Overview

Dataset statistics

Number of variables18
Number of observations100
Missing cells602
Missing cells (%)33.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 KiB
Average record size in memory156.3 B

Variable types

Categorical5
Numeric4
Text3
Unsupported6

Alerts

local_code is highly overall correlated with city_code and 2 other fieldsHigh correlation
zp_code is highly overall correlated with alsfc_confm_de and 3 other fieldsHigh correlation
main_event is highly overall correlated with main_event_nmHigh correlation
alsfc_confm_de is highly overall correlated with zp_codeHigh correlation
city_code is highly overall correlated with local_code and 3 other fieldsHigh correlation
city_nm is highly overall correlated with local_code and 3 other fieldsHigh correlation
local_nm is highly overall correlated with local_code and 3 other fieldsHigh correlation
main_event_nm is highly overall correlated with main_eventHigh correlation
city_code is highly imbalanced (80.6%)Imbalance
city_nm is highly imbalanced (80.6%)Imbalance
res_tel_no has 100 (100.0%) missing valuesMissing
addr2 has 2 (2.0%) missing valuesMissing
map_point_x has 100 (100.0%) missing valuesMissing
map_point_y has 100 (100.0%) missing valuesMissing
facil_gbn has 100 (100.0%) missing valuesMissing
item_cd_nm has 100 (100.0%) missing valuesMissing
course_nm has 100 (100.0%) missing valuesMissing
res_tel_no is an unsupported type, check if it needs cleaning or further analysisUnsupported
map_point_x is an unsupported type, check if it needs cleaning or further analysisUnsupported
map_point_y is an unsupported type, check if it needs cleaning or further analysisUnsupported
facil_gbn is an unsupported type, check if it needs cleaning or further analysisUnsupported
item_cd_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported
course_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 10:00:25.418080
Analysis finished2023-12-10 10:00:31.994592
Duration6.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

city_code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11
97 
50
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row50
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 97
97.0%
50 3
 
3.0%

Length

2023-12-10T19:00:32.142716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:00:32.318178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 97
97.0%
50 3
 
3.0%

city_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
97 
제주
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row제주
3rd row서울
4th row서울
5th row서울

Common Values

ValueCountFrequency (%)
서울 97
97.0%
제주 3
 
3.0%

Length

2023-12-10T19:00:32.511942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:00:32.781974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 97
97.0%
제주 3
 
3.0%

local_code
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12592.75
Minimum11170
Maximum50110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:33.113542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11170
5-th percentile11200
Q111290
median11410
Q311567.5
95-th percentile11740
Maximum50110
Range38940
Interquartile range (IQR)277.5

Descriptive statistics

Standard deviation6633.2322
Coefficient of variation (CV)0.52675009
Kurtosis29.85628
Mean12592.75
Median Absolute Deviation (MAD)135
Skewness5.5890228
Sum1259275
Variance43999770
MonotonicityNot monotonic
2023-12-10T19:00:33.488146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11260 9
 
9.0%
11305 7
 
7.0%
11350 7
 
7.0%
11530 7
 
7.0%
11680 6
 
6.0%
11200 6
 
6.0%
11545 5
 
5.0%
11410 5
 
5.0%
11740 5
 
5.0%
11650 4
 
4.0%
Other values (14) 39
39.0%
ValueCountFrequency (%)
11170 1
 
1.0%
11200 6
6.0%
11215 3
 
3.0%
11230 3
 
3.0%
11260 9
9.0%
11290 4
4.0%
11305 7
7.0%
11320 3
 
3.0%
11350 7
7.0%
11380 4
4.0%
ValueCountFrequency (%)
50110 3
3.0%
11740 5
5.0%
11710 2
 
2.0%
11680 6
6.0%
11650 4
4.0%
11620 4
4.0%
11590 1
 
1.0%
11560 2
 
2.0%
11545 5
5.0%
11530 7
7.0%

local_nm
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
중랑구
9 
노원구
7 
구로구
7 
강북구
7 
강남구
 
6
Other values (19)
64 

Length

Max length4
Median length3
Mean length3.1
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row강남구
2nd row제주시
3rd row송파구
4th row성동구
5th row금천구

