Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory986.3 KiB
Average record size in memory101.0 B

Variable types

Categorical4
Numeric4
Text3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15248/F/1/datasetView.do

Alerts

대여일자 has constant value ""Constant
이용건수 is highly overall correlated with 이동거리(M) and 1 other fieldsHigh correlation
이동거리(M) is highly overall correlated with 이용건수 and 1 other fieldsHigh correlation
이용시간(분) is highly overall correlated with 이용건수 and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-03-13 13:00:47.905375
Analysis finished2024-03-13 13:00:52.276491
Duration4.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
202207
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202207
2nd row202207
3rd row202207
4th row202207
5th row202207

Common Values

ValueCountFrequency (%)
202207 10000
100.0%

Length

2024-03-13T22:00:52.359824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:00:52.491741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202207 10000
100.0%

대여소번호
Real number (ℝ)

Distinct596
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean465.2354
Minimum102
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:52.611628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile135
Q1278
median455
Q3642
95-th percentile812
Maximum846
Range744
Interquartile range (IQR)364

Descriptive statistics

Standard deviation216.43414
Coefficient of variation (CV)0.46521426
Kurtosis-1.1719529
Mean465.2354
Median Absolute Deviation (MAD)183
Skewness0.0899208
Sum4652354
Variance46843.738
MonotonicityNot monotonic
2024-03-13T22:00:52.813624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
458 34
 
0.3%
420 31
 
0.3%
633 31
 
0.3%
186 30
 
0.3%
502 30
 
0.3%
796 27
 
0.3%
117 27
 
0.3%
361 26
 
0.3%
431 26
 
0.3%
184 26
 
0.3%
Other values (586) 9712
97.1%
ValueCountFrequency (%)
102 20
0.2%
103 12
0.1%
104 19
0.2%
105 18
0.2%
106 19
0.2%
107 15
0.1%
108 21
0.2%
109 17
0.2%
111 9
0.1%
112 11
0.1%
ValueCountFrequency (%)
846 20
0.2%
845 13
0.1%
844 18
0.2%
843 19
0.2%
841 17
0.2%
840 16
0.2%
839 13
0.1%
838 16
0.2%
837 13
0.1%
836 16
0.2%

대여소명
Text

Distinct596
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T22:00:53.191652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length14.4445
Min length7

Characters and Unicode

Total characters144445
Distinct characters384
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

Unique0 ?
Unique (%)0.0%

Sample

1st row526. 용답토속공원 앞
2nd row138. 신촌동 제1공영주차장 앞
3rd row133. 해담는다리
4th row314. 국립현대미술관
5th row425. DMC첨단산업센터
ValueCountFrequency (%)
앞 3496
 
11.3%
옆 663
 
2.1%
사거리 405
 
1.3%
출구 368
 
1.2%
1번출구 363
 
1.2%
뒤 292
 
0.9%
2번출구 264
 
0.9%
입구 259
 
0.8%
교차로 252
 
0.8%
3번출구 247
 
0.8%
Other values (1243) 24247
78.6%
2024-03-13T22:00:53.790178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20960
 
14.5%
. 10000
 
6.9%
1 4578
 
3.2%
2 4503
 
3.1%
4 4152
 
2.9%
3 4135
 
2.9%
앞 3769
 
2.6%
5 3610
 
2.5%
7 3438
 
2.4%
6 3258
 
2.3%
Other values (374) 82042
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76546
53.0%
Decimal Number 34602
24.0%
Space Separator 20960
 
14.5%
Other Punctuation 10030
 
6.9%
Uppercase Letter 1492
 
1.0%
Open Punctuation 390
 
0.3%
Close Punctuation 390
 
0.3%
Dash Punctuation 35
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 3769
 
4.9%
구 3241
 
4.2%
역 2567
 
3.4%
번 2329
 
3.0%
출 2309
 
3.0%
동 2276
 
3.0%
교 1721
 
2.2%
리 1492
 
1.9%
아 1353
 
1.8%
대 1336
 
1.7%
Other values (339) 54153
70.7%
Uppercase Letter
ValueCountFrequency (%)
S 212
14.2%
K 183
12.3%
C 182
12.2%
D 170
11.4%
B 150
10.1%
M 128
8.6%
I 90
6.0%
T 74
 
