Overview

Dataset statistics

Number of variables4
Number of observations9272
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory308.0 KiB
Average record size in memory34.0 B

Variable types

Categorical1
Text1
Numeric2

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15249/F/1/datasetView.do

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates

Reproduction

Analysis started2024-03-13 09:54:48.527169
Analysis finished2024-03-13 09:54:49.460792
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여소 그룹
Categorical

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size72.6 KiB
송파구
 
593
강남구
 
587
서초구
 
533
영등포구
 
528
강서구
 
510
Other values (22)
6521 

Length

Max length6
Median length3
Mean length3.1014884
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
송파구 593
 
6.4%
강남구 587
 
6.3%
서초구 533
 
5.7%
영등포구 528
 
5.7%
강서구 510
 
5.5%
마포구 478
 
5.2%
노원구 406
 
4.4%
종로구 397
 
4.3%
구로구 383
 
4.1%
성동구 378
 
4.1%
Other values (17) 4479
48.3%

Length

2024-03-13T18:54:49.524029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 593
 
6.4%
강남구 587
 
6.3%
서초구 533
 
5.7%
영등포구 528
 
5.7%
강서구 510
 
5.5%
마포구 478
 
5.2%
노원구 406
 
4.4%
종로구 397
 
4.3%
구로구 383
 
4.1%
성동구 378
 
4.1%
Other values (18) 4486
48.3%

대여소 명
Text

Distinct1555
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Memory size72.6 KiB
2024-03-13T18:54:49.808528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.446074
Min length4

Characters and Unicode

Total characters143216
Distinct characters525
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row2301. 현대고등학교 건너편
2nd row2302. 교보타워 버스정류장(신논현역 3번출구 후면)
3rd row2303. 논현역 7번출구
4th row2304. 신영 ROYAL PALACE 앞
5th row2305. MCM 본사 직영점 앞
ValueCountFrequency (%)
앞 2400
 
8.2%
옆 468
 
1.6%
출구 323
 
1.1%
1번출구 282
 
1.0%
사거리 252
 
0.9%
교차로 246
 
0.8%
입구 238
 
0.8%
뒤 233
 
0.8%
2번출구 221
 
0.8%
3번출구 209
 
0.7%
Other values (3410) 24325
83.3%
2024-03-13T18:54:50.237279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
19925
 
13.9%
. 9281
 
6.5%
1 8272
 
5.8%
2 6182
 
4.3%
3 4357
 
3.0%
5 3237
 
2.3%
구 3196
 
2.2%
0 3084
 
2.2%
4 3011
 
2.1%
앞 2881
 
2.0%
Other values (515) 79790
55.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73616
51.4%
Decimal Number 37528
26.2%
Space Separator 19925
 
13.9%
Other Punctuation 9359
 
6.5%
Uppercase Letter 1108
 
0.8%
Close Punctuation 732
 
0.5%
Open Punctuation 732
 
0.5%
Lowercase Letter 120
 
0.1%
Dash Punctuation 54
 
< 0.1%
Math Symbol 30
 
< 0.1%
Other values (2) 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3196
 
4.3%
앞 2881
 
3.9%
역 2387
 
3.2%
번 2100
 
2.9%
출 2068
 
2.8%
동 1777
 
2.4%
교 1524
 
2.1%
리 1297
 
1.8%
원 1157
 
1.6%
파 1102
 
1.5%
Other values (457) 54127
73.5%
Uppercase Letter
ValueCountFrequency (%)
K 161
14.5%
S 132
11.9%
C 120
10.8%
G 84
 
7.6%
L 84
 
7.6%
T 65
 
5.9%
B 54
 
4.9%
M 54
 
4.9%
A 54
 
4.9%
I 48
 
4.3%
Other values (14) 252
22.7%
Decimal Number
ValueCountFrequency (%)
1 8272
22.0%
2 6182
16.5%
3 4357
11.6%
5 3237
 
8.6%
0 3084
 
8.2%
4 3011
 
8.0%
6 2778
 
7.4%
9 2234
 
6.0%
7 2227
 
5.9%
8 2146
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
e 42
35.0%
t 12
 
10.0%
l 12
 
10.0%
k 12
 
10.0%
n 12
 
10.0%
y 6
 
5.0%
c 6
 
5.0%
o 6
 
5.0%
m 6
 
5.0%
s 6
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 9281
99.2%
, 36
 
0.4%
? 18
 
0.2%
& 12
 
0.1%
@ 6
 
0.1%
· 6
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 24
80.0%
+ 6
 
20.0%
Space Separator
ValueCountFrequency (%)
19925
100.0%
Close Punctuation
ValueCountFrequency (%)
) 732
100.0%
Open Punctuation
ValueCountFrequency (%)
( 732
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73622
51.4%
Common 68366
47.7%
Latin 1228
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3196
 
4.3%
앞 2881
 
3.9%
역 2387
 
3.2%
번 2100
 
2.9%
출 2068
 
2.8%
동 1777
 
2.4%
교 1524
 
2.1%
리 1297
 
1.8%
원 1157
 
1.6%
파 1102
 
1.5%
Other values (458) 54133
73.5%
Latin
ValueCountFrequency (%)
K 161
13.1%
S 132
 
10.7%
C 120
 
9.8%
G 84
 
6.8%
L 84
 
6.8%
T 65
 
5.3%
B 54
 
4.4%
M 54
 
4.4%
A 54
 
4.4%
I 48
 
3.9%
Other values (24) 372
30.3%
Common
ValueCountFrequency (%)
19925
29.1%
. 9281
13.6%
1 8272
12.1%
2 6182
 
