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/A/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 started2024-05-18 00:13:36.848968
Analysis finished2024-05-18 00:13:47.088175
Duration10.24 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

2024-05-18T09:13:47.380738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:13:47.677536image/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%
Mean466.2176
Minimum102
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:13:48.142745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile130
Q1275
median459.5
Q3647
95-th percentile811
Maximum846
Range744
Interquartile range (IQR)372

Descriptive statistics

Standard deviation217.67672
Coefficient of variation (CV)0.46689939
Kurtosis-1.1895304
Mean466.2176
Median Absolute Deviation (MAD)186.5
Skewness0.050766806
Sum4662176
Variance47383.152
MonotonicityNot monotonic
2024-05-18T09:13:48.775730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
551 31
 
0.3%
726 28
 
0.3%
117 28
 
0.3%
431 28
 
0.3%
123 28
 
0.3%
567 27
 
0.3%
765 27
 
0.3%
113 27
 
0.3%
458 27
 
0.3%
574 27
 
0.3%
Other values (586) 9722
97.2%
ValueCountFrequency (%)
102 21
0.2%
103 13
0.1%
104 22
0.2%
105 18
0.2%
106 17
0.2%
107 17
0.2%
108 17
0.2%
109 14
0.1%
111 20
0.2%
112 17
0.2%
ValueCountFrequency (%)
846 11
0.1%
845 15
0.1%
844 19
0.2%
843 14
0.1%
841 20
0.2%
840 15
0.1%
839 20
0.2%
838 19
0.2%
837 23
0.2%
836 10
0.1%

대여소명
Text

Distinct596
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:13:49.696353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length14.4252
Min length7

Characters and Unicode

Total characters144252
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 row127. 현대벤처빌 앞
2nd row322. 명동성당 앞
3rd row607. 신이문역 2번출구
4th row587. 유니베라 앞
5th row107. 신한은행 서교동지점
ValueCountFrequency (%)
앞 3504
 
11.4%
옆 650
 
2.1%
출구 369
 
1.2%
사거리 362
 
1.2%
1번출구 341
 
1.1%
2번출구 314
 
1.0%
교차로 269
 
0.9%
뒤 262
 
0.9%
4번출구 250
 
0.8%
3번출구 248
 
0.8%
Other values (1243) 24151
78.6%
2024-05-18T09:13:51.094840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20842
 
14.4%
. 10000
 
6.9%
1 4581
 
3.2%
2 4557
 
3.2%
4 4181
 
2.9%
3 4073
 
2.8%
앞 3794
 
2.6%
5 3608
 
2.5%
7 3442
 
2.4%
구 3248
 
2.3%
Other values (374) 81926
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76505
53.0%
Decimal Number 34512
23.9%
Space Separator 20842
 
14.4%
Other Punctuation 10033
 
7.0%
Uppercase Letter 1428
 
1.0%
Open Punctuation 445
 
0.3%
Close Punctuation 445
 
0.3%
Dash Punctuation 42
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 3794
 
5.0%
구 3248
 
4.2%
역 2572
 
3.4%
번 2340
 
3.1%
출 2332
 
3.0%
동 2223
 
2.9%
교 1716
 
2.2%
리 1420
 
1.9%
대 1364
 
1.8%
원 1341
 
1.8%
Other values (339) 54155
70.8%
Uppercase Letter
ValueCountFrequency (%)
S 206
14.4%
K 169
11.8%
C 168
11.8%
D 148
10.4%
B 128
9.0%
M 120
8.4%
I 110
7.7%
T 78
 
5.5%
G 53
 
3.7%
A 49
 
3.4%
Other values (9) 199
13.9%
Decimal Number
ValueCountFrequency (%)
1 4581
13.3%
2 4557
13.2%
4 4181
12.1%
3 4073
11.8%
5 3608
10.5%
7 3442
10.0%
6 3235
9.4%
8 2671
7.7%
0 2334
6.8%
9 1830
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 10000
99.7%
, 33
 
