Overview

Dataset statistics

Number of variables4
Number of observations107
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory36.2 B

Variable types

Numeric3
Text1

Dataset

Description페달로 정거장별 대여 및 반납현황입니다.(3분기)
Author안산도시공사
URLhttps://www.data.go.kr/data/15061868/fileData.do

Alerts

대여 is highly overall correlated with 반납High correlation
반납 is highly overall correlated with 대여High correlation
정거장 번호 has unique valuesUnique
정거장 명 has unique valuesUnique
대여 has 2 (1.9%) zerosZeros
반납 has 2 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-12 21:41:24.281522
Analysis finished2023-12-12 21:41:25.409383
Duration1.13 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

정거장 번호
Real number (ℝ)

UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.850467
Minimum1
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:41:25.480845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.3
Q127.5
median59
Q385.5
95-th percentile106.7
Maximum112
Range111
Interquartile range (IQR)58

Descriptive statistics

Standard deviation33.189508
Coefficient of variation (CV)0.58380362
Kurtosis-1.2742808
Mean56.850467
Median Absolute Deviation (MAD)29
Skewness-0.031669515
Sum6083
Variance1101.5435
MonotonicityStrictly increasing
2023-12-13T06:41:25.614264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
74 1
 
0.9%
85 1
 
0.9%
84 1
 
0.9%
83 1
 
0.9%
82 1
 
0.9%
81 1
 
0.9%
80 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
Other values (97) 97
90.7%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
112 1
0.9%
111 1
0.9%
110 1
0.9%
109 1
0.9%
108 1
0.9%
107 1
0.9%
106 1
0.9%
105 1
0.9%
104 1
0.9%
103 1
0.9%

정거장 명
Text

UNIQUE 

Distinct107
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size988.0 B
2023-12-13T06:41:25.866239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.2429907
Min length3

Characters and Unicode

Total characters775
Distinct characters197
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique107 ?
Unique (%)100.0%

Sample

1st row안산디자인문화고등학교
2nd row본오중학교
3rd row상록수역(1번출구)
4th row상록수역(2번출구)
5th row일동주민센터
ValueCountFrequency (%)
주민센터 5
 
3.4%
안산대학교 2
 
1.4%
와동 2
 
1.4%
초지역 2
 
1.4%
사거리 2
 
1.4%
월피동 2
 
1.4%
apt 2
 
1.4%
선부동 2
 
1.4%
10단지 1
 
0.7%
한양대 1
 
0.7%
Other values (124) 124
85.5%
2023-12-13T06:41:26.267463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38
 
4.9%
동 25
 
3.2%
주 18
 
2.3%
원 17
 
2.2%
역 16
 
2.1%
교 15
 
1.9%
장 14
 
1.8%
산 14
 
1.8%
터 14
 
1.8%
사 14
 
1.8%
Other values (187) 590
76.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 682
88.0%
Space Separator 38
 
4.9%
Decimal Number 18
 
2.3%
Open Punctuation 12
 
1.5%
Close Punctuation 12
 
1.5%
Uppercase Letter 12
 
1.5%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 25
 
3.7%
주 18
 
2.6%
원 17
 
2.5%
역 16
 
2.3%
교 15
 
2.2%
장 14
 
2.1%
산 14
 
2.1%
터 14
 
2.1%
사 14
 
2.1%
지 14
 
2.1%
Other values (170) 521
76.4%
Uppercase Letter
ValueCountFrequency (%)
G 2
16.7%
T 2
16.7%
P 2
16.7%
A 2
16.7%
U 1
8.3%
C 1
8.3%
V 1
8.3%
S 1
8.3%
Decimal Number
ValueCountFrequency (%)
2 8
44.4%
1 6
33.3%
3 2
 
11.1%
0 1
 
5.6%
5 1
 
5.6%
Space Separator
ValueCountFrequency (%)
38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 682
88.0%
Common 81
 
10.5%
Latin 12
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 25
 
3.7%
주 18
 
2.6%
원 17
 
2.5%
역 16
 
2.3%
교 15
 
2.2%
장 14
 
2.1%
산 14
 
2.1%
터 14
 
2.1%
사 14
 
2.1%
지 14
 
2.1%
Other values (170) 521
76.4%
Common
ValueCountFrequency (%)
38
46.9%
( 12
 
14.8%
) 12
 
14.8%
2 8
 
9.9%
1 6
 
7.4%
3 2
 
2.5%
- 1
 
1.2%
0 1
 
1.2%
5 1
 
1.2%
Latin
ValueCountFrequency (%)
G 2
16.7%
T 2
16.7%
P 2
16.7%
A 2
16.7%
U 1
8.3%
C 1
8.3%
V 1
8.3%
S 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 682
88.0%
ASCII 93
 
