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-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 12:59:24.799138
Analysis finished2024-03-13 12:59:28.880655
Duration4.08 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
2022-06
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-06
2nd row2022-06
3rd row2022-06
4th row2022-06
5th row2022-06

Common Values

ValueCountFrequency (%)
2022-06 10000
100.0%

Length

2024-03-13T21:59:28.967848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:59:29.074902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-06 10000
100.0%

대여소번호
Real number (ℝ)

Distinct1500
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1066.098
Minimum3
Maximum2230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:59:29.204163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile186
Q1540
median1044
Q31542
95-th percentile2089
Maximum2230
Range2227
Interquartile range (IQR)1002

Descriptive statistics

Standard deviation603.89077
Coefficient of variation (CV)0.56644958
Kurtosis-1.0901111
Mean1066.098
Median Absolute Deviation (MAD)501
Skewness0.19119676
Sum10660980
Variance364684.06
MonotonicityNot monotonic
2024-03-13T21:59:29.374812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1153 18
 
0.2%
144 17
 
0.2%
915 16
 
0.2%
734 16
 
0.2%
672 15
 
0.1%
299 15
 
0.1%
106 15
 
0.1%
249 15
 
0.1%
370 14
 
0.1%
2217 14
 
0.1%
Other values (1490) 9845
98.5%
ValueCountFrequency (%)
3 1
 
< 0.1%
102 3
 
< 0.1%
103 3
 
< 0.1%
104 6
 
0.1%
105 5
 
0.1%
106 15
0.1%
107 7
0.1%
108 7
0.1%
109 12
0.1%
111 10
0.1%
ValueCountFrequency (%)
2230 1
 
< 0.1%
2229 6
0.1%
2228 5
0.1%
2227 6
0.1%
2226 3
 
< 0.1%
2225 3
 
< 0.1%
2223 6
0.1%
2222 8
0.1%
2221 5
0.1%
2220 9
0.1%

대여소명
Text

Distinct1500
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T21:59:29.708907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length29
Mean length15.2646
Min length4

Characters and Unicode

Total characters152646
Distinct characters509
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

Unique26 ?
Unique (%)0.3%

Sample

1st row565. 옥수역 3번출구
2nd row107. 신한은행 서교동지점
3rd row1178. 개화산역 2번 출구
4th row2056. 동작구민 체육센터
5th row137. 신촌기차역입구 교차로
ValueCountFrequency (%)
앞 2677
 
8.9%
옆 483
 
1.6%
출구 390
 
1.3%
1번출구 280
 
0.9%
뒤 243
 
0.8%
사거리 242
 
0.8%
2번출구 230
 
0.8%
4번출구 230
 
0.8%
입구 224
 
0.7%
교차로 220
 
0.7%
Other values (3095) 24746
82.6%
2024-03-13T21:59:30.265792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20124
 
13.2%
. 10024
 
6.6%
1 9288
 
6.1%
2 5284
 
3.5%
3 3569
 
2.3%
4 3488
 
2.3%
0 3387
 
2.2%
구 3383
 
2.2%
5 3367
 
2.2%
6 3324
 
2.2%
Other values (499) 87408
57.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79350
52.0%
Decimal Number 40161
26.3%
Space Separator 20124
 
13.2%
Other Punctuation 10135
 
6.6%
Uppercase Letter 1142
 
0.7%
Close Punctuation 769
 
0.5%
Open Punctuation 769
 
0.5%
Lowercase Letter 100
 
0.1%
Dash Punctuation 65
 
< 0.1%
Math Symbol 15
 
< 0.1%
Other values (2) 16
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 3383
 
4.3%
앞 3076
 
3.9%
역 2619
 
3.3%
번 2380
 
3.0%
출 2285
 
2.9%
동 2003
 
2.5%
교 1713
 
2.2%
리 1386
 
1.7%
아 1253
 
1.6%
원 1249
 
1.6%
Other values (446) 58003
73.1%
Uppercase Letter
ValueCountFrequency (%)
K 160
14.0%
S 148
13.0%
T 103
9.0%
C 99
8.7%
A 82
 
7.2%
B 77
 
6.7%
I 75
 
6.6%
G 74
 
6.5%
L 53
 
4.6%
D 52
 
4.6%
Other values (11) 219
19.2%
Decimal Number
ValueCountFrequency (%)
1 9288
23.1%
2 5284
13.2%
3 3569
 
