Overview

Dataset statistics

Number of variables6
Number of observations293
Missing cells78
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.4 KiB
Average record size in memory50.5 B

Variable types

Numeric2
Categorical1
Text2
DateTime1

Dataset

Description서울특별시 양천구 다중이용시설현황(시설명, 시설군, 주소(소재지), 연면적(제곱미터) 등)의 정보를 제공합니다.
Author서울특별시 양천구
URLhttps://www.data.go.kr/data/15035155/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 시설군High correlation
연면적(제곱미터) is highly overall correlated with 시설군High correlation
시설군 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연면적(제곱미터) has 78 (26.6%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:22:36.351841
Analysis finished2023-12-12 16:22:37.246519
Duration0.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct293
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147
Minimum1
Maximum293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T01:22:37.347760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.6
Q174
median147
Q3220
95-th percentile278.4
Maximum293
Range292
Interquartile range (IQR)146

Descriptive statistics

Standard deviation84.726029
Coefficient of variation (CV)0.57636754
Kurtosis-1.2
Mean147
Median Absolute Deviation (MAD)73
Skewness0
Sum43071
Variance7178.5
MonotonicityStrictly increasing
2023-12-13T01:22:37.526581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
185 1
 
0.3%
201 1
 
0.3%
200 1
 
0.3%
199 1
 
0.3%
198 1
 
0.3%
197 1
 
0.3%
196 1
 
0.3%
195 1
 
0.3%
194 1
 
0.3%
Other values (283) 283
96.6%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
293 1
0.3%
292 1
0.3%
291 1
0.3%
290 1
0.3%
289 1
0.3%
288 1
0.3%
287 1
0.3%
286 1
0.3%
285 1
0.3%
284 1
0.3%

시설군
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
노래연습장업
215 
인터넷컴퓨터게임시설제공업
61 
영화상영관
 
17

Length

Max length13
Median length6
Mean length7.3993174
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
노래연습장업 215
73.4%
인터넷컴퓨터게임시설제공업 61
 
20.8%
영화상영관 17
 
5.8%

Length

2023-12-13T01:22:37.705955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:22:37.855955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노래연습장업 215
73.4%
인터넷컴퓨터게임시설제공업 61
 
20.8%
영화상영관 17
 
5.8%

시설명
Text

Distinct254
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-13T01:22:38.094328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length32
Mean length8.8395904
Min length3

Characters and Unicode

Total characters2590
Distinct characters312
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique229 ?
Unique (%)78.2%

Sample

1st row골드노래연습장
2nd row메들리노래연습장
3rd row찬찬찬노래연습장
4th row환상노래연습장
5th row스타노래연습장
ValueCountFrequency (%)
노래연습장 20
 
5.3%
pc 10
 
2.6%
메가박스중앙(주 9
 
2.4%
목동지점 9
 
2.4%
파티파티노래연습장 6
 
1.6%
스타노래연습장 5
 
1.3%
코인노래연습장 4
 
1.1%
목동점 4
 
1.1%
팡팡노래연습장 4
 
1.1%
sbs노래연습장 3
 
0.8%
Other values (270) 306
80.5%
2023-12-13T01:22:38.575469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
노 218
 
8.4%
래 216
 
8.3%
연 206
 
8.0%
습 205
 
7.9%
장 205
 
7.9%
87
 
3.4%
C 69
 
2.7%
P 59
 
2.3%
스 53
 
2.0%
점 33
 
1.3%
Other values (302) 1239
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2081
80.3%
Uppercase Letter 294
 
11.4%
Space Separator 87
 
3.4%
Decimal Number 43
 
1.7%
Open Punctuation 32
 
1.2%
Close Punctuation 32
 
1.2%
Lowercase Letter 21
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
노 218
 
10.5%
래 216
 
10.4%
연 206
 
9.9%
습 205
 
9.9%
장 205
 
9.9%
스 53
 
2.5%
점 33
 
1.6%
동 29
 
1.4%
목 27
 
1.3%
티 26
 
1.2%
Other values (249) 863
41.5%
Uppercase Letter
ValueCountFrequency (%)
C 69
23.5%
P 59
20.1%
O 28
9.5%
M 18
 
6.1%
T 16
 
5.4%
E 14
 
4.8%
R 12
 
4.1%
F 11
 
3.7%
B 11
 
3.7%
I 9
 
3.1%
Other values (15) 47
16.0%
Lowercase Letter
ValueCountFrequency (%)
o 5
23.8%
p 3
14.3%
x 3
14.3%
m 1
 
4.8%
s 1
 
4.8%
y 1
 
4.8%
c 1
 
4.8%
t 1
 
4.8%
l 1
 
4.8%
b 1
 
4.8%
Other values (3) 3
14.3%
Decimal Number
ValueCountFrequency (%)
1 10
23.3%
0 10
23.3%
3 5
11.6%
2 5
11.6%
4 4
 