Common Values

ValueCountFrequency (%)
중랑구 9
 
9.0%
노원구 7
 
7.0%
구로구 7
 
7.0%
강북구 7
 
7.0%
강남구 6
 
6.0%
성동구 6
 
6.0%
금천구 5
 
5.0%
서대문구 5
 
5.0%
강동구 5
 
5.0%
마포구 4
 
4.0%
Other values (14) 39
39.0%

Length

2023-12-10T19:00:33.777418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중랑구 9
 
9.0%
구로구 7
 
7.0%
강북구 7
 
7.0%
노원구 7
 
7.0%
강남구 6
 
6.0%
성동구 6
 
6.0%
금천구 5
 
5.0%
서대문구 5
 
5.0%
강동구 5
 
5.0%
성북구 4
 
4.0%
Other values (14) 39
39.0%

facil_nm
Text

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:00:34.279353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length7.85
Min length3

Characters and Unicode

Total characters785
Distinct characters194
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)96.0%

Sample

1st row화랑합기도체육관
2nd row두드림축구아카데미
3rd rowKTI한울태권도
4th row테크네 브라질리안 주짓수 스튜디오
5th row열매점핑허브다이어트
ValueCountFrequency (%)
국가대표 5
 
3.7%
용인대 5
 
3.7%
태권도 4
 
3.0%
태권도장 3
 
2.2%
한양체육관 2
 
1.5%
드림아이태권도 2
 
1.5%
필라테스 2
 
1.5%
혜성합기도 1
 
0.7%
cosery 1
 
0.7%
by 1
 
0.7%
Other values (109) 109
80.7%
2023-12-10T19:00:35.102577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
태 59
 
7.5%
도 55
 
7.0%
권 46
 
5.9%
35
 
4.5%
관 26
 
3.3%
육 24
 
3.1%
체 22
 
2.8%
대 20
 
2.5%
스 19
 
2.4%
장 14
 
1.8%
Other values (184) 465
59.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 693
88.3%
Space Separator 35
 
4.5%
Uppercase Letter 30
 
3.8%
Lowercase Letter 19
 
2.4%
Open Punctuation 3
 
0.4%
Close Punctuation 3
 
0.4%
Decimal Number 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
태 59
 
8.5%
도 55
 
7.9%
권 46
 
6.6%
관 26
 
3.8%
육 24
 
3.5%
체 22
 
3.2%
대 20
 
2.9%
스 19
 
2.7%
장 14
 
2.0%
인 11
 
1.6%
Other values (152) 397
57.3%
Lowercase Letter
ValueCountFrequency (%)
y 3
15.8%
o 2
10.5%
b 2
10.5%
e 2
10.5%
m 1
 
5.3%
g 1
 
5.3%
n 1
 
5.3%
i 1
 
5.3%
r 1
 
5.3%
s 1
 
5.3%
Other values (4) 4
21.1%
Uppercase Letter
ValueCountFrequency (%)
S 6
20.0%
M 4
13.3%
G 3
10.0%
K 3
10.0%
T 3
10.0%
A 2
 
6.7%
Y 2
 
6.7%
U 2
 
6.7%
F 1
 
3.3%
C 1
 
3.3%
Other values (3) 3
10.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 693
88.3%
Latin 49
 
6.2%
Common 43
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
태 59
 
8.5%
도 55
 
7.9%
권 46
 
6.6%
관 26
 
3.8%
육 24
 
3.5%
체 22
 
3.2%
대 20
 
2.9%
스 19
 
2.7%
장 14
 
2.0%
인 11
 
1.6%
Other values (152) 397
57.3%
Latin
ValueCountFrequency (%)
S 6
 
12.2%
M 4
 
8.2%
G 3
 
6.1%
y 3
 
6.1%
K 3
 
6.1%
T 3
 
6.1%
A 2
 
4.1%
Y 2
 
4.1%
o 2
 
4.1%
b 2
 
4.1%
Other values (17) 19
38.8%
Common
ValueCountFrequency (%)
35
81.4%
( 3
 
7.0%
) 3
 
7.0%
2 1
 
2.3%
& 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 693
88.3%
ASCII 92
 
11.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
태 59
 
8.5%
도 55
 
7.9%
권 46
 
6.6%
관 26
 
3.8%
육 24
 
3.5%
체 22
 
3.2%
대 20
 
2.9%
스 19
 
2.7%
장 14
 
2.0%
인 11
 
1.6%
Other values (152) 397
57.3%
ASCII
ValueCountFrequency (%)
35
38.0%
S 6
 
6.5%
M 4
 
4.3%
G 3
 
3.3%
y 3
 
3.3%
( 3
 
3.3%
) 3
 
3.3%
K 3
 
3.3%
T 3
 
3.3%
A 2
 
2.2%
Other values (22) 27
29.3%

pres_nm
Categorical

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
김**
20 
이**
16 
박**
8 
정**
6 
유**
6 
Other values (27)
44 