8.9%
4 3488
 
8.7%
0 3387
 
8.4%
5 3367
 
8.4%
6 3324
 
8.3%
7 3028
 
7.5%
9 2747
 
6.8%
8 2679
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
e 43
43.0%
l 10
 
10.0%
c 7
 
7.0%
m 7
 
7.0%
o 7
 
7.0%
t 7
 
7.0%
n 6
 
6.0%
s 5
 
5.0%
k 5
 
5.0%
y 3
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 10024
98.9%
, 87
 
0.9%
& 10
 
0.1%
? 10
 
0.1%
· 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
20124
100.0%
Close Punctuation
ValueCountFrequency (%)
) 769
100.0%
Open Punctuation
ValueCountFrequency (%)
( 769
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 65
100.0%
Math Symbol
ValueCountFrequency (%)
~ 15
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79356
52.0%
Common 72048
47.2%
Latin 1242
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 3383
 
4.3%
앞 3076
 
3.9%
역 2619
 
3.3%
번 2380
 
3.0%
출 2285
 
2.9%
동 2003
 
2.5%
교 1713
 
2.2%
리 1386
 
1.7%
아 1253
 
1.6%
원 1249
 
1.6%
Other values (447) 58009
73.1%
Latin
ValueCountFrequency (%)
K 160
12.9%
S 148
11.9%
T 103
 
8.3%
C 99
 
8.0%
A 82
 
6.6%
B 77
 
6.2%
I 75
 
6.0%
G 74
 
6.0%
L 53
 
4.3%
D 52
 
4.2%
Other values (21) 319
25.7%
Common
ValueCountFrequency (%)
20124
27.9%
. 10024
13.9%
1 9288
12.9%
2 5284
 
7.3%
3 3569
 
5.0%
4 3488
 
4.8%
0 3387
 
4.7%
5 3367
 
4.7%
6 3324
 
4.6%
7 3028
 
4.2%
Other values (11) 7165
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79350
52.0%
ASCII 73286
48.0%
None 10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20124
27.5%
. 10024
13.7%
1 9288
12.7%
2 5284
 
7.2%
3 3569
 
4.9%
4 3488
 
4.8%
0 3387
 
4.6%
5 3367
 
4.6%
6 3324
 
4.5%
7 3028
 
4.1%
Other values (41) 8403
11.5%
Hangul
ValueCountFrequency (%)
구 3383
 
4.3%
앞 3076
 
3.9%
역 2619
 
3.3%
번 2380
 
3.0%
출 2285
 
2.9%
동 2003
 
2.5%
교 1713
 
2.2%
리 1386
 
1.7%
아 1253
 
1.6%
원 1249
 
1.6%
Other values (446) 58003
73.1%
None
ValueCountFrequency (%)
㈜ 6
60.0%
· 4
40.0%

대여구분코드
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
정기
4920 
일일(회원)
3862 
단체
962 
일일(비회원)
 
254
10분이용권
 
2

Length

Max length7
Median length2
Mean length3.6726
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 4920
49.2%
일일(회원) 3862
38.6%
단체 962
 
9.6%
일일(비회원) 254
 
2.5%
10분이용권 2
 
< 0.1%

Length

2024-03-13T21:59:30.473709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:59:30.671423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 4920
49.2%
일일(회원 3862
38.6%
단체 962
 
9.6%
일일(비회원 254
 
2.5%
10분이용권 2
 
< 0.1%

성별
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
M
3022 
\N
2877 
F
2796 
<NA>
1304 
m
 
1

Length

Max length4
Median length1
Mean length1.6789
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
M 3022
30.2%
\N 2877
28.8%
F 2796
28.0%
<NA> 1304
13.0%
m 1
 
< 0.1%

Length

2024-03-13T21:59:30.861046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:59:31.007363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 3023
30.2%
n 2877
28.8%
f 2796
28.0%
na 1304
13.0%

연령대코드
Categorical

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20대
1776 
30대
1579 
40대
1577 
기타
1452 
50대
1263 
Other values (3)
2353 

Length

Max length5
Median length3
Mean length2.9156
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20대
2nd row40대
3rd row30대
4th row70대이상
5th row50대

Common Values

ValueCountFrequency (%)
20대 1776
17.8%
30대 1579
15.8%
40대 1577
15.8%
기타 1452
14.5%
50대 1263
12.6%
10대 1250
12.5%
60대 799
8.0%
70대이상 304
 
