Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory976.6 KiB
Average record size in memory100.0 B

Variable types

Categorical4
Numeric4
Text3

Dataset

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

Alerts

이용건수 is highly overall correlated with 이동거리 and 1 other fieldsHigh correlation
이동거리 is highly overall correlated with 이용건수 and 1 other fieldsHigh correlation
이용시간 is highly overall correlated with 이용건수 and 1 other fieldsHigh correlation
대여일자 is highly imbalanced (73.5%)Imbalance
대여소번호 is highly skewed (γ1 = 44.13539298)Skewed
이동거리 has 148 (1.5%) zerosZeros

Reproduction

Analysis started2024-04-17 19:02:50.488297
Analysis finished2024-04-17 19:02:53.616012
Duration3.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Jan-20
9550 
Feb-20
 
450

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJan-20
2nd rowJan-20
3rd rowJan-20
4th rowJan-20
5th rowJan-20

Common Values

ValueCountFrequency (%)
Jan-20 9550
95.5%
Feb-20 450
 
4.5%

Length

2024-04-18T04:02:53.666344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:02:53.744028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
jan-20 9550
95.5%
feb-20 450
 
4.5%

대여소번호
Real number (ℝ)

SKEWED 

Distinct1534
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1236.693
Minimum3
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-18T04:02:53.851947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile136
Q1456
median1170
Q31906
95-th percentile2509
Maximum99999
Range99996
Interquartile range (IQR)1450

Descriptive statistics

Standard deviation1298.9351
Coefficient of variation (CV)1.0503295
Kurtosis3342.0698
Mean1236.693
Median Absolute Deviation (MAD)716
Skewness44.135393
Sum12366930
Variance1687232.5
MonotonicityNot monotonic
2024-04-18T04:02:53.960557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104 25
 
0.2%
117 24
 
0.2%
103 24
 
0.2%
106 22
 
0.2%
133 20
 
0.2%
152 19
 
0.2%
123 19
 
0.2%
110 19
 
0.2%
131 18
 
0.2%
144 18
 
0.2%
Other values (1524) 9792
97.9%
ValueCountFrequency (%)
3 2
 
< 0.1%
5 2
 
< 0.1%
10 1
 
< 0.1%
101 8
 
0.1%
102 13
0.1%
103 24
0.2%
104 25
0.2%
105 7
 
0.1%
106 22
0.2%
107 14
0.1%
ValueCountFrequency (%)
99999 1
 
< 0.1%
9997 1
 
< 0.1%
3543 2
 
< 0.1%
3542 5
0.1%
3541 10
0.1%
3539 3
 
< 0.1%
3538 5
0.1%
3537 7
0.1%
3536 2
 
< 0.1%
3535 6
0.1%

대여소명
Text

Distinct1534
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-18T04:02:54.186044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length31
Mean length15.4362
Min length3

Characters and Unicode

Total characters154362
Distinct characters523
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

Unique29 ?
Unique (%)0.3%

Sample

1st row399. 서울역 센트럴 자이아파트
2nd row1031. 암사동 CBIS
3rd row2248. 서초리슈빌S 글로벌 앞
4th row2313. 금원빌딩 앞
5th row575. 중앙농협 중곡지점
ValueCountFrequency (%)
앞 2703
 
8.5%
옆 573
 
1.8%
출구 400
 
1.3%
1번출구 359
 
1.1%
뒤 283
 
0.9%
2번출구 276
 
0.9%
사거리 266
 
0.8%
3번출구 260
 
0.8%
입구 228
 
0.7%
교차로 227
 
0.7%
Other values (3367) 26182
82.4%
2024-04-18T04:02:54.520443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21932
 
14.2%
. 10020
 
6.5%
1 9379
 
6.1%
2 6541
 
4.2%
3 4585
 
3.0%
구 3746
 
2.4%
5 3453
 
2.2%
0 3321
 
2.2%
4 3240
 
2.1%
앞 3203
 
2.1%
Other values (513) 84942
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79064
51.2%
Decimal Number 40297
26.1%
Space Separator 21932
 
14.2%
Other Punctuation 10093
 
6.5%
Uppercase Letter 1172
 
0.8%
Open Punctuation 807
 
0.5%
Close Punctuation 807
 
0.5%
Lowercase Letter 86
 
0.1%
Dash Punctuation 68
 
< 0.1%
Math Symbol 26
 
< 0.1%
Other values (2) 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3746
 
