Overview

Dataset statistics

Number of variables10
Number of observations2657
Missing cells1322
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory210.3 KiB
Average record size in memory81.0 B

Variable types

Numeric1
Text5
Categorical3
DateTime1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-13252/F/1/datasetView.do

Alerts

대여소 번호 is highly overall correlated with 소재지(위치) and 1 other fieldsHigh correlation
소재지(위치) is highly overall correlated with 대여소 번호High correlation
운영 방식 is highly overall correlated with 대여소 번호High correlation
설치형태 is highly imbalanced (51.9%)Imbalance
설치 시기 has 28 (1.1%) missing valuesMissing
Unnamed: 8 has 1277 (48.1%) missing valuesMissing

Reproduction

Analysis started2023-12-11 06:35:35.001896
Analysis finished2023-12-11 06:35:36.813240
Duration1.81 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여소 번호
Real number (ℝ)

HIGH CORRELATION 

Distinct2653
Distinct (%)100.0%
Missing4
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2183.5243
Minimum102
Maximum5855
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2023-12-11T15:35:36.881885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile254.6
Q1950
median1928
Q33545
95-th percentile4732.2
Maximum5855
Range5753
Interquartile range (IQR)2595

Descriptive statistics

Standard deviation1464.0331
Coefficient of variation (CV)0.67049087
Kurtosis-1.0332501
Mean2183.5243
Median Absolute Deviation (MAD)1154
Skewness0.43493279
Sum5792890
Variance2143393
MonotonicityStrictly increasing
2023-12-11T15:35:37.032539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2642 1
 
< 0.1%
2644 1
 
< 0.1%
2645 1
 
< 0.1%
2646 1
 
< 0.1%
2647 1
 
< 0.1%
2648 1
 
< 0.1%
2649 1
 
< 0.1%
2650 1
 
< 0.1%
2651 1
 
< 0.1%
2652 1
 
< 0.1%
Other values (2643) 2643
99.5%
(Missing) 4
 
0.2%
ValueCountFrequency (%)
102 1
< 0.1%
103 1
< 0.1%
104 1
< 0.1%
105 1
< 0.1%
106 1
< 0.1%
107 1
< 0.1%
108 1
< 0.1%
109 1
< 0.1%
111 1
< 0.1%
112 1
< 0.1%
ValueCountFrequency (%)
5855 1
< 0.1%
5854 1
< 0.1%
5853 1
< 0.1%
5852 1
< 0.1%
5851 1
< 0.1%
5753 1
< 0.1%
5752 1
< 0.1%
5751 1
< 0.1%
5306 1
< 0.1%
5305 1
< 0.1%

보관소(대여소)명
Text

Distinct2650
Distinct (%)99.9%
Missing4
Missing (%)0.2%
Memory size20.9 KiB
2023-12-11T15:35:37.361790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length26
Mean length10.07991
Min length2

Characters and Unicode

Total characters26742
Distinct characters594
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2647 ?
Unique (%)99.8%

Sample

1st row망원역 1번출구 앞
2nd row망원역 2번출구 앞
3rd row합정역 1번출구 앞
4th row합정역 5번출구 앞
5th row합정역 7번출구 앞
ValueCountFrequency (%)
앞 696
 
12.4%
출구 109
 
1.9%
옆 102
 
1.8%
입구 71
 
1.3%
1번출구 64
 
1.1%
교차로 63
 
1.1%
사거리 56
 
1.0%
2번출구 47
 
0.8%
뒤 45
 
0.8%
3번출구 44
 
0.8%
Other values (3031) 4325
76.9%
2023-12-11T15:35:37.799259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3001
 
11.2%
앞 831
 
3.1%
구 812
 
3.0%
역 597
 
2.2%
동 583
 
2.2%
번 526
 
2.0%
출 511
 
1.9%
교 444
 
1.7%
1 421
 
1.6%
파 389
 
1.5%
Other values (584) 18627
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21355
79.9%
Space Separator 3001
 
