Overview

Dataset statistics

Number of variables11
Number of observations1951
Missing cells89
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory177.3 KiB
Average record size in memory93.1 B

Variable types

Text3
Categorical2
Numeric5
DateTime1

Dataset

Description공공데이터 중장기 개방계획에 따라 공개하는 경상남도 하천관리 시스템의 데이터 입니다. 하천관리시스템의 측량기준점현황 정보를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15093567

Alerts

구분코드 is highly imbalanced (93.9%)Imbalance
동코드 has 52 (2.7%) missing valuesMissing
기타주소 has 37 (1.9%) missing valuesMissing
평면직각좌표YTM is highly skewed (γ1 = 44.14608645)Skewed
동코드 is highly skewed (γ1 = -22.31103394)Skewed
평면직각좌표XTM has 26 (1.3%) zerosZeros
평면직각좌표YTM has 26 (1.3%) zerosZeros

Reproduction

Analysis started2023-12-11 00:21:50.232937
Analysis finished2023-12-11 00:21:54.258624
Duration4.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

하천관리코드
Text

Distinct459
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
2023-12-11T09:21:54.484265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters37069
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)1.3%

Sample

1st row20266800000F99Q9901
2nd row20266800000F99Q9901
3rd row20261902011F01Q0101
4th row20257102008F02Q0101
5th row20245602010F01Q0101
ValueCountFrequency (%)
40225802018f02q0101 57
 
2.9%
20263602019f02q0101 20
 
1.0%
20249602010f02q0101 20
 
1.0%
20272002012f02q0102 17
 
0.9%
20272002012f02q0101 17
 
0.9%
27209902014f02q0101 16
 
0.8%
20268002012f02q0101 14
 
0.7%
20255602014f02q0101 13
 
0.7%
20260202013f02q0101 12
 
0.6%
20227002010f02q0101 11
 
0.6%
Other values (449) 1754
89.9%
2023-12-11T09:21:54.908732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12810
34.6%
2 7380
19.9%
1 7053
19.0%
F 1951
 
5.3%
Q 1951
 
5.3%
7 1127
 
3.0%
4 988
 
2.7%
6 893
 
2.4%
5 820
 
2.2%
9 746
 
2.0%
Other values (2) 1350
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33167
89.5%
Uppercase Letter 3902
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12810
38.6%
2 7380
22.3%
1 7053
21.3%
7 1127
 
3.4%
4 988
 
3.0%
6 893
 
2.7%
5 820
 
2.5%
9 746
 
2.2%
3 681
 
2.1%
8 669
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
F 1951
50.0%
Q 1951
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33167
89.5%
Latin 3902
 
10.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12810
38.6%
2 7380
22.3%
1 7053
21.3%
7 1127
 
3.4%
4 988
 
3.0%
6 893
 
2.7%
5 820
 
2.5%
9 746
 
2.2%
3 681
 
2.1%
8 669
 
2.0%
Latin
ValueCountFrequency (%)
F 1951
50.0%
Q 1951
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37069
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12810
34.6%
2 7380
19.9%
1 7053
19.0%
F 1951
 
5.3%
Q 1951
 
5.3%
7 1127
 
3.0%
4 988
 
2.7%
6 893
 
2.4%
5 820
 
2.2%
9 746
 
2.0%
Other values (2) 1350
 
3.6%

구분코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
E04
1937 
H13
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE04
2nd rowE04
3rd rowE04
4th rowE04
5th rowE04

Common Values

ValueCountFrequency (%)
E04 1937
99.3%
H13 14
 
0.7%

Length

2023-12-11T09:21:55.065580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:21:55.150204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e04 1937
99.3%
h13 14
 
0.7%

일련번호
Real number (ℝ)

Distinct60
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8646848
Minimum1
Maximum1003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-12-11T09:21:55.281377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile12
Maximum1003
Range1002
Interquartile range (IQR)3

Descriptive statistics

Standard deviation50.788977
Coefficient of variation (CV)7.3985883
Kurtosis375.76987
Mean6.8646848
Median Absolute Deviation (MAD)1
Skewness19.301566
Sum13393
Variance2579.5201
MonotonicityNot monotonic
2023-12-11T09:21:55.431326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 430
22.0%
2 414
21.2%
3 353
18.1%
4 244
12.5%
5 146
 
