Overview

Dataset statistics

Number of variables8
Number of observations101
Missing cells3
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory67.3 B

Variable types

Numeric1
Categorical5
Text1
DateTime1

Alerts

last_load_dttm has constant value ""Constant
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 (86.0%)Imbalance
idx has 3 (3.0%) missing valuesMissing

Reproduction

Analysis started2024-04-17 00:41:06.660820
Analysis finished2024-04-17 00:41:07.282286
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

idx
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct70
Distinct (%)71.4%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean41.27551
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T09:41:07.341420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q122.25
median40.5
Q358.75
95-th percentile75.15
Maximum80
Range79
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation20.989335
Coefficient of variation (CV)0.50851788
Kurtosis-1.1519288
Mean41.27551
Median Absolute Deviation (MAD)18.5
Skewness0.10386728
Sum4045
Variance440.55218
MonotonicityNot monotonic
2024-04-17T09:41:07.718230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 3
 
3.0%
46 3
 
3.0%
15 3
 
3.0%
23 2
 
2.0%
24 2
 
2.0%
47 2
 
2.0%
48 2
 
2.0%
31 2
 
2.0%
25 2
 
2.0%
52 2
 
2.0%
Other values (60) 75
74.3%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
1 1
 
1.0%
3 1
 
1.0%
11 2
2.0%
12 2
2.0%
13 1
 
1.0%
14 1
 
1.0%
15 3
3.0%
16 2
2.0%
17 3
3.0%
18 2
2.0%
ValueCountFrequency (%)
80 1
1.0%
79 1
1.0%
78 1
1.0%
77 1
1.0%
76 1
1.0%
75 1
1.0%
74 1
1.0%
73 1
1.0%
72 1
1.0%
71 1
1.0%

gugun
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
부산광역시 부산진구
18 
부산광역시 연제구
15 
부산광역시 강서구
11 
부산광역시 기장군
9 
부산광역시 수영구
8 
Other values (9)
40 

Length

Max length10
Median length9
Mean length9.049505
Min length7

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 부산진구 18
17.8%
부산광역시 연제구 15
14.9%
부산광역시 강서구 11
10.9%
부산광역시 기장군 9
8.9%
부산광역시 수영구 8
7.9%
부산광역시 해운대구 7
 
6.9%
부산광역시 사상구 6
 
5.9%
부산광역시 북구 6
 
5.9%
부산광역시 동래구 6
 
5.9%
낙동강관리본부 5
 
5.0%
Other values (4) 10
9.9%

Length

2024-04-17T09:41:07.843678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 96
48.7%
부산진구 18
 
9.1%
연제구 15
 
7.6%
강서구 11
 
5.6%
기장군 9
 
4.6%
수영구 8
 
4.1%
해운대구 7
 
3.6%
사상구 6
 
3.0%
북구 6
 
3.0%
동래구 6
 
3.0%
Other values (5) 15
 
7.6%

spot
Text

Distinct96
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
2024-04-17T09:41:08.066799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length24
Mean length13.752475
Min length4

Characters and Unicode

Total characters1389
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

Unique91 ?
Unique (%)90.1%

Sample

1st row삼락자전거대여소
2nd row화명 자전거대여소 앞
3rd row대저 자전거대여소
4th row맥도 자전거무료대여소
5th row맥도 철새태마공원
ValueCountFrequency (%)
앞 26
 
8.6%
인근 13
 
4.3%
출구 12
 
4.0%
지하철 11
 
3.6%
자전거보관대 11
 
3.6%
자전거 7
 
2.3%
자전거대여소 6
 
2.0%
화명동 3
 
1.0%
5번 3
 
1.0%
옆 3
 
1.0%
Other values (166) 208
68.6%
2024-04-17T09:41:08.424990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
202
 
14.5%
대 46
 
3.3%
구 41
 
3.0%
동 36
 
2.6%
전 34
 
2.4%
역 31
 
2.2%
거 30
 
2.2%
자 29
 
2.1%
앞 27
 
1.9%
번 27
 
1.9%
Other values (191) 886
63.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1073
77.2%
Space Separator 202
 
14.5%
Decimal Number 79
 
5.7%
Close Punctuation 15
 
1.1%
Open Punctuation 14
 
1.0%
Dash Punctuation 2
 
0.1%
Uppercase Letter 2
 
0.1%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 46
 
4.3%
구 41
 
3.8%
동 36
 
3.4%
전 34
 
3.2%
역 31
 
2.9%
거 30
 
2.8%
자 29
 
2.7%
앞 27
 
2.5%
번 27
 
2.5%
출 23
 
2.1%
Other values (173) 749
69.8%
Decimal Number
ValueCountFrequency (%)
3 15
19.0%
2 13
16.5%
1 11
13.9%
0 8
10.1%
4 8
10.1%
6 6
 