11.2%
Decimal Number 1453
 
5.4%
Uppercase Letter 341
 
1.3%
Open Punctuation 235
 
0.9%
Close Punctuation 233
 
0.9%
Lowercase Letter 49
 
0.2%
Other Punctuation 41
 
0.2%
Dash Punctuation 18
 
0.1%
Control 6
 
< 0.1%
Other values (4) 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 831
 
3.9%
구 812
 
3.8%
역 597
 
2.8%
동 583
 
2.7%
번 526
 
2.5%
출 511
 
2.4%
교 444
 
2.1%
파 389
 
1.8%
아 372
 
1.7%
트 351
 
1.6%
Other values (517) 15939
74.6%
Uppercase Letter
ValueCountFrequency (%)
S 42
12.3%
K 42
12.3%
T 34
10.0%
C 31
9.1%
A 25
 
7.3%
G 21
 
6.2%
D 19
 
5.6%
M 18
 
5.3%
P 17
 
5.0%
B 16
 
4.7%
Other values (13) 76
22.3%
Lowercase Letter
ValueCountFrequency (%)
e 15
30.6%
s 7
14.3%
k 7
14.3%
t 3
 
6.1%
d 2
 
4.1%
l 2
 
4.1%
n 2
 
4.1%
g 1
 
2.0%
v 1
 
2.0%
a 1
 
2.0%
Other values (8) 8
16.3%
Decimal Number
ValueCountFrequency (%)
1 421
29.0%
2 255
17.5%
3 180
12.4%
4 140
 
9.6%
0 125
 
8.6%
5 106
 
7.3%
6 65
 
4.5%
7 61
 
4.2%
8 56
 
3.9%
9 44
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 17
41.5%
. 10
24.4%
& 8
19.5%
· 4
 
9.8%
? 2
 
4.9%
Math Symbol
ValueCountFrequency (%)
~ 4
80.0%
+ 1
 
20.0%
Other Number
ValueCountFrequency (%)
① 1
50.0%
② 1
50.0%
Space Separator
ValueCountFrequency (%)
3001
100.0%
Open Punctuation
ValueCountFrequency (%)
( 235
100.0%
Close Punctuation
ValueCountFrequency (%)
) 233
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Control
ValueCountFrequency (%)
6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21356
79.9%
Common 4996
 
18.7%
Latin 390
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 831
 
3.9%
구 812
 
3.8%
역 597
 
2.8%
동 583
 
2.7%
번 526
 
2.5%
출 511
 
2.4%
교 444
 
2.1%
파 389
 
1.8%
아 372
 
1.7%
트 351
 
1.6%
Other values (518) 15940
74.6%
Latin
ValueCountFrequency (%)
S 42
 
10.8%
K 42
 
10.8%
T 34
 
8.7%
C 31
 
7.9%
A 25
 
6.4%
G 21
 
5.4%
D 19
 
4.9%
M 18
 
4.6%
P 17
 
4.4%
B 16
 
4.1%
Other values (31) 125
32.1%
Common
ValueCountFrequency (%)
3001
60.1%
1 421
 
8.4%
2 255
 
5.1%
( 235
 
4.7%
) 233
 
4.7%
3 180
 
3.6%
4 140
 
2.8%
0 125
 
2.5%
5 106
 
2.1%
6 65
 
1.3%
Other values (15) 235
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21355
79.9%
ASCII 5380
 
20.1%
None 5
 
< 0.1%
Enclosed Alphanum 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3001
55.8%
1 421
 
