 261
29.7%
1 218
24.8%
3 106
12.1%
4 56
 
6.4%
0 43
 
4.9%
A 35
 
4.0%
B 33
 
3.8%
5 31
 
3.5%
7 30
 
3.4%
6 25
 
2.8%
Other values (5) 40
 
4.6%
Hangul
ValueCountFrequency (%)
38
34.2%
28
25.2%
12
 
10.8%
9
 
8.1%
7
 
6.3%
6
 
5.4%
4
 
3.6%
3
 
2.7%
2
 
1.8%
1
 
0.9%

FLR_INFO
Categorical

IMBALANCE 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6527 
1
2275 
2
 
530
3
 
306
4
 
128
Other values (13)
 
234

Length

Max length4
Median length4
Mean length2.9631
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row<NA>
3rd row1
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 6527
65.3%
1 2275
 
22.8%
2 530
 
5.3%
3 306
 
3.1%
4 128
 
1.3%
5 108
 
1.1%
6 41
 
0.4%
-1 35
 
0.4%
7 18
 
0.2%
8 15
 
0.1%
Other values (8) 17
 
0.2%

Length

2023-12-10T22:23:50.239224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 6527
65.3%
1 2310
 
23.1%
2 532
 
5.3%
3 306
 
3.1%
4 128
 
1.3%
5 108
 
1.1%
6 41
 
0.4%
7 18
 
0.2%
8 15
 
0.1%
10 4
 
< 0.1%
Other values (6) 11
 
0.1%

HO_INFO
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

LO
Real number (ℝ)

Distinct5930
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58041
Minimum128.50986
Maximum128.62956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:50.500630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.50986
5-th percentile128.54327
Q1128.55539
median128.58263
Q3128.60617
95-th percentile128.61843
Maximum128.62956
Range0.1197009
Interquartile range (IQR)0.0507742

Descriptive statistics

Standard deviation0.02742939
Coefficient of variation (CV)0.00021332479
Kurtosis-1.0685631
Mean128.58041
Median Absolute Deviation (MAD)0.0244191
Skewness-0.21755045
Sum1285804.1
Variance0.00075237143
MonotonicityNot monotonic
2023-12-10T22:23:50.855351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.6064082 109
 
1.1%
128.6070346 93
 
0.9%
128.5659698 89
 
0.9%
128.6103762 84
 
0.8%
128.6049939 73
 
0.7%
128.543266 52
 
0.5%
128.5559809 50
 
0.5%
128.549038 46
 
0.5%
128.5897991 35
 
0.4%
128.6096753 28
 
0.3%
Other values (5920) 9341
93.4%
ValueCountFrequency (%)
128.5098614 2
< 0.1%
128.5098625 3
< 0.1%
128.5098626 2
< 0.1%
128.5098747 2
< 0.1%
128.5098748 3
< 0.1%
128.5098824 2
< 0.1%
128.5098825 1
 
< 0.1%
128.5098835 2
< 0.1%
128.509909 3
< 0.1%
128.5103187 2
< 0.1%
ValueCountFrequency (%)
128.6295623 1
< 0.1%
128.6295375 1
< 0.1%
128.6293452 2
< 0.1%
128.6290799 1
< 0.1%
128.6289786 1
< 0.1%
128.6289336 2
< 0.1%
128.6288623 1
< 0.1%
128.6286705 1
< 0.1%
128.6286015 1
< 0.1%
128.6283057 1
< 0.1%

LA
Real number (ℝ)

Distinct5960
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.909763
Minimum35.874174
Maximum35.966034
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-10T22:23:51.107243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.874174
5-th percentile35.87988
Q135.8919
median35.904134
Q335.928758
95-th percentile35.946475
Maximum35.966034
Range0.09186051
Interquartile range (IQR)0.036858838

Descriptive statistics

Standard deviation0.022034788
Coefficient of variation (CV)0.00061361553
Kurtosis-1.139351
Mean35.909763
Median Absolute Deviation (MAD)0.016049365
Skewness0.38833676
Sum359097.63
Variance0.00048553189
MonotonicityNot monotonic
2023-12-10T22:23:51.357127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.90498099 109
 
1.1%
35.90721309 93
 
0.9%
35.8907122 89
 
0.9%
35.90523128 84
 
0.8%
35.90546582 73
 
0.7%
35.90090282 52
 
0.5%
35.94485589 50
 
0.5%
35.93302655 46
 
0.5%
35.8852347 35
 
0.4%
35.90628938 28
 
0.3%
Other values (5950) 9341
93.4%
ValueCountFrequency (%)
35.87417374 1
< 0.1%
35.87432397 1
< 0.1%
35.87433958 2
< 0.1%
35.87437972 1
< 0.1%
35.87439086 1
< 0.1%
35.87443022 2
< 0.1%
35.87449907 1
< 0.1%
35.87449915 1
< 0.1%
35.8745246 1
< 0.1%
35.87452544 1
< 0.1%
ValueCountFrequency (%)
35.96603425 1
< 0.1%
35.96569382 1
< 0.1%
35.96473899 1
< 0.1%
35.96409048 1
< 0.1%
35.96406819 1
< 0.1%
35.96309227 1
< 0.1%
35.96159642 1
< 0.1%
35.96063169 1
< 0.1%
35.96059181 1
< 0.1%
35.96042893 1
< 0.1%

Sample

wkt_geomBD_PRPS_CDBSI_INT_SNBSI_ZON_NORGST_BD_NMBD_DPN_SCBD_ENG_NMBD_SNEMD_CDEQB_SNGRND_FLCTLI_CDMNTN_YNNTFC_DTDETL_BD_NMRSC_SNRSC_SGG_CDUNDR_FLCTCM_BSSH_NOCM_NMSPOT_NMCMSC_L_CDCMSC_L_NMCMSC_M_CDCMSC_M_NMCMSC_S_CDCMSC_S_NMSIC_CDSIC_NMPNUPLT_SC_CDPLT_SC_NMLNNO_ADRESRD_NMRDNMADRO_ZIP_INFON_ZIP_INFODONG_INFOFLR_INFOHO_INFOLOLA
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