Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells397
Missing cells (%)0.3%
Duplicate rows24
Duplicate rows (%)0.2%
Total size in memory1.2 MiB
Average record size in memory127.0 B

Variable types

Text4
Numeric6
Categorical1
DateTime3

Dataset

Description산림청에서 운영하는 산림자원통합관리시스템 내 정보로 국유림을 대상으로 시행하는 조림, 숲가꾸기, 벌채 사업에 대한 계약정보
Author산림청
URLhttps://www.data.go.kr/data/15041975/fileData.do

Alerts

금회집행금액 has constant value ""Constant
Dataset has 24 (0.2%) duplicate rowsDuplicates
계약금액 is highly overall correlated with 계약보증금 and 2 other fieldsHigh correlation
계약보증금 is highly overall correlated with 계약금액 and 2 other fieldsHigh correlation
계획면적 is highly overall correlated with 계약금액 and 2 other fieldsHigh correlation
총사업일수 is highly overall correlated with 계약금액 and 2 other fieldsHigh correlation
계약자명 has 363 (3.6%) missing valuesMissing
계약보증금 has 379 (3.8%) zerosZeros
선급금 has 9843 (98.4%) zerosZeros
생산재적 has 5796 (58.0%) zerosZeros

Reproduction

Analysis started2023-12-12 20:00:33.122908
Analysis finished2023-12-12 20:00:39.956306
Duration6.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct7513
Distinct (%)75.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:00:40.147997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
Distinct characters55
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5610 ?
Unique (%)56.1%

Sample

1st row평창2016D038
2nd row춘천2014D072
3rd row태백2015D082
4th row정선2013D011
5th row홍천2015D102
ValueCountFrequency (%)
홍천2017d081 7
 
0.1%
영주2016d092 6
 
0.1%
태백2013d049 6
 
0.1%
평창2017d062 6
 
0.1%
태백2015d005 5
 
< 0.1%
강릉2015d072 5
 
< 0.1%
인제2013d010 5
 
< 0.1%
영주2015d082 5
 
< 0.1%
정선2014d179 5
 
< 0.1%
태백2014d008 5
 
< 0.1%
Other values (7503) 9945
99.5%
2023-12-13T05:00:40.528909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21449
21.4%
1 13456
13.5%
2 13387
13.4%
D 7159
 
7.2%
3 4067
 
4.1%
4 3727
 
3.7%
5 3457
 
3.5%
6 3152
 
3.2%
7 2772
 
2.8%
8 2649
 
2.6%
Other values (45) 24725
24.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70000
70.0%
Other Letter 20000
 
20.0%
Uppercase Letter 10000
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1691
 
8.5%
1517
 
7.6%
1489
 
7.4%
1395
 
7.0%
741
 
3.7%
658
 
3.3%
658
 
3.3%
612
 
3.1%
610
 
3.0%
584
 
2.9%
Other values (31) 10045
50.2%
Decimal Number
ValueCountFrequency (%)
0 21449
30.6%
1 13456
19.2%
2 13387
19.1%
3 4067
 
5.8%
4 3727
 
5.3%
5 3457
 
4.9%
6 3152
 
4.5%
7 2772
 
4.0%
8 2649
 
3.8%
9 1884
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
D 7159
71.6%
C 1650
 
16.5%
S 1085
 
10.8%
E 106
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 70000
70.0%
Hangul 20000
 
20.0%
Latin 10000
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1691
 
8.5%
1517
 
7.6%
1489
 
7.4%
1395
 
7.0%
741
 
3.7%
658
 
3.3%
658
 
3.3%
612
 
3.1%
610
 
3.0%
584
 
2.9%
Other values (31) 10045
50.2%
Common
ValueCountFrequency (%)
0 21449
30.6%
1 13456
19.2%
2 13387
19.1%
3 4067
 