4.7%
앞 3203
 
4.1%
역 2878
 
3.6%
번 2555
 
3.2%
출 2522
 
3.2%
동 1785
 
2.3%
교 1604
 
2.0%
리 1431
 
1.8%
원 1324
 
1.7%
사 1196
 
1.5%
Other values (456) 56820
71.9%
Uppercase Letter
ValueCountFrequency (%)
K 159
13.6%
S 145
12.4%
C 114
9.7%
G 96
 
8.2%
L 85
 
7.3%
T 67
 
5.7%
A 65
 
5.5%
M 62
 
5.3%
B 54
 
4.6%
I 52
 
4.4%
Other values (14) 273
23.3%
Decimal Number
ValueCountFrequency (%)
1 9379
23.3%
2 6541
16.2%
3 4585
11.4%
5 3453
 
8.6%
0 3321
 
8.2%
4 3240
 
8.0%
6 2949
 
7.3%
7 2392
 
5.9%
8 2244
 
5.6%
9 2193
 
5.4%
Lowercase Letter
ValueCountFrequency (%)
e 33
38.4%
n 14
16.3%
k 10
 
11.6%
l 8
 
9.3%
y 7
 
8.1%
t 6
 
7.0%
s 5
 
5.8%
c 1
 
1.2%
o 1
 
1.2%
m 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 10020
99.3%
, 46
 
0.5%
& 15
 
0.1%
? 8
 
0.1%
· 4
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
~ 19
73.1%
+ 7
 
26.9%
Space Separator
ValueCountFrequency (%)
21932
100.0%
Open Punctuation
ValueCountFrequency (%)
( 807
100.0%
Close Punctuation
ValueCountFrequency (%)
) 807
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79068
51.2%
Common 74036
48.0%
Latin 1258
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3746
 
4.7%
앞 3203
 
4.1%
역 2878
 
3.6%
번 2555
 
3.2%
출 2522
 
3.2%
동 1785
 
2.3%
교 1604
 
2.0%
리 1431
 
1.8%
원 1324
 
1.7%
사 1196
 
1.5%
Other values (457) 56824
71.9%
Latin
ValueCountFrequency (%)
K 159
12.6%
S 145
11.5%
C 114
 
9.1%
G 96
 
7.6%
L 85
 
6.8%
T 67
 
5.3%
A 65
 
5.2%
M 62
 
4.9%
B 54
 
4.3%
I 52
 
4.1%
Other values (24) 359
28.5%
Common
ValueCountFrequency (%)
21932
29.6%
. 10020
13.5%
1 9379
12.7%
2 6541
 
8.8%
3 4585
 
6.2%
5 3453
 
4.7%
0 3321
 
4.5%
4 3240
 
4.4%
6 2949
 
4.0%
7 2392
 
3.2%
Other values (12) 6224
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79064
51.2%
ASCII 75290
48.8%
None 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21932
29.1%
. 10020
13.3%
1 9379
12.5%
2 6541
 
8.7%
3 4585
 
6.1%
5 3453
 
4.6%
0 3321
 
4.4%
4 3240
 
4.3%
6 2949
 
3.9%
7 2392
 
3.2%
Other values (45) 7478
 
9.9%
Hangul
ValueCountFrequency (%)
구 3746
 
4.7%
앞 3203
 
4.1%
역 2878
 
3.6%
번 2555
 
3.2%
출 2522
 
3.2%
동 1785
 
2.3%
교 1604
 
2.0%
리 1431
 
1.8%
원 1324
 
1.7%
사 1196
 
1.5%
Other values (456) 56820
71.9%
None
ValueCountFrequency (%)
㈜ 4
50.0%
· 4
50.0%

대여구분코드
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
5847 
일일(회원)
3459 
단체
 
429
일일(비회원)
 
265

Length

Max length7
Median length2
Mean length3.5161
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 5847
58.5%
일일(회원) 3459
34.6%
단체 429
 
4.3%
일일(비회원) 265
 
2.6%

Length

2024-04-18T04:02:54.629228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:02:54.707098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 5847
58.5%
일일(회원 3459
34.6%
단체 429
 
4.3%
일일(비회원 265
 
2.6%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
3450 
M
2740 
F
2182 
<NA>
1624 
m
 
3

Length

Max length4
Median length2
Mean length1.8322
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row\N
2nd rowM
3rd row\N
4th row<NA>
5th rowM

Common Values

ValueCountFrequency (%)
\N 3450
34.5%
M 2740
27.4%
F 2182
21.8%
<NA> 1624
16.2%
m 3
 
< 0.1%
f 1
 
< 0.1%

Length

2024-04-18T04:02:54.794576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:02:54.878757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 3450
34.5%
m 2743
27.4%
f 2183
21.8%
na 1624
16.2%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
2270 
AGE_003
1926 
AGE_004
1679 
AGE_005
1295 
AGE_001
1230 
Other values (3)
1600 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_002
2nd rowAGE_001
3rd rowAGE_003
4th rowAGE_002
5th rowAGE_002

