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

대여년월 has constant value ""Constant
이용건수 is highly overall correlated with 이용거리(M) and 1 other fieldsHigh correlation
이용거리(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 00:16:40.691185
Analysis finished2024-05-18 00:16:48.513368
Duration7.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대여년월
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
202307
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202307
2nd row202307
3rd row202307
4th row202307
5th row202307

Common Values

ValueCountFrequency (%)
202307 10000
100.0%

Length

2024-05-18T09:16:48.715636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:16:49.000644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202307 10000
100.0%

대여소번호
Real number (ℝ)

Distinct727
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean557.0841
Minimum102
Maximum1058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:16:49.384670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile141
Q1316
median542
Q3791
95-th percentile1015
Maximum1058
Range956
Interquartile range (IQR)475

Descriptive statistics

Standard deviation277.8833
Coefficient of variation (CV)0.49881751
Kurtosis-1.1635705
Mean557.0841
Median Absolute Deviation (MAD)237
Skewness0.12902293
Sum5570841
Variance77219.131
MonotonicityNot monotonic
2024-05-18T09:16:49.817903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
508 24
 
0.2%
275 23
 
0.2%
576 22
 
0.2%
271 22
 
0.2%
735 22
 
0.2%
912 22
 
0.2%
519 22
 
0.2%
537 22
 
0.2%
746 22
 
0.2%
1008 22
 
0.2%
Other values (717) 9777
97.8%
ValueCountFrequency (%)
102 20
0.2%
103 18
0.2%
104 16
0.2%
105 12
0.1%
106 20
0.2%
107 14
0.1%
108 14
0.1%
109 15
0.1%
111 15
0.1%
112 17
0.2%
ValueCountFrequency (%)
1058 14
0.1%
1057 16
0.2%
1056 13
0.1%
1054 13
0.1%
1053 10
0.1%
1052 15
0.1%
1051 13
0.1%
1050 12
0.1%
1049 12
0.1%
1044 17
0.2%

대여소명
Text

Distinct727
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:16:50.314158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length14.6609
Min length7

Characters and Unicode

Total characters146609
Distinct characters415
Distinct categories9 ?
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 row1020. 강동경찰서
2nd row433. 을지로입구역 2번출구
3rd row647. 신이문역 1번출구
4th row1030. 미호 플랜트 앞
5th row640. 동대문경찰서 교차로
ValueCountFrequency (%)
앞 3144
 
10.3%
옆 534
 
1.8%
출구 389
 
1.3%
1번출구 324
 
1.1%
사거리 300
 
1.0%
2번출구 265
 
0.9%
4번출구 239
 
0.8%
교차로 238
 
0.8%
3번출구 220
 
0.7%
뒤 205
 
0.7%
Other values (1533) 24640
80.8%
2024-05-18T09:16:51.258310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20669
 
14.1%
. 10000
 
6.8%
1 5084
 
3.5%
2 4246
 
2.9%
3 3823
 
2.6%
4 3783
 
2.6%
앞 3521
 
2.4%
5 3437
 
2.3%
구 3286
 
2.2%
7 3091
 
2.1%
Other values (405) 85669
58.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77879
53.1%
Decimal Number 35061
23.9%
Space Separator 20669
 
14.1%
Other Punctuation 10037
 
6.8%
Uppercase Letter 1544
 
1.1%
Close Punctuation 678
 
0.5%
Open Punctuation 678
 
0.5%
Dash Punctuation 38
 
< 0.1%
Math Symbol 25
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
앞 3521
 
4.5%
구 3286
 
4.2%
역 2605
 
3.3%
번 2319
 
3.0%
출 2275
 
2.9%
동 2072
 
2.7%
교 1654
 
2.1%
리 1390
 
1.8%
원 1353
 
1.7%
아 1342
 
1.7%
Other values (370) 56062
72.0%
Uppercase Letter
ValueCountFrequency (%)
S 197
12.8%
C 196
12.7%
B 179
11.6%
K 178
11.5%
D 143
9.3%
A 111
7.2%
M 107
6.9%
I 91
5.9%
G 77
 
