Overview

Dataset statistics

Number of variables13
Number of observations314
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.2 KiB
Average record size in memory111.4 B

Variable types

Text2
Numeric7
Categorical4

Dataset

Description경주시 관리 저수지 315개소에 대한 제원과 안전등급 현황입니다(시설명, 주소, 준공연도, 시설제원(수혜면적, 유역면적, 총저수량, 유효저수량, 제방높이, 제방길이, 안전등급 등)
Author경상북도 경주시
URLhttps://www.data.go.kr/data/15034762/fileData.do

Alerts

등급사유 has constant value ""Constant
전화번호 has constant value ""Constant
관리기관명 has constant value ""Constant
수혜면적(헥타르) is highly overall correlated with 유역면적(헥타르) and 2 other fieldsHigh correlation
유역면적(헥타르) is highly overall correlated with 수혜면적(헥타르) and 3 other fieldsHigh correlation
총저수량(천세제곱미터) is highly overall correlated with 수혜면적(헥타르) and 4 other fieldsHigh correlation
유효저수량 is highly overall correlated with 수혜면적(헥타르) and 4 other fieldsHigh correlation
제방높이(미터) is highly overall correlated with 유역면적(헥타르) and 2 other fieldsHigh correlation
제방길이(미터) is highly overall correlated with 총저수량(천세제곱미터) and 1 other fieldsHigh correlation
소재지지번주소 has unique valuesUnique

Reproduction

Analysis started2024-03-14 21:26:40.759049
Analysis finished2024-03-14 21:26:56.520019
Duration15.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct302
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-03-15T06:26:58.158466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.5923567
Min length1

Characters and Unicode

Total characters814
Distinct characters221
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

Unique294 ?
Unique (%)93.6%

Sample

1st row강당
2nd row식화
3rd row능남
4th row간곡
5th row와산
ValueCountFrequency (%)
사곡 6
 
1.9%
사일 2
 
0.6%
와룡 2
 
0.6%
내곡 2
 
0.6%
골안 2
 
0.6%
대곡 2
 
0.6%
하후곡 2
 
0.6%
탑골 2
 
0.6%
새못안(상 1
 
0.3%
응용골 1
 
0.3%
Other values (292) 292
93.0%
2024-03-15T06:27:00.160560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
75
 
9.2%
53
 
6.5%
32
 
3.9%
20
 
2.5%
20
 
2.5%
14
 
1.7%
( 14
 
1.7%
) 14
 
1.7%
14
 
1.7%
13
 
1.6%
Other values (211) 545
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 786
96.6%
Open Punctuation 14
 
1.7%
Close Punctuation 14
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
75
 
9.5%
53
 
6.7%
32
 
4.1%
20
 
2.5%
20
 
2.5%
14
 
1.8%
14
 
1.8%
13
 
1.7%
13
 
1.7%
12
 
1.5%
Other values (209) 520
66.2%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 786
96.6%
Common 28
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
75
 
9.5%
53
 
6.7%
32
 
4.1%
20
 
2.5%
20
 
2.5%
14
 
1.8%
14
 
1.8%
13
 
1.7%
13
 
1.7%
12
 
1.5%
Other values (209) 520
66.2%
Common
ValueCountFrequency (%)
( 14
50.0%
) 14
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 786
96.6%
ASCII 28
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
75
 
9.5%
53
 
6.7%
32
 
4.1%
20
 
2.5%
20
 
2.5%
14
 
1.8%
14
 
1.8%
13
 
1.7%
13
 
1.7%
12
 
1.5%
Other values (209) 520
66.2%
ASCII
ValueCountFrequency (%)
( 14
50.0%
) 14
50.0%
Distinct314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-03-15T06:27:01.736662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length20.901274
Min length16

Characters and Unicode

Total characters6563
Distinct characters123
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

Unique314 ?
Unique (%)100.0%

Sample

1st row경상북도 경주시 탑동 857
2nd row경상북도 경주시 탑동 805
3rd row경상북도 경주시 서악동 728-2
4th row경상북도 경주시 서악동 690
5th row경상북도 경주시 효현동 719
ValueCountFrequency (%)
경상북도 314
20.2%
경주시 314
20.2%
서면 68
 