7.5%
6 88
 
4.5%
7 60
 
3.1%
8 43
 
2.2%
9 31
 
1.6%
10 21
 
1.1%
Other values (50) 121
 
6.2%
ValueCountFrequency (%)
1 430
22.0%
2 414
21.2%
3 353
18.1%
4 244
12.5%
5 146
 
7.5%
6 88
 
4.5%
7 60
 
3.1%
8 43
 
2.2%
9 31
 
1.6%
10 21
 
1.1%
ValueCountFrequency (%)
1003 1
0.1%
1002 2
0.1%
1001 2
0.1%
57 1
0.1%
56 1
0.1%
55 1
0.1%
54 1
0.1%
53 1
0.1%
52 1
0.1%
51 1
0.1%

측량기준점번호
Text

Distinct740
Distinct (%)37.9%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
2023-12-11T09:21:55.706210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.3490518
Min length1

Characters and Unicode

Total characters8485
Distinct characters105
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique546 ?
Unique (%)28.0%

Sample

1st rowNO.01
2nd rowNO.02
3rd row매설1
4th row표석 No.1
5th row표석1
ValueCountFrequency (%)
표석 179
 
8.1%
표석1 85
 
3.9%
표석2 83
 
3.8%
표석3 71
 
3.2%
우1 56
 
2.5%
표석4 45
 
2.0%
좌1 45
 
2.0%
좌2 39
 
1.8%
no.01 31
 
1.4%
no.02 31
 
1.4%
Other values (656) 1534
69.8%
2023-12-11T09:21:56.120432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
석 950
 
11.2%
표 940
 
11.1%
1 638
 
7.5%
. 559
 
6.6%
2 496
 
5.8%
N 398
 
4.7%
0 390
 
4.6%
3 379
 
4.5%
O 349
 
4.1%
좌 345
 
4.1%
Other values (95) 3041
35.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3548
41.8%
Decimal Number 2523
29.7%
Uppercase Letter 1207
 
14.2%
Other Punctuation 572
 
6.7%
Space Separator 251
 
3.0%
Open Punctuation 166
 
2.0%
Close Punctuation 116
 
1.4%
Dash Punctuation 54
 
0.6%
Lowercase Letter 48
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
석 950
26.8%
표 940
26.5%
좌 345
 
9.7%
우 340
 
9.6%
천 252
 
7.1%
매 104
 
2.9%
설 99
 
2.8%
단 34
 
1.0%
장 34
 
1.0%
양 26
 
0.7%
Other values (60) 424
12.0%
Uppercase Letter
ValueCountFrequency (%)
N 398
33.0%
O 349
28.9%
C 94
 
7.8%
P 88
 
7.3%
S 65
 
5.4%
M 32
 
2.7%
A 29
 
2.4%
B 27
 
2.2%
G 25
 
2.1%
J 23
 
1.9%
Other values (8) 77
 
6.4%
Decimal Number
ValueCountFrequency (%)
1 638
25.3%
2 496
19.7%
0 390
15.5%
3 379
15.0%
4 248
 
9.8%
5 146
 
5.8%
6 92
 
3.6%
7 63
 
2.5%
8 39
 
1.5%
9 32
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 559
97.7%
# 13
 
2.3%
Space Separator
ValueCountFrequency (%)
251
100.0%
Open Punctuation
ValueCountFrequency (%)
( 166
100.0%
Close Punctuation
ValueCountFrequency (%)
) 116
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3682
43.4%
Hangul 3548
41.8%
Latin 1255
 
14.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
석 950
26.8%
표 940
26.5%
좌 345
 
9.7%
우 340
 
9.6%
천 252
 
7.1%
매 104
 
2.9%
설 99
 
2.8%
단 34
 
1.0%
장 34
 
1.0%
양 26
 
0.7%
Other values (60) 424
12.0%
Latin
ValueCountFrequency (%)
N 398
31.7%
O 349
27.8%
C 94
 
7.5%
P 88
 
7.0%
S 65
 
5.2%
o 48
 
3.8%
M 32
 
2.5%
A 29
 
2.3%
B 27
 
2.2%
G 25
 
2.0%
Other values (9) 100
 
8.0%
Common
ValueCountFrequency (%)
1 638
17.3%
. 559
15.2%
2 496
13.5%
0 390
10.6%
3 379
10.3%
251
 