5.0%
T 66
 
4.3%
Other values (8) 199
12.9%
Decimal Number
ValueCountFrequency (%)
1 5084
14.5%
2 4246
12.1%
3 3823
10.9%
4 3783
10.8%
5 3437
9.8%
7 3091
8.8%
6 3085
8.8%
0 3066
8.7%
9 2788
8.0%
8 2658
7.6%
Other Punctuation
ValueCountFrequency (%)
. 10000
99.6%
, 37
 
0.4%
Space Separator
ValueCountFrequency (%)
20669
100.0%
Close Punctuation
ValueCountFrequency (%)
) 678
100.0%
Open Punctuation
ValueCountFrequency (%)
( 678
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%
Math Symbol
ValueCountFrequency (%)
~ 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77879
53.1%
Common 67186
45.8%
Latin 1544
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
앞 3521
 
4.5%
구 3286
 
4.2%
역 2605
 
3.3%
번 2319
 
3.0%
출 2275
 
2.9%
동 2072
 
2.7%
교 1654
 
2.1%
리 1390
 
1.8%
원 1353
 
1.7%
아 1342
 
1.7%
Other values (370) 56062
72.0%
Latin
ValueCountFrequency (%)
S 197
12.8%
C 196
12.7%
B 179
11.6%
K 178
11.5%
D 143
9.3%
A 111
7.2%
M 107
6.9%
I 91
5.9%
G 77
 
5.0%
T 66
 
4.3%
Other values (8) 199
12.9%
Common
ValueCountFrequency (%)
20669
30.8%
. 10000
14.9%
1 5084
 
7.6%
2 4246
 
6.3%
3 3823
 
5.7%
4 3783
 
5.6%
5 3437
 
5.1%
7 3091
 
4.6%
6 3085
 
4.6%
0 3066
 
4.6%
Other values (7) 6902
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77879
53.1%
ASCII 68730
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20669
30.1%
. 10000
14.5%
1 5084
 
7.4%
2 4246
 
6.2%
3 3823
 
5.6%
4 3783
 
5.5%
5 3437
 
5.0%
7 3091
 
4.5%
6 3085
 
4.5%
0 3066
 
4.5%
Other values (25) 8446
12.3%
Hangul
ValueCountFrequency (%)
앞 3521
 
4.5%
구 3286
 
4.2%
역 2605
 
3.3%
번 2319
 
3.0%
출 2275
 
2.9%
동 2072
 
2.7%
교 1654
 
2.1%
리 1390
 
1.8%
원 1353
 
1.7%
아 1342
 
1.7%
Other values (370) 56062
72.0%

대여구분코드
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기권
5496 
일일권
4205 
일일권(비회원)
 
299

Length

Max length8
Median length3
Mean length3.1495
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일일권
2nd row정기권
3rd row정기권
4th row일일권
5th row정기권

Common Values

ValueCountFrequency (%)
정기권 5496
55.0%
일일권 4205
42.0%
일일권(비회원) 299
 
3.0%

Length

2024-05-18T09:16:51.633421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:16:51.813114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기권 5496
55.0%
일일권 4205
42.0%
일일권(비회원 299
 
3.0%

성별
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3587 
<NA>
3261 
F
3152 

Length

Max length4
Median length1
Mean length1.9783
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 3587
35.9%
<NA> 3261
32.6%
F 3152
31.5%

Length

2024-05-18T09:16:52.174363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:16:52.502846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3587
35.9%
na 3261
32.6%
f 3152
31.5%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
1775 
30대
1497 
20대
1476 
40대
1368 
50대
1308 
Other values (3)
2576 

Length

Max length5
Median length3
Mean length3.0261
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 1775
17.8%
30대 1497
15.0%
20대 1476
14.8%
40대 1368
13.7%
50대 1308
13.1%
~10대 1224
12.2%
60대 946
9.5%
70대이상 406
 
4.1%

Length

2024-05-18T09:16:52.863990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T09:16:53.087106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 1775
17.8%
30대 1497
15.0%
20대 1476
14.8%
40대 1368
13.7%
50대 1308
13.1%
10대 1224
12.2%
60대 946
9.5%
70대이상 406
 
4.1%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct400
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.8953
Minimum1
Maximum950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:16:53.355752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median14
Q347
95-th percentile182
Maximum950
Range949
Interquartile range (IQR)43

