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

DateTime1
Numeric4
Text3
Categorical3

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15246/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
대여구분코드 is highly imbalanced (53.6%)Imbalance
이동거리(M) is highly skewed (γ1 = 31.44721103)Skewed
이용시간(분) is highly skewed (γ1 = 26.31131652)Skewed
이동거리(M) has 4336 (43.4%) zerosZeros

Reproduction

Analysis started2024-05-18 05:04:42.414892
Analysis finished2024-05-18 05:04:50.923738
Duration8.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-06-01 00:00:00
Maximum2020-06-01 00:00:00
2024-05-18T14:04:51.060893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:51.370897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

대여소번호
Real number (ℝ)

Distinct641
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean473.7346
Minimum10
Maximum954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:04:51.754170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile129
Q1252
median445
Q3663
95-th percentile913
Maximum954
Range944
Interquartile range (IQR)411

Descriptive statistics

Standard deviation246.40333
Coefficient of variation (CV)0.52012947
Kurtosis-1.1040228
Mean473.7346
Median Absolute Deviation (MAD)202
Skewness0.27201572
Sum4737346
Variance60714.6
MonotonicityNot monotonic
2024-05-18T14:04:52.334193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152 42
 
0.4%
829 39
 
0.4%
207 37
 
0.4%
166 36
 
0.4%
914 35
 
0.4%
907 35
 
0.4%
565 34
 
0.3%
583 33
 
0.3%
931 32
 
0.3%
186 32
 
0.3%
Other values (631) 9645
96.5%
ValueCountFrequency (%)
10 2
 
< 0.1%
101 11
0.1%
102 20
0.2%
103 25
0.2%
104 20
0.2%
105 13
0.1%
106 27
0.3%
107 22
0.2%
108 14
0.1%
109 23
0.2%
ValueCountFrequency (%)
954 12
0.1%
953 6
 
0.1%
952 8
0.1%
951 19
0.2%
950 19
0.2%
949 12
0.1%
948 10
0.1%
947 19
0.2%
946 12
0.1%
945 11
0.1%

대여소
Text

Distinct641
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:04:53.018849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length25
Mean length14.5139
Min length3

Characters and Unicode

Total characters145139
Distinct characters391
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st row328. 탑골공원 앞
2nd row593.자양중앙나들목
3rd row553. 중곡 성원APT 앞
4th row542. 강변역 4번출구 뒤
5th row320. 을지로입구역 4번출구 앞
ValueCountFrequency (%)
앞 3449
 
10.9%
옆 746
 
2.4%
사거리 399
 
1.3%
1번출구 383
 
1.2%
2번출구 356
 
1.1%
출구 334
 
1.1%
4번출구 331
 
1.0%
뒤 310
 
1.0%
3번출구 280
 
0.9%
입구 211
 
0.7%
Other values (1354) 24889
78.5%
2024-05-18T14:04:54.035379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21849
 
15.1%
. 9998
 
6.9%
1 4959
 
3.4%
2 4843
 
3.3%
3 4036
 
2.8%
앞 3752
 
2.6%
5 3747
 
2.6%
4 3661
 
2.5%
구 3620
 
2.5%
6 2967
 
2.0%
Other values (381) 81707
56.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76258
52.5%
Decimal Number 34323
23.6%
Space Separator 21849
 
15.1%
Other Punctuation 10017
 
6.9%
Uppercase Letter 1683
 
1.2%
Open Punctuation 486
 
0.3%
Close Punctuation 486
 
0.3%
Dash Punctuation 37
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 3752
 
4.9%
구 3620
 
4.7%
역 2926
 
3.8%
출 2673
 
3.5%
번 2660
 
3.5%
동 1829
 
2.4%
교 1538
 
2.0%
리 1419
 
1.9%
원 1306
 
1.7%
사 1106
 
1.5%
Other values (346) 53429
70.1%
Uppercase Letter
ValueCountFrequency (%)
K 235
14.0%
S 220
13.1%
C 206
12.2%
M 121
 
7.2%
B 111
 
6.6%
G 104
 
6.2%
D 101
 
6.0%
I 95
 
5.6%
L 89
 
5.3%
T 82
 
4.9%
Other values (9) 319
19.0%
Decimal Number
ValueCountFrequency (%)
1 4959
14.4%
2 4843
14.1%
3 4036
11.8%
5 3747
10.9%
4 3661
10.7%
6 2967
8.6%
7 2775
8.1%
8 2588
7.5%
0 2528
7.4%
9 2219
6.5%
Other Punctuation
ValueCountFrequency (%)
. 9998
99.8%
, 19
 