5.8%
4 3727
 
5.3%
5 3457
 
4.9%
6 3152
 
4.5%
7 2772
 
4.0%
8 2649
 
3.8%
9 1884
 
2.7%
Latin
ValueCountFrequency (%)
D 7159
71.6%
C 1650
 
16.5%
S 1085
 
10.8%
E 106
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
80.0%
Hangul 20000
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21449
26.8%
1 13456
16.8%
2 13387
16.7%
D 7159
 
8.9%
3 4067
 
5.1%
4 3727
 
4.7%
5 3457
 
4.3%
6 3152
 
3.9%
7 2772
 
3.5%
8 2649
 
3.3%
Other values (4) 4725
 
5.9%
Hangul
ValueCountFrequency (%)
1691
 
8.5%
1517
 
7.6%
1489
 
7.4%
1395
 
7.0%
741
 
3.7%
658
 
3.3%
658
 
3.3%
612
 
3.1%
610
 
3.0%
584
 
2.9%
Other values (31) 10045
50.2%
Distinct9264
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:00:40.768890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.0488
Min length1

Characters and Unicode

Total characters130488
Distinct characters103
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8780 ?
Unique (%)87.8%

Sample

1st row20160525771-00
2nd row20140517690
3rd row20150819089-00
4th row정선공사 2013-13-변경
5th row20151008116-01
ValueCountFrequency (%)
1 47
 
0.5%
2.01e+12 30
 
0.3%
정선 29
 
0.3%
2.02e+09 28
 
0.3%
정선공사 25
 
0.2%
2.02e+12 19
 
0.2%
18
 
0.2%
123456789 15
 
0.1%
2.01e+11 12
 
0.1%
0 11
 
0.1%
Other values (9254) 9846
97.7%
2023-12-13T05:00:41.396772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 37083
28.4%
1 19956
15.3%
2 17226
13.2%
- 7818
 
6.0%
3 7757
 
5.9%
6 6847
 
5.2%
4 6681
 
5.1%
5 6416
 
4.9%
8 5396
 
4.1%
7 4958
 
3.8%
Other values (93) 10350
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 116692
89.4%
Dash Punctuation 7818
 
6.0%
Uppercase Letter 3592
 
2.8%
Other Letter 1850
 
1.4%
Lowercase Letter 191
 
0.1%
Other Punctuation 134
 
0.1%
Math Symbol 129
 
0.1%
Space Separator 81
 
0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
188
10.2%
186
10.1%
186
10.1%
186
10.1%
186
10.1%
185
10.0%
158
8.5%
157
8.5%
81
 
4.4%
81
 
4.4%
Other values (50) 256
13.8%
Lowercase Letter
ValueCountFrequency (%)
c 52
27.2%
f 33
17.3%
a 28
14.7%
b 25
13.1%
e 23
12.0%
d 15
 
7.9%
n 4
 
2.1%
t 4
 
2.1%
s 2
 
1.0%
u 2
 
1.0%
Other values (3) 3
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
B 659
18.3%
E 646
18.0%
F 573
16.0%
C 568
15.8%
A 560
15.6%
D 540
15.0%
G 32
 
0.9%
J 5
 
0.1%
O 5
 
0.1%
S 2
 
0.1%
Other values (2) 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 37083
31.8%
1 19956
17.1%
2 17226
14.8%
3 7757
 
6.6%
6 6847
 
5.9%
4 6681
 
5.7%
5 6416
 
5.5%
8 5396
 
4.6%
7 4958
 
4.2%
9 4372
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 130
97.0%
/ 2
 
1.5%
, 1
 
0.7%
* 1
 
0.7%
Dash Punctuation
ValueCountFrequency (%)
- 7818
100.0%
Math Symbol
ValueCountFrequency (%)
+ 129
100.0%
Space Separator
ValueCountFrequency (%)
81
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 124855
95.7%
Latin 3783
 
2.9%
Hangul 1850
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
188
10.2%
186
10.1%
186
10.1%
186
10.1%
186
10.1%
185
10.0%
158
8.5%
157
8.5%
81
 