9.3%
7 2
 
4.7%
5 2
 
4.7%
6 2
 
4.7%
8 2
 
4.7%
9 1
 
2.3%
Open Punctuation
ValueCountFrequency (%)
( 31
96.9%
[ 1
 
3.1%
Close Punctuation
ValueCountFrequency (%)
) 31
96.9%
] 1
 
3.1%
Space Separator
ValueCountFrequency (%)
87
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2081
80.3%
Latin 315
 
12.2%
Common 194
 
7.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
노 218
 
10.5%
래 216
 
10.4%
연 206
 
9.9%
습 205
 
9.9%
장 205
 
9.9%
스 53
 
2.5%
점 33
 
1.6%
동 29
 
1.4%
목 27
 
1.3%
티 26
 
1.2%
Other values (249) 863
41.5%
Latin
ValueCountFrequency (%)
C 69
21.9%
P 59
18.7%
O 28
8.9%
M 18
 
5.7%
T 16
 
5.1%
E 14
 
4.4%
R 12
 
3.8%
F 11
 
3.5%
B 11
 
3.5%
I 9
 
2.9%
Other values (28) 68
21.6%
Common
ValueCountFrequency (%)
87
44.8%
( 31
 
16.0%
) 31
 
16.0%
1 10
 
5.2%
0 10
 
5.2%
3 5
 
2.6%
2 5
 
2.6%
4 4
 
2.1%
7 2
 
1.0%
5 2
 
1.0%
Other values (5) 7
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2081
80.3%
ASCII 509
 
19.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
노 218
 
10.5%
래 216
 
10.4%
연 206
 
9.9%
습 205
 
9.9%
장 205
 
9.9%
스 53
 
2.5%
점 33
 
1.6%
동 29
 
1.4%
목 27
 
1.3%
티 26
 
1.2%
Other values (249) 863
41.5%
ASCII
ValueCountFrequency (%)
87
17.1%
C 69
13.6%
P 59
11.6%
( 31
 
6.1%
) 31
 
6.1%
O 28
 
5.5%
M 18
 
3.5%
T 16
 
3.1%
E 14
 
2.8%
R 12
 
2.4%
Other values (43) 144
28.3%

소재지
Text

Distinct277
Distinct (%)94.5%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2023-12-13T01:22:38.866829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length43
Mean length28.648464
Min length21

Characters and Unicode

Total characters8394
Distinct characters127
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

Unique274 ?
Unique (%)93.5%

Sample

1st row서울특별시 양천구 등촌로 220 (목동)
2nd row서울특별시 양천구 목동중앙서로 10 (목동,지하1층)
3rd row서울특별시 양천구 공항대로 620 (목동,지하1층)
4th row서울특별시 양천구 신정중앙로 99 (신정동,2층)
5th row서울특별시 양천구 은행정로 16 (신정동)
ValueCountFrequency (%)
서울특별시 293
18.2%
양천구 293
18.2%
신정동 60
 
3.7%
목동 56
 
3.5%
신월동 53
 
3.3%
오목로 27
 
1.7%
신정동,지하1층 26
 
1.6%
목동동로 25
 
1.6%
지하1층 24
 
1.5%
목동,지하1층 24
 
1.5%
Other values (319) 729
45.3%
2023-12-13T01:22:39.336471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1320
 
15.7%
동 415
 
4.9%
로 308
 
3.7%
양 306
 
3.6%
서 302
 
3.6%
( 296
 
3.5%
) 296
 
3.5%
시 294
 
3.5%
특 293
 
3.5%
별 293
 
3.5%
Other values (117) 4271
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5112
60.9%
Space Separator 1320
 
15.7%
Decimal Number 1101
 
13.1%
Open Punctuation 296
 
3.5%
Close Punctuation 296
 
3.5%
Other Punctuation 235
 
2.8%
Dash Punctuation 28
 
0.3%
Math Symbol 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 415
 
8.1%
로 308
 
6.0%
양 306
 
6.0%
서 302
 
5.9%
시 294
 
5.8%
특 293
 
5.7%
별 293
 
5.7%
천 293
 
5.7%
구 293
 
5.7%
울 293
 
5.7%
Other values (98) 2022
39.6%
Decimal Number
ValueCountFrequency (%)
1 277
25.2%
2 192
17.4%
3 124
11.3%
0 88
 
8.0%
7 87
 
7.9%
5 82
 
7.4%
4 74
 
6.7%
6 65
 
5.9%
8 56
 
5.1%
9 56
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
s 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
1320
100.0%
Open Punctuation
ValueCountFrequency (%)
( 296
100.0%
Close Punctuation
ValueCountFrequency (%)
) 296
100.0%
Other Punctuation
ValueCountFrequency (%)
, 235
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5112
60.9%
Common 3278
39.1%
Latin 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 415
 