Common Values

ValueCountFrequency (%)
AGE_002 2270
22.7%
AGE_003 1926
19.3%
AGE_004 1679
16.8%
AGE_005 1295
13.0%
AGE_001 1230
12.3%
AGE_006 739
 
7.4%
AGE_008 570
 
5.7%
AGE_007 291
 
2.9%

Length

2024-04-18T04:02:54.968711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T04:02:55.051505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 2270
22.7%
age_003 1926
19.3%
age_004 1679
16.8%
age_005 1295
13.0%
age_001 1230
12.3%
age_006 739
 
7.4%
age_008 570
 
5.7%
age_007 291
 
2.9%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct186
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5791
Minimum1
Maximum342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-18T04:02:55.155133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q316
95-th percentile61
Maximum342
Range341
Interquartile range (IQR)14

Descriptive statistics

Standard deviation25.449183
Coefficient of variation (CV)1.7455935
Kurtosis26.819886
Mean14.5791
Median Absolute Deviation (MAD)4
Skewness4.2359006
Sum145791
Variance647.66091
MonotonicityNot monotonic
2024-04-18T04:02:55.263156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2025
20.2%
2 1350
13.5%
3 822
 
8.2%
4 608
 
6.1%
5 451
 
4.5%
6 382
 
3.8%
7 340
 
3.4%
8 296
 
3.0%
9 241
 
2.4%
11 197
 
2.0%
Other values (176) 3288
32.9%
ValueCountFrequency (%)
1 2025
20.2%
2 1350
13.5%
3 822
8.2%
4 608
 
6.1%
5 451
 
4.5%
6 382
 
3.8%
7 340
 
3.4%
8 296
 
3.0%
9 241
 
2.4%
10 192
 
1.9%
ValueCountFrequency (%)
342 1
< 0.1%
326 1
< 0.1%
322 1
< 0.1%
297 1
< 0.1%
273 1
< 0.1%
269 1
< 0.1%
268 1
< 0.1%
251 1
< 0.1%
248 1
< 0.1%
242 1
< 0.1%

운동량
Text

Distinct9228
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-18T04:02:55.550736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.0101
Min length1

Characters and Unicode

Total characters60101
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

Unique8728 ?
Unique (%)87.3%

Sample

1st row1081.47
2nd row1171.93
3rd row5580.51
4th row2790.23
5th row107.4
ValueCountFrequency (%)
0 136
 
1.4%
n 12
 
0.1%
43.24 7
 
0.1%
37.07 5
 
< 0.1%
37.32 5
 
< 0.1%
39.12 5
 
< 0.1%
51.74 5
 
< 0.1%
57.56 5
 
< 0.1%
39.38 5
 
< 0.1%
84.17 5
 
< 0.1%
Other values (9218) 9810
98.1%
2024-04-18T04:02:55.953736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9751
16.2%
1 7211
12.0%
2 5981
10.0%
3 5405
9.0%
4 5134
8.5%
5 4871
8.1%
6 4754
7.9%
7 4608
7.7%
8 4529
7.5%
9 4431
7.4%
Other values (3) 3426
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50326
83.7%
Other Punctuation 9763
 
16.2%
Uppercase Letter 12
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7211
14.3%
2 5981
11.9%
3 5405
10.7%
4 5134
10.2%
5 4871
9.7%
6 4754
9.4%
7 4608
9.2%
8 4529
9.0%
9 4431
8.8%
0 3402
6.8%
Other Punctuation
ValueCountFrequency (%)
. 9751
99.9%
\ 12
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60089
> 99.9%
Latin 12
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9751
16.2%
1 7211
12.0%
2 5981
10.0%
3 5405
9.0%
4 5134
8.5%
5 4871
8.1%
6 4754
7.9%
7 4608
7.7%
8 4529
7.5%
9 4431
7.4%
Other values (2) 3414
 
5.7%
Latin
ValueCountFrequency (%)
N 12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9751
16.2%
1 7211
12.0%
2 5981
10.0%
3 5405
9.0%
4 5134
8.5%
5 4871
8.1%
6 4754
7.9%
7 4608
7.7%
8 4529
7.5%
9 4431
7.4%
Other values (3) 3426
 
5.7%

탄소량
Text

Distinct3386
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-18T04:02:56.266497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1911
Min length1

Characters and Unicode

Total characters41911
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

Unique1934 ?
Unique (%)19.3%

Sample

1st row9.55
2nd row9.41
3rd row49.13
4th row25.55
5th row0.72
ValueCountFrequency (%)
0 137
 
