Overview

Dataset statistics

Number of variables6
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory54.9 B

Variable types

Categorical2
Text3
Numeric1

Alerts

집계년도 has constant value ""Constant
취수장상수도구분명 has constant value ""Constant
취수장명 has unique valuesUnique

Reproduction

Analysis started2024-03-12 23:44:59.110416
Analysis finished2024-03-12 23:44:59.649557
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024
27 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024
2nd row2024
3rd row2024
4th row2024
5th row2024

Common Values

ValueCountFrequency (%)
2024 27
100.0%

Length

2024-03-13T08:44:59.696425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:44:59.765247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024 27
100.0%

취수장수도사업자명
Text

Distinct16
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-03-13T08:44:59.869547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1111111
Min length3

Characters and Unicode

Total characters84
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)29.6%

Sample

1st row수원시
2nd row수원시
3rd row성남시
4th row의정부시
5th row평택시
ValueCountFrequency (%)
포천시 3
11.1%
가평군 3
11.1%
양평군 3
11.1%
수원시 2
 
7.4%
평택시 2
 
7.4%
남양주시 2
 
7.4%
파주시 2
 
7.4%
광주시 2
 
7.4%
성남시 1
 
3.7%
의정부시 1
 
3.7%
Other values (6) 6
22.2%
2024-03-13T08:45:00.091150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 20
23.8%
평 8
 
9.5%
주 7
 
8.3%
군 7
 
8.3%
양 5
 
6.0%
천 5
 
6.0%
남 4
 
4.8%
포 3
 
3.6%
가 3
 
3.6%
성 2
 
2.4%
Other values (15) 20
23.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 84
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 20
23.8%
평 8
 
9.5%
주 7
 
8.3%
군 7
 
8.3%
양 5
 
6.0%
천 5
 
6.0%
남 4
 
4.8%
포 3
 
3.6%
가 3
 
3.6%
성 2
 
2.4%
Other values (15) 20
23.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 84
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 20
23.8%
평 8
 
9.5%
주 7
 
8.3%
군 7
 
8.3%
양 5
 
6.0%
천 5
 
6.0%
남 4
 
4.8%
포 3
 
3.6%
가 3
 
3.6%
성 2
 
2.4%
Other values (15) 20
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 84
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 20
23.8%
평 8
 
9.5%
주 7
 
8.3%
군 7
 
8.3%
양 5
 
6.0%
천 5
 
6.0%
남 4
 
4.8%
포 3
 
3.6%
가 3
 
3.6%
성 2
 
2.4%
Other values (15) 20
23.8%

취수장상수도구분명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
지방취수장
27 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방취수장 27
100.0%

Length

2024-03-13T08:45:00.189919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:45:00.260744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방취수장 27
100.0%

취수장명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-03-13T08:45:00.393119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.7407407
Min length2

Characters and Unicode

Total characters74
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row광교
2nd row파장
3rd row한강
4th row제일
5th row송탄
ValueCountFrequency (%)
광교 1
 
3.7%
운휴)광주 1
 
3.7%
양서 1
 
3.7%
양동 1
 
3.7%
현리 1
 
3.7%
설악 1
 
3.7%
가평통합 1
 
3.7%
연천 1
 
3.7%
여주 1
 
3.7%
이동 1
 
3.7%
Other values (17) 17
63.0%
2024-03-13T08:45:00.692596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
광 3
 
4.1%
천 3
 
4.1%
양 3
 
4.1%
동 3
 
4.1%
주 3
 
4.1%
휴 3
 
4.1%
운 3
 
4.1%
( 3
 
4.1%
금 3
 
4.1%
평 3
 
4.1%
Other values (35) 44
59.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
91.9%
Open Punctuation 3
 
4.1%
Close Punctuation 3
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 3
 
4.4%
천 3
 
4.4%
양 3
 
4.4%
동 3
 
4.4%
주 3
 
4.4%
휴 3
 
4.4%
운 3
 
4.4%
금 3
 
4.4%
평 3
 
4.4%
통 2
 
2.9%
Other values (33) 39
57.4%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
91.9%
Common 6
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 3
 
4.4%
천 3
 
4.4%
양 3
 
4.4%
동 3
 
4.4%
주 3
 
4.4%
휴 3
 
4.4%
운 3
 
4.4%
금 3
 
4.4%
평 3
 
4.4%
통 2
 
2.9%
Other values (33) 39
57.4%
Common
ValueCountFrequency (%)
( 3
50.0%
) 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
91.9%
ASCII 6
 
8.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
광 3
 
4.4%
천 3
 
4.4%
양 3
 
4.4%
동 3
 
4.4%
주 3
 
4.4%
휴 3
 
4.4%
운 3
 
4.4%
금 3
 
4.4%
평 3
 
4.4%
통 2
 
2.9%
Other values (33) 39
57.4%
ASCII
ValueCountFrequency (%)
( 3
50.0%
) 3
50.0%

취수원정보
Text

Distinct19
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-03-13T08:45:00.827766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.2962963
Min length2

Characters and Unicode

Total characters89
Distinct characters42
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)51.9%

