Overview

Dataset statistics

Number of variables15
Number of observations199
Missing cells16
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.6 KiB
Average record size in memory126.7 B

Variable types

Text5
Categorical7
Numeric3

Alerts

SIDO_CD has constant value ""Constant
SIDO_NM has constant value ""Constant
SIGUNGU_CD has constant value ""Constant
SIGUNGU_NM has constant value ""Constant
BCODE is highly overall correlated with HCODE and 2 other fieldsHigh correlation
HNAME is highly overall correlated with HCODE and 3 other fieldsHigh correlation
BNAME is highly overall correlated with HCODE and 2 other fieldsHigh correlation
HCODE is highly overall correlated with POST_CD and 3 other fieldsHigh correlation
POST_CD is highly overall correlated with HCODE and 1 other fieldsHigh correlation
SMA_CATE_CD has 8 (4.0%) missing valuesMissing
SMA_CATE_NM has 8 (4.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 06:36:36.882371
Analysis finished2023-12-10 06:36:40.729310
Duration3.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BIG_CATE_NM
Text

Distinct62
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:36:40.940226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9
Mean length6.7788945
Min length2

Characters and Unicode

Total characters1349
Distinct characters157
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

Unique21 ?
Unique (%)10.6%

Sample

1st row음/식료품소매
2nd row음/식료품소매
3rd row음/식료품소매
4th row음/식료품소매
5th row음/식료품소매
ValueCountFrequency (%)
종합소매점 15
 
7.5%
음/식료품소매 13
 
6.5%
의복의류 13
 
6.5%
가방/신발/액세서리 10
 
5.0%
가정/주방/인테리어 10
 
5.0%
한식 9
 
4.5%
사무/문구/컴퓨터 6
 
3.0%
사진/광학/정밀기기소매 5
 
2.5%
건강/미용식품 5
 
2.5%
병원 5
 
2.5%
Other values (52) 108
54.3%
2023-12-10T15:36:41.491482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 180
 
13.3%
매 57
 
4.2%
소 54
 
4.0%
품 48
 
3.6%
식 38
 
2.8%
가 31
 
2.3%
의 30
 
2.2%
기 27
 
2.0%
방 26
 
1.9%
리 26
 
1.9%
Other values (147) 832
61.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1158
85.8%
Other Punctuation 180
 
13.3%
Dash Punctuation 7
 
0.5%
Uppercase Letter 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
매 57
 
4.9%
소 54
 
4.7%
품 48
 
4.1%
식 38
 
3.3%
가 31
 
2.7%
의 30
 
2.6%
기 27
 
2.3%
방 26
 
2.2%
리 26
 
2.2%
서 26
 
2.2%
Other values (143) 795
68.7%
Uppercase Letter
ValueCountFrequency (%)
P 2
50.0%
C 2
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 180
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1158
85.8%
Common 187
 
13.9%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
매 57
 
4.9%
소 54
 
4.7%
품 48
 
4.1%
식 38
 
3.3%
가 31
 
2.7%
의 30
 
2.6%
기 27
 
2.3%
방 26
 
2.2%
리 26
 
2.2%
서 26
 
2.2%
Other values (143) 795
68.7%
Common
ValueCountFrequency (%)
/ 180
96.3%
- 7
 
3.7%
Latin
ValueCountFrequency (%)
P 2
50.0%
C 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1158
85.8%
ASCII 191
 
14.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 180
94.2%
- 7
 
3.7%
P 2
 
1.0%
C 2
 
1.0%
Hangul
ValueCountFrequency (%)
매 57
 
4.9%
소 54
 
4.7%
품 48
 
4.1%
식 38
 
3.3%
가 31
 
2.7%
의 30
 
2.6%
기 27
 
2.3%
방 26
 
2.2%
리 26
 
2.2%
서 26
 
2.2%
Other values (143) 795
68.7%

MID_CATE_CD
Text

Distinct162
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:36:41.998174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1194
Distinct characters19
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

Unique125 ?
Unique (%)62.8%

Sample

1st rowD01A01
2nd rowD01A06
3rd rowD01A07
4th rowD01A11
5th rowD01A12
ValueCountFrequency (%)
d01a01 2
 
1.0%
d13a01 2
 
1.0%
d13a03 2
 
1.0%
d06a05 2
 
1.0%
d23a06 2
 
1.0%
d06a07 2
 
1.0%
d07a01 2
 
1.0%
d08a01 2
 
1.0%
d10a04 2
 
1.0%
d06a10 2
 
1.0%
Other values (152) 179
89.9%
2023-12-10T15:36:42.694438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 325
27.2%
A 188
15.7%
1 174
14.6%
D 117
 
