Overview

Dataset statistics

Number of variables13
Number of observations189
Missing cells10
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.9 KiB
Average record size in memory107.7 B

Variable types

Numeric3
Text7
Categorical2
DateTime1

Dataset

Description서산시 월별 지목별 토지통계로 번호, 년도, 월, 합계, 전, 답, 임야, 대지, 공장(용지), 공원, 체육(용지), 기타, 기준일을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=451&beforeMenuCd=DOM_000000201001001000&publicdatapk=15000655

Alerts

번호 is highly overall correlated with 년도 and 2 other fieldsHigh correlation
년도 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
공원 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
체육 is highly overall correlated with 번호 and 2 other fieldsHigh correlation

Reproduction

Analysis started2024-01-09 20:42:51.175206
Analysis finished2024-01-09 20:42:52.755012
Duration1.58 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION 

Distinct188
Distinct (%)100.0%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean94.5
Minimum1
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:52.842396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.35
Q147.75
median94.5
Q3141.25
95-th percentile178.65
Maximum188
Range187
Interquartile range (IQR)93.5

Descriptive statistics

Standard deviation54.415071
Coefficient of variation (CV)0.57582086
Kurtosis-1.2
Mean94.5
Median Absolute Deviation (MAD)47
Skewness0
Sum17766
Variance2961
MonotonicityStrictly decreasing
2024-01-10T05:42:52.987113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188 1
 
0.5%
58 1
 
0.5%
67 1
 
0.5%
66 1
 
0.5%
65 1
 
0.5%
64 1
 
0.5%
63 1
 
0.5%
62 1
 
0.5%
61 1
 
0.5%
60 1
 
0.5%
Other values (178) 178
94.2%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
188 1
0.5%
187 1
0.5%
186 1
0.5%
185 1
0.5%
184 1
0.5%
183 1
0.5%
182 1
0.5%
181 1
0.5%
180 1
0.5%
179 1
0.5%

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)8.5%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2012.3404
Minimum2005
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:53.121584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005
Q12008
median2012
Q32016
95-th percentile2019
Maximum2020
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.5375514
Coefficient of variation (CV)0.0022548627
Kurtosis-1.197768
Mean2012.3404
Median Absolute Deviation (MAD)4
Skewness0.0090514182
Sum378320
Variance20.589373
MonotonicityDecreasing
2024-01-10T05:42:53.256041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2012 12
 
6.3%
2005 12
 
6.3%
2018 12
 
6.3%
2017 12
 
6.3%
2016 12
 
6.3%
2015 12
 
6.3%
2014 12
 
6.3%
2013 12
 
6.3%
2019 12
 
6.3%
2011 12
 
6.3%
Other values (6) 68
36.0%
ValueCountFrequency (%)
2005 12
6.3%
2006 12
6.3%
2007 12
6.3%
2008 12
6.3%
2009 12
6.3%
2010 12
6.3%
2011 12
6.3%
2012 12
6.3%
2013 12
6.3%
2014 12
6.3%
ValueCountFrequency (%)
2020 8
4.2%
2019 12
6.3%
2018 12
6.3%
2017 12
6.3%
2016 12
6.3%
2015 12
6.3%
2014 12
6.3%
2013 12
6.3%
2012 12
6.3%
2011 12
6.3%

월
Real number (ℝ)

Distinct12
Distinct (%)6.4%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.4148936
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-01-10T05:42:53.385717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.443694
Coefficient of variation (CV)0.53682792
Kurtosis-1.1996116
Mean6.4148936
Median Absolute Deviation (MAD)3
Skewness0.033130473
Sum1206
Variance11.859028
MonotonicityNot monotonic
2024-01-10T05:42:53.484894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 16
8.5%
7 16
8.5%
6 16
8.5%
5 16
8.5%
4 16
8.5%
3 16
8.5%
2 16
8.5%
1 16
8.5%
12 15
7.9%
11 15
7.9%
Other values (2) 30
15.9%
ValueCountFrequency (%)
1 16
8.5%
2 16
8.5%
3 16
8.5%
4 16
8.5%
5 16
8.5%
6 16
8.5%
7 16
8.5%
8 16
8.5%
9 15
7.9%
10 15
7.9%
ValueCountFrequency (%)
12 15
7.9%
11 15
7.9%
10 15
7.9%
9 15
7.9%
8 16
8.5%
7 16
8.5%
6 16
8.5%
5 16
8.5%
4 16
8.5%
3 16
8.5%