1.4%
0.35 37
 
0.4%
0.26 36
 
0.4%
0.29 34
 
0.3%
0.32 33
 
0.3%
0.68 32
 
0.3%
0.39 31
 
0.3%
0.63 30
 
0.3%
0.6 30
 
0.3%
0.62 30
 
0.3%
Other values (3376) 9570
95.7%
2024-04-18T04:02:56.683447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9774
23.3%
1 5132
12.2%
2 3909
 
9.3%
3 3475
 
8.3%
0 3446
 
8.2%
4 3166
 
7.6%
5 2854
 
6.8%
6 2776
 
6.6%
7 2536
 
6.1%
8 2476
 
5.9%
Other values (3) 2367
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 32113
76.6%
Other Punctuation 9786
 
23.3%
Uppercase Letter 12
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5132
16.0%
2 3909
12.2%
3 3475
10.8%
0 3446
10.7%
4 3166
9.9%
5 2854
8.9%
6 2776
8.6%
7 2536
7.9%
8 2476
7.7%
9 2343
7.3%
Other Punctuation
ValueCountFrequency (%)
. 9774
99.9%
\ 12
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41899
> 99.9%
Latin 12
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9774
23.3%
1 5132
12.2%
2 3909
 
9.3%
3 3475
 
8.3%
0 3446
 
8.2%
4 3166
 
7.6%
5 2854
 
6.8%
6 2776
 
6.6%
7 2536
 
6.1%
8 2476
 
5.9%
Other values (2) 2355
 
5.6%
Latin
ValueCountFrequency (%)
N 12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41911
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9774
23.3%
1 5132
12.2%
2 3909
 
9.3%
3 3475
 
8.3%
0 3446
 
8.2%
4 3166
 
7.6%
5 2854
 
6.8%
6 2776
 
6.6%
7 2536
 
6.1%
8 2476
 
5.9%
Other values (3) 2367
 
5.6%

이동거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5781
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60963.069
Minimum0
Maximum1576050
Zeros148
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-18T04:02:56.797057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1149.5
Q15207.5
median17300
Q367737.5
95-th percentile269322
Maximum1576050
Range1576050
Interquartile range (IQR)62530

Descriptive statistics

Standard deviation108269.84
Coefficient of variation (CV)1.7759906
Kurtosis21.233328
Mean60963.069
Median Absolute Deviation (MAD)15000
Skewness3.7947126
Sum6.0963069 × 108
Variance1.1722358 × 1010
MonotonicityNot monotonic
2024-04-18T04:02:56.908194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 148
 
1.5%
2550 13
 
0.1%
1530 13
 
0.1%
1140 12
 
0.1%
2590 12
 
0.1%
1520 12
 
0.1%
1370 11
 
0.1%
2180 11
 
0.1%
2240 11
 
0.1%
3100 10
 
0.1%
Other values (5771) 9747
97.5%
ValueCountFrequency (%)
0 148
1.5%
20 1
 
< 0.1%
30 1
 
< 0.1%
40 1
 
< 0.1%
70 1
 
< 0.1%
90 2
 
< 0.1%
120 1
 
< 0.1%
200 1
 
< 0.1%
220 2
 
< 0.1%
250 1
 
< 0.1%
ValueCountFrequency (%)
1576050 1
< 0.1%
1208600 1
< 0.1%
1137590 1
< 0.1%
1106910 1
< 0.1%
1051040 1
< 0.1%
999040 1
< 0.1%
971190 1
< 0.1%
967950 1
< 0.1%
960230 1
< 0.1%
947620 1
< 0.1%

이용시간
Real number (ℝ)

HIGH CORRELATION 

Distinct1489
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.4804
Minimum0
Maximum5893
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-18T04:02:57.021648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q141
median119
Q3344
95-th percentile1231.05
Maximum5893
Range5893
Interquartile range (IQR)303

Descriptive statistics

Standard deviation489.30457
Coefficient of variation (CV)1.6230062
Kurtosis20.375427
Mean301.4804
Median Absolute Deviation (MAD)96
Skewness3.7522943
Sum3014804
Variance239418.96
MonotonicityNot monotonic
2024-04-18T04:02:57.127187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 94
 