9.8%
2 71
 
5.9%
3 56
 
4.7%
5 38
 
3.2%
6 35
 
2.9%
7 32
 
2.7%
Q 32
 
2.7%
Other values (9) 126
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 796
66.7%
Uppercase Letter 398
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 325
40.8%
1 174
21.9%
2 71
 
8.9%
3 56
 
7.0%
5 38
 
4.8%
6 35
 
4.4%
7 32
 
4.0%
4 31
 
3.9%
8 21
 
2.6%
9 13
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
A 188
47.2%
D 117
29.4%
Q 32
 
8.0%
F 25
 
6.3%
B 11
 
2.8%
R 11
 
2.8%
S 7
 
1.8%
N 5
 
1.3%
L 2
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 796
66.7%
Latin 398
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 325
40.8%
1 174
21.9%
2 71
 
8.9%
3 56
 
7.0%
5 38
 
4.8%
6 35
 
4.4%
7 32
 
4.0%
4 31
 
3.9%
8 21
 
2.6%
9 13
 
1.6%
Latin
ValueCountFrequency (%)
A 188
47.2%
D 117
29.4%
Q 32
 
8.0%
F 25
 
6.3%
B 11
 
2.8%
R 11
 
2.8%
S 7
 
1.8%
N 5
 
1.3%
L 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1194
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 325
27.2%
A 188
15.7%
1 174
14.6%
D 117
 
9.8%
2 71
 
5.9%
3 56
 
4.7%
5 38
 
3.2%
6 35
 
2.9%
7 32
 
2.7%
Q 32
 
2.7%
Other values (9) 126
 
10.6%

BNAME
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
청운효자동
139 
사직동
60 

Length

Max length5
Median length5
Mean length4.3969849
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청운효자동 139
69.8%
사직동 60
30.2%

Length

2023-12-10T15:36:42.930341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:43.124819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청운효자동 139
69.8%
사직동 60
30.2%

HCODE
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1110109 × 109
Minimum1.1110101 × 109
Maximum1.1110121 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:36:43.283465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110101 × 109
5-th percentile1.1110101 × 109
Q11.1110105 × 109
median1.1110108 × 109
Q31.1110112 × 109
95-th percentile1.1110119 × 109
Maximum1.1110121 × 109
Range2000
Interquartile range (IQR)700

Descriptive statistics

Standard deviation546.28424
Coefficient of variation (CV)4.9170017 × 10-7
Kurtosis-0.52557257
Mean1.1110109 × 109
Median Absolute Deviation (MAD)400
Skewness0.47222561
Sum2.2109117 × 1011
Variance298426.48
MonotonicityNot monotonic
2023-12-10T15:36:43.471639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1111010800 45
22.6%
1111010100 20
10.1%
1111010400 13
 
6.5%
1111010200 13
 
6.5%
1111010500 12
 
6.0%
1111011100 11
 
5.5%
1111010900 11
 
5.5%
1111011000 9
 
4.5%
1111011300 9
 
4.5%
1111011700 8
 
4.0%
Other values (10) 48
24.1%
ValueCountFrequency (%)
1111010100 20
10.1%
1111010200 13
 
6.5%
1111010300 3
 
1.5%
1111010400 13
 
6.5%
1111010500 12
 
6.0%
1111010600 4
 
2.0%
1111010700 5
 
2.5%
1111010800 45
22.6%
1111010900 11
 
5.5%
1111011000 9
 
4.5%
ValueCountFrequency (%)
1111012100 3
 
1.5%
1111012000 6
3.0%
1111011900 7
3.5%
1111011800 4
 
2.0%
1111011700 8
4.0%
1111011500 7
3.5%
1111011400 3
 
1.5%
1111011300 9
4.5%
1111011200 6
3.0%
1111011100 11
5.5%

HNAME
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
통인동
45 
청운동
20 
효자동
13 
신교동
13 
창성동
12 
Other values (15)
96 

Length

Max length5
Median length3
Mean length3.0904523
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row청운동
2nd row청운동
3rd row통인동
4th row통인동
5th row통인동

Common Values

ValueCountFrequency (%)
통인동 45
22.6%
청운동 20
10.1%
효자동 13
 
6.5%
신교동 13
 
6.5%
창성동 12
 
6.0%
옥인동 11
 
5.5%
누상동 11
 
5.5%
누하동 9
 
4.5%
필운동 9
 
4.5%
당주동 8
 
4.0%
Other values (10) 48
24.1%

Length

2023-12-10T15:36:43.689584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
통인동 45
22.6%
청운동 20
10.1%
효자동 13
 