4.4%
81
 
4.4%
Other values (50) 256
13.8%
Latin
ValueCountFrequency (%)
B 659
17.4%
E 646
17.1%
F 573
15.1%
C 568
15.0%
A 560
14.8%
D 540
14.3%
c 52
 
1.4%
f 33
 
0.9%
G 32
 
0.8%
a 28
 
0.7%
Other values (15) 92
 
2.4%
Common
ValueCountFrequency (%)
0 37083
29.7%
1 19956
16.0%
2 17226
13.8%
- 7818
 
6.3%
3 7757
 
6.2%
6 6847
 
5.5%
4 6681
 
5.4%
5 6416
 
5.1%
8 5396
 
4.3%
7 4958
 
4.0%
Other values (8) 4717
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 128638
98.6%
Hangul 1846
 
1.4%
Compat Jamo 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37083
28.8%
1 19956
15.5%
2 17226
13.4%
- 7818
 
6.1%
3 7757
 
6.0%
6 6847
 
5.3%
4 6681
 
5.2%
5 6416
 
5.0%
8 5396
 
4.2%
7 4958
 
3.9%
Other values (33) 8500
 
6.6%
Hangul
ValueCountFrequency (%)
188
10.2%
186
10.1%
186
10.1%
186
10.1%
186
10.1%
185
10.0%
158
8.6%
157
8.5%
81
 
4.4%
81
 
4.4%
Other values (47) 252
13.7%
Compat Jamo
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%

계약금액
Real number (ℝ)

HIGH CORRELATION 

Distinct5569
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59926690
Minimum40300
Maximum5.2 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:00:41.546738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40300
5-th percentile9799500
Q128800000
median48320000
Q380600000
95-th percentile1.4781025 × 108
Maximum5.2 × 108
Range5.199597 × 108
Interquartile range (IQR)51800000

Descriptive statistics

Standard deviation44343749
Coefficient of variation (CV)0.7399666
Kurtosis4.7956458
Mean59926690
Median Absolute Deviation (MAD)23920000
Skewness1.6425975
Sum5.992669 × 1011
Variance1.9663681 × 1015
MonotonicityNot monotonic
2023-12-13T05:00:41.701293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48500000 40
 
0.4%
49000000 27
 
0.3%
44000000 19
 
0.2%
49200000 19
 
0.2%
48300000 19
 
0.2%
46500000 18
 
0.2%
49400000 17
 
0.2%
48700000 17
 
0.2%
48000000 17
 
0.2%
49300000 17
 
0.2%
Other values (5559) 9790
97.9%
ValueCountFrequency (%)
40300 1
< 0.1%
54645 1
< 0.1%
545000 1
< 0.1%
635000 1
< 0.1%
670000 1
< 0.1%
870000 1
< 0.1%
880000 1
< 0.1%
909000 1
< 0.1%
950000 1
< 0.1%
990000 1
< 0.1%
ValueCountFrequency (%)
520000000 1
< 0.1%
440500000 1
< 0.1%
400000000 1
< 0.1%
347500000 2
< 0.1%
327021000 1
< 0.1%
315000000 1
< 0.1%
312853000 1
< 0.1%
306000000 1
< 0.1%
305270000 1
< 0.1%
302480000 1
< 0.1%

계약보증금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4219
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9029447.4
Minimum0
Maximum1.98 × 108
Zeros379
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:00:41.836617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile484975
Q13954000
median7178550
Q312000000
95-th percentile22627500
Maximum1.98 × 108
Range1.98 × 108
Interquartile range (IQR)8046000

Descriptive statistics

Standard deviation8544505.7
Coefficient of variation (CV)0.94629331
Kurtosis64.433004
Mean9029447.4
Median Absolute Deviation (MAD)3818550
Skewness5.2085877
Sum9.0294474 × 1010
Variance7.3008577 × 1013
MonotonicityNot monotonic
2023-12-13T05:00:41.979773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 379
 