Descriptive statistics

Standard deviation71.613313
Coefficient of variation (CV)1.70934
Kurtosis20.988734
Mean41.8953
Median Absolute Deviation (MAD)12
Skewness3.7871041
Sum418953
Variance5128.4666
MonotonicityNot monotonic
2024-05-18T09:16:53.629101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1000
 
10.0%
2 621
 
6.2%
3 516
 
5.2%
4 464
 
4.6%
5 355
 
3.5%
6 324
 
3.2%
7 311
 
3.1%
8 286
 
2.9%
9 255
 
2.5%
10 201
 
2.0%
Other values (390) 5667
56.7%
ValueCountFrequency (%)
1 1000
10.0%
2 621
6.2%
3 516
5.2%
4 464
4.6%
5 355
 
3.5%
6 324
 
3.2%
7 311
 
3.1%
8 286
 
2.9%
9 255
 
2.5%
10 201
 
2.0%
ValueCountFrequency (%)
950 1
< 0.1%
919 1
< 0.1%
825 1
< 0.1%
705 1
< 0.1%
687 1
< 0.1%
643 1
< 0.1%
624 1
< 0.1%
623 1
< 0.1%
620 1
< 0.1%
614 1
< 0.1%

운동량
Text

Distinct9786
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:16:54.308960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.3126
Min length1

Characters and Unicode

Total characters63126
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

Unique9589 ?
Unique (%)95.9%

Sample

1st row4345.71
2nd row369.15
3rd row3204.56
4th row1797.35
5th row1405.18
ValueCountFrequency (%)
0 12
 
0.1%
n 6
 
0.1%
42.41 3
 
< 0.1%
68.18 3
 
< 0.1%
32.18 3
 
< 0.1%
390.56 2
 
< 0.1%
22.14 2
 
< 0.1%
759.74 2
 
< 0.1%
108.03 2
 
< 0.1%
11.71 2
 
< 0.1%
Other values (9776) 9963
99.6%
2024-05-18T09:16:55.320378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9884
15.7%
1 7527
11.9%
2 6291
10.0%
3 5743
9.1%
4 5376
8.5%
5 5112
8.1%
6 5046
8.0%
7 4902
7.8%
8 4864
7.7%
9 4807
7.6%
Other values (3) 3574
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53230
84.3%
Other Punctuation 9890
 
15.7%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7527
14.1%
2 6291
11.8%
3 5743
10.8%
4 5376
10.1%
5 5112
9.6%
6 5046
9.5%
7 4902
9.2%
8 4864
9.1%
9 4807
9.0%
0 3562
6.7%
Other Punctuation
ValueCountFrequency (%)
. 9884
99.9%
\ 6
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63120
> 99.9%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9884
15.7%
1 7527
11.9%
2 6291
10.0%
3 5743
9.1%
4 5376
8.5%
5 5112
8.1%
6 5046
8.0%
7 4902
7.8%
8 4864
7.7%
9 4807
7.6%
Other values (2) 3568
 
5.7%
Latin
ValueCountFrequency (%)
N 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9884
15.7%
1 7527
11.9%
2 6291
10.0%
3 5743
9.1%
4 5376
8.5%
5 5112
8.1%
6 5046
8.0%
7 4902
7.8%
8 4864
7.7%
9 4807
7.6%
Other values (3) 3574
 
5.7%

탄소량
Text

Distinct4372
Distinct (%)43.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T09:16:56.141869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.3999
Min length1

Characters and Unicode

Total characters43999
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

Unique2596 ?
Unique (%)26.0%

Sample

1st row39.13
2nd row3.25
3rd row25.25
4th row14.74
5th row13.36
ValueCountFrequency (%)
0.25 26
 
0.3%
0.32 24
 
0.2%
0.29 24
 
0.2%
0.35 24
 
0.2%
0.63 23
 
0.2%
0.2 22
 
0.2%
0.42 21
 
0.2%
1.08 20
 
0.2%
0.3 20
 
0.2%
0.67 20
 
0.2%
Other values (4362) 9776
97.8%
2024-05-18T09:16:57.363396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9887
22.5%
1 5400
12.3%
2 4272
9.7%
3 3660
 