0.2%
Space Separator
ValueCountFrequency (%)
21849
100.0%
Open Punctuation
ValueCountFrequency (%)
( 486
100.0%
Close Punctuation
ValueCountFrequency (%)
) 486
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76258
52.5%
Common 67198
46.3%
Latin 1683
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 3752
 
4.9%
구 3620
 
4.7%
역 2926
 
3.8%
출 2673
 
3.5%
번 2660
 
3.5%
동 1829
 
2.4%
교 1538
 
2.0%
리 1419
 
1.9%
원 1306
 
1.7%
사 1106
 
1.5%
Other values (346) 53429
70.1%
Latin
ValueCountFrequency (%)
K 235
14.0%
S 220
13.1%
C 206
12.2%
M 121
 
7.2%
B 111
 
6.6%
G 104
 
6.2%
D 101
 
6.0%
I 95
 
5.6%
L 89
 
5.3%
T 82
 
4.9%
Other values (9) 319
19.0%
Common
ValueCountFrequency (%)
21849
32.5%
. 9998
14.9%
1 4959
 
7.4%
2 4843
 
7.2%
3 4036
 
6.0%
5 3747
 
5.6%
4 3661
 
5.4%
6 2967
 
4.4%
7 2775
 
4.1%
8 2588
 
3.9%
Other values (6) 5775
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76258
52.5%
ASCII 68881
47.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21849
31.7%
. 9998
14.5%
1 4959
 
7.2%
2 4843
 
7.0%
3 4036
 
5.9%
5 3747
 
5.4%
4 3661
 
5.3%
6 2967
 
4.3%
7 2775
 
4.0%
8 2588
 
3.8%
Other values (25) 7458
 
10.8%
Hangul
ValueCountFrequency (%)
앞 3752
 
4.9%
구 3620
 
4.7%
역 2926
 
3.8%
출 2673
 
3.5%
번 2660
 
3.5%
동 1829
 
2.4%
교 1538
 
2.0%
리 1419
 
1.9%
원 1306
 
1.7%
사 1106
 
1.5%
Other values (346) 53429
70.1%

대여구분코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
6923 
일일(회원)
2746 
일일(비회원)
 
207
단체
 
115
BIL_021
 
9

Length

Max length7
Median length2
Mean length3.2064
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일일(회원)
2nd row일일(회원)
3rd row정기
4th row정기
5th row일일(회원)

Common Values

ValueCountFrequency (%)
정기 6923
69.2%
일일(회원) 2746
 
27.5%
일일(비회원) 207
 
2.1%
단체 115
 
1.1%
BIL_021 9
 
0.1%

Length

2024-05-18T14:04:54.451554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:04:54.731908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 6923
69.2%
일일(회원 2746
 
27.5%
일일(비회원 207
 
2.1%
단체 115
 
1.1%
bil_021 9
 
0.1%

성별
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
\N
4186 
M
2629 
F
2018 
<NA>
1160 
f
 
4

Length

Max length4
Median length2
Mean length1.7666
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row\N
2nd row\N
3rd row\N
4th row\N
5th row\N

Common Values

ValueCountFrequency (%)
\N 4186
41.9%
M 2629
26.3%
F 2018
20.2%
<NA> 1160
 
11.6%
f 4
 
< 0.1%
m 3
 
< 0.1%

Length

2024-05-18T14:04:54.941048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:04:55.141145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 4186
41.9%
m 2632
26.3%
f 2022
20.2%
na 1160
 
11.6%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AGE_002
3063 
AGE_003
2294 
AGE_004
1714 
AGE_005
1234 
AGE_001
755 
Other values (3)
940 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGE_002
2nd rowAGE_002
3rd rowAGE_002
4th rowAGE_001
5th rowAGE_004

Common Values

ValueCountFrequency (%)
AGE_002 3063
30.6%
AGE_003 2294
22.9%
AGE_004 1714
17.1%
AGE_005 1234
12.3%
AGE_001 755
 