5.0%
G 47
 
3.2%
A 47
 
3.2%
Other values (9) 209
14.0%
Decimal Number
ValueCountFrequency (%)
1 4578
13.2%
2 4503
13.0%
4 4152
12.0%
3 4135
12.0%
5 3610
10.4%
7 3438
9.9%
6 3258
9.4%
8 2736
7.9%
0 2312
6.7%
9 1880
5.4%
Other Punctuation
ValueCountFrequency (%)
. 10000
99.7%
, 30
 
0.3%
Space Separator
ValueCountFrequency (%)
20960
100.0%
Open Punctuation
ValueCountFrequency (%)
( 390
100.0%
Close Punctuation
ValueCountFrequency (%)
) 390
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76546
53.0%
Common 66407
46.0%
Latin 1492
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 3769
 
4.9%
구 3241
 
4.2%
역 2567
 
3.4%
번 2329
 
3.0%
출 2309
 
3.0%
동 2276
 
3.0%
교 1721
 
2.2%
리 1492
 
1.9%
아 1353
 
1.8%
대 1336
 
1.7%
Other values (339) 54153
70.7%
Latin
ValueCountFrequency (%)
S 212
14.2%
K 183
12.3%
C 182
12.2%
D 170
11.4%
B 150
10.1%
M 128
8.6%
I 90
6.0%
T 74
 
5.0%
G 47
 
3.2%
A 47
 
3.2%
Other values (9) 209
14.0%
Common
ValueCountFrequency (%)
20960
31.6%
. 10000
15.1%
1 4578
 
6.9%
2 4503
 
6.8%
4 4152
 
6.3%
3 4135
 
6.2%
5 3610
 
5.4%
7 3438
 
5.2%
6 3258
 
4.9%
8 2736
 
4.1%
Other values (6) 5037
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76546
53.0%
ASCII 67899
47.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20960
30.9%
. 10000
14.7%
1 4578
 
6.7%
2 4503
 
6.6%
4 4152
 
6.1%
3 4135
 
6.1%
5 3610
 
5.3%
7 3438
 
5.1%
6 3258
 
4.8%
8 2736
 
4.0%
Other values (25) 6529
 
9.6%
Hangul
ValueCountFrequency (%)
앞 3769
 
4.9%
구 3241
 
4.2%
역 2567
 
3.4%
번 2329
 
3.0%
출 2309
 
3.0%
동 2276
 
3.0%
교 1721
 
2.2%
리 1492
 
1.9%
아 1353
 
1.8%
대 1336
 
1.7%
Other values (339) 54153
70.7%

대여구분코드
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기권
4826 
일일권
3866 
단체권
998 
일일권(비회원)
 
310

Length

Max length8
Median length3
Mean length3.155
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정기권
2nd row정기권
3rd row정기권
4th row일일권(비회원)
5th row정기권

Common Values

ValueCountFrequency (%)
정기권 4826
48.3%
일일권 3866
38.7%
단체권 998
 
10.0%
일일권(비회원) 310
 
3.1%

Length

2024-03-13T22:00:54.176368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:00:54.396173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기권 4826
48.3%
일일권 3866
38.7%
단체권 998
 
10.0%
일일권(비회원 310
 
3.1%

성별
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3578 
<NA>
3269 
F
3151 
f
 
2

Length

Max length4
Median length1
Mean length1.9807
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M 3578
35.8%
<NA> 3269
32.7%
F 3151
31.5%
f 2
 
< 0.1%

Length

2024-03-13T22:00:54.612615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:00:54.783895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3578
35.8%
na 3269
32.7%
f 3153
31.5%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
1661 
20대
1642 
30대
1477 
40대
1428 
~10대
1384 
Other values (3)
2408 

Length

Max length5
Median length3
Mean length3.0423
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row40대
3rd row기타
4th row기타
5th row60대

Common Values

ValueCountFrequency (%)
기타 1661
16.6%
20대 1642
16.4%
30대 1477
14.8%
40대 1428
14.3%
~10대 1384
13.8%
50대 1219
12.2%
60대 839
8.4%
70대이상 350
 