합계
Text

Distinct138
Distinct (%)73.4%
Missing1
Missing (%)0.5%
Memory size1.6 KiB
2024-01-10T05:42:53.762228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9308511
Min length5

Characters and Unicode

Total characters1303
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)53.2%

Sample

1st row742,023
2nd row742,023
3rd row742,031
4th row742.032
5th row742.036
ValueCountFrequency (%)
741.202 4
 
2.1%
740.398 4
 
2.1%
740.79 4
 
2.1%
741.313 3
 
1.6%
740.564 3
 
1.6%
740.389 3
 
1.6%
740.595 3
 
1.6%
740.661 3
 
1.6%
740.663 3
 
1.6%
740.56 2
 
1.1%
Other values (128) 156
83.0%
2024-01-10T05:42:54.190609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 233
17.9%
4 219
16.8%
. 185
14.2%
0 163
12.5%
3 83
 
6.4%
1 81
 
6.2%
2 78
 
6.0%
5 74
 
5.7%
8 63
 
4.8%
6 62
 
4.8%
Other values (2) 62
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1115
85.6%
Other Punctuation 188
 
14.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 233
20.9%
4 219
19.6%
0 163
14.6%
3 83
 
7.4%
1 81
 
7.3%
2 78
 
7.0%
5 74
 
6.6%
8 63
 
5.7%
6 62
 
5.6%
9 59
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 185
98.4%
, 3
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 233
17.9%
4 219
16.8%
. 185
14.2%
0 163
12.5%
3 83
 
6.4%
1 81
 
6.2%
2 78
 
6.0%
5 74
 
5.7%
8 63
 
4.8%
6 62
 
4.8%
Other values (2) 62
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 233
17.9%
4 219
16.8%
. 185
14.2%
0 163
12.5%
3 83
 
6.4%
1 81
 
6.2%
2 78
 
6.0%
5 74
 
5.7%
8 63
 
4.8%
6 62
 
4.8%
Other values (2) 62
 
4.8%

전
Text

Distinct177
Distinct (%)94.1%
Missing1
Missing (%)0.5%
Memory size1.6 KiB
2024-01-10T05:42:54.481003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9255319
Min length5

Characters and Unicode

Total characters1114
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167 ?
Unique (%)88.8%

Sample

1st row78,370
2nd row78,370
3rd row78,384
4th row78.409
5th row78.433
ValueCountFrequency (%)
77.811 3
 
1.6%
79.147 2
 
1.1%
77.756 2
 
1.1%
77.816 2
 
1.1%
77.778 2
 
1.1%
77.825 2
 
1.1%
78,370 2
 
1.1%
77.786 2
 
1.1%
79.157 2
 
1.1%
78.783 2
 
1.1%
Other values (167) 167
88.8%
2024-01-10T05:42:54.885618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 331
29.7%
. 185
16.6%
8 155
13.9%
9 93
 
8.3%
1 63
 
5.7%
5 58
 
5.2%
4 56
 
5.0%
6 52
 
4.7%
3 44
 
3.9%
0 39
 
3.5%
Other values (2) 38
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 926
83.1%
Other Punctuation 188
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 331
35.7%
8 155
16.7%
9 93
 
10.0%
1 63
 
6.8%
5 58
 
6.3%
4 56
 
6.0%
6 52
 
5.6%
3 44
 
4.8%
0 39
 
4.2%
2 35
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 185
98.4%
, 3
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 331
29.7%
. 185
16.6%
8 155
13.9%
9 93
 