4.4%
현곡면 37
 
2.4%
건천읍 30
 
1.9%
안강읍 28
 
1.8%
내남면 28
 
1.8%
19
 
1.2%
도리 18
 
1.2%
양남면 14
 
0.9%
Other values (419) 686
44.1%
2024-03-15T06:27:04.012417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1609
24.5%
628
 
9.6%
339
 
5.2%
330
 
5.0%
318
 
4.8%
317
 
4.8%
314
 
4.8%
271
 
4.1%
190
 
2.9%
1 187
 
2.8%
Other values (113) 2060
31.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3861
58.8%
Space Separator 1609
24.5%
Decimal Number 1025
 
15.6%
Dash Punctuation 68
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
628
16.3%
339
 
8.8%
330
 
8.5%
318
 
8.2%
317
 
8.2%
314
 
8.1%
271
 
7.0%
190
 
4.9%
83
 
2.1%
81
 
2.1%
Other values (101) 990
25.6%
Decimal Number
ValueCountFrequency (%)
1 187
18.2%
2 143
14.0%
6 102
10.0%
4 94
9.2%
3 90
8.8%
5 87
8.5%
8 87
8.5%
0 84
8.2%
7 80
7.8%
9 71
 
6.9%
Space Separator
ValueCountFrequency (%)
1609
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3861
58.8%
Common 2702
41.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
628
16.3%
339
 
8.8%
330
 
8.5%
318
 
8.2%
317
 
8.2%
314
 
8.1%
271
 
7.0%
190
 
4.9%
83
 
2.1%
81
 
2.1%
Other values (101) 990
25.6%
Common
ValueCountFrequency (%)
1609
59.5%
1 187
 
6.9%
2 143
 
5.3%
6 102
 
3.8%
4 94
 
3.5%
3 90
 
3.3%
5 87
 
3.2%
8 87
 
3.2%
0 84
 
3.1%
7 80
 
3.0%
Other values (2) 139
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3861
58.8%
ASCII 2702
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1609
59.5%
1 187
 
6.9%
2 143
 
5.3%
6 102
 
3.8%
4 94
 
3.5%
3 90
 
3.3%
5 87
 
3.2%
8 87
 
3.2%
0 84
 
3.1%
7 80
 
3.0%
Other values (2) 139
 
5.1%
Hangul
ValueCountFrequency (%)
628
16.3%
339
 
8.8%
330
 
8.5%
318
 
8.2%
317
 
8.2%
314
 
8.1%
271
 
7.0%
190
 
4.9%
83
 
2.1%
81
 
2.1%
Other values (101) 990
25.6%

준공연도
Real number (ℝ)

Distinct34
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1950.9554
Minimum1945
Maximum2013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-15T06:27:04.435210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1945
5-th percentile1945
Q11945
median1945
Q31948
95-th percentile1978
Maximum2013
Range68
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.894286
Coefficient of variation (CV)0.0060966469
Kurtosis4.4353268
Mean1950.9554
Median Absolute Deviation (MAD)0
Skewness2.1300908
Sum612600
Variance141.47404
MonotonicityNot monotonic
2024-03-15T06:27:04.910957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1945 232
73.9%
1960 9
 
2.9%
1950 5
 
1.6%
1978 5
 
1.6%
1965 5
 
1.6%
1961 5
 
1.6%
1970 4
 
1.3%
1966 4
 
1.3%
1981 4
 
1.3%
1948 3
 
1.0%
Other values (24) 38
 
12.1%
ValueCountFrequency (%)
1945 232
73.9%
1946 1
 
0.3%
1948 3
 
1.0%
1950 5
 
1.6%
1954 1
 
0.3%
1955 3
 
1.0%
1956 2
 
0.6%
1957 1
 
0.3%
1958 1
 
0.3%
1960 9
 
2.9%
ValueCountFrequency (%)
2013 1
 
0.3%
2005 1
 
0.3%
1996 1
 
0.3%
1987 1
 
0.3%
1986 1
 
0.3%
1983 2
 
0.6%
1981 4
1.3%
1979 3
1.0%
1978 5
1.6%
1977 1
 
0.3%

수혜면적(헥타르)
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.221975
Minimum0.1
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-15T06:27:05.419852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.8
Q12.725
median6
Q315.375
95-th percentile43.15
Maximum129
Range128.9
Interquartile range (IQR)12.65