Length

Max length7
Median length3
Mean length3.04
Min length3

Unique

Unique19 ?
Unique (%)19.0%

Sample

1st row이**
2nd row한**
3rd row정**
4th row이**
5th row전**

Common Values

ValueCountFrequency (%)
김** 20
20.0%
이** 16
16.0%
박** 8
 
8.0%
정** 6
 
6.0%
유** 6
 
6.0%
신** 5
 
5.0%
최** 4
 
4.0%
한** 4
 
4.0%
송** 3
 
3.0%
윤** 3
 
3.0%
Other values (22) 25
25.0%

Length

2023-12-10T19:00:35.434481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김 20
20.0%
이 16
16.0%
박 8
 
8.0%
정 6
 
6.0%
유 6
 
6.0%
신 5
 
5.0%
최 4
 
4.0%
한 4
 
4.0%
송 3
 
3.0%
윤 3
 
3.0%
Other values (21) 25
25.0%

res_tel_no
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

zp_code
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42989.6
Minimum1031
Maximum158097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:35.738158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1031
5-th percentile1364.65
Q13662.25
median6904
Q3121440
95-th percentile143662.55
Maximum158097
Range157066
Interquartile range (IQR)117777.75

Descriptive statistics

Standard deviation59492.841
Coefficient of variation (CV)1.3838891
Kurtosis-0.90619199
Mean42989.6
Median Absolute Deviation (MAD)4266
Skewness1.0049712
Sum4298960
Variance3.5393981 × 109
MonotonicityNot monotonic
2023-12-10T19:00:36.588558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15290 2
 
2.0%
6800 2
 
2.0%
2134 2
 
2.0%
13233 1
 
1.0%
133120 1
 
1.0%
7388 1
 
1.0%
5415 1
 
1.0%
8281 1
 
1.0%
6615 1
 
1.0%
157010 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
1031 1
1.0%
1041 1
1.0%
1181 1
1.0%
1194 1
1.0%
1206 1
1.0%
1373 1
1.0%
1601 1
1.0%
1708 1
1.0%
1830 1
1.0%
1831 1
1.0%
ValueCountFrequency (%)
158097 1
1.0%
158092 1
1.0%
157010 1
1.0%
153861 1
1.0%
152052 1
1.0%
143221 1
1.0%
143220 1
1.0%
142743 1
1.0%
142070 1
1.0%
140896 1
1.0%

addr1
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:00:37.395343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length22
Mean length17.04
Min length6

Characters and Unicode

Total characters1704
Distinct characters152
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)98.0%

Sample

1st row서울 강남구 일원동
2nd row제주특별자치도 제주시 은수길 25
3rd row서울특별시 송파구 문정로 55
4th row서울특별시 성동구 왕십리로24나길 20
5th row서울특별시 금천구 독산로40길 49
ValueCountFrequency (%)
서울특별시 71
 
18.4%
서울 26
 
6.8%
중랑구 9
 
2.3%
노원구 7
 
1.8%
구로구 7
 
1.8%
강북구 7
 
1.8%
강남구 6
 
1.6%
성동구 6
 
1.6%
금천구 5
 
1.3%
서대문구 5
 
1.3%
Other values (183) 236
61.3%
2023-12-10T19:00:38.517465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
285
16.7%
서 115
 
6.7%
구 107
 
6.3%
울 99
 
5.8%
로 83
 
4.9%
시 77
 
4.5%
특 74
 
4.3%
별 74
 
4.3%
1 46
 
2.7%
동 45
 
2.6%
Other values (142) 699
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1135
66.6%
Space Separator 285
 
16.7%
Decimal Number 274
 
16.1%
Dash Punctuation 8
 
0.5%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 115
 
10.1%
구 107
 
9.4%
울 99
 
8.7%
로 83
 
7.3%
시 77
 
6.8%
특 74
 
6.5%
별 74
 
6.5%
동 45
 
4.0%
길 36
 
3.2%
강 22
 
1.9%
Other values (128) 403
35.5%
Decimal Number
ValueCountFrequency (%)
1 46
16.8%
2 40
14.6%
3 38
13.9%
5 31
11.3%
4 26
9.5%
9 22
8.0%
8 21
7.7%
0 18
 