8.1%
로 308
 
6.0%
양 306
 
6.0%
서 302
 
5.9%
시 294
 
5.8%
특 293
 
5.7%
별 293
 
5.7%
천 293
 
5.7%
구 293
 
5.7%
울 293
 
5.7%
Other values (98) 2022
39.6%
Common
ValueCountFrequency (%)
1320
40.3%
( 296
 
9.0%
) 296
 
9.0%
1 277
 
8.5%
, 235
 
7.2%
2 192
 
5.9%
3 124
 
3.8%
0 88
 
2.7%
7 87
 
2.7%
5 82
 
2.5%
Other values (6) 281
 
8.6%
Latin
ValueCountFrequency (%)
B 2
50.0%
s 1
25.0%
k 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5112
60.9%
ASCII 3282
39.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1320
40.2%
( 296
 
9.0%
) 296
 
9.0%
1 277
 
8.4%
, 235
 
7.2%
2 192
 
5.9%
3 124
 
3.8%
0 88
 
2.7%
7 87
 
2.7%
5 82
 
2.5%
Other values (9) 285
 
8.7%
Hangul
ValueCountFrequency (%)
동 415
 
8.1%
로 308
 
6.0%
양 306
 
6.0%
서 302
 
5.9%
시 294
 
5.8%
특 293
 
5.7%
별 293
 
5.7%
천 293
 
5.7%
구 293
 
5.7%
울 293
 
5.7%
Other values (98) 2022
39.6%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct188
Distinct (%)87.4%
Missing78
Missing (%)26.6%
Infinite0
Infinite (%)0.0%
Mean132.69674
Minimum42.9
Maximum322.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-13T01:22:39.511095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.9
5-th percentile75.3
Q197.595
median125.97
Q3160.015
95-th percentile217.659
Maximum322.16
Range279.26
Interquartile range (IQR)62.42

Descriptive statistics

Standard deviation48.740263
Coefficient of variation (CV)0.36730564
Kurtosis2.0379455
Mean132.69674
Median Absolute Deviation (MAD)32.43
Skewness1.1761287
Sum28529.8
Variance2375.6132
MonotonicityNot monotonic
2023-12-13T01:22:39.710407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.17 6
 
2.0%
132.23 5
 
1.7%
82.64 4
 
1.4%
105.78 4
 
1.4%
82.5 3
 
1.0%
142.0 3
 
1.0%
92.56 3
 
1.0%
128.92 2
 
0.7%
148.5 2
 
0.7%
158.67 2
 
0.7%
Other values (178) 181
61.8%
(Missing) 78
26.6%
ValueCountFrequency (%)
42.9 1
0.3%
52.56 1
0.3%
56.19 1
0.3%
62.0 1
0.3%
62.56 1
0.3%
66.11 1
0.3%
67.76 1
0.3%
68.42 1
0.3%
69.42 1
0.3%
69.63 1
0.3%
ValueCountFrequency (%)
322.16 1
0.3%
316.8 1
0.3%
311.0 1
0.3%
271.23 1
0.3%
269.38 1
0.3%
258.53 1
0.3%
250.48 1
0.3%
246.01 1
0.3%
240.4 1
0.3%
221.2 1
0.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
Minimum2023-11-20 00:00:00
Maximum2023-11-20 00:00:00
2023-12-13T01:22:39.848633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:39.958129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T01:22:36.869272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:36.660533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:36.958110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:36.766079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:22:40.056660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시설군연면적(제곱미터)
연번1.0000.9090.086
시설군0.9091.000NaN
연면적(제곱미터)0.086NaN1.000
2023-12-13T01:22:40.181906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번연면적(제곱미터)시설군
연번1.0000.1220.858
연면적(제곱미터)0.1221.0001.000
시설군0.8581.0001.000

Missing values

2023-12-13T01:22:37.087087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:22:37.188417image/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

연번시설군시설명소재지연면적(제곱미터)데이터기준일자
01노래연습장업골드노래연습장서울특별시 양천구 등촌로 220 (목동)92.562023-11-20
12노래연습장업메들리노래연습장서울특별시 양천구 목동중앙서로 10 (목동,지하1층)82.52023-11-20
23노래연습장업찬찬찬노래연습장서울특별시 양천구 공항대로 620 (목동,지하1층)99.172023-11-20
34노래연습장업환상노래연습장서울특별시 양천구 신정중앙로 99 (신정동,2층)99.452023-11-20
45노래연습장업스타노래연습장서울특별시 양천구 은행정로 16 (신정동)93.02023-11-20
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