6.8%
4 248
 
6.7%
( 166
 
4.5%
5 146
 
4.0%
) 116
 
3.2%
Other values (6) 293
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4937
58.2%
Hangul 3548
41.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
석 950
26.8%
표 940
26.5%
좌 345
 
9.7%
우 340
 
9.6%
천 252
 
7.1%
매 104
 
2.9%
설 99
 
2.8%
단 34
 
1.0%
장 34
 
1.0%
양 26
 
0.7%
Other values (60) 424
12.0%
ASCII
ValueCountFrequency (%)
1 638
12.9%
. 559
11.3%
2 496
10.0%
N 398
 
8.1%
0 390
 
7.9%
3 379
 
7.7%
O 349
 
7.1%
251
 
5.1%
4 248
 
5.0%
( 166
 
3.4%
Other values (25) 1063
21.5%

표고
Real number (ℝ)

Distinct1906
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1270.2431
Minimum1.049
Maximum274966.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-12-11T09:21:56.267963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.049
5-th percentile5.7045
Q120.5895
median57.86
Q3128.9275
95-th percentile352.6135
Maximum274966.25
Range274965.2
Interquartile range (IQR)108.338

Descriptive statistics

Standard deviation16780.61
Coefficient of variation (CV)13.21055
Kurtosis246.76044
Mean1270.2431
Median Absolute Deviation (MAD)43.439
Skewness15.561017
Sum2478244.3
Variance2.8158886 × 108
MonotonicityNot monotonic
2023-12-11T09:21:56.380354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.033 2
 
0.1%
13.117 2
 
0.1%
39.469 2
 
0.1%
14.883 2
 
0.1%
17.751 2
 
0.1%
53.477 2
 
0.1%
85.703 2
 
0.1%
217.663 2
 
0.1%
4.97 2
 
0.1%
70.13 2
 
0.1%
Other values (1896) 1931
99.0%
ValueCountFrequency (%)
1.049 1
0.1%
1.143 1
0.1%
1.301 1
0.1%
1.303 1
0.1%
1.408 1
0.1%
1.756 1
0.1%
1.797 1
0.1%
1.808 1
0.1%
1.943 1
0.1%
2.164 1
0.1%
ValueCountFrequency (%)
274966.25 1
0.1%
274222.069 1
0.1%
274146.597 1
0.1%
273442.457 1
0.1%
272710.205 1
0.1%
271885.615 1
0.1%
271057.433 1
0.1%
99915.0 1
0.1%
89377.0 1
0.1%
80834.0 1
0.1%

평면직각좌표XTM
Real number (ℝ)

ZEROS 

Distinct1886
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241068.42
Minimum0
Maximum462886.89
Zeros26
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-12-11T09:21:56.523235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile149341.03
Q1199011.7
median237233.79
Q3293219.82
95-th percentile328151.42
Maximum462886.89
Range462886.89
Interquartile range (IQR)94208.12

Descriptive statistics

Standard deviation63028.049
Coefficient of variation (CV)0.26145295
Kurtosis1.2769419
Mean241068.42
Median Absolute Deviation (MAD)46270.528
Skewness-0.61792443
Sum4.7032448 × 108
Variance3.9725349 × 109
MonotonicityNot monotonic
2023-12-11T09:21:56.656841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
 
1.3%
321799.394 2
 
0.1%
155484.064 2
 
0.1%
222773.0 2
 
0.1%
212195.594 2
 
0.1%
224883.443 2
 
0.1%
325497.744 2
 
0.1%
322385.068 2
 
0.1%
325109.997 2
 
0.1%
218870.22 2
 
0.1%
Other values (1876) 1907
97.7%
ValueCountFrequency (%)
0.0 26
1.3%
112066.05 1
 
0.1%
112085.44 1
 
0.1%
112302.0 1
 
0.1%
112362.12 1
 
0.1%
112766.0 1
 
0.1%
113422.0 1
 
0.1%
113640.0 1
 
0.1%
113796.0 1
 
0.1%
117271.0 1
 
0.1%
ValueCountFrequency (%)
462886.89 1
0.1%
420650.37 1
0.1%
419925.38 1
0.1%
419109.31 1
0.1%
350861.486 1
0.1%
350697.167 1
0.1%
349769.574 1
0.1%
348940.74 1
0.1%
348873.0 1
0.1%
348167.629 1
0.1%

