Overview

Dataset statistics

Number of variables18
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.5 KiB
Average record size in memory151.7 B

Variable types

Text8
Numeric8
Categorical2

Alerts

1168011000004900000 is highly overall correlated with 1168011000104890000004921 and 3 other fieldsHigh correlation
1168011000104890000004921 is highly overall correlated with 1168011000004900000 and 3 other fieldsHigh correlation
315235 is highly overall correlated with 1168011000004900000 and 2 other fieldsHigh correlation
548014 is highly overall correlated with 1168011000004900000 and 3 other fieldsHigh correlation
5 is highly overall correlated with AHigh correlation
63.87 is highly overall correlated with 1549000000High correlation
1549000000 is highly overall correlated with 1168011000004900000 and 4 other fieldsHigh correlation
A is highly overall correlated with 5 and 1 other fieldsHigh correlation
2021 is highly imbalanced (86.5%)Imbalance
서울특별시 강남구 압구정동 490번지 한양1차아파트 6동 502호 has unique valuesUnique
서울특별시 강남구 압구정로 321 한양1차아파트 6동 502호 has unique valuesUnique
A001631106 has unique valuesUnique
63.87 has 4 (2.0%) zerosZeros
1549000000 has 4 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:16:15.795032
Analysis finished2023-12-10 06:16:44.721566
Duration28.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

A000071239
Text

Distinct173
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:16:44.968928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1990
Distinct characters14
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

Unique152 ?
Unique (%)76.4%

Sample

1st rowA000019867
2nd rowA000046002
3rd rowA000067861
4th rowB000069075
5th rowU000011968
ValueCountFrequency (%)
a000069713 5
 
2.5%
u000011968 3
 
1.5%
a000049741 3
 
1.5%
u000016603 2
 
1.0%
u000014375 2
 
1.0%
a000071019 2
 
1.0%
u000015455 2
 
1.0%
a000069078 2
 
1.0%
a000045835 2
 
1.0%
a000068696 2
 
1.0%
Other values (163) 174
87.4%
2023-12-10T15:16:45.588004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 817
41.1%
1 182
 
9.1%
A 147
 
7.4%
6 144
 
7.2%
7 106
 
5.3%
4 103
 
5.2%
5 92
 
4.6%
9 90
 
4.5%
3 86
 
4.3%
2 86
 
4.3%
Other values (4) 137
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1791
90.0%
Uppercase Letter 199
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 817
45.6%
1 182
 
10.2%
6 144
 
8.0%
7 106
 
5.9%
4 103
 
5.8%
5 92
 
5.1%
9 90
 
5.0%
3 86
 
4.8%
2 86
 
4.8%
8 85
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
A 147
73.9%
B 23
 
11.6%
U 20
 
10.1%
X 9
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1791
90.0%
Latin 199
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 817
45.6%
1 182
 
10.2%
6 144
 
8.0%
7 106
 
5.9%
4 103
 
5.8%
5 92
 
5.1%
9 90
 
5.0%
3 86
 
4.8%
2 86
 
4.8%
8 85
 
4.7%
Latin
ValueCountFrequency (%)
A 147
73.9%
B 23
 
11.6%
U 20
 
10.1%
X 9
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 817
41.1%
1 182
 
9.1%
A 147
 
7.4%
6 144
 
7.2%
7 106
 
5.3%
4 103
 
5.2%
5 92
 
4.6%
9 90
 
4.5%
3 86
 
4.3%
2 86
 
4.3%
Other values (4) 137
 
6.9%

A000083687
Text

Distinct191
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:16:46.020067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1990
Distinct characters14
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

Unique183 ?
Unique (%)92.0%

Sample

1st rowA001020676
2nd rowA000053724
3rd rowA000079369
4th rowB000081042
5th rowU000018502
ValueCountFrequency (%)
a000083365 2
 
1.0%
a000084161 2
 
1.0%
a000081877 2
 
1.0%
a000057871 2
 
1.0%
a000081046 2
 
1.0%
u000026046 2
 
1.0%
a000053745 2
 
1.0%
a000057845 2
 
1.0%
a000025205 1
 
0.5%
u000033615 1
 
0.5%
Other values (181) 181
91.0%
2023-12-10T15:16:46.639556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 863
43.4%
A 142
 
7.1%
8 133
 
6.7%
2 129
 
6.5%
5 122
 
6.1%
1 108
 
5.4%
3 106
 
5.3%
7 104
 
5.2%
4 93
 
4.7%
6 67
 
3.4%
Other values (4) 123
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1791
90.0%
Uppercase Letter 199
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 863
48.2%
8 133
 
7.4%
2 129
 
7.2%
5 122
 
6.8%
1 108
 
6.0%
3 106
 
5.9%
7 104
 
5.8%
4 93
 
5.2%
6 67
 
3.7%
9 66
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
A 142
71.4%
B 28
 
14.1%
U 20
 
10.1%
X 9
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1791
90.0%
Latin 199
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 863
48.2%
8 133
 
7.4%
2 129
 
7.2%
5 122
 
6.8%
1 108
 
6.0%
3 106
 
5.9%
7 104
 
5.8%
4 93
 
5.2%
6 67
 
3.7%
9 66
 
3.7%
Latin
ValueCountFrequency (%)
A 142
71.4%
B 28
 
14.1%
U 20
 
10.1%
X 9
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 863
43.4%
A 142
 
7.1%
8 133
 
6.7%
2 129
 
6.5%
5 122
 
6.1%
1 108
 
5.4%
3 106
 
5.3%
7 104
 
5.2%
4 93
 
4.7%
6 67
 
3.4%
Other values (4) 123
 
6.2%

1168011000004900000
Real number (ℝ)

HIGH CORRELATION 

Distinct172
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.153621 × 1018
Minimum1.1305101 × 1018
Maximum1.1680118 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:16:46.903594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1305101 × 1018
5-th percentile1.1305101 × 1018
Q11.1500102 × 1018
median1.1500105 × 1018
Q31.1680104 × 1018
95-th percentile1.1680115 × 1018
Maximum1.1680118 × 1018
Range3.75017 × 1016
Interquartile range (IQR)1.80002 × 1016