0.3%
Space Separator
ValueCountFrequency (%)
20842
100.0%
Open Punctuation
ValueCountFrequency (%)
( 445
100.0%
Close Punctuation
ValueCountFrequency (%)
) 445
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76505
53.0%
Common 66319
46.0%
Latin 1428
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 3794
 
5.0%
구 3248
 
4.2%
역 2572
 
3.4%
번 2340
 
3.1%
출 2332
 
3.0%
동 2223
 
2.9%
교 1716
 
2.2%
리 1420
 
1.9%
대 1364
 
1.8%
원 1341
 
1.8%
Other values (339) 54155
70.8%
Latin
ValueCountFrequency (%)
S 206
14.4%
K 169
11.8%
C 168
11.8%
D 148
10.4%
B 128
9.0%
M 120
8.4%
I 110
7.7%
T 78
 
5.5%
G 53
 
3.7%
A 49
 
3.4%
Other values (9) 199
13.9%
Common
ValueCountFrequency (%)
20842
31.4%
. 10000
15.1%
1 4581
 
6.9%
2 4557
 
6.9%
4 4181
 
6.3%
3 4073
 
6.1%
5 3608
 
5.4%
7 3442
 
5.2%
6 3235
 
4.9%
8 2671
 
4.0%
Other values (6) 5129
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76505
53.0%
ASCII 67747
47.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20842
30.8%
. 10000
14.8%
1 4581
 
6.8%
2 4557
 
6.7%
4 4181
 
6.2%
3 4073
 
6.0%
5 3608
 
5.3%
7 3442
 
5.1%
6 3235
 
4.8%
8 2671
 
3.9%
Other values (25) 6557
 
9.7%
Hangul
ValueCountFrequency (%)
앞 3794
 
5.0%
구 3248
 
4.2%
역 2572
 
3.4%
번 2340
 
3.1%
출 2332
 
3.0%
동 2223
 
2.9%
교 1716
 
2.2%
리 1420
 
1.9%
대 1364
 
1.8%
원 1341
 
1.8%
Other values (339) 54155
70.8%

대여구분코드
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기권
4757 
일일권
3900 
단체권
1027 
일일권(비회원)
 
316

Length

Max length8
Median length3
Mean length3.158
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기권 4757
47.6%
일일권 3900
39.0%
단체권 1027
 
10.3%
일일권(비회원) 316
 
3.2%

Length

2024-05-18T09:13:51.660643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:13:52.095016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기권 4757
47.6%
일일권 3900
39.0%
단체권 1027
 
10.3%
일일권(비회원 316
 
3.2%

성별
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3493 
<NA>
3301 
F
3204 
f
 
2

Length

Max length4
Median length1
Mean length1.9903
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 3493
34.9%
<NA> 3301
33.0%
F 3204
32.0%
f 2
 
< 0.1%

Length

2024-05-18T09:13:52.605730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:13:52.990099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3493
34.9%
na 3301
33.0%
f 3206
32.1%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
1664 
20대
1613 
40대
1486 
30대
1431 
~10대
1363 
Other values (3)
2443 

Length

Max length5
Median length3
Mean length3.0395
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20대
2nd row60대
3rd row30대
4th row기타
5th row60대

Common Values

ValueCountFrequency (%)
기타 1664
16.6%
20대 1613
16.1%
40대 1486
14.9%
30대 1431
14.3%
~10대 1363
13.6%
50대 1227
12.3%
60대 868
8.7%
70대이상 348
 
3.5%

Length

2024-05-18T09:13:53.408891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:13:53.863535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 1664
16.6%
20대 1613
16.1%
40대 1486
14.9%
30대 1431
14.3%
10대 1363
13.6%
50대 1227
12.3%
60대 868
8.7%
70대이상 348
 
3.5%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct402
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.0725
Minimum1
Maximum968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:13:54.463836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median13
Q344
95-th percentile180
Maximum968
Range967
Interquartile range (IQR)40