9.0%
3 4357
 
6.4%
5 3237
 
4.7%
0 3084
 
4.5%
4 3011
 
4.4%
6 2778
 
4.1%
9 2234
 
3.3%
Other values (13) 6005
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73616
51.4%
ASCII 69588
48.6%
None 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
19925
28.6%
. 9281
13.3%
1 8272
11.9%
2 6182
 
8.9%
3 4357
 
6.3%
5 3237
 
4.7%
0 3084
 
4.4%
4 3011
 
4.3%
6 2778
 
4.0%
9 2234
 
3.2%
Other values (46) 7227
 
10.4%
Hangul
ValueCountFrequency (%)
구 3196
 
4.3%
앞 2881
 
3.9%
역 2387
 
3.2%
번 2100
 
2.9%
출 2068
 
2.8%
동 1777
 
2.4%
교 1524
 
2.1%
리 1297
 
1.8%
원 1157
 
1.6%
파 1102
 
1.5%
Other values (457) 54127
73.5%
None
ValueCountFrequency (%)
· 6
50.0%
㈜ 6
50.0%

대여 일자 / 월
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201908.5
Minimum201906
Maximum201911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.6 KiB
2024-03-13T18:54:50.350115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201906
5-th percentile201906
Q1201907
median201909
Q3201910
95-th percentile201911
Maximum201911
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7078744
Coefficient of variation (CV)8.458655 × 10-6
Kurtosis-1.2686801
Mean201908.5
Median Absolute Deviation (MAD)1
Skewness-0.0013317669
Sum1.8720956 × 109
Variance2.9168348
MonotonicityIncreasing
2024-03-13T18:54:50.453348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
201909 1547
16.7%
201910 1547
16.7%
201911 1547
16.7%
201907 1545
16.7%
201906 1543
16.6%
201908 1543
16.6%
ValueCountFrequency (%)
201906 1543
16.6%
201907 1545
16.7%
201908 1543
16.6%
201909 1547
16.7%
201910 1547
16.7%
201911 1547
16.7%
ValueCountFrequency (%)
201911 1547
16.7%
201910 1547
16.7%
201909 1547
16.7%
201908 1543
16.6%
201907 1545
16.7%
201906 1543
16.6%

대여 건수
Real number (ℝ)

Distinct3120
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1360.7385
Minimum0
Maximum15284
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size81.6 KiB
2024-03-13T18:54:50.576026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile238.55
Q1627
median1101.5
Q31752
95-th percentile3349
Maximum15284
Range15284
Interquartile range (IQR)1125

Descriptive statistics

Standard deviation1125.1187
Coefficient of variation (CV)0.8268442
Kurtosis18.206019
Mean1360.7385
Median Absolute Deviation (MAD)538.5
Skewness2.9591615
Sum12616767
Variance1265892.1
MonotonicityNot monotonic
2024-03-13T18:54:50.701953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
371 12
 
0.1%
734 12
 
0.1%
259 12
 
0.1%
554 12
 
0.1%
622 11
 
0.1%
360 11
 
0.1%
1197 11
 
0.1%
962 11
 
0.1%
749 11
 
0.1%
969 11
 
0.1%
Other values (3110) 9158
98.8%
ValueCountFrequency (%)
0 10
0.1%
1 8
0.1%
2 6
0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
15284 1
< 0.1%
14025 1
< 0.1%
13863 1
< 0.1%
13302 1
< 0.1%
13284 1
< 0.1%
12998 1
< 0.1%
12345 1
< 0.1%
11938 1
< 0.1%
10673 1
< 0.1%
10304 1
< 0.1%

Interactions

2024-03-13T18:54:49.091119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:48.895611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:49.179227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T18:54:48.987303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T18:54:50.811214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소 그룹대여 일자 / 월대여 건수
대여소 그룹1.0000.0000.302
대여 일자 / 월0.0001.0000.145
대여 건수0.3020.1451.000
2024-03-13T18:54:50.890476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여 일자 / 월대여 건수대여소 그룹
대여 일자 / 월1.000-0.1020.000
대여 건수-0.1021.0000.113
대여소 그룹0.0000.1131.000

Missing values

2024-03-13T18:54:49.329306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T18:54:49.423193image/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

대여소 그룹대여소 명대여 일자 / 월대여 건수
0강남구2301. 현대고등학교 건너편2019063909
1강남구2302. 교보타워 버스정류장(신논현역 3번출구 후면)2019062432
2강남구2303. 논현역 7번출구2019061567
3강남구2304. 신영 ROYAL PALACE 앞201906559
4강남구2305. MCM 본사 직영점 앞201906730
5강남구2306. 압구정역 2번 출구 옆2019061926
6강남구2307. 압구정 한양 3차 아파트2019062326
7강남구2308. 압구정파출소 앞2019061154
8강남구2309. 청담역(우리들병원 앞)201906584
9강남구2310. 청담동 맥도날드 옆(위치)201906917
대여소 그룹대여소 명대여 일자 / 월대여 건수
9262중랑구1450. 화랑대역 7번출구201911711
9263중랑구1451. 중랑세무서2019111306
9264중랑구1452. 겸재교 진입부2019111065
9265중랑구1453. 중랑캠핑숲201911206
9266중랑구1454. 한국전력공사(동대문 중랑지사)201911804
9267중랑구1455. 상봉역 2번 출구2019111205
9268중랑구1456. 상아빌딩(우림시장 교차로)201911764
9269중랑구1457. 동원사거리201911577
9270중랑구1458. 상봉터미널22019111243
9271중랑구1459. 용마한신아파트사거리201911339

Duplicate rows

Most frequently occurring

대여소 그룹대여소 명대여 일자 / 월대여 건수# duplicates
0그룹명 없음대여소명 없음20190702
1그룹명 없음대여소명 없음20191002