7.8%
2 255
 
4.7%
( 235
 
4.4%
) 233
 
4.3%
3 180
 
3.3%
4 140
 
2.6%
0 125
 
2.3%
5 106
 
2.0%
6 65
 
1.2%
Other values (53) 619
 
11.5%
Hangul
ValueCountFrequency (%)
앞 831
 
3.9%
구 812
 
3.8%
역 597
 
2.8%
동 583
 
2.7%
번 526
 
2.5%
출 511
 
2.4%
교 444
 
2.1%
파 389
 
1.8%
아 372
 
1.7%
트 351
 
1.6%
Other values (517) 15939
74.6%
None
ValueCountFrequency (%)
· 4
80.0%
㈜ 1
 
20.0%
Enclosed Alphanum
ValueCountFrequency (%)
① 1
50.0%
② 1
50.0%

소재지(위치)
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
송파구
209 
강서구
184 
강남구
 
159
영등포구
 
146
서초구
 
141
Other values (22)
1818 

Length

Max length4
Median length3
Mean length3.0843056
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row자치구
3rd row<NA>
4th row<NA>
5th row마포구

Common Values

ValueCountFrequency (%)
송파구 209
 
7.9%
강서구 184
 
6.9%
강남구 159
 
6.0%
영등포구 146
 
5.5%
서초구 141
 
5.3%
노원구 127
 
4.8%
마포구 122
 
4.6%
강동구 119
 
4.5%
종로구 106
 
4.0%
양천구 106
 
4.0%
Other values (17) 1238
46.6%

Length

2023-12-11T15:35:37.961406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 209
 
7.9%
강서구 184
 
6.9%
강남구 159
 
6.0%
영등포구 146
 
5.5%
서초구 141
 
5.3%
노원구 127
 
4.8%
마포구 122
 
4.6%
강동구 119
 
4.5%
종로구 106
 
4.0%
양천구 106
 
4.0%
Other values (17) 1238
46.6%

Unnamed: 3
Text

Distinct2553
Distinct (%)96.2%
Missing3
Missing (%)0.1%
Memory size20.9 KiB
2023-12-11T15:35:38.334555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length79
Median length38
Mean length14.769781
Min length3

Characters and Unicode

Total characters39199
Distinct characters386
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2467 ?
Unique (%)93.0%

Sample

1st row상세주소
2nd row서울특별시 마포구 월드컵로 72
3rd row서울특별시 마포구 월드컵로 79
4th row서울특별시 마포구 양화로 59
5th row서울특별시 마포구 양화로 48
ValueCountFrequency (%)
서울특별시 1221
 
14.1%
강서구 148
 
1.7%
송파구 131
 
1.5%
노원구 127
 
1.5%
영등포구 122
 
1.4%
마포구 117
 
1.3%
강남구 112
 
1.3%
지하 106
 
1.2%
구로구 104
 
1.2%
서초구 99
 
1.1%
Other values (2692) 6381
73.6%
2023-12-11T15:35:39.009750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6186
 
15.8%
구 2105
 
5.4%
로 1834
 
4.7%
1 1760
 
4.5%
서 1712
 
4.4%
동 1466
 
3.7%
울 1320
 
3.4%
2 1285
 
3.3%
시 1285
 
3.3%
별 1223
 
3.1%
Other values (376) 19023
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22840
58.3%
Decimal Number 9076
 
23.2%
Space Separator 6186
 
15.8%
Dash Punctuation 882
 
2.3%
Open Punctuation 75
 
0.2%
Close Punctuation 75
 
0.2%
Control 34
 
0.1%
Other Punctuation 15
 
< 0.1%
Uppercase Letter 11
 
< 0.1%
Lowercase Letter 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 2105
 
9.2%
로 1834
 
8.0%
서 1712
 
7.5%
동 1466
 
6.4%
울 1320
 
5.8%
시 1285
 
5.6%
별 1223
 
5.4%
특 1222
 
5.4%
강 425
 
1.9%
길 388
 
1.7%
Other values (343) 9860
43.2%
Decimal Number
ValueCountFrequency (%)
1 1760
19.4%
2 1285
14.2%
3 1014
11.2%
4 831
9.2%
5 783
8.6%
6 763
8.4%
7 759
8.4%
0 707
7.8%
9 590
 