3.5%

Length

2024-03-13T22:00:54.961831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:00:55.135830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 1661
16.6%
20대 1642
16.4%
30대 1477
14.8%
40대 1428
14.3%
10대 1384
13.8%
50대 1219
12.2%
60대 839
8.4%
70대이상 350
 
3.5%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct414
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.8459
Minimum1
Maximum968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:55.330294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median13
Q345
95-th percentile183
Maximum968
Range967
Interquartile range (IQR)41

Descriptive statistics

Standard deviation75.022543
Coefficient of variation (CV)1.792829
Kurtosis25.913634
Mean41.8459
Median Absolute Deviation (MAD)11
Skewness4.1540634
Sum418459
Variance5628.382
MonotonicityNot monotonic
2024-03-13T22:00:55.523316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 941
 
9.4%
2 939
 
9.4%
3 554
 
5.5%
4 452
 
4.5%
5 371
 
3.7%
6 348
 
3.5%
7 274
 
2.7%
8 242
 
2.4%
9 215
 
2.1%
10 180
 
1.8%
Other values (404) 5484
54.8%
ValueCountFrequency (%)
1 941
9.4%
2 939
9.4%
3 554
5.5%
4 452
4.5%
5 371
 
3.7%
6 348
 
3.5%
7 274
 
2.7%
8 242
 
2.4%
9 215
 
2.1%
10 180
 
1.8%
ValueCountFrequency (%)
968 1
< 0.1%
951 1
< 0.1%
939 1
< 0.1%
930 1
< 0.1%
794 1
< 0.1%
760 1
< 0.1%
743 1
< 0.1%
738 1
< 0.1%
713 2
< 0.1%
697 1
< 0.1%

운동량
Text

Distinct9777
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T22:00:55.955230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.3351
Min length1

Characters and Unicode

Total characters63351
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9575 ?
Unique (%)95.8%

Sample

1st row221.83
2nd row862.72
3rd row10120.25
4th row113.51
5th row145.53
ValueCountFrequency (%)
0 9
 
0.1%
n 5
 
< 0.1%
21.11 4
 
< 0.1%
45.81 3
 
< 0.1%
42.97 3
 
< 0.1%
22.91 3
 
< 0.1%
35.26 3
 
< 0.1%
176.2 3
 
< 0.1%
61.78 3
 
< 0.1%
40.15 3
 
< 0.1%
Other values (9767) 9961
99.6%
2024-03-13T22:00:56.709728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9885
15.6%
1 7701
12.2%
2 6268
9.9%
3 5843
9.2%
4 5371
8.5%
5 5227
8.3%
6 4945
7.8%
7 4905
7.7%
9 4820
7.6%
8 4771
7.5%
Other values (3) 3615
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53456
84.4%
Other Punctuation 9890
 
15.6%
Uppercase Letter 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7701
14.4%
2 6268
11.7%
3 5843
10.9%
4 5371
10.0%
5 5227
9.8%
6 4945
9.3%
7 4905
9.2%
9 4820
9.0%
8 4771
8.9%
0 3605
6.7%
Other Punctuation
ValueCountFrequency (%)
. 9885
99.9%
\ 5
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63346
> 99.9%
Latin 5
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9885
15.6%
1 7701
12.2%
2 6268
9.9%
3 5843
9.2%
4 5371
8.5%
5 5227
8.3%
6 4945
7.8%
7 4905
7.7%
9 4820
7.6%
8 4771
7.5%
Other values (2) 3610
 
5.7%
Latin
ValueCountFrequency (%)
N 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9885
15.6%
1 7701
12.2%
2 6268
9.9%
3 5843
9.2%
4 5371
8.5%
5 5227
8.3%
6 4945
7.8%
7 4905
7.7%
9 4820
7.6%
8 4771
7.5%
Other values (3) 3615
 
5.7%

탄소량
Text

Distinct4458
Distinct (%)44.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T22:00:57.234506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.4178
Min length1

Characters and Unicode

Total characters44178
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2666 ?
Unique (%)26.7%

Sample

1st row2.19
2nd row8.09
3rd row87.73
4th row1.02
5th row1.22
ValueCountFrequency (%)
0.45 23
 