7.5%
AGE_006 445
 
4.5%
AGE_008 372
 
3.7%
AGE_007 123
 
1.2%

Length

2024-05-18T14:04:55.513386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T14:04:55.836763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
age_002 3063
30.6%
age_003 2294
22.9%
age_004 1714
17.1%
age_005 1234
12.3%
age_001 755
 
7.5%
age_006 445
 
4.5%
age_008 372
 
3.7%
age_007 123
 
1.2%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5678
Minimum1
Maximum210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:04:56.195675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile11
Maximum210
Range209
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.404047
Coefficient of variation (CV)1.5146721
Kurtosis418.81951
Mean3.5678
Median Absolute Deviation (MAD)1
Skewness13.534819
Sum35678
Variance29.203724
MonotonicityNot monotonic
2024-05-18T14:04:56.522380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4051
40.5%
2 1890
18.9%
3 1096
 
11.0%
4 737
 
7.4%
5 494
 
4.9%
6 374
 
3.7%
7 283
 
2.8%
8 218
 
2.2%
9 148
 
1.5%
10 136
 
1.4%
Other values (43) 573
 
5.7%
ValueCountFrequency (%)
1 4051
40.5%
2 1890
18.9%
3 1096
 
11.0%
4 737
 
7.4%
5 494
 
4.9%
6 374
 
3.7%
7 283
 
2.8%
8 218
 
2.2%
9 148
 
1.5%
10 136
 
1.4%
ValueCountFrequency (%)
210 1
< 0.1%
203 1
< 0.1%
89 1
< 0.1%
74 1
< 0.1%
61 1
< 0.1%
59 1
< 0.1%
58 1
< 0.1%
57 1
< 0.1%
49 2
< 0.1%
47 1
< 0.1%

운동량
Text

Distinct4909
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:04:57.004603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length4.9603
Min length2

Characters and Unicode

Total characters49603
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

Unique4365 ?
Unique (%)43.6%

Sample

1st row18.75
2nd row69.68
3rd row708.99
4th row0.00
5th row0.00
ValueCountFrequency (%)
0.00 4312
43.1%
n 24
 
0.2%
17.50 7
 
0.1%
77.22 7
 
0.1%
47.36 7
 
0.1%
52.51 7
 
0.1%
27.80 6
 
0.1%
43.24 6
 
0.1%
13.64 5
 
< 0.1%
50.97 5
 
< 0.1%
Other values (4899) 5614
56.1%
2024-05-18T14:04:58.027354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15117
30.5%
. 9976
20.1%
1 3971
 
8.0%
2 3205
 
6.5%
3 2850
 
5.7%
4 2771
 
5.6%
5 2573
 
5.2%
6 2382
 
4.8%
9 2293
 
4.6%
8 2211
 
4.5%
Other values (3) 2254
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39579
79.8%
Other Punctuation 10000
 
20.2%
Uppercase Letter 24
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15117
38.2%
1 3971
 
10.0%
2 3205
 
8.1%
3 2850
 
7.2%
4 2771
 
7.0%
5 2573
 
6.5%
6 2382
 
6.0%
9 2293
 
5.8%
8 2211
 
5.6%
7 2206
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 9976
99.8%
\ 24
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 49579
> 99.9%
Latin 24
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15117
30.5%
. 9976
20.1%
1 3971
 
8.0%
2 3205
 
6.5%
3 2850
 
5.7%
4 2771
 
5.6%
5 2573
 
5.2%
6 2382
 
4.8%
9 2293
 
4.6%
8 2211
 
4.5%
Other values (2) 2230
 
4.5%
Latin
ValueCountFrequency (%)
N 24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15117
30.5%
. 9976
20.1%
1 3971
 
8.0%
2 3205
 
6.5%
3 2850
 
5.7%
4 2771
 
5.6%
5 2573
 
5.2%
6 2382
 
4.8%
9 2293
 
4.6%
8 2211
 
4.5%
Other values (3) 2254
 
4.5%

탄소량
Text

Distinct1187
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T14:04:58.841845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.0465
Min length2

Characters and Unicode

Total characters40465
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

Unique569 ?
Unique (%)5.7%

Sample

1st row0.17
2nd row0.80
3rd row7.05
4th row0.00
5th row0.00
ValueCountFrequency (%)
0.00 4317
43.2%
0.32 52
 