평면직각좌표YTM
Real number (ℝ)

SKEWED  ZEROS 

Distinct1885
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276015.27
Minimum0
Maximum1.633699 × 108
Zeros26
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-12-11T09:21:56.776414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile112423.93
Q1139657.8
median171289.97
Q3265183.28
95-th percentile298686
Maximum1.633699 × 108
Range1.633699 × 108
Interquartile range (IQR)125525.48

Descriptive statistics

Standard deviation3694965.8
Coefficient of variation (CV)13.386817
Kurtosis1949.5836
Mean276015.27
Median Absolute Deviation (MAD)45525.681
Skewness44.146086
Sum5.3850579 × 108
Variance1.3652772 × 1013
MonotonicityNot monotonic
2023-12-11T09:21:56.913100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 26
 
1.3%
181422.169 2
 
0.1%
190985.363 2
 
0.1%
169548.098 2
 
0.1%
189568.258 2
 
0.1%
124774.819 2
 
0.1%
184831.66 2
 
0.1%
281587.447 2
 
0.1%
113573.478 2
 
0.1%
167912.068 2
 
0.1%
Other values (1875) 1907
97.7%
ValueCountFrequency (%)
0.0 26
1.3%
4.194 1
 
0.1%
4.306 1
 
0.1%
5.318 1
 
0.1%
5.999 1
 
0.1%
8.561 1
 
0.1%
13.758 1
 
0.1%
16.334 1
 
0.1%
14471.0 1
 
0.1%
17579.849 1
 
0.1%
ValueCountFrequency (%)
163369896.0 1
0.1%
381166.271 1
0.1%
381090.657 1
0.1%
353186.0 1
0.1%
353153.0 1
0.1%
352455.0 1
0.1%
352170.0 1
0.1%
351670.0 1
0.1%
351455.0 1
0.1%
350499.0 1
0.1%

동코드
Real number (ℝ)

MISSING  SKEWED 

Distinct731
Distinct (%)38.5%
Missing52
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean4.8585177 × 109
Minimum3.171034 × 109
Maximum4.889046 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2023-12-11T09:21:57.048604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.171034 × 109
5-th percentile4.816031 × 109
Q14.827035 × 109
median4.873036 × 109
Q34.886025 × 109
95-th percentile4.889034 × 109
Maximum4.889046 × 109
Range1.718012 × 109
Interquartile range (IQR)58990009

Descriptive statistics

Standard deviation48484989
Coefficient of variation (CV)0.009979379
Kurtosis772.71233
Mean4.8585177 × 109
Median Absolute Deviation (MAD)14994998
Skewness-22.311034
Sum9.226325 × 1012
Variance2.3507942 × 1015
MonotonicityNot monotonic
2023-12-11T09:21:57.206351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4885031027 22
 
1.1%
4885031024 14
 
0.7%
4889033034 11
 
0.6%
4812725022 11
 
0.6%
4873037023 10
 
0.5%
4827035032 9
 
0.5%
4827039021 9
 
0.5%
4827025022 9
 
0.5%
4888034025 9
 
0.5%
4833032024 9
 
0.5%
Other values (721) 1786
91.5%
(Missing) 52
 
2.7%
ValueCountFrequency (%)
3171034027 1
 
0.1%
4811013300 4
0.2%
4811013700 2
0.1%
4811014700 3
0.2%
4811016000 1
 
0.1%
4811025023 1
 
0.1%
4811025035 1
 
0.1%
4811025040 1
 
0.1%
4811025042 1
 
0.1%
4811032031 1
 
0.1%
ValueCountFrequency (%)
4889046035 1
 
0.1%
4889046033 3
0.2%
4889046032 3
0.2%
4889045029 4
0.2%
4889045027 2
 
0.1%
4889045025 1
 
0.1%
4889044028 7
0.4%
4889044027 3
0.2%
4889044026 5
0.3%
4889044025 4
0.2%

기타주소
Text

MISSING 

Distinct777
Distinct (%)40.6%
Missing37
Missing (%)1.9%
Memory size15.4 KiB
2023-12-11T09:21:57.517456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length16
Mean length15.726228
Min length10