Descriptive statistics

Standard deviation1.3742972 × 1016
Coefficient of variation (CV)0.0119129
Kurtosis-1.0029778
Mean1.153621 × 1018
Median Absolute Deviation (MAD)1.79998 × 1016
Skewness-0.43676663
Sum8.2096576 × 1018
Variance1.8886927 × 1032
MonotonicityNot monotonic
2023-12-10T15:16:47.187997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1168010600003160000 5
 
2.5%
1150010300011650000 3
 
1.5%
1150010400014820000 3
 
1.5%
1130510100013530000 2
 
1.0%
1150010300011590000 2
 
1.0%
1130510200002370000 2
 
1.0%
1168011400007410000 2
 
1.0%
1168010400000430000 2
 
1.0%
1150010200005620005 2
 
1.0%
1150010500007410000 2
 
1.0%
Other values (162) 174
87.4%
ValueCountFrequency (%)
1130510100000770017 1
0.5%
1130510100000840039 1
0.5%
1130510100001600003 1
0.5%
1130510100002230031 1
0.5%
1130510100002580404 1
0.5%
1130510100003420001 1
0.5%
1130510100007670051 1
0.5%
1130510100007911982 1
0.5%
1130510100008110000 1
0.5%
1130510100008130000 1
0.5%
ValueCountFrequency (%)
1168011800008690000 1
0.5%
1168011800005380003 1
0.5%
1168011800005270000 1
0.5%
1168011800004670000 1
0.5%
1168011800004640000 2
1.0%
1168011800000910005 1
0.5%
1168011500007990000 1
0.5%
1168011500007460000 1
0.5%
1168011500007360000 1
0.5%
1168011500007120000 1
0.5%

1168011000104890000004921
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.153621 × 1024
Minimum1.1305101 × 1024
Maximum1.1680118 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:16:47.475044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1305101 × 1024
5-th percentile1.1305101 × 1024
Q11.1500102 × 1024
median1.1500105 × 1024
Q31.1680104 × 1024
95-th percentile1.1680115 × 1024
Maximum1.1680118 × 1024
Range3.75017 × 1022
Interquartile range (IQR)1.80002 × 1022

Descriptive statistics

Standard deviation1.3742971 × 1022
Coefficient of variation (CV)0.011912899
Kurtosis-1.002977
Mean1.153621 × 1024
Median Absolute Deviation (MAD)1.79998 × 1022
Skewness-0.43676733
Sum2.2957059 × 1026
Variance1.8886925 × 1044
MonotonicityNot monotonic
2023-12-10T15:16:47.776079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.16801060010316e+24 5
 
2.5%
1.15001030010424e+24 4
 
2.0%
1.15001040011482e+24 3
 
1.5%
1.15001030011025e+24 3
 
1.5%
1.16801010010754e+24 3
 
1.5%
1.16801030010185e+24 2
 
1.0%
1.15001070010067e+24 2
 
1.0%
1.16801030010012e+24 2
 
1.0%
1.16801030010656e+24 2
 
1.0%
1.15001050010741e+24 2
 
1.0%
Other values (155) 171
85.9%
ValueCountFrequency (%)
1.13051010010077e+24 1
0.5%
1.13051010010084e+24 1
0.5%
1.1305101001016e+24 1
0.5%
1.13051010010223e+24 1
0.5%
1.1305101001025804e+24 1
0.5%
1.13051010010342e+24 1
0.5%
1.13051010010767e+24 1
0.5%
1.130510100107912e+24 1
0.5%
1.13051010010811e+24 1
0.5%
1.13051010010813e+24 1
0.5%
ValueCountFrequency (%)
1.16801180010869e+24 1
0.5%
1.16801180010538e+24 1
0.5%
1.16801180010527e+24 1
0.5%
1.16801180010467e+24 1
0.5%
1.16801180010464e+24 2
1.0%
1.16801180010091e+24 1
0.5%
1.16801150010746e+24 1
0.5%
1.16801150010736e+24 1
0.5%
1.16801150010712e+24 1
0.5%
1.16801150010707e+24 1
0.5%

서울특별시 강남구 압구정동 490번지 한양1차아파트 6동 502호
Text

UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:16:48.294160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length40
Mean length36.663317
Min length26

Characters and Unicode

Total characters7296
Distinct characters208
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

Unique199 ?
Unique (%)100.0%

Sample

1st row서울특별시 강북구 미아동 1353번지 에스케이북한산시티아파트 151동 2012호
2nd row서울특별시 강서구 등촌동 687번지 등촌주공1단지아파트 105동 507호
3rd row서울특별시 강남구 역삼동 628-1번지 한양럭스 303호
4th row서울특별시 강남구 청담동 40-13번지 드림빌 1동 402호
5th row서울특별시 강서구 화곡동 1165번지 강서힐스테이트 121동 302호
ValueCountFrequency (%)
서울특별시 199
 
14.9%
강서구 82
 
6.2%
강남구 80
 
6.0%
강북구 37
 
2.8%
화곡동 31
 
2.3%
미아동 15
 
1.1%
수유동 15
 
1.1%
등촌동 15
 
1.1%
역삼동 15
 
1.1%
101동 14
 
1.1%
Other values (522) 830
62.3%
2023-12-10T15:16:48.981936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1134
 
15.5%
1 436
 
6.0%
동 360
 
4.9%
0 338
 
4.6%
서 294
 
4.0%
지 246
 
3.4%
2 238
 
3.3%
강 206
 
2.8%
번 205
 
2.8%
구 204
 
2.8%
Other values (198) 3635
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4196
57.5%
Decimal Number 1860
25.5%
Space Separator 1134
 
15.5%
Dash Punctuation 95
 
1.3%
Uppercase Letter 11
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 360
 