Descriptive statistics

Standard deviation73.67773
Coefficient of variation (CV)1.7938458
Kurtosis25.313526
Mean41.0725
Median Absolute Deviation (MAD)11
Skewness4.100476
Sum410725
Variance5428.4079
MonotonicityNot monotonic
2024-05-18T09:13:55.026807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1015
 
10.2%
1 905
 
9.0%
3 577
 
5.8%
4 472
 
4.7%
5 382
 
3.8%
6 345
 
3.5%
7 275
 
2.8%
9 232
 
2.3%
8 226
 
2.3%
11 199
 
2.0%
Other values (392) 5372
53.7%
ValueCountFrequency (%)
1 905
9.0%
2 1015
10.2%
3 577
5.8%
4 472
4.7%
5 382
 
3.8%
6 345
 
3.5%
7 275
 
2.8%
8 226
 
2.3%
9 232
 
2.3%
10 193
 
1.9%
ValueCountFrequency (%)
968 1
< 0.1%
951 1
< 0.1%
895 1
< 0.1%
872 1
< 0.1%
794 1
< 0.1%
760 1
< 0.1%
738 1
< 0.1%
713 1
< 0.1%
697 1
< 0.1%
690 1
< 0.1%

운동량
Text

Distinct9775
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:13:56.082558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.3282
Min length1

Characters and Unicode

Total characters63282
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

Unique9568 ?
Unique (%)95.7%

Sample

1st row174.82
2nd row155.96
3rd row3555.55
4th row68.26
5th row284.7
ValueCountFrequency (%)
0 8
 
0.1%
n 5
 
< 0.1%
45.3 3
 
< 0.1%
14.41 3
 
< 0.1%
66.15 3
 
< 0.1%
21.62 3
 
< 0.1%
217.45 3
 
< 0.1%
43.76 3
 
< 0.1%
246.3 3
 
< 0.1%
56.77 3
 
< 0.1%
Other values (9765) 9963
99.6%
2024-05-18T09:13:57.768353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9897
15.6%
1 7739
12.2%
2 6229
9.8%
3 5784
9.1%
4 5379
8.5%
5 5190
8.2%
6 5098
8.1%
7 4881
7.7%
8 4749
7.5%
9 4645
7.3%
Other values (3) 3691
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53375
84.3%
Other Punctuation 9902
 
15.6%
Uppercase Letter 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7739
14.5%
2 6229
11.7%
3 5784
10.8%
4 5379
10.1%
5 5190
9.7%
6 5098
9.6%
7 4881
9.1%
8 4749
8.9%
9 4645
8.7%
0 3681
6.9%
Other Punctuation
ValueCountFrequency (%)
. 9897
99.9%
\ 5
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63277
> 99.9%
Latin 5
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9897
15.6%
1 7739
12.2%
2 6229
9.8%
3 5784
9.1%
4 5379
8.5%
5 5190
8.2%
6 5098
8.1%
7 4881
7.7%
8 4749
7.5%
9 4645
7.3%
Other values (2) 3686
 
5.8%
Latin
ValueCountFrequency (%)
N 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9897
15.6%
1 7739
12.2%
2 6229
9.8%
3 5784
9.1%
4 5379
8.5%
5 5190
8.2%
6 5098
8.1%
7 4881
7.7%
8 4749
7.5%
9 4645
7.3%
Other values (3) 3691
 
5.8%

탄소량
Text

Distinct4410
Distinct (%)44.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:13:59.135686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.4184
Min length1

Characters and Unicode

Total characters44184
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

Unique2652 ?
Unique (%)26.5%

Sample

1st row1.97
2nd row1.27
3rd row31.35
4th row0.62
5th row3.03
ValueCountFrequency (%)
0.34 23
 
0.2%
0.49 22
 
0.2%
0.48 21
 
0.2%
0.56 21
 
0.2%
0.65 21
 
0.2%
0.32 20
 
0.2%
0.35 20
 
0.2%
0.19 20
 
0.2%
1.23 20
 
0.2%
0.43 19
 
0.2%
Other values (4400) 9793
97.9%
2024-05-18T09:14:01.216915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9906
22.4%
1 5586
12.6%
2 4101
9.3%
3 3660
 