Descriptive statistics

Standard deviation16.842992
Coefficient of variation (CV)1.3780909
Kurtosis19.534908
Mean12.221975
Median Absolute Deviation (MAD)4.3
Skewness3.6950353
Sum3837.7
Variance283.68638
MonotonicityNot monotonic
2024-03-15T06:27:05.895418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 14
 
4.5%
4.0 10
 
3.2%
2.0 10
 
3.2%
5.0 8
 
2.5%
7.0 7
 
2.2%
1.3 7
 
2.2%
6.0 6
 
1.9%
2.5 6
 
1.9%
10.0 5
 
1.6%
3.3 5
 
1.6%
Other values (147) 236
75.2%
ValueCountFrequency (%)
0.1 1
 
0.3%
0.2 2
 
0.6%
0.3 2
 
0.6%
0.4 3
 
1.0%
0.6 3
 
1.0%
0.7 3
 
1.0%
0.8 3
 
1.0%
0.9 1
 
0.3%
1.0 14
4.5%
1.1 1
 
0.3%
ValueCountFrequency (%)
129.0 1
0.3%
126.0 1
0.3%
124.2 1
0.3%
69.2 1
0.3%
60.7 1
0.3%
60.0 1
0.3%
56.3 1
0.3%
53.5 1
0.3%
51.0 1
0.3%
47.9 1
0.3%

유역면적(헥타르)
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.03121
Minimum2
Maximum623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-15T06:27:06.383875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q115
median29
Q366.75
95-th percentile189.35
Maximum623
Range621
Interquartile range (IQR)51.75

Descriptive statistics

Standard deviation72.823017
Coefficient of variation (CV)1.2996867
Kurtosis17.384579
Mean56.03121
Median Absolute Deviation (MAD)21
Skewness3.3995693
Sum17593.8
Variance5303.1918
MonotonicityNot monotonic
2024-03-15T06:27:06.839009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.0 11
 
3.5%
5.0 10
 
3.2%
9.0 9
 
2.9%
24.0 7
 
2.2%
17.0 7
 
2.2%
15.0 7
 
2.2%
22.0 7
 
2.2%
4.0 7
 
2.2%
11.0 7
 
2.2%
18.0 7
 
2.2%
Other values (118) 235
74.8%
ValueCountFrequency (%)
2.0 4
 
1.3%
3.0 4
 
1.3%
4.0 7
2.2%
5.0 10
3.2%
6.0 4
 
1.3%
6.5 1
 
0.3%
7.0 11
3.5%
8.0 4
 
1.3%
9.0 9
2.9%
10.0 6
1.9%
ValueCountFrequency (%)
623.0 1
0.3%
515.0 1
0.3%
327.0 1
0.3%
326.0 1
0.3%
281.0 2
0.6%
261.0 1
0.3%
250.0 2
0.6%
246.0 1
0.3%
226.0 1
0.3%
204.0 1
0.3%

총저수량(천세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct220
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.831369
Minimum0.2
Maximum514.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-15T06:27:07.273991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.8
Q12.3
median6.75
Q317.6
95-th percentile75.76
Maximum514.2
Range514
Interquartile range (IQR)15.3

Descriptive statistics

Standard deviation47.136978
Coefficient of variation (CV)2.3768897
Kurtosis55.055392
Mean19.831369
Median Absolute Deviation (MAD)5.25
Skewness6.5697782
Sum6227.05
Variance2221.8947
MonotonicityNot monotonic
2024-03-15T06:27:07.635160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.0 5
 
1.6%
2.3 5
 
1.6%
3.1 5
 
1.6%
4.4 5
 
1.6%
1.4 5
 
1.6%
2.1 4
 
1.3%
1.8 4
 
1.3%
6.9 4
 
1.3%
8.9 4
 
1.3%
3.7 4
 
1.3%
Other values (210) 269
85.7%
ValueCountFrequency (%)
0.2 1
0.3%
0.3 1
0.3%
0.35 1
0.3%
0.4 1
0.3%
0.44 1
0.3%
0.5 1
0.3%
0.53 1
0.3%
0.54 1
0.3%
0.55 1
0.3%
0.56 1
0.3%
ValueCountFrequency (%)
514.2 1
0.3%
398.5 1
0.3%
258.4 1
0.3%
256.9 1
0.3%
163.0 1
0.3%
140.0 1
0.3%
131.0 1
0.3%
125.0 1
0.3%
119.39 1
0.3%
112.2 1
0.3%