6.5%
8 584
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
C 2
18.2%
B 2
18.2%
A 1
9.1%
M 1
9.1%
J 1
9.1%
K 1
9.1%
I 1
9.1%
S 1
9.1%
G 1
9.1%
Other Punctuation
ValueCountFrequency (%)
, 5
33.3%
' 4
26.7%
? 3
20.0%
. 2
 
13.3%
@ 1
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
t 2
50.0%
i 1
25.0%
c 1
25.0%
Space Separator
ValueCountFrequency (%)
6186
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 882
100.0%
Open Punctuation
ValueCountFrequency (%)
( 75
100.0%
Close Punctuation
ValueCountFrequency (%)
) 75
100.0%
Control
ValueCountFrequency (%)
34
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22840
58.3%
Common 16344
41.7%
Latin 15
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 2105
 
9.2%
로 1834
 
8.0%
서 1712
 
7.5%
동 1466
 
6.4%
울 1320
 
5.8%
시 1285
 
5.6%
별 1223
 
5.4%
특 1222
 
5.4%
강 425
 
1.9%
길 388
 
1.7%
Other values (343) 9860
43.2%
Common
ValueCountFrequency (%)
6186
37.8%
1 1760
 
10.8%
2 1285
 
7.9%
3 1014
 
6.2%
- 882
 
5.4%
4 831
 
5.1%
5 783
 
4.8%
6 763
 
4.7%
7 759
 
4.6%
0 707
 
4.3%
Other values (11) 1374
 
8.4%
Latin
ValueCountFrequency (%)
C 2
13.3%
t 2
13.3%
B 2
13.3%
A 1
6.7%
M 1
6.7%
J 1
6.7%
i 1
6.7%
c 1
6.7%
K 1
6.7%
I 1
6.7%
Other values (2) 2
13.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22840
58.3%
ASCII 16359
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6186
37.8%
1 1760
 
10.8%
2 1285
 
7.9%
3 1014
 
6.2%
- 882
 
5.4%
4 831
 
5.1%
5 783
 
4.8%
6 763
 
4.7%
7 759
 
4.6%
0 707
 
4.3%
Other values (23) 1389
 
8.5%
Hangul
ValueCountFrequency (%)
구 2105
 
9.2%
로 1834
 
8.0%
서 1712
 
7.5%
동 1466
 
6.4%
울 1320
 
5.8%
시 1285
 
5.6%
별 1223
 
5.4%
특 1222
 
5.4%
강 425
 
1.9%
길 388
 
1.7%
Other values (343) 9860
43.2%

Unnamed: 4
Text

Distinct2574
Distinct (%)97.0%
Missing3
Missing (%)0.1%
Memory size20.9 KiB
2023-12-11T15:35:39.373057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.888847
Min length2

Characters and Unicode

Total characters28899
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2494 ?
Unique (%)94.0%

Sample

1st row위도
2nd row37.5556488
3rd row37.55495071
4th row37.55062866
5th row37.55000687
ValueCountFrequency (%)
37.5586319 2
 
0.1%
37.48163223 2
 
0.1%
37.58369827 2
 
0.1%
37.52069473 2
 
0.1%
37.50723267 2
 
0.1%
37.49449921 2
 
0.1%
37.55894852 2
 
0.1%
37.51758957 2
 
0.1%
37.49535751 2
 
0.1%
37.50508881 2
 
0.1%
Other values (2564) 2634
99.2%
2023-12-11T15:35:39.859685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 4508
15.6%
3 4361
15.1%
5 3548
12.3%
. 2653
9.2%
6 2376
8.2%
4 2311
8.0%
8 1968
6.8%
9 1902
6.6%
1 1891
6.5%
2 1749
 
6.1%
Other values (3) 1632
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26244
90.8%
Other Punctuation 2653
 