8.3%
1 63
 
5.7%
5 58
 
5.2%
4 56
 
5.0%
6 52
 
4.7%
3 44
 
3.9%
0 39
 
3.5%
Other values (2) 38
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 331
29.7%
. 185
16.6%
8 155
13.9%
9 93
 
8.3%
1 63
 
5.7%
5 58
 
5.2%
4 56
 
5.0%
6 52
 
4.7%
3 44
 
3.9%
0 39
 
3.5%
Other values (2) 38
 
3.4%

답
Text

Distinct180
Distinct (%)95.7%
Missing1
Missing (%)0.5%
Memory size1.6 KiB
2024-01-10T05:42:55.205172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.8723404
Min length3

Characters and Unicode

Total characters1292
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)92.0%

Sample

1st row192,381
2nd row192,381
3rd row192,401
4th row192.472
5th row192.504
ValueCountFrequency (%)
198.321 3
 
1.6%
197.052 2
 
1.1%
197.676 2
 
1.1%
197.136 2
 
1.1%
197.758 2
 
1.1%
192,381 2
 
1.1%
197.203 2
 
1.1%
197.188 1
 
0.5%
198.261 1
 
0.5%
197.148 1
 
0.5%
Other values (170) 170
90.4%
2024-01-10T05:42:55.627793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 248
19.2%
9 227
17.6%
. 184
14.2%
7 119
9.2%
3 105
8.1%
8 95
 
7.4%
2 77
 
6.0%
5 75
 
5.8%
6 74
 
5.7%
4 49
 
3.8%
Other values (2) 39
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1105
85.5%
Other Punctuation 187
 
14.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 248
22.4%
9 227
20.5%
7 119
10.8%
3 105
9.5%
8 95
 
8.6%
2 77
 
7.0%
5 75
 
6.8%
6 74
 
6.7%
4 49
 
4.4%
0 36
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 184
98.4%
, 3
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1292
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 248
19.2%
9 227
17.6%
. 184
14.2%
7 119
9.2%
3 105
8.1%
8 95
 
7.4%
2 77
 
6.0%
5 75
 
5.8%
6 74
 
5.7%
4 49
 
3.8%
Other values (2) 39
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1292
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 248
19.2%
9 227
17.6%
. 184
14.2%
7 119
9.2%
3 105
8.1%
8 95
 
7.4%
2 77
 
6.0%
5 75
 
5.8%
6 74
 
5.7%
4 49
 
3.8%
Other values (2) 39
 
3.0%

임야
Text

Distinct185
Distinct (%)98.4%
Missing1
Missing (%)0.5%
Memory size1.6 KiB
2024-01-10T05:42:55.921108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.8510638
Min length5

Characters and Unicode

Total characters1288
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)96.8%

Sample

1st row287,313
2nd row287,313
3rd row287,463
4th row287.555
5th row287.651
ValueCountFrequency (%)
287,313 2
 
1.1%
308.979 2
 
1.1%
309.13 2
 
1.1%
313.464 1
 
0.5%
300.912 1
 
0.5%
308.823 1
 
0.5%
310.087 1
 
0.5%
310.032 1
 
0.5%
308.875 1
 
0.5%
308.879 1
 
0.5%
Other values (175) 175
93.1%
2024-01-10T05:42:56.364228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 193
15.0%
. 185
14.4%
3 159
12.3%
9 157
12.2%
1 136
10.6%
8 99
7.7%
0 99
7.7%
7 81
6.3%
6 60
 
4.7%
4 60
 
4.7%
Other values (2) 59
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1100
85.4%
Other Punctuation 188
 
14.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 193
17.5%
3 159
14.5%
9 157
14.3%
1 136
12.4%
8 99
9.0%
0 99
9.0%
7 81
7.4%
6 60
 
5.5%
4 60
 
5.5%
5 56
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 185
98.4%
, 3
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1288
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 193
15.0%
. 185
14.4%
3 159
12.3%
9 157
12.2%
1 136
10.6%
8 99
7.7%
0 99
7.7%
7 81
6.3%
6 60
 
