Overview

Dataset statistics

Number of variables8
Number of observations124
Missing cells3
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory67.1 B

Variable types

Numeric1
Categorical5
Text1
DateTime1

Alerts

gugun is highly overall correlated with idx and 3 other fieldsHigh correlation
instt_code is highly overall correlated with idx and 3 other fieldsHigh correlation
idx is highly overall correlated with gugun and 2 other fieldsHigh correlation
pumpgubun is highly overall correlated with gugun and 2 other fieldsHigh correlation
pumpcnt is highly overall correlated with pumpsetcostHigh correlation
pumpsetcost is highly overall correlated with idx and 4 other fieldsHigh correlation
pumpcnt is highly imbalanced (88.1%)Imbalance
idx has 3 (2.4%) missing valuesMissing

Reproduction

Analysis started2024-04-17 00:40:56.160010
Analysis finished2024-04-17 00:40:56.854186
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

idx
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct70
Distinct (%)57.9%
Missing3
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean45.586777
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-04-17T09:40:56.910106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q125
median47
Q365
95-th percentile77
Maximum80
Range79
Interquartile range (IQR)40

Descriptive statistics

Standard deviation21.628557
Coefficient of variation (CV)0.47444805
Kurtosis-1.1806334
Mean45.586777
Median Absolute Deviation (MAD)20
Skewness-0.11489239
Sum5516
Variance467.79449
MonotonicityNot monotonic
2024-04-17T09:40:57.022062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 5
 
4.0%
48 4
 
3.2%
47 4
 
3.2%
15 3
 
2.4%
17 3
 
2.4%
76 2
 
1.6%
58 2
 
1.6%
80 2
 
1.6%
79 2
 
1.6%
52 2
 
1.6%
Other values (60) 92
74.2%
(Missing) 3
 
2.4%
ValueCountFrequency (%)
1 1
 
0.8%
3 1
 
0.8%
11 2
1.6%
12 2
1.6%
13 1
 
0.8%
14 1
 
0.8%
15 3
2.4%
16 2
1.6%
17 3
2.4%
18 2
1.6%
ValueCountFrequency (%)
80 2
1.6%
79 2
1.6%
78 2
1.6%
77 2
1.6%
76 2
1.6%
75 2
1.6%
74 2
1.6%
73 2
1.6%
72 2
1.6%
71 2
1.6%

gugun
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
부산광역시 연제구
30 
부산광역시 부산진구
18 
부산광역시 수영구
16 
부산광역시 강서구
11 
부산광역시 기장군
9 
Other values (9)
40 

Length

Max length10
Median length9
Mean length9.0403226
Min length7

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 연제구 30
24.2%
부산광역시 부산진구 18
14.5%
부산광역시 수영구 16
12.9%
부산광역시 강서구 11
 
8.9%
부산광역시 기장군 9
 
7.3%
부산광역시 해운대구 7
 
5.6%
부산광역시 사상구 6
 
4.8%
부산광역시 동래구 6
 
4.8%
부산광역시 북구 6
 
4.8%
낙동강관리본부 5
 
4.0%
Other values (4) 10
 
8.1%

Length

2024-04-17T09:40:57.128590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 119
49.0%
연제구 30
 
12.3%
부산진구 18
 
7.4%
수영구 16
 
6.6%
강서구 11
 
4.5%
기장군 9
 
3.7%
해운대구 7
 
2.9%
사상구 6
 
2.5%
동래구 6
 
2.5%
북구 6
 
2.5%
Other values (5) 15
 
6.2%

spot
Text

Distinct96
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-04-17T09:40:57.334787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length24
Mean length14.379032
Min length4

Characters and Unicode

Total characters1783
Distinct characters201
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)59.7%

Sample

1st row삼락자전거대여소
2nd row화명 자전거대여소 앞
3rd row대저 자전거대여소
4th row맥도 자전거무료대여소
5th row맥도 철새태마공원
ValueCountFrequency (%)
앞 29
 
7.4%
인근 23
 
5.8%
자전거보관대 21
 
5.3%
지하철 19
 
4.8%
출구 18
 
4.6%
자전거대여소 9
 
2.3%
자전거 7
 
1.8%
민락동 6
 
1.5%
하부 5
 
1.3%
수영강변 5
 
1.3%
Other values (166) 252
64.0%
2024-04-17T09:40:57.670820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
270
 
15.1%
대 64
 
3.6%
구 50
 
2.8%
동 48
 
2.7%
전 47
 
2.6%
거 45
 
2.5%
자 42
 
2.4%
역 39
 
2.2%
번 35
 
2.0%
관 32
 
1.8%
Other values (191) 1111
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1377
77.2%
Space Separator 270
 
15.1%
Decimal Number 99
 
5.6%
Close Punctuation 16
 
0.9%
Open Punctuation 15
 
0.8%
Dash Punctuation 2
 
0.1%
Uppercase Letter 2
 
0.1%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 64
 
4.6%
구 50
 
3.6%
동 48
 
3.5%
전 47
 
3.4%
거 45
 
3.3%
자 42
 
3.1%
역 39
 
2.8%
번 35
 
2.5%
관 32
 
2.3%
출 31
 
2.3%
Other values (173) 944
68.6%
Decimal Number
ValueCountFrequency (%)
3 17
17.2%
2 17
17.2%
1 14
14.1%
4 11
11.1%
0 10
10.1%
7 8
8.1%
6 7
7.1%
5 6
 