6.6%
7 17
 
6.2%
6 15
 
5.5%
Space Separator
ValueCountFrequency (%)
285
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1135
66.6%
Common 569
33.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 115
 
10.1%
구 107
 
9.4%
울 99
 
8.7%
로 83
 
7.3%
시 77
 
6.8%
특 74
 
6.5%
별 74
 
6.5%
동 45
 
4.0%
길 36
 
3.2%
강 22
 
1.9%
Other values (128) 403
35.5%
Common
ValueCountFrequency (%)
285
50.1%
1 46
 
8.1%
2 40
 
7.0%
3 38
 
6.7%
5 31
 
5.4%
4 26
 
4.6%
9 22
 
3.9%
8 21
 
3.7%
0 18
 
3.2%
7 17
 
3.0%
Other values (4) 25
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1135
66.6%
ASCII 569
33.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
285
50.1%
1 46
 
8.1%
2 40
 
7.0%
3 38
 
6.7%
5 31
 
5.4%
4 26
 
4.6%
9 22
 
3.9%
8 21
 
3.7%
0 18
 
3.2%
7 17
 
3.0%
Other values (4) 25
 
4.4%
Hangul
ValueCountFrequency (%)
서 115
 
10.1%
구 107
 
9.4%
울 99
 
8.7%
로 83
 
7.3%
시 77
 
6.8%
특 74
 
6.5%
별 74
 
6.5%
동 45
 
4.0%
길 36
 
3.2%
강 22
 
1.9%
Other values (128) 403
35.5%

addr2
Text

MISSING 

Distinct98
Distinct (%)100.0%
Missing2
Missing (%)2.0%
Memory size932.0 B
2023-12-10T19:00:39.191329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length23
Mean length14.428571
Min length2

Characters and Unicode

Total characters1414
Distinct characters213
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)100.0%

Sample

1st row663-84층
2nd row1층(연동)
3rd row1차 상가 2층 KTI한울태권도(문정동, 문정푸르지오아파트)
4th row지하1 테크네 주짓수(도선동)
5th row3층(시흥동)
ValueCountFrequency (%)
2층 16
 
7.1%
3층 13
 
5.8%
상가 9
 
4.0%
4층 3
 
1.3%
202호 2
 
0.9%
1층 2
 
0.9%
5층 2
 
0.9%
청룡체육관 2
 
0.9%
힐스테이트 2
 
0.9%
용인대 2
 
0.9%
Other values (170) 171
76.3%
2023-12-10T19:00:39.930217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
127
 
9.0%
동 79
 
5.6%
층 78
 
5.5%
) 73
 
5.2%
( 73
 
5.2%
2 54
 
3.8%
3 47
 
3.3%
1 33
 
2.3%
도 30
 
2.1%
태 27
 
1.9%
Other values (203) 793
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 870
61.5%
Decimal Number 236
 
16.7%
Space Separator 127
 
9.0%
Close Punctuation 73
 
5.2%
Open Punctuation 73
 
5.2%
Dash Punctuation 16
 
1.1%
Other Punctuation 12
 
0.8%
Uppercase Letter 5
 
0.4%
Lowercase Letter 1
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 79
 
9.1%
층 78
 
9.0%
도 30
 
3.4%
태 27
 
3.1%
가 26
 
3.0%
권 21
 
2.4%
상 21
 
2.4%
호 17
 
2.0%
지 16
 
1.8%
아 16
 
1.8%
Other values (181) 539
62.0%
Decimal Number
ValueCountFrequency (%)
2 54
22.9%
3 47
19.9%
1 33
14.0%
4 25
10.6%
0 23
9.7%
6 14
 
5.9%
9 13
 
5.5%
5 11
 
4.7%
8 8
 
3.4%
7 8
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
F 1
20.0%
C 1
20.0%
I 1
20.0%
T 1
20.0%
K 1
20.0%
Space Separator
ValueCountFrequency (%)
127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Open Punctuation
ValueCountFrequency (%)
( 73
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Other Punctuation
ValueCountFrequency (%)
, 12
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 870
61.5%
Common 538
38.0%
Latin 6
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 79
 