3.0%

Length

2024-03-13T21:59:31.183596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:59:31.318056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 1776
17.8%
30대 1579
15.8%
40대 1577
15.8%
기타 1452
14.5%
50대 1263
12.6%
10대 1250
12.5%
60대 799
8.0%
70대이상 304
 
3.0%

이용건수
Real number (ℝ)

HIGH CORRELATION 

Distinct372
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.2492
Minimum1
Maximum1212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:59:31.491842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median11
Q335
95-th percentile148
Maximum1212
Range1211
Interquartile range (IQR)32

Descriptive statistics

Standard deviation66.144949
Coefficient of variation (CV)1.9312845
Kurtosis50.634084
Mean34.2492
Median Absolute Deviation (MAD)9
Skewness5.4644099
Sum342492
Variance4375.1542
MonotonicityNot monotonic
2024-03-13T21:59:31.702410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1119
 
11.2%
2 990
 
9.9%
3 575
 
5.8%
4 529
 
5.3%
5 427
 
4.3%
6 353
 
3.5%
7 300
 
3.0%
8 244
 
2.4%
9 244
 
2.4%
10 191
 
1.9%
Other values (362) 5028
50.3%
ValueCountFrequency (%)
1 1119
11.2%
2 990
9.9%
3 575
5.8%
4 529
5.3%
5 427
 
4.3%
6 353
 
3.5%
7 300
 
3.0%
8 244
 
2.4%
9 244
 
2.4%
10 191
 
1.9%
ValueCountFrequency (%)
1212 1
< 0.1%
1052 1
< 0.1%
1002 1
< 0.1%
995 1
< 0.1%
925 1
< 0.1%
908 1
< 0.1%
855 1
< 0.1%
791 1
< 0.1%
750 1
< 0.1%
686 1
< 0.1%

운동량
Text

Distinct9716
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T21:59:32.510526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.3639
Min length2

Characters and Unicode

Total characters63639
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

Unique9488 ?
Unique (%)94.9%

Sample

1st row76508.64
2nd row316.25
3rd row2947.94
4th row3355.46
5th row193.47
ValueCountFrequency (%)
0.00 37
 
0.4%
n 7
 
0.1%
18.79 4
 
< 0.1%
67.95 4
 
< 0.1%
30.89 3
 
< 0.1%
135.08 3
 
< 0.1%
15.70 3
 
< 0.1%
94.98 3
 
< 0.1%
29.09 3
 
< 0.1%
132.23 3
 
< 0.1%
Other values (9706) 9930
99.3%
2024-03-13T21:59:33.161545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9993
15.7%
1 7411
11.6%
2 6225
9.8%
3 5697
9.0%
4 5445
8.6%
5 5138
8.1%
6 5000
7.9%
7 4794
7.5%
0 4651
7.3%
9 4650
7.3%
Other values (3) 4635
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53632
84.3%
Other Punctuation 10000
 
15.7%
Uppercase Letter 7
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7411
13.8%
2 6225
11.6%
3 5697
10.6%
4 5445
10.2%
5 5138
9.6%
6 5000
9.3%
7 4794
8.9%
0 4651
8.7%
9 4650
8.7%
8 4621
8.6%
Other Punctuation
ValueCountFrequency (%)
. 9993
99.9%
\ 7
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63632
> 99.9%
Latin 7
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9993
15.7%
1 7411
11.6%
2 6225
9.8%
3 5697
9.0%
4 5445
8.6%
5 5138
8.1%
6 5000
7.9%
7 4794
7.5%
0 4651
7.3%
9 4650
7.3%
Other values (2) 4628
7.3%
Latin
ValueCountFrequency (%)
N 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9993
15.7%
1 7411
11.6%
2 6225
9.8%
3 5697
9.0%
4 5445
8.6%
5 5138
8.1%
6 5000
7.9%
7 4794
7.5%
0 4651
7.3%
9 4650
7.3%
Other values (3) 4635
7.3%

탄소량
Text

Distinct4059
Distinct (%)40.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T21:59:33.666276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.4689
Min length2

Characters and Unicode

Total characters44689
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

Unique2352 ?
Unique (%)23.5%

Sample

1st row670.77
2nd row3.11
3rd row26.30
4th row30.24
5th row1.73
ValueCountFrequency (%)
0.00 35
 