7.6%
7 6
 
7.6%
5 5
 
6.3%
9 4
 
5.1%
8 3
 
3.8%
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 (%)
202
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1072
77.2%
Common 312
 
22.5%
Latin 4
 
0.3%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 46
 
4.3%
구 41
 
3.8%
동 36
 
3.4%
전 34
 
3.2%
역 31
 
2.9%
거 30
 
2.8%
자 29
 
2.7%
앞 27
 
2.5%
번 27
 
2.5%
출 23
 
2.1%
Other values (172) 748
69.8%
Common
ValueCountFrequency (%)
202
64.7%
3 15
 
4.8%
) 15
 
4.8%
( 14
 
4.5%
2 13
 
4.2%
1 11
 
3.5%
0 8
 
2.6%
4 8
 
2.6%
6 6
 
1.9%
7 6
 
1.9%
Other values (4) 14
 
4.5%
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 1072
77.2%
ASCII 316
 
22.8%
CJK 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
202
63.9%
3 15
 
4.7%
) 15
 
4.7%
( 14
 
4.4%
2 13
 
4.1%
1 11
 
3.5%
0 8
 
2.5%
4 8
 
2.5%
6 6
 
1.9%
7 6
 
1.9%
Other values (8) 18
 
5.7%
Hangul
ValueCountFrequency (%)
대 46
 
4.3%
구 41
 
3.8%
동 36
 
3.4%
전 34
 
3.2%
역 31
 
2.9%
거 30
 
2.8%
자 29
 
2.7%
앞 27
 
2.5%
번 27
 
2.5%
출 23
 
2.1%
Other values (172) 748
69.8%
CJK
ValueCountFrequency (%)
舊 1
100.0%

pumpgubun
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
태양광
68 
수동식
16 
태양광(소형)
7 
전기식
 
4
태양광(중형)
 
4
Other values (2)
 
2

Length

Max length9
Median length3
Mean length3.5346535
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
태양광 68
67.3%
수동식 16
 
15.8%
태양광(소형) 7
 
6.9%
전기식 4
 
4.0%
태양광(중형) 4
 
4.0%
전기식(콤프레샤) 1
 
1.0%
전기식,수동식 1
 
1.0%

Length

2024-04-17T09:41:08.532053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T09:41:08.645735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
태양광 68
67.3%
수동식 16
 
15.8%
태양광(소형 7
 
6.9%
전기식 4
 
4.0%
태양광(중형 4
 
4.0%
전기식(콤프레샤 1
 
1.0%
전기식,수동식 1
 
1.0%

pumpcnt
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
1
99 
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 99
98.0%
2 2
 
2.0%

Length

2024-04-17T09:41:08.741191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T09:41:08.815292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 99
98.0%
2 2
 
2.0%

pumpsetcost
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Memory size940.0 B
1799000
15 
1650000
8 
1730000
7 
10000
 
5
2200000
 
5
Other values (28)
61 

Length

Max length10
Median length7
Mean length7.0792079
Min length5

Unique

Unique8 ?
Unique (%)7.9%

Sample

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

Common Values

ValueCountFrequency (%)
1799000 15
 
14.9%
1650000 8
 
7.9%
1730000 7
 
6.9%
10000 5
 
5.0%
2200000 5
 
5.0%
600000 4
 
4.0%
3,300,000 4
 
4.0%
1990000 4
 
4.0%
544500 3
 
3.0%
1,833,000 3
 
3.0%
Other values (23) 43
42.6%

Length

2024-04-17T09:41:08.897334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1799000 15
 
14.9%
1650000 8
 
7.9%
1730000 7
 
6.9%
10000 5
 
5.0%
2200000 5
 
5.0%
600000 4
 
4.0%
3,300,000 4
 
4.0%
1990000 4
 
4.0%
1750850 3
 
3.0%
1894000 3
 
3.0%
Other values (23) 43
42.6%

instt_code
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size940.0 B
3290000
18 
3370000
15 
3360000
11 
3400000
9 
3380000
8 
Other values (9)
40 

Length

Max length9
Median length7
Mean length7.0990099
Min length7

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
3290000 18
17.8%
3370000 15
14.9%
3360000 11
10.9%
3400000 9
8.9%
3380000 8
7.9%
3330000 7
 