4.7%
4 60
 
4.7%
Other values (2) 59
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1288
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 193
15.0%
. 185
14.4%
3 159
12.3%
9 157
12.2%
1 136
10.6%
8 99
7.7%
0 99
7.7%
7 81
6.3%
6 60
 
4.7%
4 60
 
4.7%
Other values (2) 59
 
4.6%

대지
Text

Distinct185
Distinct (%)98.4%
Missing1
Missing (%)0.5%
Memory size1.6 KiB
2024-01-10T05:42:56.710585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9255319
Min length5

Characters and Unicode

Total characters1114
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)96.8%

Sample

1st row22,096
2nd row22,096
3rd row22,031
4th row21.998
5th row21.977
ValueCountFrequency (%)
22,096 2
 
1.1%
16.142 2
 
1.1%
16.062 2
 
1.1%
14.224 1
 
0.5%
16.97 1
 
0.5%
16.292 1
 
0.5%
15.69 1
 
0.5%
15.696 1
 
0.5%
16.268 1
 
0.5%
16.243 1
 
0.5%
Other values (175) 175
93.1%
2024-01-10T05:42:57.143437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 226
20.3%
. 185
16.6%
2 112
10.1%
6 94
8.4%
5 83
 
7.5%
4 82
 
7.4%
9 79
 
7.1%
8 78
 
7.0%
7 69
 
6.2%
3 53
 
4.8%
Other values (2) 53
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 926
83.1%
Other Punctuation 188
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 226
24.4%
2 112
12.1%
6 94
10.2%
5 83
 
9.0%
4 82
 
8.9%
9 79
 
8.5%
8 78
 
8.4%
7 69
 
7.5%
3 53
 
5.7%
0 50
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 185
98.4%
, 3
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1114
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 226
20.3%
. 185
16.6%
2 112
10.1%
6 94
8.4%
5 83
 
7.5%
4 82
 
7.4%
9 79
 
7.1%
8 78
 
7.0%
7 69
 
6.2%
3 53
 
4.8%
Other values (2) 53
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1114
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 226
20.3%
. 185
16.6%
2 112
10.1%
6 94
8.4%
5 83
 
7.5%
4 82
 
7.4%
9 79
 
7.1%
8 78
 
7.0%
7 69
 
6.2%
3 53
 
4.8%
Other values (2) 53
 
4.8%

공장
Text

Distinct137
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-01-10T05:42:57.742503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.8730159
Min length2

Characters and Unicode

Total characters1110
Distinct characters14
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

Unique100 ?
Unique (%)52.9%

Sample

1st row용지
2nd row18,989
3rd row18,989
4th row18,989
5th row18.984
ValueCountFrequency (%)
11.261 5
 
2.6%
17.093 4
 
2.1%
17.754 4
 
2.1%
11.653 4
 
2.1%
11.272 3
 
1.6%
18,989 3
 
1.6%
15.482 3
 
1.6%
11.378 3
 
1.6%
11.64 3
 
1.6%
10.953 3
 
1.6%
Other values (127) 154
81.5%
2024-01-10T05:42:58.171082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 307
27.7%
. 185
16.7%
7 123
11.1%
9 83
 
7.5%
8 76
 
6.8%
6 64
 
5.8%
3 61
 
5.5%
5 56
 
5.0%
2 54
 
4.9%
4 53
 
4.8%
Other values (4) 48
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 920
82.9%
Other Punctuation 188
 
16.9%
Other Letter 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 307
33.4%
7 123
13.4%
9 83
 
9.0%
8 76
 
8.3%
6 64
 
7.0%
3 61
 
6.6%
5 56
 
6.1%
2 54
 
5.9%
4 53
 
5.8%
0 43
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 185
98.4%
, 3
 
1.6%
Other Letter
ValueCountFrequency (%)
용 1
50.0%
지 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1108
99.8%
Hangul 2
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 307
27.7%
. 185
16.7%
7 123
11.1%
9 83
 