0.2%
0.5 23
 
0.2%
0.49 22
 
0.2%
0.56 21
 
0.2%
0.31 21
 
0.2%
0.28 21
 
0.2%
0.21 20
 
0.2%
0.65 20
 
0.2%
0.42 20
 
0.2%
0.48 19
 
0.2%
Other values (4448) 9790
97.9%
2024-03-13T22:00:57.967188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9899
22.4%
1 5452
12.3%
2 4285
9.7%
3 3578
 
8.1%
4 3365
 
7.6%
5 3248
 
7.4%
6 3089
 
7.0%
0 2896
 
6.6%
7 2881
 
6.5%
8 2806
 
6.4%
Other values (3) 2679
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34269
77.6%
Other Punctuation 9904
 
22.4%
Uppercase Letter 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5452
15.9%
2 4285
12.5%
3 3578
10.4%
4 3365
9.8%
5 3248
9.5%
6 3089
9.0%
0 2896
8.5%
7 2881
8.4%
8 2806
8.2%
9 2669
7.8%
Other Punctuation
ValueCountFrequency (%)
. 9899
99.9%
\ 5
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44173
> 99.9%
Latin 5
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9899
22.4%
1 5452
12.3%
2 4285
9.7%
3 3578
 
8.1%
4 3365
 
7.6%
5 3248
 
7.4%
6 3089
 
7.0%
0 2896
 
6.6%
7 2881
 
6.5%
8 2806
 
6.4%
Other values (2) 2674
 
6.1%
Latin
ValueCountFrequency (%)
N 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9899
22.4%
1 5452
12.3%
2 4285
9.7%
3 3578
 
8.1%
4 3365
 
7.6%
5 3248
 
7.4%
6 3089
 
7.0%
0 2896
 
6.6%
7 2881
 
6.5%
8 2806
 
6.4%
Other values (3) 2679
 
6.1%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION 

Distinct9751
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112466.42
Minimum0
Maximum5424524.6
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:58.166084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1790
Q110741.283
median39039.645
Q3126823.28
95-th percentile460889.58
Maximum5424524.6
Range5424524.6
Interquartile range (IQR)116082

Descriptive statistics

Standard deviation206269.44
Coefficient of variation (CV)1.8340536
Kurtosis97.690624
Mean112466.42
Median Absolute Deviation (MAD)34476.785
Skewness6.646733
Sum1.1246642 × 109
Variance4.2547084 × 1010
MonotonicityNot monotonic
2024-03-13T22:00:58.363206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
0.1%
820.0 7
 
0.1%
1340.0 6
 
0.1%
2170.0 5
 
0.1%
2800.0 5
 
0.1%
890.0 4
 
< 0.1%
1270.0 4
 
< 0.1%
1200.0 4
 
< 0.1%
1540.0 4
 
< 0.1%
1420.0 4
 
< 0.1%
Other values (9741) 9944
99.4%
ValueCountFrequency (%)
0.0 13
0.1%
0.39 1
 
< 0.1%
8.01 1
 
< 0.1%
10.0 1
 
< 0.1%
88.14 1
 
< 0.1%
110.0 1
 
< 0.1%
130.0 2
 
< 0.1%
141.9 1
 
< 0.1%
142.67 1
 
< 0.1%
170.0 1
 
< 0.1%
ValueCountFrequency (%)
5424524.59 1
< 0.1%
4746202.91 1
< 0.1%
3492548.42 1
< 0.1%
3031636.74 1
< 0.1%
2604879.04 1
< 0.1%
2386255.3 1
< 0.1%
2324493.45 1
< 0.1%
2157284.94 1
< 0.1%
2154853.43 1
< 0.1%
2148433.78 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct2831
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean954.85
Minimum0
Maximum51494
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T22:00:58.549324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.95
Q1100
median346
Q31101.25
95-th percentile3898.05
Maximum51494
Range51494
Interquartile range (IQR)1001.25

Descriptive statistics

Standard deviation1699.1633
Coefficient of variation (CV)1.7795081
Kurtosis131.7276
Mean954.85
Median Absolute Deviation (MAD)301
Skewness7.2785508
Sum9548500
Variance2887155.9
MonotonicityNot monotonic
2024-03-13T22:00:58.776897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 45
 