9.2%
Other Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 4508
17.2%
3 4361
16.6%
5 3548
13.5%
6 2376
9.1%
4 2311
8.8%
8 1968
7.5%
9 1902
7.2%
1 1891
7.2%
2 1749
 
6.7%
0 1630
 
6.2%
Other Letter
ValueCountFrequency (%)
위 1
50.0%
도 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2653
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28897
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
7 4508
15.6%
3 4361
15.1%
5 3548
12.3%
. 2653
9.2%
6 2376
8.2%
4 2311
8.0%
8 1968
6.8%
9 1902
6.6%
1 1891
6.5%
2 1749
 
6.1%
Hangul
ValueCountFrequency (%)
위 1
50.0%
도 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28897
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 4508
15.6%
3 4361
15.1%
5 3548
12.3%
. 2653
9.2%
6 2376
8.2%
4 2311
8.0%
8 1968
6.8%
9 1902
6.6%
1 1891
6.5%
2 1749
 
6.1%
Hangul
ValueCountFrequency (%)
위 1
50.0%
도 1
50.0%

Unnamed: 5
Text

Distinct2570
Distinct (%)96.8%
Missing3
Missing (%)0.1%
Memory size20.9 KiB
2023-12-11T15:35:40.159195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.882442
Min length2

Characters and Unicode

Total characters28882
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2491 ?
Unique (%)93.9%

Sample

1st row경도
2nd row126.9106293
3rd row126.9108353
4th row126.9149857
5th row126.9148254
ValueCountFrequency (%)
127.0162888 3
 
0.1%
127.0427704 3
 
0.1%
127.0446091 3
 
0.1%
126.8274384 3
 
0.1%
127.0446014 3
 
0.1%
127.0469971 2
 
0.1%
126.8257599 2
 
0.1%
127.0152512 2
 
0.1%
126.9661789 2
 
0.1%
126.9077988 2
 
0.1%
Other values (2560) 2629
99.1%
2023-12-11T15:35:40.568723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4543
15.7%
2 4276
14.8%
7 2995
10.4%
6 2905
10.1%
. 2653
9.2%
9 2332
8.1%
0 2273
7.9%
8 2136
7.4%
3 1630
 
5.6%
4 1596
 
5.5%
Other values (3) 1543
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26227
90.8%
Other Punctuation 2653
 
9.2%
Other Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4543
17.3%
2 4276
16.3%
7 2995
11.4%
6 2905
11.1%
9 2332
8.9%
0 2273
8.7%
8 2136
8.1%
3 1630
 
6.2%
4 1596
 
6.1%
5 1541
 
5.9%
Other Letter
ValueCountFrequency (%)
경 1
50.0%
도 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2653
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28880
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4543
15.7%
2 4276
14.8%
7 2995
10.4%
6 2905
10.1%
. 2653
9.2%
9 2332
8.1%
0 2273
7.9%
8 2136
7.4%
3 1630
 
5.6%
4 1596
 
5.5%
Hangul
ValueCountFrequency (%)
경 1
50.0%
도 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28880
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4543
15.7%
2 4276
14.8%
7 2995
10.4%
6 2905
10.1%
. 2653
9.2%
9 2332
8.1%
0 2273
7.9%
8 2136
7.4%
3 1630
 
5.6%
4 1596
 
5.5%
Hangul
ValueCountFrequency (%)
경 1
50.0%
도 1
50.0%

설치 시기
Date

MISSING 

Distinct455
Distinct (%)17.3%
Missing28
Missing (%)1.1%
Memory size20.9 KiB
Minimum2015-09-06 00:00:00
Maximum2022-06-28 00:00:00
2023-12-11T15:35:40.772005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:40.930830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

설치형태
Categorical

IMBALANCE 

Distinct30
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
<NA>
1365 
10
621 
15
191 
20
 
122
8
 
75
Other values (25)
283 

Length

Max length5
Median length4
Mean length2.968009
Min length1

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st rowLCD
2nd row<NA>
3rd row거치 대수
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1365
51.4%
10 621
23.4%
15 191
 