8.6%
서 294
 
7.0%
지 246
 
5.9%
강 206
 
4.9%
번 205
 
4.9%
구 204
 
4.9%
시 203
 
4.8%
별 199
 
4.7%
특 199
 
4.7%
울 199
 
4.7%
Other values (184) 1881
44.8%
Decimal Number
ValueCountFrequency (%)
1 436
23.4%
0 338
18.2%
2 238
12.8%
3 158
 
8.5%
4 156
 
8.4%
5 152
 
8.2%
6 106
 
5.7%
7 106
 
5.7%
8 95
 
5.1%
9 75
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
B 8
72.7%
A 3
 
27.3%
Space Separator
ValueCountFrequency (%)
1134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4196
57.5%
Common 3089
42.3%
Latin 11
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 360
 
8.6%
서 294
 
7.0%
지 246
 
5.9%
강 206
 
4.9%
번 205
 
4.9%
구 204
 
4.9%
시 203
 
4.8%
별 199
 
4.7%
특 199
 
4.7%
울 199
 
4.7%
Other values (184) 1881
44.8%
Common
ValueCountFrequency (%)
1134
36.7%
1 436
 
14.1%
0 338
 
10.9%
2 238
 
7.7%
3 158
 
5.1%
4 156
 
5.1%
5 152
 
4.9%
6 106
 
3.4%
7 106
 
3.4%
- 95
 
3.1%
Other values (2) 170
 
5.5%
Latin
ValueCountFrequency (%)
B 8
72.7%
A 3
 
27.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4196
57.5%
ASCII 3100
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1134
36.6%
1 436
 
14.1%
0 338
 
10.9%
2 238
 
7.7%
3 158
 
5.1%
4 156
 
5.0%
5 152
 
4.9%
6 106
 
3.4%
7 106
 
3.4%
- 95
 
3.1%
Other values (4) 181
 
5.8%
Hangul
ValueCountFrequency (%)
동 360
 
8.6%
서 294
 
7.0%
지 246
 
5.9%
강 206
 
4.9%
번 205
 
4.9%
구 204
 
4.9%
시 203
 
4.8%
별 199
 
4.7%
특 199
 
4.7%
울 199
 
4.7%
Other values (184) 1881
44.8%

서울특별시 강남구 압구정로 321 한양1차아파트 6동 502호
Text

UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:16:49.452230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length40
Mean length36.271357
Min length11

Characters and Unicode

Total characters7218
Distinct characters227
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

Unique199 ?
Unique (%)100.0%

Sample

1st row서울특별시 강북구 솔샘로 174 에스케이북한산시티아파트 151동 2012호
2nd row서울특별시 강서구 강서로68길 36 등촌주공1단지아파트 105동 507호
3rd row서울특별시 강남구 테헤란로25길 58 한양럭스 303호
4th row서울특별시 강남구 선릉로134길 7 드림빌 1동 402호
5th row서울특별시 강서구 우현로 67 강서힐스테이트 121동 302호
ValueCountFrequency (%)
서울특별시 198
 
14.9%
강서구 82
 
6.2%
강남구 79
 
5.9%
강북구 37
 
2.8%
101동 14
 
1.1%
201호 14
 
1.1%
202호 12
 
0.9%
삼성로 9
 
0.7%
허준로 7
 
0.5%
102동 7
 
0.5%
Other values (592) 870
65.5%
2023-12-10T15:16:50.173542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1330
 
18.4%
1 440
 
6.1%
0 343
 
4.8%
서 307
 
4.3%
2 251
 
3.5%
강 221
 
3.1%
구 205
 
2.8%
시 202
 
2.8%
로 200
 
2.8%
호 199
 
2.8%
Other values (217) 3520
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4009
55.5%
Decimal Number 1837
25.5%
Space Separator 1330
 
18.4%
Dash Punctuation 31
 
0.4%
Uppercase Letter 11
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 307
 
7.7%
강 221
 
5.5%
구 205
 
5.1%
시 202
 
5.0%
로 200
 
5.0%
호 199
 
5.0%
울 198
 
4.9%
별 198
 
4.9%
특 198
 
4.9%
동 170
 
4.2%
Other values (203) 1911
47.7%
Decimal Number
ValueCountFrequency (%)
1 440
24.0%
0 343
18.7%
2 251
13.7%
3 174
 
9.5%
5 146
 
7.9%
4 130
 
7.1%
6 108
 
5.9%
7 89
 
4.8%
8 84
 
4.6%
9 72
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
B 8
72.7%
A 3
 
27.3%
Space Separator
ValueCountFrequency (%)
1330
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4009
55.5%
Common 3198
44.3%
Latin 11
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 307
 
7.7%
강 221
 
5.5%
구 205
 
5.1%
시 202
 
5.0%
로 200
 
5.0%
호 199
 
5.0%
울 198
 
4.9%
별 198
 
4.9%
특 198
 
4.9%
동 170
 
4.2%
Other values (203) 1911
47.7%
Common
ValueCountFrequency (%)
1330
41.6%
1 440
 
13.8%
0 343
 
10.7%
2 251
 
7.8%
3 174
 
5.4%
5 146
 
4.6%
4 130
 
4.1%
6 108
 
3.4%
7 89
 
2.8%
8 84
 
2.6%
Other values (2) 103
 
3.2%
Latin
ValueCountFrequency (%)
B 8
72.7%
A 3
 
27.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4009
55.5%
ASCII 3209
44.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1330
41.4%
1 440
 