12.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38
40.9%
( 12
 
12.9%
) 12
 
12.9%
2 8
 
8.6%
1 6
 
6.5%
G 2
 
2.2%
T 2
 
2.2%
3 2
 
2.2%
P 2
 
2.2%
A 2
 
2.2%
Other values (7) 7
 
7.5%
Hangul
ValueCountFrequency (%)
동 25
 
3.7%
주 18
 
2.6%
원 17
 
2.5%
역 16
 
2.3%
교 15
 
2.2%
장 14
 
2.1%
산 14
 
2.1%
터 14
 
2.1%
사 14
 
2.1%
지 14
 
2.1%
Other values (170) 521
76.4%

대여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct106
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3794.5421
Minimum0
Maximum16430
Zeros2
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:41:26.410421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile160.2
Q11404
median3496
Q35157
95-th percentile9512.6
Maximum16430
Range16430
Interquartile range (IQR)3753

Descriptive statistics

Standard deviation3188.9618
Coefficient of variation (CV)0.84040754
Kurtosis2.7649346
Mean3794.5421
Median Absolute Deviation (MAD)1868
Skewness1.396863
Sum406016
Variance10169477
MonotonicityNot monotonic
2023-12-13T06:41:26.545456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
1.9%
2845 1
 
0.9%
2631 1
 
0.9%
4843 1
 
0.9%
997 1
 
0.9%
3524 1
 
0.9%
188 1
 
0.9%
416 1
 
0.9%
9926 1
 
0.9%
6629 1
 
0.9%
Other values (96) 96
89.7%
ValueCountFrequency (%)
0 2
1.9%
130 1
0.9%
147 1
0.9%
149 1
0.9%
156 1
0.9%
170 1
0.9%
188 1
0.9%
249 1
0.9%
257 1
0.9%
276 1
0.9%
ValueCountFrequency (%)
16430 1
0.9%
14083 1
0.9%
13313 1
0.9%
12846 1
0.9%
10016 1
0.9%
9926 1
0.9%
8548 1
0.9%
8184 1
0.9%
7989 1
0.9%
7884 1
0.9%

반납
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3787.271
Minimum0
Maximum18640
Zeros2
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T06:41:26.685469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile171.6
Q11177.5
median3374
Q35227.5
95-th percentile9467.7
Maximum18640
Range18640
Interquartile range (IQR)4050

Descriptive statistics

Standard deviation3282.7224
Coefficient of variation (CV)0.8667778
Kurtosis4.1976854
Mean3787.271
Median Absolute Deviation (MAD)1980
Skewness1.6141885
Sum405238
Variance10776267
MonotonicityNot monotonic
2023-12-13T06:41:26.833579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
1.9%
188 2
 
1.9%
2778 1
 
0.9%
2336 1
 
0.9%
774 1
 
0.9%
3168 1
 
0.9%
399 1
 
0.9%
11709 1
 
0.9%
5658 1
 
0.9%
3774 1
 
0.9%
Other values (95) 95
88.8%
ValueCountFrequency (%)
0 2
1.9%
96 1
0.9%
125 1
0.9%
160 1
0.9%
171 1
0.9%
173 1
0.9%
175 1
0.9%
188 2
1.9%
273 1
0.9%
274 1
0.9%
ValueCountFrequency (%)
18640 1
0.9%
14715 1
0.9%
13239 1
0.9%
11709 1
0.9%
10334 1
0.9%
9543 1
0.9%
9292 1
0.9%
9017 1
0.9%
8195 1
0.9%
7663 1
0.9%

Interactions

2023-12-13T06:41:25.001045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:41:24.466659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:41:24.724761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:41:25.080780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:41:24.544974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:41:24.821981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:41:25.172221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:41:24.627483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:41:24.909640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:41:27.237784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정거장 번호대여반납
정거장 번호1.0000.5250.411
대여0.5251.0000.925
반납0.4110.9251.000
2023-12-13T06:41:27.317253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정거장 번호대여반납
정거장 번호1.000-0.283-0.238
대여-0.2831.0000.984
반납-0.2380.9841.000

Missing values

2023-12-13T06:41:25.287506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:41:25.371550image/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

정거장 번호정거장 명대여반납
01안산디자인문화고등학교28452778
12본오중학교26032623
23상록수역(1번출구)56324722
34상록수역(2번출구)56735251
45일동주민센터28863078
56부곡도서관29363019
67조각공원19651590
78한양대앞역(농수산물 도매시장)17641511
89한양대앞역(환승주차장)1001610334
910은하수공원27523054
정거장 번호정거장 명대여반납
97103검찰청정문33263206
98104중앙역맞은편1331314715
99105고잔1동주민센터46275371
100106시화호조력발전소257273
101107초지역 서해선 2번출구37923679
102108선부광장50865354
103109달미역15921260
104110방아머리 구선착장00
105111탄도항170173
106112대부도해양본부130125