6.9%
3390000 6
 
5.9%
3320000 6
 
5.9%
3300000 6
 
5.9%
e-낙동강관리본부 5
 
5.0%
Other values (4) 10
9.9%

Length

2024-04-17T09:41:08.997575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3290000 18
17.8%
3370000 15
14.9%
3360000 11
10.9%
3400000 9
8.9%
3380000 8
7.9%
3330000 7
 
6.9%
3390000 6
 
5.9%
3320000 6
 
5.9%
3300000 6
 
5.9%
e-낙동강관리본부 5
 
5.0%
Other values (4) 10
9.9%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size940.0 B
Minimum2021-01-05 14:20:05
Maximum2021-01-05 14:20:05
2024-04-17T09:41:09.090712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T09:41:09.163558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T09:41:07.037711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T09:41:09.221548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idxgugunspotpumpgubunpumpcntpumpsetcostinstt_code
idx1.0000.8870.9920.5650.0000.9370.887
gugun0.8871.0001.0000.8730.1990.9931.000
spot0.9921.0001.0000.7001.0000.0001.000
pumpgubun0.5650.8730.7001.0000.2110.9860.873
pumpcnt0.0000.1991.0000.2111.0000.7590.199
pumpsetcost0.9370.9930.0000.9860.7591.0000.993
instt_code0.8871.0001.0000.8730.1990.9931.000
2024-04-17T09:41:09.307991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
guguninstt_codepumpgubunpumpsetcostpumpcnt
gugun1.0001.0000.5040.8240.142
instt_code1.0001.0000.5040.8240.142
pumpgubun0.5040.5041.0000.7830.219
pumpsetcost0.8240.8240.7831.0000.548
pumpcnt0.1420.1420.2190.5481.000
2024-04-17T09:41:09.391795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idxgugunpumpgubunpumpcntpumpsetcostinstt_code
idx1.0000.6300.3230.0000.6020.630
gugun0.6301.0000.5040.1420.8241.000
pumpgubun0.3230.5041.0000.2190.7830.504
pumpcnt0.0000.1420.2191.0000.5480.142
pumpsetcost0.6020.8240.7830.5481.0000.824
instt_code0.6301.0000.5040.1420.8241.000

Missing values

2024-04-17T09:41:07.137042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T09:41:07.240729image/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-낙동강관리본부2021-01-05 14:20:05
117낙동강관리본부화명 자전거대여소 앞전기식(콤프레샤)1150000e-낙동강관리본부2021-01-05 14:20:05
218낙동강관리본부대저 자전거대여소수동식110000e-낙동강관리본부2021-01-05 14:20:05
319낙동강관리본부맥도 자전거무료대여소수동식110000e-낙동강관리본부2021-01-05 14:20:05
420낙동강관리본부맥도 철새태마공원수동식110000e-낙동강관리본부2021-01-05 14:20:05
536부산광역시 사상구감전역 3번 출구전기식1560000033900002021-01-05 14:20:05
637부산광역시 사상구주례역 1번 출구전기식1560000033900002021-01-05 14:20:05
738부산광역시 사상구사상달리미 자전거대여소수동식160000033900002021-01-05 14:20:05
839부산광역시 사상구르네시떼역 경전철 아래수동식160000033900002021-01-05 14:20:05
940부산광역시 사상구국토종주자전거길 전망대쉼터수동식160000033900002021-01-05 14:20:05
idxgugunspotpumpgubunpumpcntpumpsetcostinstt_codelast_load_dttm
9172부산광역시 연제구연산9동 망미주공아파트 자전거보관대 인근태양광1179900033700002021-01-05 14:20:05
9273부산광역시 연제구지하철 배산역 3번 출구 자전거보관대 인근태양광1179900033700002021-01-05 14:20:05
9374부산광역시 연제구지하철 물만골역 4번 출구 자전거보관대 인근태양광1179900033700002021-01-05 14:20:05
9475부산광역시 연제구연산2동 이마트 연제점태양광1179900033700002021-01-05 14:20:05
9576부산광역시 연제구거제1동 홈플러스 아시아드점태양광1179900033700002021-01-05 14:20:05
9677부산광역시 연제구연산1동 홈플러스 연산점태양광1179900033700002021-01-05 14:20:05
9778부산광역시 연제구지하철 연산역 14번출구 자전거보관대 인근태양광1179900033700002021-01-05 14:20:05
9879부산광역시 연제구지하철 교대역 3번출구 국제신문 앞 자전거보관대 인근태양광1179900033700002021-01-05 14:20:05
9980부산광역시 연제구부산교대 후문 입구태양광1179900033700002021-01-05 14:20:05
10065부산광역시 부산진구범천동 사학연금매장 앞태양광1175085032900002021-01-05 14:20:05