6.1%
9 6
 
6.1%
8 3
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
U 1
50.0%
N 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
270
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1376
77.2%
Common 402
 
22.5%
Latin 4
 
0.2%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 64
 
4.7%
구 50
 
3.6%
동 48
 
3.5%
전 47
 
3.4%
거 45
 
3.3%
자 42
 
3.1%
역 39
 
2.8%
번 35
 
2.5%
관 32
 
2.3%
출 31
 
2.3%
Other values (172) 943
68.5%
Common
ValueCountFrequency (%)
270
67.2%
3 17
 
4.2%
2 17
 
4.2%
) 16
 
4.0%
( 15
 
3.7%
1 14
 
3.5%
4 11
 
2.7%
0 10
 
2.5%
7 8
 
2.0%
6 7
 
1.7%
Other values (4) 17
 
4.2%
Latin
ValueCountFrequency (%)
U 1
25.0%
N 1
25.0%
s 1
25.0%
k 1
25.0%
Han
ValueCountFrequency (%)
舊 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1376
77.2%
ASCII 406
 
22.8%
CJK 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
270
66.5%
3 17
 
4.2%
2 17
 
4.2%
) 16
 
3.9%
( 15
 
3.7%
1 14
 
3.4%
4 11
 
2.7%
0 10
 
2.5%
7 8
 
2.0%
6 7
 
1.7%
Other values (8) 21
 
5.2%
Hangul
ValueCountFrequency (%)
대 64
 
4.7%
구 50
 
3.6%
동 48
 
3.5%
전 47
 
3.4%
거 45
 
3.3%
자 42
 
3.1%
역 39
 
2.8%
번 35
 
2.5%
관 32
 
2.3%
출 31
 
2.3%
Other values (172) 943
68.5%
CJK
ValueCountFrequency (%)
舊 1
100.0%

pumpgubun
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
태양광
88 
수동식
19 
태양광(소형)
 
7
전기식
 
4
태양광(중형)
 
4
Other values (2)
 
2

Length

Max length9
Median length3
Mean length3.4354839
Min length3

Unique

Unique2 ?
Unique (%)1.6%

Sample

1st row수동식
2nd row전기식(콤프레샤)
3rd row수동식
4th row수동식
5th row수동식

Common Values

ValueCountFrequency (%)
태양광 88
71.0%
수동식 19
 
15.3%
태양광(소형) 7
 
5.6%
전기식 4
 
3.2%
태양광(중형) 4
 
3.2%
전기식(콤프레샤) 1
 
0.8%
전기식,수동식 1
 
0.8%

Length

2024-04-17T09:40:57.780746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T09:40:57.872146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
태양광 88
71.0%
수동식 19
 
15.3%
태양광(소형 7
 
5.6%
전기식 4
 
3.2%
태양광(중형 4
 
3.2%
전기식(콤프레샤 1
 
0.8%
전기식,수동식 1
 
0.8%

pumpcnt
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
122 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 122
98.4%
2 2
 
1.6%

Length

2024-04-17T09:40:57.968281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T09:40:58.051531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 122
98.4%
2 2
 
1.6%

pumpsetcost
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1799000
30 
1650000
8 
1730000
7 
544500
 
6
1894000
 
6
Other values (28)
67 

Length

Max length10
Median length7
Mean length7.0403226
Min length5

Unique

Unique8 ?
Unique (%)6.5%

Sample

1st row10000
2nd row150000
3rd row10000
4th row10000
5th row10000

Common Values

ValueCountFrequency (%)
1799000 30
24.2%
1650000 8
 
6.5%
1730000 7
 
5.6%
544500 6
 
4.8%
1894000 6
 
4.8%
10000 5
 
4.0%
2200000 5
 
4.0%
600000 4
 
3.2%
3,300,000 4
 
3.2%
2125000 4
 
3.2%
Other values (23) 45
36.3%

Length

2024-04-17T09:40:58.150016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1799000 30
24.2%
1650000 8
 
6.5%
1730000 7
 
5.6%
544500 6
 
4.8%
1894000 6
 
4.8%
10000 5
 
4.0%
2200000 5
 
4.0%
2125000 4
 
3.2%
1990000 4
 
3.2%
3,300,000 4
 
3.2%
Other values (23) 45
36.3%

instt_code
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
68 
3370000
15 
3360000
11 
3380000
8 
3330000
7 
Other values (4)
15 

Length

Max length9
Median length4
Mean length5.4354839
Min length4

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st rowe-낙동강관리본부
2nd rowe-낙동강관리본부
3rd rowe-낙동강관리본부
4th rowe-낙동강관리본부
5th rowe-낙동강관리본부