유효저수량
Real number (ℝ)

HIGH CORRELATION 

Distinct227
Distinct (%)72.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.147006
Minimum0.2
Maximum467.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-15T06:27:08.044360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.7
Q12.025
median6.025
Q316.025
95-th percentile72.8375
Maximum467.48
Range467.28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation42.557025
Coefficient of variation (CV)2.3451265
Kurtosis53.598639
Mean18.147006
Median Absolute Deviation (MAD)4.715
Skewness6.4435002
Sum5698.16
Variance1811.1004
MonotonicityNot monotonic
2024-03-15T06:27:08.485416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1 5
 
1.6%
1.8 4
 
1.3%
2.5 4
 
1.3%
4.1 4
 
1.3%
0.84 4
 
1.3%
2.0 4
 
1.3%
7.8 4
 
1.3%
0.9 3
 
1.0%
1.9 3
 
1.0%
5.7 3
 
1.0%
Other values (217) 276
87.9%
ValueCountFrequency (%)
0.2 2
0.6%
0.25 1
 
0.3%
0.34 1
 
0.3%
0.4 2
0.6%
0.43 1
 
0.3%
0.45 1
 
0.3%
0.5 1
 
0.3%
0.54 3
1.0%
0.63 1
 
0.3%
0.68 2
0.6%
ValueCountFrequency (%)
467.48 1
0.3%
339.1 1
0.3%
242.35 1
0.3%
233.6 1
0.3%
148.0 1
0.3%
132.6 1
0.3%
119.0 1
0.3%
113.7 1
0.3%
111.69 1
0.3%
102.0 1
0.3%

제방높이(미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0242038
Minimum1.8
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-15T06:27:08.875648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.6
Q14
median5.5
Q37.5
95-th percentile11
Maximum23
Range21.2
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.9367975
Coefficient of variation (CV)0.4874997
Kurtosis5.5366476
Mean6.0242038
Median Absolute Deviation (MAD)1.7
Skewness1.747762
Sum1891.6
Variance8.6247797
MonotonicityNot monotonic
2024-03-15T06:27:09.238649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.0 12
 
3.8%
3.0 11
 
3.5%
4.2 11
 
3.5%
8.0 10
 
3.2%
4.5 9
 
2.9%
7.0 9
 
2.9%
5.1 8
 
2.5%
5.5 8
 
2.5%
4.0 8
 
2.5%
7.5 7
 
2.2%
Other values (85) 221
70.4%
ValueCountFrequency (%)
1.8 3
1.0%
1.9 1
 
0.3%
2.0 2
 
0.6%
2.2 2
 
0.6%
2.4 3
1.0%
2.5 1
 
0.3%
2.6 7
2.2%
2.7 1
 
0.3%
2.8 5
1.6%
2.9 1
 
0.3%
ValueCountFrequency (%)
23.0 1
0.3%
20.0 1
0.3%
18.0 1
0.3%
16.0 1
0.3%
15.0 1
0.3%
14.1 1
0.3%
14.0 2
0.6%
13.0 2
0.6%
12.5 1
0.3%
11.7 1
0.3%

제방길이(미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.410828
Minimum10
Maximum501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-15T06:27:09.515606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile25
Q140
median60
Q392
95-th percentile199.55
Maximum501
Range491
Interquartile range (IQR)52

Descriptive statistics

Standard deviation61.366182
Coefficient of variation (CV)0.79273383
Kurtosis11.127024
Mean77.410828
Median Absolute Deviation (MAD)24
Skewness2.8135241
Sum24307
Variance3765.8083
MonotonicityNot monotonic
2024-03-15T06:27:09.933583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 13
 