3.8%
7275000 39
 
0.4%
7350000 27
 
0.3%
7410000 24
 
0.2%
7380000 22
 
0.2%
6600000 21
 
0.2%
7245000 19
 
0.2%
7200000 18
 
0.2%
7305000 18
 
0.2%
6750000 17
 
0.2%
Other values (4209) 9416
94.2%
ValueCountFrequency (%)
0 379
3.8%
1 9
 
0.1%
20 1
 
< 0.1%
152 1
 
< 0.1%
1111 1
 
< 0.1%
3232 1
 
< 0.1%
3240 2
 
< 0.1%
5394 1
 
< 0.1%
10000 1
 
< 0.1%
11111 1
 
< 0.1%
ValueCountFrequency (%)
198000000 1
< 0.1%
160064000 1
< 0.1%
124334000 1
< 0.1%
122715000 1
< 0.1%
108214050 1
< 0.1%
107820000 2
< 0.1%
106989000 1
< 0.1%
104775000 2
< 0.1%
103195550 1
< 0.1%
102720000 1
< 0.1%

선급금
Real number (ℝ)

ZEROS 

Distinct102
Distinct (%)1.0%
Missing34
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean733617.61
Minimum0
Maximum1.738 × 108
Zeros9843
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:00:42.108409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1.738 × 108
Range1.738 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7498288.2
Coefficient of variation (CV)10.220976
Kurtosis180.40038
Mean733617.61
Median Absolute Deviation (MAD)0
Skewness12.482949
Sum7.3112331 × 109
Variance5.6224327 × 1013
MonotonicityNot monotonic
2023-12-13T05:00:42.247486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9843
98.4%
30000000 6
 
0.1%
70000000 5
 
0.1%
50000000 3
 
< 0.1%
36000000 3
 
< 0.1%
75000000 3
 
< 0.1%
90000000 2
 
< 0.1%
43610001 2
 
< 0.1%
40000000 2
 
< 0.1%
60000001 2
 
< 0.1%
Other values (92) 95
 
0.9%
(Missing) 34
 
0.3%
ValueCountFrequency (%)
0 9843
98.4%
4400000 1
 
< 0.1%
12000000 1
 
< 0.1%
13370000 1
 
< 0.1%
19180000 1
 
< 0.1%
19950000 1
 
< 0.1%
22000000 1
 
< 0.1%
24360001 1
 
< 0.1%
24600000 1
 
< 0.1%
24700000 1
 
< 0.1%
ValueCountFrequency (%)
173800000 1
< 0.1%
160000000 1
< 0.1%
154280000 1
< 0.1%
145250001 1
< 0.1%
120000000 1
< 0.1%
118440000 1
< 0.1%
113645000 1
< 0.1%
113155000 1
< 0.1%
110530000 1
< 0.1%
108400001 1
< 0.1%

금회집행금액
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-13T05:00:42.384400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:00:42.479180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

계약자명
Text

MISSING 

Distinct1568
Distinct (%)16.3%
Missing363
Missing (%)3.6%
Memory size156.2 KiB
2023-12-13T05:00:42.653409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length10.054685
Min length1

Characters and Unicode

Total characters96897
Distinct characters310
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique584 ?
Unique (%)6.1%

Sample

1st row봉평임업 대표 김중래
2nd row푸른숲영림단 대표 김성길
3rd row장성삼림기업
4th row임계기능인영림단 대표 홍순학
5th row풍천기계화영림단 대표 엄성철
ValueCountFrequency (%)
대표 3222
 
17.7%
솔향영림단 111
 
0.6%
장성삼림기업 91
 
0.5%
심석빈 89
 
0.5%
86
 
0.5%
용화산림경영단 86
 
0.5%
삼척산림경영단 82
 
0.5%
푸른숲영림단 81
 
0.4%
주식회사 80
 
0.4%
태화산영림단 78
 
0.4%
Other values (1202) 14155
77.9%
2023-12-13T05:00:42.960539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9063
 