6.5%
신교동 13
 
6.5%
창성동 12
 
6.0%
옥인동 11
 
5.5%
누상동 11
 
5.5%
누하동 9
 
4.5%
필운동 9
 
4.5%
당주동 8
 
4.0%
Other values (10) 48
24.1%

POST_CD
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110169.65
Minimum110030
Maximum110999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:36:43.890115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110030
5-th percentile110030
Q1110034
median110043
Q3110052.5
95-th percentile110807.4
Maximum110999
Range969
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation287.9826
Coefficient of variation (CV)0.002613992
Kurtosis1.3309223
Mean110169.65
Median Absolute Deviation (MAD)9
Skewness1.7980989
Sum21923761
Variance82933.975
MonotonicityNot monotonic
2023-12-10T15:36:44.187406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
110043 45
22.6%
110030 20
 
10.1%
110033 13
 
6.5%
110032 13
 
6.5%
110034 12
 
6.0%
110035 11
 
5.5%
110041 11
 
5.5%
110044 9
 
4.5%
110054 8
 
4.0%
110045 6
 
3.0%
Other values (24) 51
25.6%
ValueCountFrequency (%)
110030 20
10.1%
110031 3
 
1.5%
110032 13
 
6.5%
110033 13
 
6.5%
110034 12
 
6.0%
110035 11
 
5.5%
110040 4
 
2.0%
110041 11
 
5.5%
110043 45
22.6%
110044 9
 
4.5%
ValueCountFrequency (%)
110999 2
 
1.0%
110872 2
 
1.0%
110871 1
 
0.5%
110822 3
1.5%
110820 2
 
1.0%
110806 3
1.5%
110805 6
3.0%
110796 1
 
0.5%
110783 1
 
0.5%
110766 2
 
1.0%

CNT
Real number (ℝ)

Distinct20
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.919598
Minimum1
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:36:44.390020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile15.1
Maximum64
Range63
Interquartile range (IQR)3

Descriptive statistics

Standard deviation7.1353132
Coefficient of variation (CV)1.8204197
Kurtosis34.476106
Mean3.919598
Median Absolute Deviation (MAD)1
Skewness5.2860836
Sum780
Variance50.912695
MonotonicityNot monotonic
2023-12-10T15:36:44.587785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 88
44.2%
2 39
19.6%
3 21
 
10.6%
4 12
 
6.0%
5 9
 
4.5%
9 5
 
2.5%
8 5
 
2.5%
6 4
 
2.0%
7 4
 
2.0%
21 2
 
1.0%
Other values (10) 10
 
5.0%
ValueCountFrequency (%)
1 88
44.2%
2 39
19.6%
3 21
 
10.6%
4 12
 
6.0%
5 9
 
4.5%
6 4
 
2.0%
7 4
 
2.0%
8 5
 
2.5%
9 5
 
2.5%
13 1
 
0.5%
ValueCountFrequency (%)
64 1
0.5%
47 1
0.5%
39 1
0.5%
26 1
0.5%
24 1
0.5%
21 2
1.0%
18 1
0.5%
17 1
0.5%
16 1
0.5%
15 1
0.5%

MID_CATE_NM
Text

Distinct162
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:36:45.524704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.8693467
Min length2

Characters and Unicode

Total characters1168
Distinct characters250
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

Unique125 ?
Unique (%)62.8%

Sample

1st row식료품점
2nd row육류소매
3rd row반찬가게
4th row건어물상
5th row수산물소매
ValueCountFrequency (%)
식료품점 2
 
1.0%
서점 2
 
1.0%
서적/신문소매 2
 
1.0%
액세서리판매 2
 
1.0%
자동차부품판매 2
 
1.0%
양품점 2
 
1.0%
인테리어/욕실용품/커튼 2
 
1.0%
사무/문구용품 2
 
1.0%
건강식품판매 2
 
1.0%
가방/가죽제품소매 2
 
1.0%
Other values (152) 179
89.9%
2023-12-10T15:36:47.443999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 82
 
7.0%
매 60
 
5.1%
품 37
 
3.2%
판 35
 
3.0%
점 28
 
2.4%
소 26
 
2.2%
문 21
 
1.8%
전 21
 
1.8%
용 20
 
1.7%
기 20
 
1.7%
Other values (240) 818
70.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1073
91.9%
Other Punctuation 82
 