4.1%
60 12
 
3.8%
50 11
 
3.5%
32 10
 
3.2%
40 10
 
3.2%
45 7
 
2.2%
65 7
 
2.2%
58 6
 
1.9%
92 6
 
1.9%
95 6
 
1.9%
Other values (112) 226
72.0%
ValueCountFrequency (%)
10 1
 
0.3%
11 1
 
0.3%
16 1
 
0.3%
18 2
 
0.6%
19 2
 
0.6%
20 3
1.0%
21 1
 
0.3%
22 2
 
0.6%
24 2
 
0.6%
25 5
1.6%
ValueCountFrequency (%)
501 1
0.3%
361 1
0.3%
340 1
0.3%
312 1
0.3%
302 1
0.3%
295 1
0.3%
285 1
0.3%
278 1
0.3%
273 1
0.3%
268 1
0.3%

점검결과
Categorical

Distinct4
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
C등급
205 
B등급
80 
D등급
27 
A등급
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB등급
2nd rowC등급
3rd rowC등급
4th rowB등급
5th rowC등급

Common Values

ValueCountFrequency (%)
C등급 205
65.3%
B등급 80
 
25.5%
D등급 27
 
8.6%
A등급 2
 
0.6%

Length

2024-03-15T06:27:10.164059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:27:10.505946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c등급 205
65.3%
b등급 80
 
25.5%
d등급 27
 
8.6%
a등급 2
 
0.6%

등급사유
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023년 점검 결과
314 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023년 점검 결과
2nd row2023년 점검 결과
3rd row2023년 점검 결과
4th row2023년 점검 결과
5th row2023년 점검 결과

Common Values

ValueCountFrequency (%)
2023년 점검 결과 314
100.0%

Length

2024-03-15T06:27:11.041203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:27:11.349055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023년 314
33.3%
점검 314
33.3%
결과 314
33.3%

전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
054-779-6412
314 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row054-779-6412
2nd row054-779-6412
3rd row054-779-6412
4th row054-779-6412
5th row054-779-6412

Common Values

ValueCountFrequency (%)
054-779-6412 314
100.0%

Length

2024-03-15T06:27:11.702545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:27:11.975109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
054-779-6412 314
100.0%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
경상북도 경주시청
314 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도 경주시청
2nd row경상북도 경주시청
3rd row경상북도 경주시청
4th row경상북도 경주시청
5th row경상북도 경주시청

Common Values

ValueCountFrequency (%)
경상북도 경주시청 314
100.0%

Length

2024-03-15T06:27:12.279643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:27:12.645151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 314
50.0%
경주시청 314
50.0%

Interactions

2024-03-15T06:26:53.366344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:41.392881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:43.737644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:45.631825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:47.551381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:49.628193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:51.555401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:53.582922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:41.718760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:44.012667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:45.877004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:47.867205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:49.968741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:51.816058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:53.802991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:42.104162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:44.282298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:46.133990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:48.145799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:50.287544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:52.024015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:54.276209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:42.492591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:44.577716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:46.464644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:48.420165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:50.546055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:52.279979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:54.528369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:42.810135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:44.847702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:46.762445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:48.736450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:50.794326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:52.637034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:54.829550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:43.212083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:45.141738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:47.015199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:49.029858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:51.036776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:52.928607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:55.155522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:43.454768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:45.417293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:47.265314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:49.306279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:51.270808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:26:53.146221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T06:27:12.985607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공연도수혜면적(헥타르)유역면적(헥타르)총저수량(천세제곱미터)유효저수량제방높이(미터)제방길이(미터)점검결과
준공연도1.0000.3060.6550.7570.8960.7920.2780.000
수혜면적(헥타르)0.3061.0000.5330.6010.6000.5500.3740.122
유역면적(헥타르)0.6550.5331.0000.8760.8780.8060.3190.737
총저수량(천세제곱미터)0.7570.6010.8761.0001.0000.7130.5780.319
유효저수량0.8960.6000.8781.0001.0000.7120.5690.324
제방높이(미터)0.7920.5500.8060.7130.7121.0000.0000.617
제방길이(미터)0.2780.3740.3190.5780.5690.0001.0000.000
점검결과0.0000.1220.7370.3190.3240.6170.0001.000
2024-03-15T06:27:13.432757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공연도수혜면적(헥타르)유역면적(헥타르)총저수량(천세제곱미터)유효저수량제방높이(미터)제방길이(미터)점검결과
준공연도1.0000.1430.1470.1030.1030.248-0.1240.086
수혜면적(헥타르)0.1431.0000.6210.6350.6360.4830.4400.083
유역면적(헥타르)0.1470.6211.0000.6430.6480.5100.4100.403
총저수량(천세제곱미터)0.1030.6350.6431.0000.9980.6190.6440.137
유효저수량0.1030.6360.6480.9981.0000.6170.6480.149
제방높이(미터)0.2480.4830.5100.6190.6171.0000.1590.416
제방길이(미터)-0.1240.4400.4100.6440.6480.1591.0000.000
점검결과0.0860.0830.4030.1370.1490.4160.0001.000

