Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory43.3 B

Variable types

Text2
Categorical1
Numeric2

Alerts

관측소명() has unique valuesUnique
행정구역명() has unique valuesUnique
X좌표() has unique valuesUnique
Y좌표() has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:26:42.460196
Analysis finished2023-12-10 12:26:43.622800
Duration1.16 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관측소명()
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:26:44.002165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.06
Min length4

Characters and Unicode

Total characters406
Distinct characters119
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row가평가평
2nd row가평북면
3rd row가평상면
4th row강릉연곡
5th row강릉왕산
ValueCountFrequency (%)
가평가평 1
 
1.0%
동두천상패 1
 
1.0%
밀양단장 1
 
1.0%
밀양가곡 1
 
1.0%
문경영순 1
 
1.0%
문경문경 1
 
1.0%
문경농암 1
 
1.0%
무주무풍 1
 
1.0%
무주무주 1
 
1.0%
무안해제 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T21:26:44.724498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 24
 
5.9%
주 15
 
3.7%
성 13
 
3.2%
부 12
 
3.0%
고 11
 
2.7%
양 9
 
2.2%
창 9
 
2.2%
대 8
 
2.0%
김 8
 
2.0%
보 8
 
2.0%
Other values (109) 289
71.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 405
99.8%
Connector Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 24
 
5.9%
주 15
 
3.7%
성 13
 
3.2%
부 12
 
3.0%
고 11
 
2.7%
양 9
 
2.2%
창 9
 
2.2%
대 8
 
2.0%
김 8
 
2.0%
보 8
 
2.0%
Other values (108) 288
71.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 405
99.8%
Common 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 24
 
5.9%
주 15
 
3.7%
성 13
 
3.2%
부 12
 
3.0%
고 11
 
2.7%
양 9
 
2.2%
창 9
 
2.2%
대 8
 
2.0%
김 8
 
2.0%
보 8
 
2.0%
Other values (108) 288
71.1%
Common
ValueCountFrequency (%)
_ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 405
99.8%
ASCII 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 24
 
5.9%
주 15
 
3.7%
성 13
 
3.2%
부 12
 
3.0%
고 11
 
2.7%
양 9
 
2.2%
창 9
 
2.2%
대 8
 
2.0%
김 8
 
2.0%
보 8
 
2.0%
Other values (108) 288
71.1%
ASCII
ValueCountFrequency (%)
_ 1
100.0%

하천()
Categorical

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
낙동강
27 
금강
15 
한강
11 
새만금
9 
한강동해
6 
Other values (10)
32 

Length

Max length5
Median length4
Mean length3.12
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row한강
2nd row한강
3rd row한강
4th row한강동해
5th row한강

Common Values

ValueCountFrequency (%)
낙동강 27
27.0%
금강 15
15.0%
한강 11
11.0%
새만금 9
 
9.0%
한강동해 6
 
6.0%
섬진강 5
 
5.0%
금강서해 5
 
5.0%
섬진강남해 4
 
4.0%
낙동강남해 4
 
4.0%
영산강서해 4
 
4.0%
Other values (5) 10
 
10.0%

Length

2023-12-10T21:26:45.021768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
낙동강 27
27.0%
금강 15
15.0%
한강 11
11.0%
새만금 9
 
9.0%
한강동해 6
 
6.0%
섬진강 5
 
5.0%
금강서해 5
 
5.0%
섬진강남해 4
 
4.0%
낙동강남해 4
 
4.0%
영산강서해 4
 
4.0%
Other values (5) 10
 
10.0%

행정구역명()
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:26:45.340979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length10.05
Min length8

Characters and Unicode

Total characters1005
Distinct characters124
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row경기도가평군가평읍
2nd row경기도가평군북면
3rd row경기도가평군상면
4th row강원도강릉시연곡면
5th row강원도강릉시왕산면
ValueCountFrequency (%)
경기도가평군가평읍 1
 