9.1%
층 78
 
9.0%
도 30
 
3.4%
태 27
 
3.1%
가 26
 
3.0%
권 21
 
2.4%
상 21
 
2.4%
호 17
 
2.0%
지 16
 
1.8%
아 16
 
1.8%
Other values (181) 539
62.0%
Common
ValueCountFrequency (%)
127
23.6%
) 73
13.6%
( 73
13.6%
2 54
10.0%
3 47
 
8.7%
1 33
 
6.1%
4 25
 
4.6%
0 23
 
4.3%
- 16
 
3.0%
6 14
 
2.6%
Other values (6) 53
9.9%
Latin
ValueCountFrequency (%)
F 1
16.7%
b 1
16.7%
C 1
16.7%
I 1
16.7%
T 1
16.7%
K 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 870
61.5%
ASCII 544
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
127
23.3%
) 73
13.4%
( 73
13.4%
2 54
9.9%
3 47
 
8.6%
1 33
 
6.1%
4 25
 
4.6%
0 23
 
4.2%
- 16
 
2.9%
6 14
 
2.6%
Other values (12) 59
10.8%
Hangul
ValueCountFrequency (%)
동 79
 
9.1%
층 78
 
9.0%
도 30
 
3.4%
태 27
 
3.1%
가 26
 
3.0%
권 21
 
2.4%
상 21
 
2.4%
호 17
 
2.0%
지 16
 
1.8%
아 16
 
1.8%
Other values (181) 539
62.0%

main_event
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.99
Minimum1
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:40.200983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q122
median22
Q322
95-th percentile76.1
Maximum79
Range78
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.227699
Coefficient of variation (CV)0.79413001
Kurtosis1.0789272
Mean27.99
Median Absolute Deviation (MAD)0
Skewness1.5163178
Sum2799
Variance494.07061
MonotonicityNot monotonic
2023-12-10T19:00:40.439972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
22 58
58.0%
3 7
 
7.0%
76 6
 
6.0%
75 5
 
5.0%
17 4
 
4.0%
1 4
 
4.0%
12 4
 
4.0%
79 4
 
4.0%
25 3
 
3.0%
20 2
 
2.0%
Other values (2) 3
 
3.0%
ValueCountFrequency (%)
1 4
 
4.0%
3 7
 
7.0%
12 4
 
4.0%
17 4
 
4.0%
20 2
 
2.0%
21 2
 
2.0%
22 58
58.0%
25 3
 
3.0%
75 5
 
5.0%
76 6
 
6.0%
ValueCountFrequency (%)
79 4
 
4.0%
78 1
 
1.0%
76 6
 
6.0%
75 5
 
5.0%
25 3
 
3.0%
22 58
58.0%
21 2
 
2.0%
20 2
 
2.0%
17 4
 
4.0%
12 4
 
4.0%

main_event_nm
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
태권도
58 
기타종목
7 
복싱
6 
합기도
 
5
유도
 
4
Other values (7)
20 

Length

Max length4
Median length3
Mean length2.85
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row합기도
2nd row축구
3rd row태권도
4th row기타종목
5th row기타종목

Common Values

ValueCountFrequency (%)
태권도 58
58.0%
기타종목 7
 
7.0%
복싱 6
 
6.0%
합기도 5
 
5.0%
유도 4
 
4.0%
검도 4
 
4.0%
수영 4
 
4.0%
필라테스 4
 
4.0%
헬스 3
 
3.0%
축구 2
 
2.0%
Other values (2) 3
 
3.0%

Length

2023-12-10T19:00:40.735007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
태권도 58
58.0%
기타종목 7
 
7.0%
복싱 6
 
6.0%
합기도 5
 
5.0%
유도 4
 
4.0%
검도 4
 
4.0%
수영 4
 
4.0%
필라테스 4
 
4.0%
헬스 3
 
3.0%
축구 2
 
2.0%
Other values (2) 3
 
3.0%

map_point_x
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

map_point_y
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

facil_gbn
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

item_cd_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

course_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

alsfc_confm_de
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20164222
Minimum20090401
Maximum20210916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:00:41.037882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20090401
5-th percentile20109760
Q120127846
median20170370
Q320200206
95-th percentile20210626
Maximum20210916
Range120515
Interquartile range (IQR)72360.5