7.0%
Dash Punctuation 9
 
0.8%
Uppercase Letter 2
 
0.2%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
매 60
 
5.6%
품 37
 
3.4%
판 35
 
3.3%
점 28
 
2.6%
소 26
 
2.4%
문 21
 
2.0%
전 21
 
2.0%
용 20
 
1.9%
기 20
 
1.9%
가 16
 
1.5%
Other values (234) 789
73.5%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 82
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1073
91.9%
Common 93
 
8.0%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
매 60
 
5.6%
품 37
 
3.4%
판 35
 
3.3%
점 28
 
2.6%
소 26
 
2.4%
문 21
 
2.0%
전 21
 
2.0%
용 20
 
1.9%
기 20
 
1.9%
가 16
 
1.5%
Other values (234) 789
73.5%
Common
ValueCountFrequency (%)
/ 82
88.2%
- 9
 
9.7%
) 1
 
1.1%
( 1
 
1.1%
Latin
ValueCountFrequency (%)
C 1
50.0%
P 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1073
91.9%
ASCII 95
 
8.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 82
86.3%
- 9
 
9.5%
) 1
 
1.1%
( 1
 
1.1%
C 1
 
1.1%
P 1
 
1.1%
Hangul
ValueCountFrequency (%)
매 60
 
5.6%
품 37
 
3.4%
판 35
 
3.3%
점 28
 
2.6%
소 26
 
2.4%
문 21
 
2.0%
전 21
 
2.0%
용 20
 
1.9%
기 20
 
1.9%
가 16
 
1.5%
Other values (234) 789
73.5%

SMA_CATE_CD
Text

MISSING 

Distinct97
Distinct (%)50.8%
Missing8
Missing (%)4.0%
Memory size1.7 KiB
2023-12-10T15:36:47.901075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1146
Distinct characters21
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

Unique47 ?
Unique (%)24.6%

Sample

1st rowG47219
2nd rowG47212
3rd rowG47219
4th rowG47213
5th rowG47213
ValueCountFrequency (%)
i56111 12
 
6.3%
g47419 6
 
3.1%
g47190 5
 
2.6%
g47216 5
 
2.6%
g47519 5
 
2.6%
g47416 5
 
2.6%
g47219 5
 
2.6%
g47611 4
 
2.1%
i56114 4
 
2.1%
g47841 3
 
1.6%
Other values (87) 137
71.7%
2023-12-10T15:36:48.632955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 219
19.1%
4 152
13.3%
7 119
10.4%
G 116
10.1%
2 99
8.6%
9 93
8.1%
5 80
 
7.0%
6 76
 
6.6%
3 42
 
3.7%
8 38
 
3.3%
Other values (11) 112
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 955
83.3%
Uppercase Letter 191
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 116
60.7%
I 33
 
17.3%
S 11
 
5.8%
P 9
 
4.7%
Q 6
 
3.1%
R 4
 
2.1%
N 3
 
1.6%
C 3
 
1.6%
M 3
 
1.6%
L 2
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 219
22.9%
4 152
15.9%
7 119
12.5%
2 99
10.4%
9 93
9.7%
5 80
 
8.4%
6 76
 
8.0%
3 42
 
4.4%
8 38
 
4.0%
0 37
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 955
83.3%
Latin 191
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 116
60.7%
I 33
 
17.3%
S 11
 
5.8%
P 9
 
4.7%
Q 6
 
3.1%
R 4
 
2.1%
N 3
 
1.6%
C 3
 
1.6%
M 3
 
1.6%
L 2
 
1.0%
Common
ValueCountFrequency (%)
1 219
22.9%
4 152
15.9%
7 119
12.5%
2 99
10.4%
9 93
9.7%
5 80
 
8.4%
6 76
 
8.0%
3 42
 
4.4%
8 38
 
4.0%
0 37
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 219
19.1%
4 152
13.3%
7 119
10.4%
G 116
10.1%
2 99
8.6%
9 93
8.1%
5 80
 
7.0%
6 76
 
6.6%
3 42
 
3.7%
8 38
 
3.3%
Other values (11) 112
9.8%

SMA_CATE_NM
Text

MISSING 

Distinct97
Distinct (%)50.8%
Missing8
Missing (%)4.0%
Memory size1.7 KiB
2023-12-10T15:36:49.178782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length10.947644
Min length3

Characters and Unicode

Total characters2091
Distinct characters176
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