Sample

1st row광교취수장
2nd row파장취수장
3rd row한강
4th row제일저수지
5th row진위천
ValueCountFrequency (%)
한강 4
14.8%
북한강 3
 
11.1%
남한강 2
 
7.4%
지하수 2
 
7.4%
임진강 2
 
7.4%
광교취수장 1
 
3.7%
한탄강 1
 
3.7%
계정천 1
 
3.7%
조종천 1
 
3.7%
미원천 1
 
3.7%
Other values (9) 9
33.3%
2024-03-13T08:45:01.072694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
강 12
 
13.5%
한 10
 
11.2%
천 8
 
9.0%
수 5
 
5.6%
북 3
 
3.4%
지 3
 
3.4%
진 3
 
3.4%
장 3
 
3.4%
계 2
 
2.2%
도 2
 
2.2%
Other values (32) 38
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 87
97.8%
Close Punctuation 1
 
1.1%
Open Punctuation 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
강 12
 
13.8%
한 10
 
11.5%
천 8
 
9.2%
수 5
 
5.7%
북 3
 
3.4%
지 3
 
3.4%
진 3
 
3.4%
장 3
 
3.4%
계 2
 
2.3%
도 2
 
2.3%
Other values (30) 36
41.4%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 87
97.8%
Common 2
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
강 12
 
13.8%
한 10
 
11.5%
천 8
 
9.2%
수 5
 
5.7%
북 3
 
3.4%
지 3
 
3.4%
진 3
 
3.4%
장 3
 
3.4%
계 2
 
2.3%
도 2
 
2.3%
Other values (30) 36
41.4%
Common
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 87
97.8%
ASCII 2
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
강 12
 
13.8%
한 10
 
11.5%
천 8
 
9.2%
수 5
 
5.7%
북 3
 
3.4%
지 3
 
3.4%
진 3
 
3.4%
장 3
 
3.4%
계 2
 
2.3%
도 2
 
2.3%
Other values (30) 36
41.4%
ASCII
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

취수장설계시설용량(㎥/일)
Real number (ℝ)

Distinct23
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54318.519
Minimum1000
Maximum330000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-13T08:45:01.165717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1100
Q15500
median20000
Q360250
95-th percentile263600
Maximum330000
Range329000
Interquartile range (IQR)54750

Descriptive statistics

Standard deviation84905.658
Coefficient of variation (CV)1.563107
Kurtosis6.3872429
Mean54318.519
Median Absolute Deviation (MAD)18900
Skewness2.5652254
Sum1466600
Variance7.2089708 × 109
MonotonicityNot monotonic
2024-03-13T08:45:01.251594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
50000 2
 
7.4%
10000 2
 
7.4%
15000 2
 
7.4%
1100 2
 
7.4%
1900 1
 
3.7%
26000 1
 
3.7%
5000 1
 
3.7%
1000 1
 
3.7%
2000 1
 
3.7%
20000 1
 
3.7%
Other values (13) 13
48.1%
ValueCountFrequency (%)
1000 1
3.7%
1100 2
7.4%
1700 1
3.7%
1900 1
3.7%
2000 1
3.7%
5000 1
3.7%
6000 1
3.7%
10000 2
7.4%
15000 2
7.4%
17600 1
3.7%
ValueCountFrequency (%)
330000 1
3.7%
314000 1
3.7%
146000 1
3.7%
100800 1
3.7%
77000 1
3.7%
66000 1
3.7%
63000 1
3.7%
57500 1
3.7%
52500 1
3.7%
50000 2
7.4%

Interactions

2024-03-13T08:44:59.434636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:45:01.315018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
취수장수도사업자명취수장명취수원정보취수장설계시설용량(㎥/일)
취수장수도사업자명1.0001.0000.0000.914
취수장명1.0001.0001.0001.000
취수원정보0.0001.0001.0000.000
취수장설계시설용량(㎥/일)0.9141.0000.0001.000

Missing values

2024-03-13T08:44:59.529424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:44:59.613925image/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

집계년도취수장수도사업자명취수장상수도구분명취수장명취수원정보취수장설계시설용량(㎥/일)
02024수원시지방취수장광교광교취수장50000
12024수원시지방취수장파장파장취수장50000
22024성남시지방취수장한강한강314000
32024의정부시지방취수장제일제일저수지10000
42024평택시지방취수장송탄진위천15000
52024평택시지방취수장유천안성천15000
62024구리시지방취수장토평한강63000
72024남양주시지방취수장금남북한강57500
82024남양주시지방취수장도곡한강17600
92024하남시지방취수장하남한강77000
집계년도취수장수도사업자명취수장상수도구분명취수장명취수원정보취수장설계시설용량(㎥/일)
172024포천시지방취수장내촌(운휴)왕숙천1100
182024포천시지방취수장이동도평천(도마치계곡)1700
192024여주시지방취수장여주남한강52500
202024연천군지방취수장연천임진강146000
212024가평군지방취수장가평통합북한강20000
222024가평군지방취수장설악미원천2000
232024가평군지방취수장현리조종천10000
242024양평군지방취수장양동계정천1000
252024양평군지방취수장양서북한강5000
262024양평군지방취수장양평통합흑천26000