7.5%
8 76
 
6.9%
6 64
 
5.8%
3 61
 
5.5%
5 56
 
5.1%
2 54
 
4.9%
4 53
 
4.8%
Other values (2) 46
 
4.2%
Hangul
ValueCountFrequency (%)
용 1
50.0%
지 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1108
99.8%
Hangul 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 307
27.7%
. 185
16.7%
7 123
11.1%
9 83
 
7.5%
8 76
 
6.9%
6 64
 
5.8%
3 61
 
5.5%
5 56
 
5.1%
2 54
 
4.9%
4 53
 
4.8%
Other values (2) 46
 
4.2%
Hangul
ValueCountFrequency (%)
용 1
50.0%
지 1
50.0%

공원
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0.418
22 
1.778
18 
1.034
12 
1.669
 
10
0.46
 
10
Other values (36)
117 

Length

Max length5
Median length5
Mean length4.8941799
Min length3

Unique

Unique13 ?
Unique (%)6.9%

Sample

1st row<NA>
2nd row2,142
3rd row2,142
4th row2,142
5th row2.142

Common Values

ValueCountFrequency (%)
0.418 22
 
11.6%
1.778 18
 
9.5%
1.034 12
 
6.3%
1.669 10
 
5.3%
0.46 10
 
5.3%
1.816 9
 
4.8%
0.395 9
 
4.8%
1.659 9
 
4.8%
0.459 7
 
3.7%
0.462 7
 
3.7%
Other values (31) 76
40.2%

Length

2024-01-10T05:42:58.316593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.418 22
 
11.6%
1.778 18
 
9.5%
1.034 12
 
6.3%
1.669 10
 
5.3%
0.46 10
 
5.3%
1.816 9
 
4.8%
0.395 9
 
4.8%
1.659 9
 
4.8%
0.462 7
 
3.7%
0.459 7
 
3.7%
Other values (31) 76
40.2%

체육
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0.196
20 
0.982
19 
0.156
15 
0.968
14 
0.229
12 
Other values (31)
109 

Length

Max length5
Median length5
Mean length4.9470899
Min length2

Unique

Unique9 ?
Unique (%)4.8%

Sample

1st row용지
2nd row1,177
3rd row1,177
4th row1,177
5th row1.171

Common Values

ValueCountFrequency (%)
0.196 20
 
10.6%
0.982 19
 
10.1%
0.156 15
 
7.9%
0.968 14
 
7.4%
0.229 12
 
6.3%
1.066 12
 
6.3%
1.069 10
 
5.3%
1.074 9
 
4.8%
1.165 7
 
3.7%
0.999 6
 
3.2%
Other values (26) 65
34.4%

Length

2024-01-10T05:42:58.442511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.196 20
 
10.6%
0.982 19
 
10.1%
0.156 15
 
7.9%
0.968 14
 
7.4%
0.229 12
 
6.3%
1.066 12
 
6.3%
1.069 10
 
5.3%
1.074 9
 
4.8%
1.165 7
 
3.7%
1.063 6
 
3.2%
Other values (26) 65
34.4%

기타
Text

Distinct182
Distinct (%)96.8%
Missing1
Missing (%)0.5%
Memory size1.6 KiB
2024-01-10T05:42:58.765102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.8989362
Min length5

Characters and Unicode

Total characters1297
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)93.6%

Sample

1st row139,555
2nd row139,555
3rd row139,444
4th row139.301
5th row139.184
ValueCountFrequency (%)
134.621 2
 
1.1%
127.077 2
 
1.1%
128.314 2
 
1.1%
128.042 2
 
1.1%
139,555 2
 
1.1%
134.682 2
 
1.1%
128.142 1
 
0.5%
127.005 1
 
0.5%
127.716 1
 
0.5%
128.123 1
 
0.5%
Other values (172) 172
91.5%
2024-01-10T05:42:59.232807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 252
19.4%
. 185
14.3%
3 164
12.6%
2 146
11.3%
7 103
7.9%
6 98
 
7.6%
8 91
 
7.0%
5 78
 
6.0%
4 73
 
5.6%
9 63
 
4.9%
Other values (2) 44
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1109
85.5%
Other Punctuation 188
 