8.3%
4 3456
 
7.9%
5 3071
 
7.0%
6 2994
 
6.8%
7 2947
 
6.7%
8 2855
 
6.5%
0 2762
 
6.3%
Other values (3) 2695
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34100
77.5%
Other Punctuation 9893
 
22.5%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5400
15.8%
2 4272
12.5%
3 3660
10.7%
4 3456
10.1%
5 3071
9.0%
6 2994
8.8%
7 2947
8.6%
8 2855
8.4%
0 2762
8.1%
9 2683
7.9%
Other Punctuation
ValueCountFrequency (%)
. 9887
99.9%
\ 6
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43993
> 99.9%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9887
22.5%
1 5400
12.3%
2 4272
9.7%
3 3660
 
8.3%
4 3456
 
7.9%
5 3071
 
7.0%
6 2994
 
6.8%
7 2947
 
6.7%
8 2855
 
6.5%
0 2762
 
6.3%
Other values (2) 2689
 
6.1%
Latin
ValueCountFrequency (%)
N 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9887
22.5%
1 5400
12.3%
2 4272
9.7%
3 3660
 
8.3%
4 3456
 
7.9%
5 3071
 
7.0%
6 2994
 
6.8%
7 2947
 
6.7%
8 2855
 
6.5%
0 2762
 
6.3%
Other values (3) 2695
 
6.1%

이용거리(M)
Real number (ℝ)

HIGH CORRELATION 

Distinct9813
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102498.15
Minimum0
Maximum3917832.7
Zeros10
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:16:57.876266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1770
Q110938.675
median38188.355
Q3119167.75
95-th percentile414311.6
Maximum3917832.7
Range3917832.7
Interquartile range (IQR)108229.08

Descriptive statistics

Standard deviation176897.17
Coefficient of variation (CV)1.7258571
Kurtosis56.348852
Mean102498.15
Median Absolute Deviation (MAD)33135.45
Skewness5.2875495
Sum1.0249815 × 109
Variance3.1292608 × 1010
MonotonicityNot monotonic
2024-05-18T09:16:58.280523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
0.1%
1130.0 5
 
0.1%
1090.0 5
 
0.1%
1310.0 4
 
< 0.1%
1380.0 4
 
< 0.1%
3120.0 4
 
< 0.1%
1250.0 4
 
< 0.1%
1830.0 4
 
< 0.1%
1530.0 4
 
< 0.1%
1400.0 4
 
< 0.1%
Other values (9803) 9952
99.5%
ValueCountFrequency (%)
0.0 10
0.1%
10.0 1
 
< 0.1%
50.0 1
 
< 0.1%
88.13 1
 
< 0.1%
111.2 1
 
< 0.1%
130.0 1
 
< 0.1%
150.0 1
 
< 0.1%
176.18 1
 
< 0.1%
180.0 1
 
< 0.1%
199.37 1
 
< 0.1%
ValueCountFrequency (%)
3917832.66 1
< 0.1%
3299945.96 1
< 0.1%
2561448.77 1
< 0.1%
2412165.3 1
< 0.1%
2177957.61 1
< 0.1%
2045370.21 1
< 0.1%
2024766.59 1
< 0.1%
1975539.96 1
< 0.1%
1792015.74 1
< 0.1%
1715758.97 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct2733
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean899.2101
Minimum1
Maximum33924
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T09:16:58.559482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q198
median337
Q31043.25
95-th percentile3669.05
Maximum33924
Range33923
Interquartile range (IQR)945.25

Descriptive statistics

Standard deviation1510.2366
Coefficient of variation (CV)1.6795147
Kurtosis56.470545
Mean899.2101
Median Absolute Deviation (MAD)292
Skewness5.021684
Sum8992101
Variance2280814.6
MonotonicityNot monotonic
2024-05-18T09:16:58.846668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 48
 
0.5%
8 47
 
0.5%
9 47
 
0.5%
18 44
 
0.4%
7 42
 
0.4%
11 40
 
0.4%
13 39
 
0.4%
14 39
 
0.4%
20 39
 
0.4%
29 36
 
0.4%
Other values (2723) 9579
95.8%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 17
 