9.4%
8540
 
8.8%
7895
 
8.1%
7135
 
7.4%
4214
 
4.3%
4004
 
4.1%
3602
 
3.7%
2875
 
3.0%
2486
 
2.6%
2190
 
2.3%
Other values (300) 44893
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85330
88.1%
Space Separator 8540
 
8.8%
Decimal Number 1788
 
1.8%
Open Punctuation 553
 
0.6%
Close Punctuation 550
 
0.6%
Other Punctuation 121
 
0.1%
Lowercase Letter 10
 
< 0.1%
Dash Punctuation 3
 
< 0.1%
Other Symbol 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9063
 
10.6%
7895
 
9.3%
7135
 
8.4%
4214
 
4.9%
4004
 
4.7%
3602
 
4.2%
2875
 
3.4%
2486
 
2.9%
2190
 
2.6%
1386
 
1.6%
Other values (274) 40480
47.4%
Decimal Number
ValueCountFrequency (%)
2 428
23.9%
1 382
21.4%
3 329
18.4%
5 166
 
9.3%
4 130
 
7.3%
6 127
 
7.1%
7 114
 
6.4%
9 54
 
3.0%
8 49
 
2.7%
0 9
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
o 2
20.0%
s 1
10.0%
p 1
10.0%
f 1
10.0%
r 1
10.0%
e 1
10.0%
t 1
10.0%
i 1
10.0%
a 1
10.0%
Space Separator
ValueCountFrequency (%)
8540
100.0%
Open Punctuation
ValueCountFrequency (%)
( 553
100.0%
Close Punctuation
ValueCountFrequency (%)
) 550
100.0%
Other Punctuation
ValueCountFrequency (%)
: 121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85331
88.1%
Common 11555
 
11.9%
Latin 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9063
 
10.6%
7895
 
9.3%
7135
 
8.4%
4214
 
4.9%
4004
 
4.7%
3602
 
4.2%
2875
 
3.4%
2486
 
2.9%
2190
 
2.6%
1386
 
1.6%
Other values (275) 40481
47.4%
Common
ValueCountFrequency (%)
8540
73.9%
( 553
 
4.8%
) 550
 
4.8%
2 428
 
3.7%
1 382
 
3.3%
3 329
 
2.8%
5 166
 
1.4%
4 130
 
1.1%
6 127
 
1.1%
: 121
 
1.0%
Other values (5) 229
 
2.0%
Latin
ValueCountFrequency (%)
o 2
18.2%
D 1
9.1%
s 1
9.1%
p 1
9.1%
f 1
9.1%
r 1
9.1%
e 1
9.1%
t 1
9.1%
i 1
9.1%
a 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85324
88.1%
ASCII 11566
 
11.9%
Compat Jamo 6
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
9063
 
10.6%
7895
 
9.3%
7135
 
8.4%
4214
 
4.9%
4004
 
4.7%
3602
 
4.2%
2875
 
3.4%
2486
 
2.9%
2190
 
2.6%
1386
 
1.6%
Other values (270) 40474
47.4%
ASCII
ValueCountFrequency (%)
8540
73.8%
( 553
 
4.8%
) 550
 
4.8%
2 428
 
3.7%
1 382
 
3.3%
3 329
 
2.8%
5 166
 
1.4%
4 130
 
1.1%
6 127
 
1.1%
: 121
 
1.0%
Other values (15) 240
 
2.1%
Compat Jamo
ValueCountFrequency (%)
2
33.3%
2
33.3%
1
16.7%
1
16.7%
None
ValueCountFrequency (%)
1
100.0%
Distinct1751
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2012-01-09 00:00:00
Maximum2019-09-16 00:00:00
2023-12-13T05:00:43.090682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:43.200895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2340
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2012-01-31 00:00:00
Maximum2019-10-21 00:00:00
2023-12-13T05:00:43.314034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:43.436061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct194
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:00:43.653873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length4
Mean length5.4498
Min length1

Characters and Unicode

Total characters54498
Distinct characters129
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)0.9%