1.0%
경기도동두천시상패동 1
 
1.0%
경상남도밀양시단장면 1
 
1.0%
경상남도밀양시가곡동 1
 
1.0%
경상북도문경시영순면 1
 
1.0%
경상북도문경시문경읍 1
 
1.0%
경상북도문경시농암면 1
 
1.0%
전라북도무주군무풍면 1
 
1.0%
전라북도무주군무주읍 1
 
1.0%
전라남도무안군해제면 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T21:26:45.881655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 93
 
9.3%
면 60
 
6.0%
시 58
 
5.8%
군 51
 
5.1%
남 50
 
5.0%
경 48
 
4.8%
상 40
 
4.0%
북 37
 
3.7%
전 29
 
2.9%
동 28
 
2.8%
Other values (114) 511
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 998
99.3%
Decimal Number 7
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 93
 
9.3%
면 60
 
6.0%
시 58
 
5.8%
군 51
 
5.1%
남 50
 
5.0%
경 48
 
4.8%
상 40
 
4.0%
북 37
 
3.7%
전 29
 
2.9%
동 28
 
2.8%
Other values (111) 504
50.5%
Decimal Number
ValueCountFrequency (%)
1 5
71.4%
2 1
 
14.3%
3 1
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 998
99.3%
Common 7
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 93
 
9.3%
면 60
 
6.0%
시 58
 
5.8%
군 51
 
5.1%
남 50
 
5.0%
경 48
 
4.8%
상 40
 
4.0%
북 37
 
3.7%
전 29
 
2.9%
동 28
 
2.8%
Other values (111) 504
50.5%
Common
ValueCountFrequency (%)
1 5
71.4%
2 1
 
14.3%
3 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 998
99.3%
ASCII 7
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 93
 
9.3%
면 60
 
6.0%
시 58
 
5.8%
군 51
 
5.1%
남 50
 
5.0%
경 48
 
4.8%
상 40
 
4.0%
북 37
 
3.7%
전 29
 
2.9%
동 28
 
2.8%
Other values (111) 504
50.5%
ASCII
ValueCountFrequency (%)
1 5
71.4%
2 1
 
14.3%
3 1
 
14.3%

X좌표()
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262059.38
Minimum139885.44
Maximum414495.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:26:46.110680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum139885.44
5-th percentile180345.21
Q1205236.96
median248239.18
Q3306856.09
95-th percentile385191.33
Maximum414495.29
Range274609.85
Interquartile range (IQR)101619.12

Descriptive statistics

Standard deviation68474.804
Coefficient of variation (CV)0.261295
Kurtosis-0.68371883
Mean262059.38
Median Absolute Deviation (MAD)50682.595
Skewness0.51481654
Sum26205938
Variance4.6887988 × 109
MonotonicityNot monotonic
2023-12-10T21:26:46.322172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
239668.1517 1
 
1.0%
328880.0629 1
 
1.0%
226846.241 1
 
1.0%
236070.8566 1
 
1.0%
189565.0972 1
 
1.0%
389084.4068 1
 
1.0%
285661.2227 1
 
1.0%
335504.2446 1
 
1.0%
198182.3241 1
 
1.0%
353525.2777 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
139885.4371 1
1.0%
141569.5473 1
1.0%
163560.5608 1
1.0%
169332.8409 1
1.0%
169971.3407 1
1.0%
180891.2054 1
1.0%
182724.9258 1
1.0%
183532.9599 1
1.0%
184423.9377 1
1.0%
184978.4209 1
1.0%
ValueCountFrequency (%)
414495.2898 1
1.0%
408799.7549 1
1.0%
406126.9762 1
1.0%
389084.4068 1
1.0%
386355.8181 1
1.0%
385130.0402 1
1.0%
384882.2793 1
1.0%
382537.8534 1
1.0%
382077.3902 1
1.0%
376428.1724 1
1.0%