Unique47 ?
Unique (%)24.6%

Sample

1st row기타 식료품 소매업
2nd row육류 소매업
3rd row기타 식료품 소매업
4th row수산물 소매업
5th row수산물 소매업
ValueCountFrequency (%)
소매업 106
 
17.3%
및 72
 
11.7%
기타 50
 
8.1%
음식점업 21
 
3.4%
한식 12
 
2.0%
종합 11
 
1.8%
그외 10
 
1.6%
의복 6
 
1.0%
섬유 6
 
1.0%
직물 6
 
1.0%
Other values (150) 314
51.1%
2023-12-10T15:36:49.958340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
423
20.2%
업 172
 
8.2%
매 110
 
5.3%
소 109
 
5.2%
기 74
 
3.5%
및 72
 
3.4%
식 61
 
2.9%
품 57
 
2.7%
타 52
 
2.5%
용 36
 
1.7%
Other values (166) 925
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1647
78.8%
Space Separator 423
 
20.2%
Other Punctuation 21
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 172
 
10.4%
매 110
 
6.7%
소 109
 
6.6%
기 74
 
4.5%
및 72
 
4.4%
식 61
 
3.7%
품 57
 
3.5%
타 52
 
3.2%
용 36
 
2.2%
점 35
 
2.1%
Other values (163) 869
52.8%
Other Punctuation
ValueCountFrequency (%)
. 18
85.7%
· 3
 
14.3%
Space Separator
ValueCountFrequency (%)
423
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1647
78.8%
Common 444
 
21.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 172
 
10.4%
매 110
 
6.7%
소 109
 
6.6%
기 74
 
4.5%
및 72
 
4.4%
식 61
 
3.7%
품 57
 
3.5%
타 52
 
3.2%
용 36
 
2.2%
점 35
 
2.1%
Other values (163) 869
52.8%
Common
ValueCountFrequency (%)
423
95.3%
. 18
 
4.1%
· 3
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1647
78.8%
ASCII 441
 
21.1%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
423
95.9%
. 18
 
4.1%
Hangul
ValueCountFrequency (%)
업 172
 
10.4%
매 110
 
6.7%
소 109
 
6.6%
기 74
 
4.5%
및 72
 
4.4%
식 61
 
3.7%
품 57
 
3.5%
타 52
 
3.2%
용 36
 
2.2%
점 35
 
2.1%
Other values (163) 869
52.8%
None
ValueCountFrequency (%)
· 3
100.0%

SIDO_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
11
199 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 199
100.0%

Length

2023-12-10T15:36:50.206302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:50.383334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 199
100.0%

SIDO_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
서울특별시
199 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 199
100.0%

Length

2023-12-10T15:36:50.560677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:50.838042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 199
100.0%

SIGUNGU_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
11110
199 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11110
2nd row11110
3rd row11110
4th row11110
5th row11110

Common Values

ValueCountFrequency (%)
11110 199
100.0%

Length

2023-12-10T15:36:51.084012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:51.259885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11110 199
100.0%

SIGUNGU_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
종로구
199 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
종로구 199
100.0%

Length

2023-12-10T15:36:51.488161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:51.677195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종로구 199
100.0%

BCODE
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1111051500
139 
1111053000
60 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1111051500
2nd row1111051500
3rd row1111051500
4th row1111051500
5th row1111051500

Common Values

ValueCountFrequency (%)
1111051500 139
69.8%
1111053000 60
30.2%

Length

2023-12-10T15:36:51.842353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:52.104452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1111051500 139
69.8%
1111053000 60
30.2%

Interactions

2023-12-10T15:36:39.484900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:38.450825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:38.990643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:39.687893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:38.649665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:39.167333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:39.839401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:38.825224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:36:39.291769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:36:52.303658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BIG_CATE_NMBNAMEHCODEHNAMEPOST_CDCNTSMA_CATE_CDSMA_CATE_NMBCODE
BIG_CATE_NM1.0000.2120.0000.0000.0000.0000.9990.9990.212
BNAME0.2121.0000.9991.0000.5070.0000.0000.0001.000
HCODE0.0000.9991.0001.0000.7560.0000.3770.3770.999
HNAME0.0001.0001.0001.0000.8490.0000.0000.0001.000
POST_CD0.0000.5070.7560.8491.0000.0410.3370.3370.507
CNT0.0000.0000.0000.0000.0411.0000.0000.0000.000
SMA_CATE_CD0.9990.0000.3770.0000.3370.0001.0001.0000.000
SMA_CATE_NM0.9990.0000.3770.0000.3370.0001.0001.0000.000
BCODE0.2121.0000.9991.0000.5070.0000.0000.0001.000
2023-12-10T15:36:52.533284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
BCODEHNAMEBNAME
BCODE1.0000.9350.988
HNAME0.9351.0000.935
BNAME0.9880.9351.000
2023-12-10T15:36:52.712303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
HCODEPOST_CDCNTBNAMEHNAMEBCODE
HCODE1.0000.8630.0640.9590.9730.959
POST_CD0.8631.0000.0670.3650.6300.365
CNT0.0640.0671.0000.0000.0000.000
BNAME0.9590.3650.0001.0000.9350.988
HNAME0.9730.6300.0000.9351.0000.935
BCODE0.9590.3650.0000.9880.9351.000