Descriptive statistics

Standard deviation37126.432
Coefficient of variation (CV)0.0018412033
Kurtosis-1.1851789
Mean20164222
Median Absolute Deviation (MAD)30094.5
Skewness-0.38013868
Sum2.0164222 × 109
Variance1.378372 × 109
MonotonicityNot monotonic
2023-12-10T19:00:41.360457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200121 2
 
2.0%
20120822 2
 
2.0%
20201110 2
 
2.0%
20170427 2
 
2.0%
20210722 2
 
2.0%
20120210 2
 
2.0%
20120118 1
 
1.0%
20130415 1
 
1.0%
20210916 1
 
1.0%
20170228 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
20090401 1
1.0%
20090414 1
1.0%
20091208 1
1.0%
20100506 1
1.0%
20101206 1
1.0%
20110210 1
1.0%
20110215 1
1.0%
20110330 1
1.0%
20110817 1
1.0%
20110818 1
1.0%
ValueCountFrequency (%)
20210916 1
1.0%
20210905 1
1.0%
20210824 1
1.0%
20210722 2
2.0%
20210621 1
1.0%
20210601 1
1.0%
20210426 1
1.0%
20210422 1
1.0%
20210414 1
1.0%
20210309 1
1.0%

Interactions

2023-12-10T19:00:30.504385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:28.178517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:28.928017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:29.693804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:30.704078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:28.372116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:29.130341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:29.907029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:30.878579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:28.567084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:29.339177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:30.111402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:31.083148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:28.750806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:29.524183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:00:30.311112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:00:41.580908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_codecity_nmlocal_codelocal_nmfacil_nmpres_nmzp_codeaddr1addr2main_eventmain_event_nmalsfc_confm_de
city_code1.0000.9630.9631.0001.0000.4901.0001.0001.0000.0000.6090.000
city_nm0.9631.0000.9631.0001.0000.4901.0001.0001.0000.0000.6090.000
local_code0.9630.9631.0001.0001.0000.4841.0001.0001.0000.0000.6070.000
local_nm1.0001.0001.0001.0000.9820.7040.8301.0001.0000.7330.7150.000
facil_nm1.0001.0001.0000.9821.0000.9910.9690.9971.0000.9420.9890.954
pres_nm0.4900.4900.4840.7040.9911.0000.1900.9981.0000.0000.0000.000
zp_code1.0001.0001.0000.8300.9690.1901.0001.0001.0000.0000.3990.735
addr11.0001.0001.0001.0000.9970.9981.0001.0001.0000.8560.9370.612
addr21.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
main_event0.0000.0000.0000.7330.9420.0000.0000.8561.0001.0001.0000.203
main_event_nm0.6090.6090.6070.7150.9890.0000.3990.9371.0001.0001.0000.273
alsfc_confm_de0.0000.0000.0000.0000.9540.0000.7350.6121.0000.2030.2731.000
2023-12-10T19:00:41.936959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
city_nmmain_event_nmpres_nmcity_codelocal_nm
city_nm1.0000.4510.3220.8260.881
main_event_nm0.4511.0000.0000.4510.262
pres_nm0.3220.0001.0000.3220.212
city_code0.8260.4510.3221.0000.881
local_nm0.8810.2620.2120.8811.000
2023-12-10T19:00:42.215256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
local_codezp_codemain_eventalsfc_confm_decity_codecity_nmlocal_nmpres_nmmain_event_nm
local_code1.0000.144-0.0010.2260.8260.8260.8810.3220.451
zp_code0.1441.000-0.067-0.5570.9850.9850.5220.0670.223
main_event-0.001-0.0671.0000.1190.0000.0000.4110.0000.962
alsfc_confm_de0.226-0.5570.1191.0000.0000.0000.0000.0000.083
city_code0.8260.9850.0000.0001.0000.8260.8810.3220.451
city_nm0.8260.9850.0000.0000.8261.0000.8810.3220.451
local_nm0.8810.5220.4110.0000.8810.8811.0000.2120.262
pres_nm0.3220.0670.0000.0000.3220.3220.2121.0000.000
main_event_nm0.4510.2230.9620.0830.4510.4510.2620.0001.000