13.7%
0 343
 
10.7%
2 251
 
7.8%
3 174
 
5.4%
5 146
 
4.5%
4 130
 
4.1%
6 108
 
3.4%
7 89
 
2.8%
8 84
 
2.6%
Other values (4) 114
 
3.6%
Hangul
ValueCountFrequency (%)
서 307
 
7.7%
강 221
 
5.5%
구 205
 
5.1%
시 202
 
5.0%
로 200
 
5.0%
호 199
 
5.0%
울 198
 
4.9%
별 198
 
4.9%
특 198
 
4.9%
동 170
 
4.2%
Other values (203) 1911
47.7%

315235
Real number (ℝ)

HIGH CORRELATION 

Distinct189
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308384.08
Minimum294813
Maximum320837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:16:50.528595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum294813
5-th percentile295804.8
Q1298206.5
median313541
Q3315953
95-th percentile318854
Maximum320837
Range26024
Interquartile range (IQR)17746.5

Descriptive statistics

Standard deviation9020.6119
Coefficient of variation (CV)0.029251224
Kurtosis-1.7560251
Mean308384.08
Median Absolute Deviation (MAD)4358
Skewness-0.30376436
Sum61368432
Variance81371439
MonotonicityNot monotonic
2023-12-10T15:16:50.809116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
313199 2
 
1.0%
298291 2
 
1.0%
299149 2
 
1.0%
316065 2
 
1.0%
315674 2
 
1.0%
312901 2
 
1.0%
317448 2
 
1.0%
299571 2
 
1.0%
318854 2
 
1.0%
314815 2
 
1.0%
Other values (179) 179
89.9%
ValueCountFrequency (%)
294813 1
0.5%
295032 1
0.5%
295063 1
0.5%
295184 1
0.5%
295298 1
0.5%
295350 1
0.5%
295427 1
0.5%
295463 1
0.5%
295687 1
0.5%
295803 1
0.5%
ValueCountFrequency (%)
320837 1
0.5%
320618 1
0.5%
320477 1
0.5%
320114 1
0.5%
320020 1
0.5%
319866 1
0.5%
319729 1
0.5%
319417 1
0.5%
319191 1
0.5%
318854 2
1.0%

548014
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean549824.86
Minimum540924
Maximum561461
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:16:51.100689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum540924
5-th percentile543090
Q1544609.5
median549534
Q3551926
95-th percentile559814.2
Maximum561461
Range20537
Interquartile range (IQR)7316.5

Descriptive statistics

Standard deviation5380.8126
Coefficient of variation (CV)0.0097864119
Kurtosis-0.67273468
Mean549824.86
Median Absolute Deviation (MAD)3503
Skewness0.55590433
Sum1.0941515 × 108
Variance28953144
MonotonicityNot monotonic
2023-12-10T15:16:51.365637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
546143 2
 
1.0%
543456 2
 
1.0%
551671 2
 
1.0%
546647 2
 
1.0%
543090 2
 
1.0%
551893 2
 
1.0%
544379 2
 
1.0%
551904 2
 
1.0%
551702 2
 
1.0%
550231 1
 
0.5%
Other values (180) 180
90.5%
ValueCountFrequency (%)
540924 1
0.5%
541922 1
0.5%
542218 1
0.5%
542264 1
0.5%
542449 1
0.5%
542564 1
0.5%
542692 1
0.5%
542925 1
0.5%
542986 1
0.5%
543090 2
1.0%
ValueCountFrequency (%)
561461 1
0.5%
561138 1
0.5%
561094 1
0.5%
560855 1
0.5%
560679 1
0.5%
560588 1
0.5%
560003 1
0.5%
559977 1
0.5%
559950 1
0.5%
559897 1
0.5%

24509
Real number (ℝ)

Distinct189
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210983.95
Minimum11245
Maximum509248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:16:51.585441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11245
5-th percentile14505.8
Q124429
median221539
Q3350535
95-th percentile417174.1
Maximum509248
Range498003
Interquartile range (IQR)326106

Descriptive statistics

Standard deviation156026.32
Coefficient of variation (CV)0.73951745
Kurtosis-1.3447893
Mean210983.95
Median Absolute Deviation (MAD)168868
Skewness-0.036097406
Sum41985807
Variance2.4344211 × 1010
MonotonicityNot monotonic
2023-12-10T15:16:51.837841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31291 2
 
1.0%
260158 2
 
1.0%
413691 2
 
1.0%
13612 2
 
1.0%
221539 2
 
1.0%
24453 2
 
1.0%
272729 2
 
1.0%
15849 2
 
1.0%
18819 2
 
1.0%
271923 2
 
1.0%
Other values (179) 179
89.9%
ValueCountFrequency (%)
11245 1
0.5%
13503 1
0.5%
13601 1
0.5%
13606 1
0.5%
13612 2
1.0%
13625 1
0.5%
14121 1
0.5%
14138 1
0.5%
14441 1
0.5%
14513 1
0.5%
ValueCountFrequency (%)
509248 1
0.5%
509205 1
0.5%
509199 1
0.5%
509196 1
0.5%
501959 1
0.5%
501254 1
0.5%
422436 1
0.5%
419902 1
0.5%
419059 1
0.5%
417463 1
0.5%

A
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
A
134 
V
65 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowV
5th rowA

Common Values

ValueCountFrequency (%)
A 134
67.3%
V 65
32.7%

Length

2023-12-10T15:16:52.117561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:52.271588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 134
67.3%
v 65
32.7%

A001631106
Text

UNIQUE 

Distinct199
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:16:52.702364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1990
Distinct characters13
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

Unique199 ?
Unique (%)100.0%

Sample

1st rowA010064825
2nd rowA001007720
3rd rowA001527563
4th rowB001571571
5th rowU000925353
ValueCountFrequency (%)
a010064825 1
 
0.5%
a001099313 1
 
0.5%
a001592289 1
 
0.5%
a012957390 1
 
0.5%
a011129178 1
 
0.5%
a000425404 1
 
0.5%
a001552465 1
 
0.5%
a011842227 1
 
0.5%
u002241201 1
 
0.5%
a001609336 1
 
0.5%
Other values (189) 189
95.0%
2023-12-10T15:16:53.354341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 548
27.5%
1 301
15.1%
A 149
 