0.4%
31 45
 
0.4%
19 43
 
0.4%
12 42
 
0.4%
15 41
 
0.4%
11 41
 
0.4%
24 40
 
0.4%
16 39
 
0.4%
34 39
 
0.4%
53 39
 
0.4%
Other values (2821) 9586
95.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 5
 
0.1%
2 8
 
0.1%
3 23
0.2%
4 33
0.3%
5 24
0.2%
6 33
0.3%
7 32
0.3%
8 32
0.3%
9 34
0.3%
ValueCountFrequency (%)
51494 1
< 0.1%
38027 1
< 0.1%
35653 1
< 0.1%
19648 1
< 0.1%
19559 1
< 0.1%
18360 1
< 0.1%
16609 1
< 0.1%
16448 1
< 0.1%
14723 1
< 0.1%
14518 1
< 0.1%

Interactions

2024-03-13T22:00:51.332965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:49.329494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:50.308309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:50.817221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:51.454615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:49.923398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:50.444459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:50.955213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:51.578017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:50.040045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:50.563685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:51.074980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:51.759375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:50.178529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:50.686585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:00:51.203316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:00:58.980684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0130.0000.0000.0800.0610.054
대여구분코드0.0131.0000.0240.4940.3070.1500.121
성별0.0000.0241.0000.0430.1630.1250.056
연령대코드0.0000.4940.0431.0000.2540.1470.134
이용건수0.0800.3070.1630.2541.0000.6960.747
이동거리(M)0.0610.1500.1250.1470.6961.0000.945
이용시간(분)0.0540.1210.0560.1340.7470.9451.000
2024-03-13T22:00:59.151285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여구분코드성별연령대코드
대여구분코드1.0000.0230.238
성별0.0231.0000.027
연령대코드0.2380.0271.000
2024-03-13T22:00:59.285962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.0000.007-0.017-0.0240.0080.0000.000
이용건수0.0071.0000.9450.9490.1880.0720.124
이동거리(M)-0.0170.9451.0000.9790.0960.0520.072
이용시간(분)-0.0240.9490.9791.0000.0840.0420.072
대여구분코드0.0080.1880.0960.0841.0000.0230.238
성별0.0000.0720.0520.0420.0231.0000.027
연령대코드0.0000.1240.0720.0720.2380.0271.000

Missing values

2024-03-13T22:00:51.960822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:00:52.183084image/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

대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
16053202207526526. 용답토속공원 앞정기권F기타8221.832.199458.81116
1556202207138138. 신촌동 제1공영주차장 앞정기권F40대5862.728.0934854.61200
1400202207133133. 해담는다리정기권F기타7810120.2587.73378177.292959
8263202207314314. 국립현대미술관일일권(비회원)F기타1113.511.024410.0110
12271202207425425. DMC첨단산업센터정기권M60대1145.531.225250.029
9587202207352352. 중앙고입구 삼거리일일권F~10대15.130.06240.020
14575202207488488.푸르메병원정기권<NA>40대321376.4910.1843923.66366
20874202207664664. 서울시립대 건설공학관정기권M70대이상1101.720.783380.025
3424202207188188. 홍은동 정원여중 입구일일권<NA>50대1126.251.144904.7829
15928202207523523. 옥수동성당 옆정기권F~10대1139.431.175030.031
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
1128202207126126. 서강대 후문 옆정기권F기타5245.292.6611466.1105
11067202207398398. 을지로3가역 3번출구단체권F20대2205.151.857970.042
8124202207308308. 광화문 S타워 앞정기권F30대955822.0754.03232812.252331
6254202207259259. 대방역6번출구정기권M60대878035.8364.31277048.881940
8103202207308308. 광화문 S타워 앞일일권F30대13688.827.231047.96288
7558202207292292. 영일 어린이공원일일권F~10대8472.334.5419579.08219
13098202207446446. 상명대입구정기권M30대394515.6437.65162239.161093
6804202207275275. 신동아아파트일일권M50대6447.474.5219482.99245
23433202207752752. 성원2차 아파트일일권F~10대6494.195.1122013.75192
10781202207389389. 을지로4가역 1번출구정기권F30대582336.3922.3296161.342537