Missing values

2023-12-10T15:36:40.056885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:36:40.404455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-10T15:36:40.615342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

BIG_CATE_NMMID_CATE_CDBNAMEHCODEHNAMEPOST_CDCNTMID_CATE_NMSMA_CATE_CDSMA_CATE_NMSIDO_CDSIDO_NMSIGUNGU_CDSIGUNGU_NMBCODE
0음/식료품소매D01A01청운효자동1111010100청운동1100304식료품점G47219기타 식료품 소매업11서울특별시11110종로구1111051500
1음/식료품소매D01A06청운효자동1111010100청운동1100304육류소매G47212육류 소매업11서울특별시11110종로구1111051500
2음/식료품소매D01A07청운효자동1111010800통인동1100438반찬가게G47219기타 식료품 소매업11서울특별시11110종로구1111051500
3음/식료품소매D01A11청운효자동1111010800통인동1100431건어물상G47213수산물 소매업11서울특별시11110종로구1111051500
4음/식료품소매D01A12청운효자동1111010800통인동1100431수산물소매G47213수산물 소매업11서울특별시11110종로구1111051500
5음/식료품소매D01A13청운효자동1111010800통인동1100439청과물소매G47214과실 및 채소 소매업11서울특별시11110종로구1111051500
6음/식료품소매D01A15청운효자동1111010800통인동1100431곡물소매G47211곡물 소매업11서울특별시11110종로구1111051500
7음/식료품소매D01A19청운효자동1111010100청운동1100301생수판매G47221음료 소매업11서울특별시11110종로구1111051500
8음/식료품소매D01A21청운효자동1111011000누하동1108051우유판매G47221음료 소매업11서울특별시11110종로구1111051500
9선물/팬시/기념품D02A01청운효자동1111010500창성동1100341선물용품판매G47842관광 민예품 및 선물용품 소매업11서울특별시11110종로구1111051500
BIG_CATE_NMMID_CATE_CDBNAMEHCODEHNAMEPOST_CDCNTMID_CATE_NMSMA_CATE_CDSMA_CATE_NMSIDO_CDSIDO_NMSIGUNGU_CDSIGUNGU_NMBCODE
189철물/난방/건설자재소매D21A07사직동1111011200체부동1100451열쇠판매/수리G47511철물 및 난방용구 소매업11서울특별시11110종로구1111053000
190페인트/유리제품소매D22A01사직동1111011500사직동1100541페인트/유리제품소매G47519페인트. 유리 및 기타 건설자재 소매업11서울특별시11110종로구1111053000
191자동차/자동차용품D23A01사직동1111011900세종로1108222자동차판매G45110자동차 신품 판매업11서울특별시11110종로구1111053000
192자동차/자동차용품D23A02사직동1111011500사직동1100541중고자동차판매G45120중고 자동차 판매업11서울특별시11110종로구1111053000
193자동차/자동차용품D23A06사직동1111011200체부동1100453자동차부품판매G45219기타 자동차신품 부품 및 내장품 판매업11서울특별시11110종로구1111053000
194중고품소매/교환D24A02사직동1111010700적선동1107562재활용/고물수집G47869기타 중고상품 소매업11서울특별시11110종로구1111053000
195기타판매업D25A23사직동1111012100신문로2가1100625통신판매G47911전자상거래업11서울특별시11110종로구1111053000
196시계/귀금속소매D26A01사직동1111011700당주동1100713시계/귀금속G47830시계 및 귀금속 소매업11서울특별시11110종로구1111053000
197이/미용/건강F01A01사직동1111011200체부동11004526여성미용실S96112두발미용업11서울특별시11110종로구1111053000
198이/미용/건강F01A02사직동1111011800내수동1108724발/네일케어S96119기타미용업11서울특별시11110종로구1111053000