7.5%
5 144
 
7.2%
4 130
 
6.5%
9 122
 
6.1%
2 121
 
6.1%
3 121
 
6.1%
6 106
 
5.3%
8 100
 
5.0%
Other values (3) 148
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1791
90.0%
Uppercase Letter 199
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 548
30.6%
1 301
16.8%
5 144
 
8.0%
4 130
 
7.3%
9 122
 
6.8%
2 121
 
6.8%
3 121
 
6.8%
6 106
 
5.9%
8 100
 
5.6%
7 98
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
A 149
74.9%
B 26
 
13.1%
U 24
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1791
90.0%
Latin 199
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 548
30.6%
1 301
16.8%
5 144
 
8.0%
4 130
 
7.3%
9 122
 
6.8%
2 121
 
6.8%
3 121
 
6.8%
6 106
 
5.9%
8 100
 
5.6%
7 98
 
5.5%
Latin
ValueCountFrequency (%)
A 149
74.9%
B 26
 
13.1%
U 24
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1990
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 548
27.5%
1 301
15.1%
A 149
 
7.5%
5 144
 
7.2%
4 130
 
6.5%
9 122
 
6.1%
2 121
 
6.1%
3 121
 
6.1%
6 106
 
5.3%
8 100
 
5.0%
Other values (3) 148
 
7.4%

한양1차아파트
Text

Distinct165
Distinct (%)82.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:16:53.797521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.7487437
Min length2

Characters and Unicode

Total characters1343
Distinct characters195
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141 ?
Unique (%)70.9%

Sample

1st row에스케이북한산시티아파트
2nd row등촌주공1단지아파트
3rd row한양럭스
4th row드림빌
5th row강서힐스테이트
ValueCountFrequency (%)
명칭없음 6
 
3.0%
은마 5
 
2.5%
동성아파트 3
 
1.5%
강서힐스테이트 3
 
1.5%
가양8단지아파트 3
 
1.5%
개나리래미안 2
 
1.0%
에스케이북한산시티아파트 2
 
1.0%
임광아파트 2
 
1.0%
개포한신아파트 2
 
1.0%
개포래미안포레스트 2
 
1.0%
Other values (155) 169
84.9%
2023-12-10T15:16:54.482727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 95
 
7.1%
트 93
 
6.9%
파 92
 
6.9%
지 47
 
3.5%
단 37
 
2.8%
빌 36
 
2.7%
스 32
 
2.4%
이 28
 
2.1%
우 20
 
1.5%
미 20
 
1.5%
Other values (185) 843
62.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1284
95.6%
Decimal Number 59
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 95
 
7.4%
트 93
 
7.2%
파 92
 
7.2%
지 47
 
3.7%
단 37
 
2.9%
빌 36
 
2.8%
스 32
 
2.5%
이 28
 
2.2%
우 20
 
1.6%
미 20
 
1.6%
Other values (176) 784
61.1%
Decimal Number
ValueCountFrequency (%)
1 16
27.1%
2 9
15.3%
5 8
13.6%
8 6
 
10.2%
6 6
 
10.2%
4 5
 
8.5%
3 5
 
8.5%
7 2
 
3.4%
9 2
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1284
95.6%
Common 59
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 95
 
7.4%
트 93
 
7.2%
파 92
 
7.2%
지 47
 
3.7%
단 37
 
2.9%
빌 36
 
2.8%
스 32
 
2.5%
이 28
 
2.2%
우 20
 
1.6%
미 20
 
1.6%
Other values (176) 784
61.1%
Common
ValueCountFrequency (%)
1 16
27.1%
2 9
15.3%
5 8
13.6%
8 6
 
10.2%
6 6
 
10.2%
4 5
 
8.5%
3 5
 
8.5%
7 2
 
3.4%
9 2
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1284
95.6%
ASCII 59
 
4.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 95
 
7.4%
트 93
 
7.2%
파 92
 
7.2%
지 47
 
3.7%
단 37
 
2.9%
빌 36
 
2.8%
스 32
 
2.5%
이 28
 
2.2%
우 20
 
1.6%
미 20
 
1.6%
Other values (176) 784
61.1%
ASCII
ValueCountFrequency (%)
1 16
27.1%
2 9
15.3%
5 8
13.6%
8 6
 
10.2%
6 6
 
10.2%
4 5
 
8.5%
3 5
 
8.5%
7 2
 
3.4%
9 2
 
3.4%

6
Text

Distinct69
Distinct (%)34.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:16:54.843734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3
Min length1

Characters and Unicode

Total characters597
Distinct characters18
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

Unique35 ?
Unique (%)17.6%

Sample

1st row151
2nd row105
3rd row동명없음
4th row1
5th row121
ValueCountFrequency (%)
동명없음 54
27.1%
101 14
 
7.0%
b 7
 
3.5%
104 7
 
3.5%
102 7
 
3.5%
1 5
 
2.5%
103 5
 
2.5%
107 4
 
2.0%
105 4
 
2.0%
111 3
 
1.5%
Other values (59) 89
44.7%
2023-12-10T15:16:55.385444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 127
21.3%
0 88
14.7%
동 54
9.0%
없 54
9.0%
음 54
9.0%
명 54
9.0%
2 43
 
7.2%
4 23
 
3.9%
3 22
 
3.7%
5 20
 
3.4%
Other values (8) 58
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 369
61.8%
Other Letter 218
36.5%
Uppercase Letter 10
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 127
34.4%
0 88
23.8%
2 43
 
11.7%
4 23
 
6.2%
3 22
 
6.0%
5 20
 
5.4%
7 14
 
3.8%
6 12
 
3.3%
9 10
 
2.7%
8 10
 
2.7%
Other Letter
ValueCountFrequency (%)
동 54
24.8%
없 54
24.8%
음 54
24.8%
명 54
24.8%
다 1
 
0.5%
가 1
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
B 7
70.0%
A 3
30.0%

Most occurring scripts

ValueCountFrequency (%)
Common 369
61.8%
Hangul 218
36.5%
Latin 10
 
1.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 127
34.4%
0 88
23.8%
2 43
 