0.4%
0.48 27
 
0.3%
0.44 25
 
0.2%
0.33 25
 
0.2%
0.40 24
 
0.2%
0.37 22
 
0.2%
0.56 21
 
0.2%
0.97 20
 
0.2%
0.24 20
 
0.2%
0.87 19
 
0.2%
Other values (4049) 9762
97.6%
2024-03-13T21:59:34.375632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9993
22.4%
1 5488
12.3%
2 4173
9.3%
0 4064
9.1%
3 3623
 
8.1%
4 3331
 
7.5%
5 3055
 
6.8%
6 2961
 
6.6%
7 2745
 
6.1%
8 2687
 
6.0%
Other values (3) 2569
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34682
77.6%
Other Punctuation 10000
 
22.4%
Uppercase Letter 7
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5488
15.8%
2 4173
12.0%
0 4064
11.7%
3 3623
10.4%
4 3331
9.6%
5 3055
8.8%
6 2961
8.5%
7 2745
7.9%
8 2687
7.7%
9 2555
7.4%
Other Punctuation
ValueCountFrequency (%)
. 9993
99.9%
\ 7
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
N 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44682
> 99.9%
Latin 7
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9993
22.4%
1 5488
12.3%
2 4173
9.3%
0 4064
9.1%
3 3623
 
8.1%
4 3331
 
7.5%
5 3055
 
6.8%
6 2961
 
6.6%
7 2745
 
6.1%
8 2687
 
6.0%
Other values (2) 2562
 
5.7%
Latin
ValueCountFrequency (%)
N 7
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44689
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9993
22.4%
1 5488
12.3%
2 4173
9.3%
0 4064
9.1%
3 3623
 
8.1%
4 3331
 
7.5%
5 3055
 
6.8%
6 2961
 
6.6%
7 2745
 
6.1%
8 2687
 
6.0%
Other values (3) 2569
 
5.7%

이동거리(M)
Real number (ℝ)

HIGH CORRELATION 

Distinct9660
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94112.633
Minimum0
Maximum6448859.1
Zeros40
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:59:34.603188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1582.7225
Q18833.2625
median30886.21
Q398733.512
95-th percentile384451.39
Maximum6448859.1
Range6448859.1
Interquartile range (IQR)89900.25

Descriptive statistics

Standard deviation198159.26
Coefficient of variation (CV)2.1055543
Kurtosis234.38976
Mean94112.633
Median Absolute Deviation (MAD)26767.35
Skewness10.614687
Sum9.4112633 × 108
Variance3.9267093 × 1010
MonotonicityNot monotonic
2024-03-13T21:59:34.777708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 40
 
0.4%
1850.0 6
 
0.1%
1900.0 6
 
0.1%
1390.0 5
 
0.1%
940.0 5
 
0.1%
2580.0 5
 
0.1%
1940.0 5
 
0.1%
1720.0 5
 
0.1%
4160.0 4
 
< 0.1%
2640.0 4
 
< 0.1%
Other values (9650) 9915
99.2%
ValueCountFrequency (%)
0.0 40
0.4%
0.1 1
 
< 0.1%
20.0 1
 
< 0.1%
60.0 2
 
< 0.1%
62.23 1
 
< 0.1%
88.24 1
 
< 0.1%
100.0 1
 
< 0.1%
111.3 1
 
< 0.1%
142.12 1
 
< 0.1%
142.32 1
 
< 0.1%
ValueCountFrequency (%)
6448859.07 1
< 0.1%
5420760.96 1
< 0.1%
5225865.85 1
< 0.1%
3458311.46 1
< 0.1%
3151871.31 1
< 0.1%
2945974.43 1
< 0.1%
2891102.01 1
< 0.1%
2006440.57 1
< 0.1%
1863189.6 1
< 0.1%
1857490.67 1
< 0.1%

이용시간(분)
Real number (ℝ)

HIGH CORRELATION 

Distinct2554
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean784.4945
Minimum1
Maximum53362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T21:59:34.969065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q182
median272
Q3836
95-th percentile3183.05
Maximum53362
Range53361
Interquartile range (IQR)754

Descriptive statistics

Standard deviation1583.9736
Coefficient of variation (CV)2.0191009
Kurtosis274.81577
Mean784.4945
Median Absolute Deviation (MAD)234
Skewness11.220966
Sum7844945
Variance2508972.3
MonotonicityNot monotonic
2024-03-13T21:59:35.229773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 58
 