Common Values

ValueCountFrequency (%)
<NA> 68
54.8%
3370000 15
 
12.1%
3360000 11
 
8.9%
3380000 8
 
6.5%
3330000 7
 
5.6%
3390000 6
 
4.8%
e-낙동강관리본부 5
 
4.0%
3280000 3
 
2.4%
3350000 1
 
0.8%

Length

2024-04-17T09:40:58.273757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T09:40:58.409010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 68
54.8%
3370000 15
 
12.1%
3360000 11
 
8.9%
3380000 8
 
6.5%
3330000 7
 
5.6%
3390000 6
 
4.8%
e-낙동강관리본부 5
 
4.0%
3280000 3
 
2.4%
3350000 1
 
0.8%

last_load_dttm
Date

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2021-04-01 05:42:02
Maximum2021-04-01 05:42:03
2024-04-17T09:40:58.534274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:40:58.645733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

Interactions

2024-04-17T09:40:56.612009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T09:40:58.725594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idxgugunspotpumpgubunpumpcntpumpsetcostinstt_codelast_load_dttm
idx1.0000.8930.9990.6050.0000.9470.9740.128
gugun0.8931.0001.0000.8800.2970.9941.0000.402
spot0.9991.0001.0000.9811.0000.9951.0001.000
pumpgubun0.6050.8800.9811.0000.1970.9870.7840.000
pumpcnt0.0000.2971.0000.1971.0000.7920.2320.000
pumpsetcost0.9470.9940.9950.9870.7921.0000.9770.000
instt_code0.9741.0001.0000.7840.2320.9771.0000.350
last_load_dttm0.1280.4021.0000.0000.0000.0000.3501.000
2024-04-17T09:40:58.843202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
guguninstt_codepumpgubunpumpsetcostpumpcnt
gugun1.0001.0000.5200.8530.218
instt_code1.0001.0000.5750.8350.157
pumpgubun0.5200.5751.0000.8170.205
pumpsetcost0.8530.8350.8171.0000.601
pumpcnt0.2180.1570.2050.6011.000
2024-04-17T09:40:58.947725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idxgugunpumpgubunpumpcntpumpsetcostinstt_code
idx1.0000.6470.3560.0000.6520.750
gugun0.6471.0000.5200.2180.8531.000
pumpgubun0.3560.5201.0000.2050.8170.575
pumpcnt0.0000.2180.2051.0000.6010.157
pumpsetcost0.6520.8530.8170.6011.0000.835
instt_code0.7501.0000.5750.1570.8351.000

Missing values

2024-04-17T09:40:56.718722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T09:40:56.813945image/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

idxgugunspotpumpgubunpumpcntpumpsetcostinstt_codelast_load_dttm
016낙동강관리본부삼락자전거대여소수동식110000e-낙동강관리본부2021-04-01 05:42:02
117낙동강관리본부화명 자전거대여소 앞전기식(콤프레샤)1150000e-낙동강관리본부2021-04-01 05:42:03
218낙동강관리본부대저 자전거대여소수동식110000e-낙동강관리본부2021-04-01 05:42:03
319낙동강관리본부맥도 자전거무료대여소수동식110000e-낙동강관리본부2021-04-01 05:42:03
420낙동강관리본부맥도 철새태마공원수동식110000e-낙동강관리본부2021-04-01 05:42:03
536부산광역시 사상구감전역 3번 출구전기식1560000033900002021-04-01 05:42:03
637부산광역시 사상구주례역 1번 출구전기식1560000033900002021-04-01 05:42:03
738부산광역시 사상구사상달리미 자전거대여소수동식160000033900002021-04-01 05:42:03
839부산광역시 사상구르네시떼역 경전철 아래수동식160000033900002021-04-01 05:42:03
940부산광역시 사상구국토종주자전거길 전망대쉼터수동식160000033900002021-04-01 05:42:03
idxgugunspotpumpgubunpumpcntpumpsetcostinstt_codelast_load_dttm
11471부산광역시 연제구온천천 안락교(연산9동) 자전거보관대 인근태양광11799000<NA>2021-04-01 05:42:03
11572부산광역시 연제구연산9동 망미주공아파트 자전거보관대 인근태양광11799000<NA>2021-04-01 05:42:03
11673부산광역시 연제구지하철 배산역 3번 출구 자전거보관대 인근태양광11799000<NA>2021-04-01 05:42:03
11774부산광역시 연제구지하철 물만골역 4번 출구 자전거보관대 인근태양광11799000<NA>2021-04-01 05:42:03
11875부산광역시 연제구연산2동 이마트 연제점태양광11799000<NA>2021-04-01 05:42:03
11976부산광역시 연제구거제1동 홈플러스 아시아드점태양광11799000<NA>2021-04-01 05:42:03
12077부산광역시 연제구연산1동 홈플러스 연산점태양광11799000<NA>2021-04-01 05:42:03
12178부산광역시 연제구지하철 연산역 14번출구 자전거보관대 인근태양광11799000<NA>2021-04-01 05:42:03
12279부산광역시 연제구지하철 교대역 3번출구 국제신문 앞 자전거보관대 인근태양광11799000<NA>2021-04-01 05:42:03
12380부산광역시 연제구부산교대 후문 입구태양광11799000<NA>2021-04-01 05:42:03