11.7%
4 23
 
6.2%
3 22
 
6.0%
5 20
 
5.4%
7 14
 
3.8%
6 12
 
3.3%
9 10
 
2.7%
8 10
 
2.7%
Hangul
ValueCountFrequency (%)
동 54
24.8%
없 54
24.8%
음 54
24.8%
명 54
24.8%
다 1
 
0.5%
가 1
 
0.5%
Latin
ValueCountFrequency (%)
B 7
70.0%
A 3
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 379
63.5%
Hangul 218
36.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 127
33.5%
0 88
23.2%
2 43
 
11.3%
4 23
 
6.1%
3 22
 
5.8%
5 20
 
5.3%
7 14
 
3.7%
6 12
 
3.2%
9 10
 
2.6%
8 10
 
2.6%
Other values (2) 10
 
2.6%
Hangul
ValueCountFrequency (%)
동 54
24.8%
없 54
24.8%
음 54
24.8%
명 54
24.8%
다 1
 
0.5%
가 1
 
0.5%

502
Text

Distinct102
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:16:55.827887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2914573
Min length3

Characters and Unicode

Total characters655
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

Unique61 ?
Unique (%)30.7%

Sample

1st row2012
2nd row507
3rd row303
4th row402
5th row302
ValueCountFrequency (%)
201 14
 
7.0%
202 12
 
6.0%
401 7
 
3.5%
302 6
 
3.0%
303 5
 
2.5%
402 5
 
2.5%
102 5
 
2.5%
503 5
 
2.5%
501 4
 
2.0%
1302 4
 
2.0%
Other values (92) 132
66.3%
2023-12-10T15:16:56.527130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 202
30.8%
1 130
19.8%
2 105
16.0%
3 57
 
8.7%
4 42
 
6.4%
5 39
 
6.0%
8 23
 
3.5%
7 23
 
3.5%
6 19
 
2.9%
9 14
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 654
99.8%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 202
30.9%
1 130
19.9%
2 105
16.1%
3 57
 
8.7%
4 42
 
6.4%
5 39
 
6.0%
8 23
 
3.5%
7 23
 
3.5%
6 19
 
2.9%
9 14
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 654
99.8%
Latin 1
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 202
30.9%
1 130
19.9%
2 105
16.1%
3 57
 
8.7%
4 42
 
6.4%
5 39
 
6.0%
8 23
 
3.5%
7 23
 
3.5%
6 19
 
2.9%
9 14
 
2.1%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 655
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 202
30.8%
1 130
19.8%
2 105
16.0%
3 57
 
8.7%
4 42
 
6.4%
5 39
 
6.0%
8 23
 
3.5%
7 23
 
3.5%
6 19
 
2.9%
9 14
 
2.1%

5
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1457286
Minimum-1
Maximum24
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.5%
Memory size1.9 KiB
2023-12-10T15:16:57.133585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median5
Q310
95-th percentile17.1
Maximum24
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.2995015
Coefficient of variation (CV)0.74163207
Kurtosis0.43649184
Mean7.1457286
Median Absolute Deviation (MAD)3
Skewness0.99925056
Sum1422
Variance28.084717
MonotonicityNot monotonic
2023-12-10T15:16:57.383647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 30
15.1%
4 23
11.6%
3 18
9.0%
5 18
9.0%
8 13
 
6.5%
1 12
 
6.0%
10 12
 
6.0%
13 12
 
6.0%
9 12
 
6.0%
6 9
 
4.5%
Other values (14) 40
20.1%
ValueCountFrequency (%)
-1 1
 
0.5%
1 12
 
6.0%
2 30
15.1%
3 18
9.0%
4 23
11.6%
5 18
9.0%
6 9
 
4.5%
7 5
 
2.5%
8 13
6.5%
9 12
 
6.0%
ValueCountFrequency (%)
24 1
 
0.5%
23 1
 
0.5%
22 1
 
0.5%
21 3
1.5%
20 2
1.0%
18 2
1.0%
17 4
2.0%
16 2
1.0%
15 4
2.0%
14 3
1.5%

63.87
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct167
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-434.56955
Minimum-99999
Maximum219.67
Zeros4
Zeros (%)2.0%
Negative1
Negative (%)0.5%
Memory size1.9 KiB
2023-12-10T15:16:57.600641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99999
5-th percentile22.687
Q140.595
median59.96
Q384.945
95-th percentile133.044
Maximum219.67
Range100218.67
Interquartile range (IQR)44.35

Descriptive statistics

Standard deviation7093.6728
Coefficient of variation (CV)-16.323447
Kurtosis198.9896
Mean-434.56955
Median Absolute Deviation (MAD)24.57
Skewness-14.106186
Sum-86479.34
Variance50320193
MonotonicityNot monotonic
2023-12-10T15:16:57.842341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.37 5
 
2.5%
0.0 4
 
2.0%
84.43 4
 
2.0%
84.99 3
 
1.5%
34.44 3
 
1.5%
84.97 3
 
1.5%
40.32 2
 
1.0%
59.97 2
 
1.0%
84.98 2
 
1.0%
83.21 2
 
1.0%
Other values (157) 169
84.9%
ValueCountFrequency (%)
-99999.0 1
 
0.5%
0.0 4
2.0%
13.79 1
 
0.5%
14.18 1
 
0.5%
17.84 1
 
0.5%
18.65 1
 
0.5%
19.24 1
 
0.5%
23.07 1
 
0.5%
23.57 1
 
0.5%
23.76 1
 
0.5%
ValueCountFrequency (%)
219.67 1
0.5%
176.03 1
0.5%
165.43 1
0.5%
164.97 1
0.5%
153.71 1
0.5%
144.55 1
0.5%
144.11 1
0.5%
137.9 1
0.5%
134.94 1
0.5%
134.88 1
0.5%