0.2%
3 21
0.2%
4 30
0.3%
5 36
0.4%
6 34
0.3%
7 42
0.4%
8 47
0.5%
9 47
0.5%
10 34
0.3%
ValueCountFrequency (%)
33924 1
< 0.1%
32305 1
< 0.1%
19267 1
< 0.1%
15612 1
< 0.1%
15483 1
< 0.1%
15173 1
< 0.1%
13130 1
< 0.1%
13073 1
< 0.1%
13004 1
< 0.1%
12834 1
< 0.1%

Interactions

2024-05-18T09:16:46.226372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:42.630041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:43.681017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:44.954629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:46.477085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:42.875470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:43.975590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:45.345196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:46.815162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:43.124052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:44.173112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:45.619893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:47.336811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:43.405050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:44.547008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T09:16:45.928184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T09:16:59.126182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이용거리(M)이용시간(분)
대여소번호1.0000.0000.0000.0000.1040.0750.070
대여구분코드0.0001.0000.0110.3920.3360.2700.198
성별0.0000.0111.0000.0140.1150.1160.104
연령대코드0.0000.3920.0141.0000.2580.1770.175
이용건수0.1040.3360.1150.2581.0000.6980.779
이용거리(M)0.0750.2700.1160.1770.6981.0000.884
이용시간(분)0.0700.1980.1040.1750.7790.8841.000
2024-05-18T09:16:59.441049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연령대코드성별대여구분코드
연령대코드1.0000.0110.271
성별0.0111.0000.018
대여구분코드0.2710.0181.000
2024-05-18T09:16:59.630885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이용거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.043-0.071-0.0780.0000.0000.000
이용건수-0.0431.0000.9450.9470.2140.1150.126
이용거리(M)-0.0710.9451.0000.9790.1230.0870.087
이용시간(분)-0.0780.9470.9791.0000.1350.0750.094
대여구분코드0.0000.2140.1230.1351.0000.0180.271
성별0.0000.1150.0870.0750.0181.0000.011
연령대코드0.0000.1260.0870.0940.2710.0111.000

Missing values

2024-05-18T09:16:47.758942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T09:16:48.284776image/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

대여년월대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이용거리(M)이용시간(분)
2753520230710201020. 강동경찰서일일권<NA>30대424345.7139.13168697.81363
10943202307433433. 을지로입구역 2번출구정기권F60대7369.153.2514054.55326
17595202307647647. 신이문역 1번출구정기권M30대853204.5625.25108812.441076
2792420230710301030. 미호 플랜트 앞일일권M20대321797.3514.7463556.74558
17366202307640640. 동대문경찰서 교차로정기권F30대391405.1813.3657538.48543
24422202307912912. 응암오거리일일권<NA>30대191463.8814.2461367.17431
2732720230710151015. 샛마을 근린공원일일권<NA>50대3397.253.3614475.21164
12983202307498498.연남동주민센터 앞정기권M70대이상133.540.281210.09
3393202307204204. 국회의사당역 5번출구 옆정기권<NA>30대1506577.8657.82249207.163298
5091202307251251. 서울지방병무청 버스정류장정기권<NA>60대7345.32.9212535.17396
대여년월대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이용거리(M)이용시간(분)
19173202307716716.신정6동 주민센터 인근정기권F30대972612.6126.92116012.56991
6924202307305305. 종로구청 옆정기권<NA>30대1499403.6978.11336680.22858
19637202307734734. 신트리공원 입구정기권F40대1025603.7555.51239267.151720
19246202307720720. 서울강월초등학교 앞정기권<NA>30대494904.3740.87176326.461542
26294202307975975.백련산 힐스테이트상가앞정기권F~10대150.190.451950.012
14568202307551551. 구의삼성쉐르빌 앞일일권M30대141525.5412.5153933.951046
10499202307421421. 마포구청 앞일일권F20대373433.1833.46148367.431301
18093202307666666.이문대성유니드아파트 앞일일권M40대8783.486.3227241.42169
11774202307458458. 광화문역 5번출구정기권M40대36430176.99229.5989397.947338
2733920230710151015. 샛마을 근린공원일일권M50대148.030.472021.4215