8.3%
4 3348
 
7.6%
5 3246
 
7.3%
6 3136
 
7.1%
7 2869
 
6.5%
9 2794
 
6.3%
0 2791
 
6.3%
Other values (3) 2747
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34268
77.6%
Other Punctuation 9911
 
22.4%
Uppercase Letter 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5586
16.3%
2 4101
12.0%
3 3660
10.7%
4 3348
9.8%
5 3246
9.5%
6 3136
9.2%
7 2869
8.4%
9 2794
8.2%
0 2791
8.1%
8 2737
8.0%
Other Punctuation
ValueCountFrequency (%)
. 9906
99.9%
\ 5
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44179
> 99.9%
Latin 5
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9906
22.4%
1 5586
12.6%
2 4101
9.3%
3 3660
 
8.3%
4 3348
 
7.6%
5 3246
 
7.3%
6 3136
 
7.1%
7 2869
 
6.5%
9 2794
 
6.3%
0 2791
 
6.3%
Other values (2) 2742
 
6.2%
Latin
ValueCountFrequency (%)
N 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9906
22.4%
1 5586
12.6%
2 4101
9.3%
3 3660
 
8.3%
4 3348
 
7.6%
5 3246
 
7.3%
6 3136
 
7.1%
7 2869
 
6.5%
9 2794
 
6.3%
0 2791
 
6.3%
Other values (3) 2747
 
6.2%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION 

Distinct9783
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110263.16
Minimum0
Maximum5424524.6
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:14:01.782138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1759.5165
Q110487.75
median38057.3
Q3125466.03
95-th percentile460855.38
Maximum5424524.6
Range5424524.6
Interquartile range (IQR)114978.28

Descriptive statistics

Standard deviation202931.39
Coefficient of variation (CV)1.8404278
Kurtosis118.76033
Mean110263.16
Median Absolute Deviation (MAD)33469.685
Skewness7.2667725
Sum1.1026316 × 109
Variance4.1181149 × 1010
MonotonicityNot monotonic
2024-05-18T09:14:02.368770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 12
 
0.1%
1210.0 4
 
< 0.1%
850.0 4
 
< 0.1%
7730.0 4
 
< 0.1%
1760.0 4
 
< 0.1%
920.0 4
 
< 0.1%
2400.0 4
 
< 0.1%
2270.0 3
 
< 0.1%
2560.0 3
 
< 0.1%
5260.0 3
 
< 0.1%
Other values (9773) 9955
99.6%
ValueCountFrequency (%)
0.0 12
0.1%
8.01 1
 
< 0.1%
10.0 1
 
< 0.1%
20.0 1
 
< 0.1%
27.78 1
 
< 0.1%
30.0 1
 
< 0.1%
44.34 1
 
< 0.1%
70.0 1
 
< 0.1%
110.0 2
 
< 0.1%
119.02 1
 
< 0.1%
ValueCountFrequency (%)
5424524.59 1
< 0.1%
4746202.91 1
< 0.1%
4515949.65 1
< 0.1%
3492548.42 1
< 0.1%
2604879.04 1
< 0.1%
2230809.86 1
< 0.1%
2188625.98 1
< 0.1%
2089011.92 1
< 0.1%
2061875.08 1
< 0.1%
2014129.5 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct2853
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean950.1892
Minimum0
Maximum51494
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:14:02.954934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q198
median341.5
Q31102
95-th percentile3876.05
Maximum51494
Range51494
Interquartile range (IQR)1004

Descriptive statistics

Standard deviation1716.6623
Coefficient of variation (CV)1.8066531
Kurtosis149.31419
Mean950.1892
Median Absolute Deviation (MAD)294.5
Skewness7.8955249
Sum9501892
Variance2946929.4
MonotonicityNot monotonic
2024-05-18T09:14:03.456033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 42
 