1549000000
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct180
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7689548 × 108
Minimum-99999
Maximum2.934 × 109
Zeros4
Zeros (%)2.0%
Negative1
Negative (%)0.5%
Memory size1.9 KiB
2023-12-10T15:16:58.071077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99999
5-th percentile86940000
Q11.675 × 108
median3.89 × 108
Q31.0265 × 109
95-th percentile2.065 × 109
Maximum2.934 × 109
Range2.9341 × 109
Interquartile range (IQR)8.59 × 108

Descriptive statistics

Standard deviation6.7359279 × 108
Coefficient of variation (CV)0.99512084
Kurtosis0.45914763
Mean6.7689548 × 108
Median Absolute Deviation (MAD)2.53 × 108
Skewness1.2286073
Sum1.347022 × 1011
Variance4.5372725 × 1017
MonotonicityNot monotonic
2023-12-10T15:16:58.347420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
2.0%
169000000 3
 
1.5%
122000000 3
 
1.5%
174000000 2
 
1.0%
340000000 2
 
1.0%
183000000 2
 
1.0%
136000000 2
 
1.0%
158000000 2
 
1.0%
1638000000 2
 
1.0%
1181000000 2
 
1.0%
Other values (170) 175
87.9%
ValueCountFrequency (%)
-99999 1
 
0.5%
0 4
2.0%
42000000 1
 
0.5%
45300000 1
 
0.5%
57400000 1
 
0.5%
62500000 1
 
0.5%
72000000 1
 
0.5%
88600000 1
 
0.5%
89800000 1
 
0.5%
96700000 1
 
0.5%
ValueCountFrequency (%)
2934000000 1
0.5%
2624000000 1
0.5%
2477000000 1
0.5%
2376000000 1
0.5%
2294000000 1
0.5%
2269000000 1
0.5%
2238000000 1
0.5%
2160000000 1
0.5%
2084000000 1
0.5%
2083000000 1
0.5%

2021
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2021
193 
X
 
5
2018
 
1

Length

Max length4
Median length4
Mean length3.9246231
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row2018
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 193
97.0%
X 5
 
2.5%
2018 1
 
0.5%

Length

2023-12-10T15:16:58.581568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:16:58.765438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 193
97.0%
x 5
 
2.5%
2018 1
 
0.5%

Interactions

2023-12-10T15:16:41.269959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:17.366853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:19.758232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:29.016610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:31.508744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:33.770041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:36.686766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:38.869170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:41.429676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:17.538556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:20.736599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:29.163425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:31.671837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:33.923127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:36.864484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:38.998890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:42.869148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:18.927417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:22.765041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:30.658478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:33.012522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:35.834877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:38.104426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:40.459714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:43.366022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:19.075129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:23.645121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:30.785984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:33.145602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:35.972705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:38.231085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:40.594395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:43.501116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:19.194767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:24.579823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:30.915182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:33.255044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:36.113819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:38.331191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:40.726125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:43.629232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:19.326024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:25.611388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:31.053390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:33.391827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:36.246187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:38.439100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:40.859321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:43.750948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:19.467351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:26.542250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:31.190284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:33.512034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:36.386090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:38.568189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:40.977885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:43.896176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:19.613541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:27.927779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:31.309814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:33.622778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:36.553216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:38.733596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:16:41.116080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:16:58.885767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1168011000004900000116801100010489000000492131523554801424509A6563.8715490000002021
11680110000049000001.0001.0000.8480.9650.9200.1710.0000.328NaN0.6430.204
11680110001048900000049211.0001.0000.8480.9650.9200.1710.0000.328NaN0.6430.204
3152350.8480.8481.0000.7620.5570.4230.5560.337NaN0.5320.000
5480140.9650.9650.7621.0000.6530.4800.6660.332NaN0.6520.305
245090.9200.9200.5570.6531.0000.3950.8250.020NaN0.4740.000
A0.1710.1710.4230.4800.3951.0000.9150.787NaN0.7940.000
60.0000.0000.5560.6660.8250.9151.0000.750NaN0.8360.955
50.3280.3280.3370.3320.0200.7870.7501.000NaN0.4590.569
63.87NaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaN
15490000000.6430.6430.5320.6520.4740.7940.8360.459NaN1.0000.000
20210.2040.2040.0000.3050.0000.0000.9550.569NaN0.0001.000
2023-12-10T15:16:59.113025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2021A
20211.0000.000
A0.0001.000
2023-12-10T15:16:59.267762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1168011000004900000116801100010489000000492131523554801424509563.871549000000A2021
11680110000049000001.0000.9990.508-0.8280.0510.3090.2560.6150.2900.061
11680110001048900000049210.9991.0000.509-0.8290.0420.3060.2460.6040.3630.000
3152350.5080.5091.000-0.6020.1390.2350.2090.4670.4400.000
548014-0.828-0.829-0.6021.000-0.132-0.262-0.217-0.5280.3640.168
245090.0510.0420.139-0.1321.0000.0400.2140.2580.3860.000
50.3090.3060.235-0.2620.0401.0000.2270.4250.6450.406
63.870.2560.2460.209-0.2170.2140.2271.0000.7700.0000.432
15490000000.6150.6040.467-0.5280.2580.4250.7701.0000.6180.000
A0.2900.3630.4400.3640.3860.6450.0000.6181.0000.000
20210.0610.0000.0000.1680.0000.4060.4320.0000.0001.000

Missing values

2023-12-10T15:16:44.134446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:16:44.520444image/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.