0.5%
0.39 48
 
0.5%
0.23 46
 
0.5%
0.26 40
 
0.4%
0.20 39
 
0.4%
0.43 39
 
0.4%
0.42 39
 
0.4%
0.35 39
 
0.4%
0.16 38
 
0.4%
Other values (1177) 5303
53.0%
2024-05-18T14:05:00.501731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16429
40.6%
. 9976
24.7%
1 2865
 
7.1%
2 2076
 
5.1%
3 1726
 
4.3%
4 1459
 
3.6%
5 1342
 
3.3%
6 1240
 
3.1%
7 1164
 
2.9%
8 1095
 
2.7%
Other values (3) 1093
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30441
75.2%
Other Punctuation 10000
 
24.7%
Uppercase Letter 24
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16429
54.0%
1 2865
 
9.4%
2 2076
 
6.8%
3 1726
 
5.7%
4 1459
 
4.8%
5 1342
 
4.4%
6 1240
 
4.1%
7 1164
 
3.8%
8 1095
 
3.6%
9 1045
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 9976
99.8%
\ 24
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
N 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40441
99.9%
Latin 24
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16429
40.6%
. 9976
24.7%
1 2865
 
7.1%
2 2076
 
5.1%
3 1726
 
4.3%
4 1459
 
3.6%
5 1342
 
3.3%
6 1240
 
3.1%
7 1164
 
2.9%
8 1095
 
2.7%
Other values (2) 1069
 
2.6%
Latin
ValueCountFrequency (%)
N 24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40465
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16429
40.6%
. 9976
24.7%
1 2865
 
7.1%
2 2076
 
5.1%
3 1726
 
4.3%
4 1459
 
3.6%
5 1342
 
3.3%
6 1240
 
3.1%
7 1164
 
2.9%
8 1095
 
2.7%
Other values (3) 1093
 
2.7%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2407
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21269.769
Minimum0
Maximum13245002
Zeros4336
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:05:01.159206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1235
Q36842.5
95-th percentile42082.5
Maximum13245002
Range13245002
Interquartile range (IQR)6842.5

Descriptive statistics

Standard deviation417355.79
Coefficient of variation (CV)19.622018
Kurtosis989.85295
Mean21269.769
Median Absolute Deviation (MAD)1235
Skewness31.447211
Sum2.1269769 × 108
Variance1.7418586 × 1011
MonotonicityNot monotonic
2024-05-18T14:05:01.700252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4336
43.4%
1390.0 17
 
0.2%
680.0 17
 
0.2%
1020.0 14
 
0.1%
3010.0 14
 
0.1%
1680.0 14
 
0.1%
970.0 13
 
0.1%
1600.0 13
 
0.1%
1660.0 13
 
0.1%
1710.0 13
 
0.1%
Other values (2397) 5536
55.4%
ValueCountFrequency (%)
0.0 4336
43.4%
10.0 5
 
0.1%
30.0 1
 
< 0.1%
40.0 1
 
< 0.1%
50.0 3
 
< 0.1%
60.0 1
 
< 0.1%
110.0 2
 
< 0.1%
120.0 1
 
< 0.1%
130.0 2
 
< 0.1%
150.0 1
 
< 0.1%
ValueCountFrequency (%)
13245001.6 1
< 0.1%
13213033.4 1
< 0.1%
13199341.42 1
< 0.1%
13196319.7 1
< 0.1%
13193786.87 1
< 0.1%
13182102.98 1
< 0.1%
13177429.45 1
< 0.1%
13177221.72 1
< 0.1%
13176974.77 1
< 0.1%
13170923.01 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct713
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.9069
Minimum0
Maximum13279
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T14:05:02.171701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q126
median62
Q3135
95-th percentile381
Maximum13279
Range13279
Interquartile range (IQR)109

Descriptive statistics

Standard deviation246.63974
Coefficient of variation (CV)2.0918177
Kurtosis1210.5424
Mean117.9069
Median Absolute Deviation (MAD)44
Skewness26.311317
Sum1179069
Variance60831.162
MonotonicityNot monotonic
2024-05-18T14:05:02.957494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 140
 