7.2%
20 122
 
4.6%
8 75
 
2.8%
12 49
 
1.8%
9 36
 
1.4%
7 32
 
1.2%
13 30
 
1.1%
14 27
 
1.0%
Other values (20) 109
 
4.1%

Length

2023-12-11T15:35:41.094588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1365
51.4%
10 621
23.4%
15 191
 
7.2%
20 122
 
4.6%
8 75
 
2.8%
12 49
 
1.8%
9 36
 
1.4%
7 32
 
1.2%
13 30
 
1.1%
14 27
 
1.0%
Other values (21) 110
 
4.1%

Unnamed: 8
Text

MISSING 

Distinct71
Distinct (%)5.1%
Missing1277
Missing (%)48.1%
Memory size20.9 KiB
2023-12-11T15:35:41.263958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.2057971
Min length1

Characters and Unicode

Total characters3044
Distinct characters19
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

Unique23 ?
Unique (%)1.7%

Sample

1st rowQR
2nd row거치 대수
3rd row15
4th row14
5th row13
ValueCountFrequency (%)
10 619
44.8%
15 140
 
10.1%
8 99
 
7.2%
20 83
 
6.0%
5 64
 
4.6%
7 50
 
3.6%
12 45
 
3.3%
6 41
 
3.0%
9 41
 
3.0%
13 26
 
1.9%
Other values (39) 173
 
12.5%
2023-12-11T15:35:41.539811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 947
31.1%
0 777
25.5%
401
13.2%
2 237
 
7.8%
5 236
 
7.8%
8 117
 
3.8%
3 68
 
2.2%
7 65
 
2.1%
6 57
 
1.9%
9 54
 
1.8%
Other values (9) 85
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2590
85.1%
Space Separator 401
 
13.2%
Dash Punctuation 46
 
1.5%
Other Letter 4
 
0.1%
Uppercase Letter 2
 
0.1%
Control 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 947
36.6%
0 777
30.0%
2 237
 
9.2%
5 236
 
9.1%
8 117
 
4.5%
3 68
 
2.6%
7 65
 
2.5%
6 57
 
2.2%
9 54
 
2.1%
4 32
 
1.2%
Other Letter
ValueCountFrequency (%)
거 1
25.0%
치 1
25.0%
대 1
25.0%
수 1
25.0%
Uppercase Letter
ValueCountFrequency (%)
Q 1
50.0%
R 1
50.0%
Space Separator
ValueCountFrequency (%)
401
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3038
99.8%
Hangul 4
 
0.1%
Latin 2
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 947
31.2%
0 777
25.6%
401
13.2%
2 237
 
7.8%
5 236
 
7.8%
8 117
 
3.9%
3 68
 
2.2%
7 65
 
2.1%
6 57
 
1.9%
9 54
 
1.8%
Other values (3) 79
 
2.6%
Hangul
ValueCountFrequency (%)
거 1
25.0%
치 1
25.0%
대 1
25.0%
수 1
25.0%
Latin
ValueCountFrequency (%)
Q 1
50.0%
R 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3040
99.9%
Hangul 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 947
31.2%
0 777
25.6%
401
13.2%
2 237
 
7.8%
5 236
 
7.8%
8 117
 
3.8%
3 68
 
2.2%
7 65
 
2.1%
6 57
 
1.9%
9 54
 
1.8%
Other values (5) 81
 
2.7%
Hangul
ValueCountFrequency (%)
거 1
25.0%
치 1
25.0%
대 1
25.0%
수 1
25.0%

운영 방식
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
QR
1353 
LCD
1276 
<NA>
 
28

Length

Max length4
Median length2
Mean length2.5013173
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th rowQR