Sample

A000071239A00008368711680110000049000001168011000104890000004921서울특별시 강남구 압구정동 490번지 한양1차아파트 6동 502호서울특별시 강남구 압구정로 321 한양1차아파트 6동 502호31523554801424509AA001631106한양1차아파트6502563.8715490000002021
0A000019867A00102067611305101000135300001130510100113530000024590서울특별시 강북구 미아동 1353번지 에스케이북한산시티아파트 151동 2012호서울특별시 강북구 솔샘로 174 에스케이북한산시티아파트 151동 2012호313199557900216097AA010064825에스케이북한산시티아파트15120122032.791020000002018
1A000046002A00005372411500102000068700001150010200106870000024999서울특별시 강서구 등촌동 687번지 등촌주공1단지아파트 105동 507호서울특별시 강서구 강서로68길 36 등촌주공1단지아파트 105동 507호29787455208614925AA001007720등촌주공1단지아파트105507526.371650000002021
2A000067861A00007936911680101000062800011168010100106280001027039서울특별시 강남구 역삼동 628-1번지 한양럭스 303호서울특별시 강남구 테헤란로25길 58 한양럭스 303호314836545117275665AA001527563한양럭스동명없음303344.02370000002021
3B000069075B00008104211680104000004000131168010400100400013000001서울특별시 강남구 청담동 40-13번지 드림빌 1동 402호서울특별시 강남구 선릉로134길 7 드림빌 1동 402호315462546734270344VB001571571드림빌1402452.792680000002021
4U000011968U00001850211500103000116500001150010300110250033029418서울특별시 강서구 화곡동 1165번지 강서힐스테이트 121동 302호서울특별시 강서구 우현로 67 강서힐스테이트 121동 302호297730550129509196AU000925353강서힐스테이트121302384.998190000002021
5A000049737A00005779911500104000147400001150010400114740000009559서울특별시 강서구 가양동 1474번지 대림아파트 103동 603호서울특별시 강서구 허준로 121 대림아파트 103동 603호29898155223416427AA001070074대림아파트1036036131.47840000002021
6A106006275A00200833311305101000016000031130510100101600003031521서울특별시 강북구 미아동 160-3번지 수유역푸르지오시티 717호서울특별시 강북구 도봉로 290 수유역푸르지오시티 717호314036559547220423AA011306842수유역푸르지오시티동명없음717718.651010000002021
7A106010140A00201432511500103000042400291150010300104240029023618서울특별시 강서구 화곡동 424-29번지 에스엠파크빌 501호서울특별시 강서구 곰달래로19가길 16-10 에스엠파크빌 501호297813548268336178VA011553237에스엠파크빌동명없음501531.271410000002021
8A000048294A00005616611500103000046100041150010300104610004023339서울특별시 강서구 화곡동 461-4번지 화곡예다움아파트 101동 801호서울특별시 강서구 등촌로13자길 79 화곡예다움아파트 101동 801호29891254889213601AA001044088화곡예다움아파트101801864.212400000002021
9A106039798A00205498111680108000021900261168010800102190026007598서울특별시 강남구 논현동 219-26번지 토브미하우스 202호서울특별시 강남구 학동로30길 43-16 토브미하우스 202호31481954593032084VA012517390토브미하우스동명없음202228.132830000002021
A000071239A00008368711680110000049000001168011000104890000004921서울특별시 강남구 압구정동 490번지 한양1차아파트 6동 502호서울특별시 강남구 압구정로 321 한양1차아파트 6동 502호31523554801424509AA001631106한양1차아파트6502563.8715490000002021
189A000019129A00002208711305101000025804041130510100102580404029939서울특별시 강북구 미아동 258-404번지 진한하이츠 401호서울특별시 강북구 오패산로60길 13-3 진한하이츠 401호314584558651220495VA000380598진한하이츠동명없음401444.391310000002021
190A000071397A00008405011680115000073600001168011500107360000001470서울특별시 강남구 수서동 736번지 신동아아파트 707동 912호서울특별시 강남구 광평로47길 17 신동아아파트 707동 912호320477543102151726AA001648467신동아아파트707912949.967980000002021
191A000068696A00008040311680103000018500001168010300101850000019804서울특별시 강남구 개포동 185번지 주공6단지아파트 609동 1003호서울특별시 강남구 개포로 516 주공6단지아파트 609동 1003호318121543213344264AA001553228주공6단지아파트60910031083.2117360000002021
192A000071239A00008368811680110000049000001168011000104890000004921서울특별시 강남구 압구정동 490번지 한양1차아파트 5동 1005호서울특별시 강남구 압구정로 321 한양1차아파트 5동 1005호31521754796824519AA001631035한양1차아파트510051078.0517880000002021
193A000071495A00008425711680118000086900001168011800108690000000108서울특별시 강남구 도곡동 869번지 삼익아파트 1동 1107호서울특별시 강남구 논현로 218 삼익아파트 1동 1107호315499543661351025AA001662263삼익아파트1110710153.7116140000002021
194A106051215A00207234611305103000070900091130510300107090009010696서울특별시 강북구 수유동 709-9번지 명진주택 B동 503호서울특별시 강북구 노해로35가길 19 명진주택 B동 503호313833561094213440VA013066047명진주택B503551.32030000002021
195A106054522A00207683511680103000128100001168010300101380000019580서울특별시 강남구 개포동 1281번지 디에이치아너힐즈 316동 1702호서울특별시 강남구 삼성로 11 디에이치아너힐즈 316동 1702호317899542692280252AA013177959디에이치아너힐즈31617021759.8715600000002021
196A000068257A00007979311680101000073900121168010100107390012024137서울특별시 강남구 역삼동 739-12번지 뉴현대빌라 303호서울특별시 강남구 테헤란로22길 27 뉴현대빌라 303호314944544438355764VA001535093뉴현대빌라동명없음303378.844570000002021
197A000071019A00008336511680108000021200131168010800102120013000001서울특별시 강남구 논현동 212-13번지 논현동월드메르디앙 101동 405호서울특별시 강남구 학동로38길 22 논현동월드메르디앙 101동 405호31481554614331291AA001619109논현동월드메르디앙1014054115.7510360000002021
198A000069078A00008104611680104000004300001168010400100430000000001서울특별시 강남구 청담동 43번지 청담우방 101동 1005호서울특별시 강남구 학동로 409 청담우방 101동 1005호31567454664724453AA001571855청담우방10110051059.979490000002021