1.4%
7 130
 
1.3%
8 125
 
1.2%
13 124
 
1.2%
21 122
 
1.2%
10 118
 
1.2%
12 116
 
1.2%
17 111
 
1.1%
14 110
 
1.1%
6 109
 
1.1%
Other values (703) 8795
87.9%
ValueCountFrequency (%)
0 5
 
0.1%
1 4
 
< 0.1%
2 35
 
0.4%
3 63
0.6%
4 89
0.9%
5 91
0.9%
6 109
1.1%
7 130
1.3%
8 125
1.2%
9 140
1.4%
ValueCountFrequency (%)
13279 1
< 0.1%
10975 1
< 0.1%
4450 1
< 0.1%
3668 1
< 0.1%
3565 1
< 0.1%
2676 1
< 0.1%
2501 1
< 0.1%
2373 1
< 0.1%
2341 1
< 0.1%
2211 1
< 0.1%

Interactions

2024-05-18T14:04:48.765742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:44.871608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:46.342767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:47.553108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:49.087625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:45.214222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:46.651677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:47.844039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:49.344221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:45.642341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:46.973263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:48.116622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:49.621053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:46.043613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:47.259535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T14:04:48.442980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T14:05:03.308380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0730.0420.0460.0460.0180.000
대여구분코드0.0731.0000.2530.5620.0100.0000.020
성별0.0420.2531.0000.2250.0700.0000.030
연령대코드0.0460.5620.2251.0000.0950.0000.033
이용건수0.0460.0100.0700.0951.0000.1220.966
이동거리(M)0.0180.0000.0000.0000.1221.0000.000
이용시간(분)0.0000.0200.0300.0330.9660.0001.000
2024-05-18T14:05:03.675795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.1400.389
성별0.1401.0000.097
대여구분코드0.3890.0971.000
2024-05-18T14:05:04.022303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.082-0.096-0.0900.0300.0170.022
이용건수-0.0821.0000.5350.7700.0070.0470.053
이동거리(M)-0.0960.5351.0000.5090.0000.0000.000
이용시간(분)-0.0900.7700.5091.0000.0130.0200.018
대여구분코드0.0300.0070.0000.0131.0000.0970.389
성별0.0170.0470.0000.0200.0971.0000.140
연령대코드0.0220.0530.0000.0180.3890.1401.000

Missing values

2024-05-18T14:04:50.001028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T14:04:50.618872image/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

대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
41812020-06-01328328. 탑골공원 앞일일(회원)\NAGE_002118.750.17740.07
79202020-06-01593593.자양중앙나들목일일(회원)\NAGE_002669.680.803450.0255
72082020-06-01553553. 중곡 성원APT 앞정기\NAGE_00214708.997.0530400.0369
70052020-06-01542542. 강변역 4번출구 뒤정기\NAGE_00120.000.000.079
40792020-06-01320320. 을지로입구역 4번출구 앞일일(회원)\NAGE_00410.000.000.044
71732020-06-01551551. 구의삼성쉐르빌 앞정기<NA>AGE_00310.000.000.04
26882020-06-01240240. 문래역 4번출구 앞정기MAGE_0049156.531.285480.0219
18882020-06-01205205. 산업은행 앞정기<NA>AGE_0042398.063.3314360.0117
47842020-06-01370370. 시청역(2호선) 9번출구 뒤일일(회원)\NAGE_002248.610.381640.013
73092020-06-01559559. 왕십리역 4번 출구 건너편정기MAGE_00120.000.000.0113
대여일자대여소번호대여소대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
12502020-06-01166166. 가재울 초등학교일일(회원)\NAGE_0051271.552.5711060.0166
26582020-06-01239239. 유스호스텔 앞정기<NA>AGE_00310.000.000.025
22392020-06-01221221. 여의도초교 앞일일(회원)\NAGE_002211215.6110.1943900.01158
44212020-06-01344344. 성균관대 E하우스 앞일일(회원)\NAGE_00217.600.06240.03
14272020-06-01178178. 증산3교 앞일일(회원)\NAGE_001188.100.723090.021
33932020-06-01277277. 영등포뉴타운지하상가 2번게이트일일(회원)MAGE_00210.000.000.0102
35692020-06-01284284. 센트럴 푸르지오 시티 앞정기MAGE_00420.000.000.0198
93852020-06-01723723. SBS방송국정기<NA>AGE_00510.000.000.029
62042020-06-01485485.서울역4번출구정기\NAGE_00220.000.000.071
64492020-06-01505505. 자양사거리 광진아크로텔 앞정기FAGE_00313390.964.0017280.0233