Sample

1st row수의계약
2nd row수의계약
3rd row수의계약
4th row전자계약
5th rowg2b 전자계약
ValueCountFrequency (%)
수의계약 5923
56.7%
수의계약,국유림영림단 1554
 
14.9%
수의 962
 
9.2%
g2b 467
 
4.5%
전자계약 340
 
3.3%
g2b전자계약 191
 
1.8%
전자수의계약 84
 
0.8%
수의계약,선택 82
 
0.8%
수의계약,산림조합 76
 
0.7%
전자수의계약체결 70
 
0.7%
Other values (147) 691
 
6.6%
2023-12-13T05:00:43.983131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9049
16.6%
9040
16.6%
8731
16.0%
8729
16.0%
3243
 
6.0%
, 1785
 
3.3%
1589
 
2.9%
1583
 
2.9%
1583
 
2.9%
1556
 
2.9%
Other values (119) 7610
14.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49111
90.1%
Other Punctuation 1788
 
3.3%
Decimal Number 977
 
1.8%
Uppercase Letter 951
 
1.7%
Lowercase Letter 831
 
1.5%
Space Separator 441
 
0.8%
Open Punctuation 180
 
0.3%
Close Punctuation 178
 
0.3%
Dash Punctuation 40
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9049
18.4%
9040
18.4%
8731
17.8%
8729
17.8%
3243
 
6.6%
1589
 
3.2%
1583
 
3.2%
1583
 
3.2%
1556
 
3.2%
970
 
2.0%
Other values (89) 3038
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 861
88.1%
0 39
 
4.0%
1 28
 
2.9%
3 10
 
1.0%
6 9
 
0.9%
4 9
 
0.9%
9 6
 
0.6%
5 6
 
0.6%
7 5
 
0.5%
8 4
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
b 410
49.3%
g 404
48.6%
t 5
 
0.6%
i 3
 
0.4%
a 3
 
0.4%
n 3
 
0.4%
r 3
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
G 461
48.5%
B 458
48.2%
T 28
 
2.9%
D 3
 
0.3%
C 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 1785
99.8%
. 3
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 179
99.4%
{ 1
 
0.6%
Space Separator
ValueCountFrequency (%)
441
100.0%
Close Punctuation
ValueCountFrequency (%)
) 178
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49111
90.1%
Common 3605
 
6.6%
Latin 1782
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9049
18.4%
9040
18.4%
8731
17.8%
8729
17.8%
3243
 
6.6%
1589
 
3.2%
1583
 
3.2%
1583
 
3.2%
1556
 
3.2%
970
 
2.0%
Other values (89) 3038
 
6.2%
Common
ValueCountFrequency (%)
, 1785
49.5%
2 861
23.9%
441
 
12.2%
( 179
 
5.0%
) 178
 
4.9%
- 40
 
1.1%
0 39
 
1.1%
1 28
 
0.8%
3 10
 
0.3%
6 9
 
0.2%
Other values (8) 35
 
1.0%
Latin
ValueCountFrequency (%)
G 461
25.9%
B 458
25.7%
b 410
23.0%
g 404
22.7%
T 28
 
1.6%
t 5
 
0.3%
i 3
 
0.2%
a 3
 
0.2%
D 3
 
0.2%
n 3
 
0.2%
Other values (2) 4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49102
90.1%
ASCII 5387
 
9.9%
Compat Jamo 9
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
9049
18.4%
9040
18.4%
8731
17.8%
8729
17.8%
3243
 
6.6%
1589
 
3.2%
1583
 
3.2%
1583
 
3.2%
1556
 
3.2%
970
 
2.0%
Other values (86) 3029
 
6.2%
ASCII
ValueCountFrequency (%)
, 1785
33.1%
2 861
16.0%
G 461
 
8.6%
B 458
 
8.5%
441
 
8.2%
b 410
 
7.6%
g 404
 
7.5%
( 179
 
3.3%
) 178
 
3.3%
- 40
 
0.7%
Other values (20) 170
 
3.2%
Compat Jamo
ValueCountFrequency (%)
6
66.7%
2
 
22.2%
1
 
11.1%
Distinct1719
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2012-01-05 00:00:00
Maximum2019-09-11 00:00:00
2023-12-13T05:00:44.095988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:44.208033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