Common Values

ValueCountFrequency (%)
QR 1353
50.9%
LCD 1276
48.0%
<NA> 28
 
1.1%

Length

2023-12-11T15:35:41.669040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:35:41.769899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
qr 1353
50.9%
lcd 1276
48.0%
na 28
 
1.1%

Interactions

2023-12-11T15:35:35.939566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:35:41.825381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소 번호소재지(위치)설치형태Unnamed: 8운영 방식
대여소\n번호1.0000.8750.1510.6460.815
소재지(위치)0.8751.0000.1940.6680.218
설치형태0.1510.1941.0000.9070.221
Unnamed: 80.6460.6680.9071.0000.833
운영\n방식0.8150.2180.2210.8331.000
2023-12-11T15:35:41.905664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지(위치)설치형태운영 방식
소재지(위치)1.0000.0490.187
설치형태0.0491.0000.188
운영\n방식0.1870.1881.000
2023-12-11T15:35:41.979171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소 번호소재지(위치)설치형태운영 방식
대여소\n번호1.0000.5440.0660.647
소재지(위치)0.5441.0000.0490.187
설치형태0.0660.0491.0000.188
운영\n방식0.6470.1870.1881.000

Missing values

2023-12-11T15:35:36.077520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:35:36.225602image/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.
2023-12-11T15:35:36.668286image/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

대여소 번호보관소(대여소)명소재지(위치)Unnamed: 3Unnamed: 4Unnamed: 5설치 시기설치형태Unnamed: 8운영 방식
0<NA><NA><NA><NA><NA><NA><NA>LCDQR<NA>
1<NA><NA>자치구상세주소위도경도<NA><NA><NA><NA>
2<NA><NA><NA><NA><NA><NA><NA>거치 대수거치 대수<NA>
3<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4102망원역 1번출구 앞마포구서울특별시 마포구 월드컵로 7237.5556488126.91062932022-05-03<NA>15QR
5103망원역 2번출구 앞마포구서울특별시 마포구 월드컵로 7937.55495071126.91083532022-05-03<NA>14QR
6104합정역 1번출구 앞마포구서울특별시 마포구 양화로 5937.55062866126.91498572022-05-12<NA>13QR
7105합정역 5번출구 앞마포구서울특별시 마포구 양화로 4837.55000687126.91482542015-09-06<NA>5QR
8106합정역 7번출구 앞마포구서울특별시 마포구 독막로 437.54864502126.91282652022-05-12<NA>12QR
9107신한은행 서교동지점마포구서울특별시 마포구 월드컵북로 3537.55751038126.91850282021-12-28<NA>5QR
대여소 번호보관소(대여소)명소재지(위치)Unnamed: 3Unnamed: 4Unnamed: 5설치 시기설치형태Unnamed: 8운영 방식
26475305대륜E&S 서울지사 앞노원구노원구 덕릉로70길 5437.64218903127.0592882022-05-16<NA>10QR
26485306동신아파트 후문 옆노원구노원구 공릉로 21337.62786102127.07637792022-05-16<NA>6QR
26495751송파여성축구장송파구방이동 88-937.52471542127.11743932022-05-19<NA>10QR
26505752풍납백제문화공원 옆 인근송파구풍납동 219-337.53488159127.11522682022-05-19<NA>6QR
26515753호반써밋송파구송파구 거여동 607-137.48294449127.14044192022-05-19<NA>9QR
26525851도림천 건널목 위영등포구영등포구 대림동 646-137.50208282126.89318852022-02-08<NA>15QR
26535852대림동현대2차 201동 앞영등포구영등포구 대림동 640-237.50300598126.89382942022-02-08<NA>7QR
26545853여의도역2번출구 앞영등포구여의도동 의사당대로 8837.52260971126.92301182022-05-11<NA>10QR
26555854신한금융투자 앞영등포구여의대로 7037.52508926126.92407992022-06-09<NA>26QR
26565855하이투자증권 앞영등포구여의대로 6637.52464294126.92342382022-06-09<NA>31QR