Characters and Unicode

Total characters30100
Distinct characters248
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique282 ?
Unique (%)14.7%

Sample

1st row경상남도 의령군 지정면 성당리
2nd row진주시 미천면 반지리
3rd row경상남도 산청군 생초면 신연리
4th row경상남도 진주시 사봉면 방촌리
5th row경상남도 함안군 대산면 구혜리
ValueCountFrequency (%)
경상남도 1817
24.0%
하동군 178
 
2.4%
밀양시 163
 
2.2%
산청군 146
 
1.9%
함안군 129
 
1.7%
고성군 128
 
1.7%
의령군 126
 
1.7%
합천군 123
 
1.6%
함양군 113
 
1.5%
양산시 109
 
1.4%
Other values (857) 4528
59.9%
2023-12-11T09:21:58.385994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5646
18.8%
남 2044
 
6.8%
상 1985
 
6.6%
도 1904
 
6.3%
경 1894
 
6.3%
리 1786
 
5.9%
면 1654
 
5.5%
군 1223
 
4.1%
시 741
 
2.5%
산 574
 
1.9%
Other values (238) 10649
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24387
81.0%
Space Separator 5646
 
18.8%
Decimal Number 56
 
0.2%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 2044
 
8.4%
상 1985
 
8.1%
도 1904
 
7.8%
경 1894
 
7.8%
리 1786
 
7.3%
면 1654
 
6.8%
군 1223
 
5.0%
시 741
 
3.0%
산 574
 
2.4%
동 518
 
2.1%
Other values (227) 10064
41.3%
Decimal Number
ValueCountFrequency (%)
1 11
19.6%
4 9
16.1%
3 8
14.3%
7 7
12.5%
5 6
10.7%
2 6
10.7%
0 4
 
7.1%
9 3
 
5.4%
6 2
 
3.6%
Space Separator
ValueCountFrequency (%)
5646
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24387
81.0%
Common 5713
 
19.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 2044
 
8.4%
상 1985
 
8.1%
도 1904
 
7.8%
경 1894
 
7.8%
리 1786
 
7.3%
면 1654
 
6.8%
군 1223
 
5.0%
시 741
 
3.0%
산 574
 
2.4%
동 518
 
2.1%
Other values (227) 10064
41.3%
Common
ValueCountFrequency (%)
5646
98.8%
- 11
 
0.2%
1 11
 
0.2%
4 9
 
0.2%
3 8
 
0.1%
7 7
 
0.1%
5 6
 
0.1%
2 6
 
0.1%
0 4
 
0.1%
9 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24387
81.0%
ASCII 5713
 
19.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5646
98.8%
- 11
 
0.2%
1 11
 
0.2%
4 9
 
0.2%
3 8
 
0.1%
7 7
 
0.1%
5 6
 
0.1%
2 6
 
0.1%
0 4
 
0.1%
9 3
 
0.1%
Hangul
ValueCountFrequency (%)
남 2044
 
8.4%
상 1985
 
8.1%
도 1904
 
7.8%
경 1894
 
7.8%
리 1786
 
7.3%
면 1654
 
6.8%
군 1223
 
5.0%
시 741
 
3.0%
산 574
 
2.4%
동 518
 
2.1%
Other values (227) 10064
41.3%

좌우안 코드
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
D01
967 
D02
951 
D00
 
33

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD00
2nd rowD00
3rd rowD00
4th rowD00
5th rowD00

Common Values

ValueCountFrequency (%)
D01 967
49.6%
D02 951
48.7%
D00 33
 
1.7%

Length

2023-12-11T09:21:58.536674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:21:58.629244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
d01 967
49.6%
d02 951
48.7%
d00 33
 