0.4%
5 42
 
0.4%
24 40
 
0.4%
17 39
 
0.4%
12 38
 
0.4%
16 38
 
0.4%
14 38
 
0.4%
58 38
 
0.4%
13 37
 
0.4%
11 37
 
0.4%
Other values (2843) 9611
96.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 4
 
< 0.1%
2 14
 
0.1%
3 28
0.3%
4 32
0.3%
5 42
0.4%
6 23
0.2%
7 35
0.4%
8 33
0.3%
9 42
0.4%
ValueCountFrequency (%)
51494 1
< 0.1%
39730 1
< 0.1%
38027 1
< 0.1%
35653 1
< 0.1%
18360 1
< 0.1%
18010 1
< 0.1%
17382 1
< 0.1%
14723 1
< 0.1%
14512 1
< 0.1%
14473 1
< 0.1%

Interactions

2024-05-18T09:13:44.714495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:40.210163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:41.762640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:43.038232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:45.096159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:40.662170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:42.117227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:43.580260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:45.524153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:41.042203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:42.400249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:43.930728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:45.880815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:41.376481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:42.732517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:13:44.338780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T09:14:03.795750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0290.0360.0000.0840.0580.054
대여구분코드0.0291.0000.0000.5000.3070.2070.123
성별0.0360.0001.0000.0490.1630.0590.088
연령대코드0.0000.5000.0491.0000.2560.2060.135
이용건수0.0840.3070.1630.2561.0000.7250.756
이동거리(M)0.0580.2070.0590.2060.7251.0000.944
이용시간(분)0.0540.1230.0880.1350.7560.9441.000
2024-05-18T09:14:04.135524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.0300.242
성별0.0301.0000.000
대여구분코드0.2420.0001.000
2024-05-18T09:14:04.684213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.000-0.020-0.0270.0170.0210.000
이용건수-0.0001.0000.9450.9490.1880.0720.125
이동거리(M)-0.0200.9451.0000.9780.0940.0390.070
이용시간(분)-0.0270.9490.9781.0000.0850.0360.072
대여구분코드0.0170.1880.0940.0851.0000.0000.242
성별0.0210.0720.0390.0360.0001.0000.030
연령대코드0.0000.1250.0700.0720.2420.0301.000

Missing values

2024-05-18T09:13:46.263781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T09:13:46.835133image/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)
1136202207127127. 현대벤처빌 앞단체권F20대2174.821.978490.086
8476202207322322. 명동성당 앞단체권M60대2155.961.275470.0158
18930202207607607. 신이문역 2번출구정기권<NA>30대573555.5531.35135223.81949
18330202207587587. 유니베라 앞일일권(비회원)M기타168.260.622651.8928
293202207107107. 신한은행 서교동지점정기권F60대5284.73.0313071.67189
22529202207731731. 서울시 도로환경관리센터일일권F~10대392336.722.6697691.211114
26765202207839839. 보광동 삼성리버빌아파트 앞단체권F40대7959.037.9434263.68391
15736202207519519. 양지사거리단체권M20대129.340.261140.09
20640202207658658. 촬영소 사거리일일권F50대2335.693.2814128.1264
6843202207276276. 영등포로터리2단체권M~10대5270.192.5611004.9398
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
20164202207641641. 용두역 4번출구정기권<NA>40대1198695.8672.47312411.132706
6312202207262262. 영문초등학교 사거리일일권<NA>30대695425.2249.75214330.871866
13099202207446446. 상명대입구정기권M40대599086.7976.79331186.242026
6356202207263263. 근로자회관 사거리단체권<NA>20대250.70.52170.017
2479202207163163. 명지전문대학교 정문 앞정기권F~10대3160.941.566688.2684
14953202207498498.연남동주민센터 앞일일권F40대7718.287.1430758.96217
10279202207371371. 동대입구역 6번출구 뒤정기권F기타7714.46.5928385.97350
5821202207248248. 초원아파트 앞정기권<NA>20대13914342.49120.04517458.83662
9962202207362362. 청계8가 사거리정기권F기타552599.1124.03103415.69901
21779202207706706. 신정네거리역정기권F30대281323.6112.5854326.71585