Missing values

2024-03-15T06:26:55.555705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T06:26:56.186505image/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

시설명소재지지번주소준공연도수혜면적(헥타르)유역면적(헥타르)총저수량(천세제곱미터)유효저수량제방높이(미터)제방길이(미터)점검결과등급사유전화번호관리기관명
0강당경상북도 경주시 탑동 857194515.099.032.029.04.9105B등급2023년 점검 결과054-779-6412경상북도 경주시청
1식화경상북도 경주시 탑동 80519784.37.02.62.54.138C등급2023년 점검 결과054-779-6412경상북도 경주시청
2능남경상북도 경주시 서악동 728-219452.133.08.017.35.747C등급2023년 점검 결과054-779-6412경상북도 경주시청
3간곡경상북도 경주시 서악동 69019450.461.54.844.634.560B등급2023년 점검 결과054-779-6412경상북도 경주시청
4와산경상북도 경주시 효현동 71919459.335.017.0115.515.175C등급2023년 점검 결과054-779-6412경상북도 경주시청
5와룡경상북도 경주시 효현동 77119458.832.011.2510.644.692B등급2023년 점검 결과054-779-6412경상북도 경주시청
6제공경상북도 경주시 광명동 95919458.082.018.216.477.584B등급2023년 점검 결과054-779-6412경상북도 경주시청
7화절경상북도 경주시 광명동 86619456.0113.08.988.175.552B등급2023년 점검 결과054-779-6412경상북도 경주시청
8광동경상북도 경주시 광명동 20919450.422.03.12.924.640C등급2023년 점검 결과054-779-6412경상북도 경주시청
9형산경상북도 경주시 도지동 31319458.79.02.82.55.195C등급2023년 점검 결과054-779-6412경상북도 경주시청
시설명소재지지번주소준공연도수혜면적(헥타르)유역면적(헥타르)총저수량(천세제곱미터)유효저수량제방높이(미터)제방길이(미터)점검결과등급사유전화번호관리기관명
304손실곡경상북도 경주시 천북면 성지리 49919455.710.010.89.73.695B등급2023년 점검 결과054-779-6412경상북도 경주시청
305중방곡경상북도 경주시 천북면 성지리 9819452.425.04.23.96.562B등급2023년 점검 결과054-779-6412경상북도 경주시청
306동산경상북도 경주시 천북면 동산리 232194547.925.018.918.04.0136B등급2023년 점검 결과054-779-6412경상북도 경주시청
307휘미지경상북도 경주시 천북면 동산리 20619458.524.08.27.983.895B등급2023년 점검 결과054-779-6412경상북도 경주시청
308부곡경상북도 경주시 천북면 동산리 산6-519453.528.04.44.44.246B등급2023년 점검 결과054-779-6412경상북도 경주시청
309하불암경상북도 경주시 천북면 화산리 산49-419455.417.06.86.33.6124C등급2023년 점검 결과054-779-6412경상북도 경주시청
310서당골경상북도 경주시 천북면 화산리 70019457.726.011.09.96.6110B등급2023년 점검 결과054-779-6412경상북도 경주시청
311나리경상북도 경주시 천북면 화산리 1138194551.0148.0112.2102.05.3235B등급2023년 점검 결과054-779-6412경상북도 경주시청
312이반경상북도 경주시 천북면 신당리 233194536.29.012.511.48.295C등급2023년 점검 결과054-779-6412경상북도 경주시청
313보둥경상북도 경주시 천북면 신당리 26519459.922.01.81.62.692B등급2023년 점검 결과054-779-6412경상북도 경주시청