0.9%
11 87
 
0.9%
12 80
 
0.8%
21 78
 
0.8%
6 78
 
0.8%
8 76
 
0.8%
13 76
 
0.8%
10 74
 
0.7%
14 73
 
0.7%
7 71
 
0.7%
Other values (1479) 9213
92.1%
ValueCountFrequency (%)
0 11
 
0.1%
1 10
 
0.1%
2 31
 
0.3%
3 50
0.5%
4 63
0.6%
5 68
0.7%
6 78
0.8%
7 71
0.7%
8 76
0.8%
9 94
0.9%
ValueCountFrequency (%)
5893 1
< 0.1%
5199 1
< 0.1%
5191 1
< 0.1%
5152 1
< 0.1%
4964 1
< 0.1%
4948 1
< 0.1%
4872 1
< 0.1%
4867 1
< 0.1%
4749 1
< 0.1%
4570 1
< 0.1%

Interactions

2024-04-18T04:02:53.073982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:51.932029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:52.437060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:52.744040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:53.147396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:52.204520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:52.511001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:52.822145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:53.224262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:52.279761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:52.586735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:52.908527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:53.310008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:52.361940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:52.668215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:02:52.995470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T04:02:57.207111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자대여소번호대여구분코드성별연령대코드이용건수이동거리이용시간
대여일자1.0000.0000.0000.0090.0190.0000.0080.071
대여소번호0.0001.0000.0000.0000.0000.0000.0000.012
대여구분코드0.0000.0001.0000.1340.7540.2450.1840.207
성별0.0090.0000.1341.0000.2490.1670.1030.135
연령대코드0.0190.0000.7540.2491.0000.1990.1690.190
이용건수0.0000.0000.2450.1670.1991.0000.7250.874
이동거리0.0080.0000.1840.1030.1690.7251.0000.752
이용시간0.0710.0120.2070.1350.1900.8740.7521.000
2024-04-18T04:02:57.292308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여일자연령대코드대여구분코드성별
대여일자1.0000.0140.0000.011
연령대코드0.0141.0000.4210.155
대여구분코드0.0000.4211.0000.110
성별0.0110.1550.1101.000
2024-04-18T04:02:57.630422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리이용시간대여일자대여구분코드성별연령대코드
대여소번호1.000-0.051-0.050-0.0680.0000.0000.0000.000
이용건수-0.0511.0000.8640.8870.0000.1490.0700.096
이동거리-0.0500.8641.0000.9040.0080.1190.0590.083
이용시간-0.0680.8870.9041.0000.0540.1250.0570.091
대여일자0.0000.0000.0080.0541.0000.0000.0110.014
대여구분코드0.0000.1490.1190.1250.0001.0000.1100.421
성별0.0000.0700.0590.0570.0110.1101.0000.155
연령대코드0.0000.0960.0830.0910.0140.4210.1551.000

Missing values

2024-04-18T04:02:53.424773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T04:02:53.556084image/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

대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리이용시간
9861Jan-20399399. 서울역 센트럴 자이아파트정기\NAGE_002191081.479.5541220312
22993Jan-2010311031. 암사동 CBIS정기MAGE_00111171.939.414054014
46213Jan-2022482248. 서초리슈빌S 글로벌 앞정기\NAGE_003345580.5149.13211840598
47896Jan-2023132313. 금원빌딩 앞정기<NA>AGE_002282790.2325.55110160541
14101Jan-20575575. 중앙농협 중곡지점정기MAGE_0024107.40.72312027
17906Jan-20746746. 목동2단지 상가정기MAGE_00112421.464.0317330117
45011Jan-2022022202.청계산입구역 1번출구정기FAGE_004191522.5614.8263800269
42298Jan-2020252025. 흑석역 1번출구일일(회원)<NA>AGE_0051292.452.451055034
44727Jan-2021772177. 신대방역 2번 출구일일(회원)MAGE_004156.710.4217909
38885Jan-2018281828. 한양수자인아파트 앞일일(회원)FAGE_003117.630.1984010
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리이용시간
16815Jan-20707707. 신정3동주민센터정기\NAGE_0015314.593.071324075
3094Jan-20191191. 서우빌딩(바른학원)일일(회원)\NAGE_0051105.280.95409028
17625Jan-20737737. 장수공원정기\NAGE_007379.070.713090132
12603Jan-20525525. 한양대병원사거리일일(회원)FAGE_002375402.2153.682314401264
3207Jan-20195195. 모래내고가차도정기\NAGE_006137.320.34145051
42753Jan-2020572057. 상도 아이파크 아파트단체\NAGE_0022105.530.95410075
49425Jan-2023642364. 도산대로 렉서스 앞정기\NAGE_002172276.1320.8389880302
39574Jan-2018561856. 모두의학교정기\NAGE_004394289.0433.59144810668
33343Jan-2014561456. 상아빌딩(우림시장 교차로)일일(회원)FAGE_0031105.530.95410022
17125Jan-20719719. 홍익병원앞 교차로정기\NAGE_004527544.8459.142549701218