1.7%

매설연월일
Date

Distinct209
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size15.4 KiB
Minimum1905-07-02 00:00:00
Maximum2018-11-11 00:00:00
2023-12-11T09:21:58.761897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:58.943483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-11T09:21:53.235524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:50.916817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:51.692369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:52.169771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:52.664753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:53.352025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:51.015299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:51.781364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:52.250120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:52.785305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:53.458060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:51.369184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:51.865064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:52.339379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:52.911287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:53.555311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:51.457882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:51.964833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:52.445442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:53.032719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:53.679220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:51.588795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:52.064095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:52.559368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:53.138700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:21:59.043386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분코드일련번호표고평면직각좌표XTM평면직각좌표YTM동코드좌우안 코드
구분코드1.0000.0000.0000.2890.000NaN0.000
일련번호0.0001.0000.0000.0810.000NaN0.000
표고0.0000.0001.0000.0810.000NaN0.000
평면직각좌표XTM0.2890.0810.0811.0000.000NaN0.096
평면직각좌표YTM0.0000.0000.0000.0001.000NaN0.000
동코드NaNNaNNaNNaNNaN1.000NaN
좌우안 코드0.0000.0000.0000.0960.000NaN1.000
2023-12-11T09:21:59.156761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
좌우안 코드구분코드
좌우안 코드1.0000.000
구분코드0.0001.000
2023-12-11T09:21:59.239995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호표고평면직각좌표XTM평면직각좌표YTM동코드구분코드좌우안 코드
일련번호1.0000.2370.048-0.0100.0540.0000.000
표고0.2371.0000.2050.2180.4960.0000.000
평면직각좌표XTM0.0480.2051.0000.0620.1620.2170.060
평면직각좌표YTM-0.0100.2180.0621.0000.2260.0000.000
동코드0.0540.4960.1620.2261.0000.0000.000
구분코드0.0000.0000.2170.0000.0001.0000.000
좌우안 코드0.0000.0000.0600.0000.0000.0001.000

Missing values

2023-12-11T09:21:53.841136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:21:54.049800image/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.
2023-12-11T09:21:54.196334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

하천관리코드구분코드일련번호측량기준점번호표고평면직각좌표XTM평면직각좌표YTM동코드기타주소좌우안 코드매설연월일
020266800000F99Q9901E041NO.0149.413222338.633346082.573<NA><NA>D002006-09-25
120266800000F99Q9901E042NO.0257.666222498.299345559.914<NA><NA>D002006-09-25
220261902011F01Q0101E041매설113.988307251.228143955.9764872037029경상남도 의령군 지정면 성당리D002010-08-25
320257102008F02Q0101E041표석 No.124.142197832.836118990.0494817043023진주시 미천면 반지리D002000-10-01
420245602010F01Q0101E041표석1122.174277710.0221388.04886033026경상남도 산청군 생초면 신연리D002009-07-01
520257702016F02Q0101E041표석120.5170.00.04817038022경상남도 진주시 사봉면 방촌리D002016-09-01
620262002016F02Q0101E041표석19.864306868.821147333.5624873034027경상남도 함안군 대산면 구혜리D002016-09-01
740225802018F02Q0101E041CP.121.663288169.599256744.2134885031023경상남도 하동군 화개면 탑리D002016-07-26
820257102008F02Q0101E042표석 No.226.212198615.12118946.9234817043022진주시 미천면 향양리D002000-10-01
920257702016F02Q0101E042표석222.6360.00.04817038023경상남도 진주시 사봉면 무촌리D002016-09-01
하천관리코드구분코드일련번호측량기준점번호표고평면직각좌표XTM평면직각좌표YTM동코드기타주소좌우안 코드매설연월일
194127205702020F02Q0101H132우0142.167261211.26145852.5364822034027경남 통영시 광도면 안정리D022018-11-11
194227206302020F02Q0101H134좌014.625254240.004166017.1124831010900경남 거제시 고현동D012018-11-11
194327206302020F02Q0101H133우014.632255220.586165697.1994831010900경남 거제시 고현동D022018-11-11
194427206302020F02Q0101H135좌0214.612253461.951166493.5344831011000경남 거제시 상동동D012018-11-11
194527206302020F02Q0101H136좌0326.577252665.888167152.774831011000경남 거제시 상동동D012018-11-11
194627206302020F02Q0101H137우0239.557251914.255167601.544831011100경남 거제시 문동동D022018-11-11
194727206302020F02Q0101H138우0252.795251300.689167947.1354831011100경남 거제시 문동동D022018-11-11
194820259202020F02Q0101H1312PS.0445.751129084.62204014.084872031028경남 의령군 가례면 대천리D012018-03-03
194920259202020F02Q0101H1313PS.0550.58128320.41204002.814872031029경남 의령군 가례면 봉두리D012018-03-03
195020259202020F02Q0101H1314PS.0654.599127870.0203711.754872031029경남 의령군 가례면 봉두리D012018-03-03