계획면적
Real number (ℝ)

HIGH CORRELATION 

Distinct1247
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.001332
Minimum0
Maximum248.9
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:00:44.321547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q118
median30
Q347
95-th percentile94.3
Maximum248.9
Range248.9
Interquartile range (IQR)29

Descriptive statistics

Standard deviation29.276594
Coefficient of variation (CV)0.7912308
Kurtosis6.9053617
Mean37.001332
Median Absolute Deviation (MAD)13.5
Skewness2.145968
Sum370013.32
Variance857.11894
MonotonicityNot monotonic
2023-12-13T05:00:44.448157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.0 138
 
1.4%
10.0 134
 
1.3%
18.0 131
 
1.3%
24.0 118
 
1.2%
30.0 111
 
1.1%
17.0 100
 
1.0%
23.0 98
 
1.0%
16.0 97
 
1.0%
29.0 97
 
1.0%
22.0 94
 
0.9%
Other values (1237) 8882
88.8%
ValueCountFrequency (%)
0.0 20
0.2%
0.5 1
 
< 0.1%
0.6 4
 
< 0.1%
0.7 3
 
< 0.1%
0.8 1
 
< 0.1%
1.0 27
0.3%
1.1 3
 
< 0.1%
1.2 6
 
0.1%
1.3 2
 
< 0.1%
1.4 3
 
< 0.1%
ValueCountFrequency (%)
248.9 1
 
< 0.1%
239.8 1
 
< 0.1%
237.3 1
 
< 0.1%
237.2 1
 
< 0.1%
237.0 3
< 0.1%
232.2 1
 
< 0.1%
228.5 1
 
< 0.1%
225.6 1
 
< 0.1%
224.6 1
 
< 0.1%
216.0 1
 
< 0.1%

생산재적
Real number (ℝ)

ZEROS 

Distinct3502
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234.87017
Minimum0
Maximum5247.22
Zeros5796
Zeros (%)58.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:00:44.570758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3391.15
95-th percentile1024.5435
Maximum5247.22
Range5247.22
Interquartile range (IQR)391.15

Descriptive statistics

Standard deviation390.75608
Coefficient of variation (CV)1.6637109
Kurtosis10.247276
Mean234.87017
Median Absolute Deviation (MAD)0
Skewness2.4875146
Sum2348701.7
Variance152690.31
MonotonicityNot monotonic
2023-12-13T05:00:44.692212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5796
58.0%
295.7 8
 
0.1%
1122.59 6
 
0.1%
581.1 6
 
0.1%
116.0 5
 
0.1%
559.86 5
 
0.1%
399.49 5
 
0.1%
618.68 4
 
< 0.1%
509.95 4
 
< 0.1%
428.95 4
 
< 0.1%
Other values (3492) 4157
41.6%
ValueCountFrequency (%)
0.0 5796
58.0%
0.18 1
 
< 0.1%
1.18 1
 
< 0.1%
2.0 1
 
< 0.1%
3.72 1
 
< 0.1%
3.91 1
 
< 0.1%
5.6 1
 
< 0.1%
7.94 3
 
< 0.1%
8.58 1
 
< 0.1%
8.87 1
 
< 0.1%
ValueCountFrequency (%)
5247.22 1
 
< 0.1%
4275.71 1
 
< 0.1%
3357.74 1
 
< 0.1%
3268.82 1
 
< 0.1%
3224.98 3
< 0.1%
3169.02 1
 
< 0.1%
3118.96 1
 
< 0.1%
2959.16 1
 
< 0.1%
2809.71 1
 
< 0.1%
2782.9 1
 
< 0.1%

총사업일수
Real number (ℝ)