Missing values

2023-12-10T19:00:31.375957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:00:31.846457image/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

city_codecity_nmlocal_codelocal_nmfacil_nmpres_nmres_tel_nozp_codeaddr1addr2main_eventmain_event_nmmap_point_xmap_point_yfacil_gbnitem_cd_nmcourse_nmalsfc_confm_de
011서울11680강남구화랑합기도체육관이**<NA>135231서울 강남구 일원동663-84층75합기도<NA><NA><NA><NA><NA>20101206
150제주50110제주시두드림축구아카데미한**<NA>63141제주특별자치도 제주시 은수길 251층(연동)20축구<NA><NA><NA><NA><NA>20160930
211서울11710송파구KTI한울태권도정**<NA>5797서울특별시 송파구 문정로 551차 상가 2층 KTI한울태권도(문정동, 문정푸르지오아파트)22태권도<NA><NA><NA><NA><NA>20170120
311서울11200성동구테크네 브라질리안 주짓수 스튜디오이**<NA>4709서울특별시 성동구 왕십리로24나길 20지하1 테크네 주짓수(도선동)3기타종목<NA><NA><NA><NA><NA>20180226
411서울11545금천구열매점핑허브다이어트전**<NA>8566서울특별시 금천구 독산로40길 493층(시흥동)3기타종목<NA><NA><NA><NA><NA>20210127
511서울11305강북구국제체육관미아점복싱무에타이MMA주짓수김**<NA>1194서울특별시 강북구 솔샘로 2543층(미아동)76복싱<NA><NA><NA><NA><NA>20180423
611서울11620관악구대호체육도장강******<NA>8793서울특별시 관악구 인헌6길 243층 한양대 대호 태권도장(봉천동)22태권도<NA><NA><NA><NA><NA>20180111
750제주50110제주시한라유도클럽서**<NA>63183제주특별자치도 제주시 서광로 197-13층 한라유도클럽(삼도일동)17유도<NA><NA><NA><NA><NA>20200221
811서울11410서대문구국제태권도홍**<NA>3612서울특별시 서대문구 세검정로1길 93상가 1층 국제태권도(홍은동, 홍은동벽산아파트)22태권도<NA><NA><NA><NA><NA>20200204
911서울11680강남구강남구청정**<NA>135220서울 강남구 수서동 718번지<NA>1검도<NA><NA><NA><NA><NA>20110817
city_codecity_nmlocal_codelocal_nmfacil_nmpres_nmres_tel_nozp_codeaddr1addr2main_eventmain_event_nmmap_point_xmap_point_yfacil_gbnitem_cd_nmcourse_nmalsfc_confm_de
9011서울11350노원구용인대 금메달체육관2관이**<NA>139745서울 노원구 상계1동 동방미주아파트상가 201호22태권도<NA><NA><NA><NA><NA>20111018
9111서울11590동작구궁중무예 합기도육**<NA>7008서울특별시 동작구 동작대로29길 573층 궁중무예합기도(사당동)75합기도<NA><NA><NA><NA><NA>20161229
9211서울11410서대문구태성검도관김**<NA>3679서울특별시 서대문구 응암로 73b1(북가좌동)1검도<NA><NA><NA><NA><NA>20180307
9311서울11305강북구태선문체육관이**<NA>1181서울특별시 강북구 삼양로 2312층 (미아동, 승진빌딩)22태권도<NA><NA><NA><NA><NA>20120530
9411서울11320도봉구태사자태권도장박**<NA>132814서울특별시 도봉구 도봉로181길 35567-2 3층(도봉동)22태권도<NA><NA><NA><NA><NA>20141229
9511서울11740강동구태사모태권도아카데미박**<NA>5312서울특별시 강동구 구천면로42길 652층(천호동)22태권도<NA><NA><NA><NA><NA>20210208
9611서울11305강북구청훈체육관정**<NA>1206서울특별시 강북구 도봉로13길 553층 청훈태권도장(미아동)22태권도<NA><NA><NA><NA><NA>20170612
9711서울11290성북구UFC국제GYM박**<NA>2861서울특별시 성북구 보문로 189좌측면 3층 국제복싱체육관(삼선동4가)76복싱<NA><NA><NA><NA><NA>20190211
9811서울11170용산구청파동 헬스장이**<NA>140896서울특별시 용산구 청파로 93길 272층25헬스<NA><NA><NA><NA><NA>20141112
9911서울11680강남구청춘필라테스김**<NA>6137서울특별시 강남구 봉은사로38길 123층, 청춘필라테스(역삼동)79필라테스<NA><NA><NA><NA><NA>20201105