Y좌표()
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416225.3
Minimum289276.42
Maximum527496.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:26:46.536097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum289276.42
5-th percentile319325.95
Q1366865.12
median423132.09
Q3460867.01
95-th percentile506991.11
Maximum527496.45
Range238220.03
Interquartile range (IQR)94001.887

Descriptive statistics

Standard deviation59966.466
Coefficient of variation (CV)0.14407213
Kurtosis-0.80838742
Mean416225.3
Median Absolute Deviation (MAD)43199.472
Skewness-0.15157385
Sum41622530
Variance3.5959771 × 109
MonotonicityNot monotonic
2023-12-10T21:26:46.749613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
417451.4261 1
 
1.0%
445627.9041 1
 
1.0%
345410.6981 1
 
1.0%
328180.0935 1
 
1.0%
308578.6708 1
 
1.0%
439115.0996 1
 
1.0%
392358.0095 1
 
1.0%
521876.2775 1
 
1.0%
480451.2652 1
 
1.0%
459534.6055 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
289276.4211 1
1.0%
295261.2725 1
1.0%
308578.6708 1
1.0%
309225.4916 1
1.0%
314785.7197 1
1.0%
319564.906 1
1.0%
322815.2379 1
1.0%
326346.2001 1
1.0%
327156.5954 1
1.0%
328180.0935 1
1.0%
ValueCountFrequency (%)
527496.4532 1
1.0%
525250.8636 1
1.0%
524542.1348 1
1.0%
521876.2775 1
1.0%
508288.014 1
1.0%
506922.8482 1
1.0%
506552.0641 1
1.0%
506281.063 1
1.0%
506131.6163 1
1.0%
497844.1394 1
1.0%

Interactions

2023-12-10T21:26:43.131128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:26:42.896160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:26:43.251654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:26:43.009453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:26:46.944469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소명()하천()행정구역명()X좌표()Y좌표()
관측소명()1.0001.0001.0001.0001.000
하천()1.0001.0001.0000.2230.201
행정구역명()1.0001.0001.0001.0001.000
X좌표()1.0000.2231.0001.0000.000
Y좌표()1.0000.2011.0000.0001.000
2023-12-10T21:26:47.093493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
X좌표()Y좌표()하천()
X좌표()1.0000.0780.000
Y좌표()0.0781.0000.000
하천()0.0000.0001.000

Missing values

2023-12-10T21:26:43.412741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:26:43.568585image/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

관측소명()하천()행정구역명()X좌표()Y좌표()
0가평가평한강경기도가평군가평읍239668.1517417451.4261
1가평북면한강경기도가평군북면203761.1201489548.0498
2가평상면한강경기도가평군상면310770.2307408811.3152
3강릉연곡한강동해강원도강릉시연곡면204816.9453470565.9089
4강릉왕산한강강원도강릉시왕산면223836.0483506552.0641
5강릉홍제한강동해강원도강릉시홍제동305610.419525250.8636
6강진성전탐진강전라남도강진군성전면192616.9663390459.1721
7강진칠량섬진강남해전라남도강진군칠량면303447.9451469680.8694
8거제신현낙동강남해경상남도거제시상문동185019.9619484537.2798
9거창거창낙동강경상남도거창군거창읍366059.827473642.647
관측소명()하천()행정구역명()X좌표()Y좌표()
90부여옥산금강충청남도부여군옥산면242338.7947351282.2794
91부여은산금강충청남도부여군은산면310593.0941343345.2728
92사천사천낙동강남해경상남도사천시사천읍384882.2793319564.906
93산청단성낙동강경상남도산청군단성면240884.5627373142.2282
94산청산청낙동강경상남도산청군산청읍139885.4371364015.1332
95삼척마평한강동해강원도삼척시성내동182724.9258353990.5028
96삼척신기한강동해강원도삼척시신기면247983.6643314785.7197
97상주공성낙동강경상북도상주시공성면213218.1424309225.4916
98상주서문낙동강경상북도상주시동문동197809.3498334524.9952
99서산석남금강서해충청남도서산시석남동386355.8181347572.3931