14.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 252
22.7%
3 164
14.8%
2 146
13.2%
7 103
9.3%
6 98
 
8.8%
8 91
 
8.2%
5 78
 
7.0%
4 73
 
6.6%
9 63
 
5.7%
0 41
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 185
98.4%
, 3
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1297
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 252
19.4%
. 185
14.3%
3 164
12.6%
2 146
11.3%
7 103
7.9%
6 98
 
7.6%
8 91
 
7.0%
5 78
 
6.0%
4 73
 
5.6%
9 63
 
4.9%
Other values (2) 44
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1297
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 252
19.4%
. 185
14.3%
3 164
12.6%
2 146
11.3%
7 103
7.9%
6 98
 
7.6%
8 91
 
7.0%
5 78
 
6.0%
4 73
 
5.6%
9 63
 
4.9%
Other values (2) 44
 
3.4%

기준일
Date

Distinct187
Distinct (%)99.5%
Missing1
Missing (%)0.5%
Memory size1.6 KiB
Minimum2005-01-31 00:00:00
Maximum2020-08-31 00:00:00
2024-01-10T05:42:59.372254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:59.503578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-01-10T05:42:52.067201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:51.552713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:51.800026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:52.159461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:51.637174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:51.892243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:52.238756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:51.724497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:42:51.984920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:42:59.590111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호년도월공원체육
번호1.0000.9840.0000.9960.982
년도0.9841.0000.0000.9810.987
월0.0000.0001.0000.0000.000
공원0.9960.9810.0001.0000.965
체육0.9820.9870.0000.9651.000
2024-01-10T05:42:59.691148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체육공원
체육1.0000.559
공원0.5591.000
2024-01-10T05:42:59.787691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호년도월공원체육
번호1.0000.9980.0220.8150.792
년도0.9981.000-0.0410.7870.828
월0.022-0.0411.0000.0000.000
공원0.8150.7870.0001.0000.559
체육0.7920.8280.0000.5591.000

Missing values

2024-01-10T05:42:52.347148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:42:52.492997image/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.
2024-01-10T05:42:52.632259image/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

번호년도월합계전답임야대지공장공원체육기타기준일
0<NA><NA><NA><NA><NA><NA><NA><NA>용지<NA>용지<NA><NA>
118820208742,02378,370192,381287,31322,09618,9892,1421,177139,5552020.08.31
218720207742,02378,370192,381287,31322,09618,9892,1421,177139,5552020.07.31
318620206742,03178,384192,401287,46322,03118,9892,1421,177139,4442020.06.30
418520205742.03278.409192.472287.55521.99818.9842.1421.171139.3012020.05.31
518420204742.03678.433192.504287.65121.97718.9822.1341.171139.1842020.04.30
618320203742.03878.431192.613287.74421.9718.9822.1341.171138.9932020.03.31
718220202742.03778.427192.626287.77221.9518.9792.1291.171138.9832020.02.29
818120201742.04178.425192.641287.80721.92918.9722.1261.166138.9752020.01.31
9180201912742.02678.449192.754288.03121.89918.9721.9361.165138.822019.12.31
번호년도월합계전답임야대지공장공원체육기타기준일
17910200510740.37578.001198.214312.414.40110.9470.3950.156125.8612005.10.31
180920059740.40678.018198.229312.45214.37210.9420.3950.156125.8422005.09.30
181820058740.41278.03198.243312.50414.34110.9140.3950.156125.8292005.08.31
182720057740.40378.054198.27312.55114.32510.8710.3950.156125.7812005.07.31
183620056740.39678.081198.29312.57814.28610.8540.3950.156125.7562005.06.30
184520055740.39878.026198.306312.66514.25610.850.3950.156125.7442005.05.31
185420054740.40378.044198.321312.68314.21610.8450.3950.156125.7432005.04.30
186320053739.42378.138198.62313.46414.2249.2710.0570.156125.4932005.03.31
187220052739.43978.149198.609313.49714.2039.2720.0570.156125.4962005.02.28
188120051739.39378.15198.617313.5114.1899.2720.0570.156125.4422005.01.31