0.6%
14 53
 
0.5%
29 51
 
0.5%
12 51
 
0.5%
15 50
 
0.5%
5 46
 
0.5%
17 43
 
0.4%
11 43
 
0.4%
6 41
 
0.4%
7 41
 
0.4%
Other values (2544) 9523
95.2%
ValueCountFrequency (%)
1 6
 
0.1%
2 22
 
0.2%
3 25
0.2%
4 26
0.3%
5 46
0.5%
6 41
0.4%
7 41
0.4%
8 30
0.3%
9 40
0.4%
10 58
0.6%
ValueCountFrequency (%)
53362 1
< 0.1%
48437 1
< 0.1%
43294 1
< 0.1%
24800 1
< 0.1%
23391 1
< 0.1%
18670 1
< 0.1%
16960 1
< 0.1%
16056 1
< 0.1%
13833 1
< 0.1%
13586 1
< 0.1%

Interactions

2024-03-13T21:59:27.902499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:26.366377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:26.873020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:27.402627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:28.016911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:26.463502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:26.999748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:27.514884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:28.196889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:26.602543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:27.123892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:27.643985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:28.375175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:26.734819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:27.264158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:59:27.772206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:59:35.370192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호대여구분코드성별연령대코드이용건수이동거리(M)이용시간(분)
대여소번호1.0000.0220.0000.0330.0750.0450.042
대여구분코드0.0221.0000.1450.3170.2670.0760.070
성별0.0000.1451.0000.1040.0430.0240.000
연령대코드0.0330.3170.1041.0000.1950.1540.104
이용건수0.0750.2670.0430.1951.0000.8530.826
이동거리(M)0.0450.0760.0240.1540.8531.0000.917
이용시간(분)0.0420.0700.0000.1040.8260.9171.000
2024-03-13T21:59:35.525599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여구분코드성별연령대코드
대여구분코드1.0000.1190.200
성별0.1191.0000.047
연령대코드0.2000.0471.000
2024-03-13T21:59:35.653647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대여소번호이용건수이동거리(M)이용시간(분)대여구분코드성별연령대코드
대여소번호1.000-0.024-0.031-0.0430.0090.0000.016
이용건수-0.0241.0000.9380.9400.1140.0260.094
이동거리(M)-0.0310.9381.0000.9760.0470.0110.052
이용시간(분)-0.0430.9400.9761.0000.0440.0000.055
대여구분코드0.0090.1140.0470.0441.0000.1190.200
성별0.0000.0260.0110.0000.1191.0000.047
연령대코드0.0160.0940.0520.0550.2000.0471.000

Missing values

2024-03-13T21:59:28.558913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:59:28.771155image/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)
207902022-06565565. 옥수역 3번출구정기\N20대43576508.64670.772891102.0116056
3332022-06107107. 신한은행 서교동지점일일(회원)F40대4316.253.1113411.54129
443362022-0611781178. 개화산역 2번 출구정기\N30대482947.9426.30113399.421093
730312022-0620562056. 동작구민 체육센터정기M70대이상123355.4630.24130359.21857
17992022-06137137. 신촌기차역입구 교차로정기<NA>50대2193.471.737422.36112
481382022-0612621262. 송파여성문화회관 앞정기M40대1469145.6571.10306585.863365
569522022-0615091509. 서울북부수도사업소정기F기타6361.874.4619245.01164
5542022-06112112. 극동방송국 앞일일(회원)F40대1413.353.7316058.6111
519402022-0613621362. 보문역6번출구 앞정기F30대704733.0548.13207468.21863
64172022-06236236. 문래동자이아파트 앞정기M기타1126674.6052.49226075.42178
대여일자대여소번호대여소명대여구분코드성별연령대코드이용건수운동량탄소량이동거리(M)이용시간(분)
359732022-06975975.백련산 힐스테이트상가앞정기<NA>40대231.400.281220.017
396722022-0610641064.중앙보훈병원역 1번출구정기F기타11623.785.8024947.61170
172892022-06488488.푸르메병원일일(회원)\N10대7162.392.4110369.64116
403262022-0610771077.강동역 1번출구 앞정기M60대221155.2210.1943874.62410
577972022-0615311531. 미아사거리 1번 출구일일(비회원)F기타152.800.482051.422
326832022-06863863.이촌역2번 출구정기F10대232.770.391697.7916
106262022-06331331. 을지로2가 사거리 북측정기M20대513401.2228.34122077.93877
387642022-0610421042. 강일리버파크 2~5단지정기F10대14510.664.9921501.43538
15472022-06131131. 증산2교단체\N30대6793.248.1335079.0268
653882022-0617611761.방학중학교 앞정기F40대14596.406.0726109.52581