HIGH CORRELATION 

Distinct196
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.4122
Minimum0
Maximum503
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:00:44.816712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q120
median29
Q347
95-th percentile106
Maximum503
Range503
Interquartile range (IQR)27

Descriptive statistics

Standard deviation31.383773
Coefficient of variation (CV)0.81702617
Kurtosis11.505341
Mean38.4122
Median Absolute Deviation (MAD)13
Skewness2.4243891
Sum384122
Variance984.94119
MonotonicityNot monotonic
2023-12-13T05:00:44.951934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 652
 
6.5%
20 392
 
3.9%
40 355
 
3.5%
25 342
 
3.4%
15 318
 
3.2%
10 272
 
2.7%
28 271
 
2.7%
30 227
 
2.3%
50 215
 
2.1%
35 202
 
2.0%
Other values (186) 6754
67.5%
ValueCountFrequency (%)
0 5
 
0.1%
1 53
 
0.5%
2 39
 
0.4%
3 43
 
0.4%
4 87
0.9%
5 110
1.1%
6 81
0.8%
7 141
1.4%
8 139
1.4%
9 102
1.0%
ValueCountFrequency (%)
503 1
< 0.1%
394 1
< 0.1%
283 1
< 0.1%
257 1
< 0.1%
241 1
< 0.1%
238 2
< 0.1%
235 1
< 0.1%
232 1
< 0.1%
218 2
< 0.1%
214 2
< 0.1%

Interactions

2023-12-13T05:00:38.906581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:35.187351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:35.852878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:36.579472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:37.412003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:38.199033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:39.006493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:35.295330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:35.981909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:36.691765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:37.550777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:38.309102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:39.111791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:35.405785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:36.114394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:36.838666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:37.714325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:38.468387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:39.209239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:35.510888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:36.248170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:36.962558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:37.852841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:38.622673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:39.297591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:35.621915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:36.364099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:37.088530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:37.973495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:38.719955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:39.387161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:35.753435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:36.479213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:37.239841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:38.079301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:00:38.809048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:00:45.043546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계약금액계약보증금선급금계획면적생산재적총사업일수
계약금액1.0000.6520.1600.7730.5210.485
계약보증금0.6521.0000.2220.4430.4300.311
선급금0.1600.2221.0000.1660.0520.067
계획면적0.7730.4430.1661.0000.1270.365
생산재적0.5210.4300.0520.1271.0000.316
총사업일수0.4850.3110.0670.3650.3161.000
2023-12-13T05:00:45.186458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계약금액계약보증금선급금계획면적생산재적총사업일수
계약금액1.0000.9130.0790.7300.4820.790
계약보증금0.9131.0000.0760.6730.4380.739
선급금0.0790.0761.0000.059-0.0180.055
계획면적0.7300.6730.0591.0000.0250.611
생산재적0.4820.438-0.0180.0251.0000.451
총사업일수0.7900.7390.0550.6110.4511.000

Missing values

2023-12-13T05:00:39.529122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:00:39.752717image/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.
2023-12-13T05:00:39.893687image/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

사업번호전자계약번호계약금액계약보증금선급금금회집행금액계약자명사업시작일사업종료일계약체결방법계약일계획면적생산재적총사업일수
7288평창2016D03820160525771-00877000001315500000봉평임업 대표 김중래2016-05-162016-07-06수의계약2016-05-1631.01013.2452
4317춘천2014D0722014051769018710000280650000푸른숲영림단 대표 김성길2014-05-142014-05-28수의계약2014-05-1412.80.015
4543태백2015D08220150819089-00636800095520000장성삼림기업2015-08-192015-08-26수의계약2015-08-1813.00.08
547정선2013D011정선공사 2013-13-변경48500000727500000임계기능인영림단 대표 홍순학2013-02-252013-03-16전자계약2013-02-2529.5270.220
5033홍천2015D10220151008116-0146750000701250000풍천기계화영림단 대표 엄성철2015-10-072015-11-30g2b 전자계약2015-11-1730.40.055
8548영주2016D01020160643683-001034000001551000000국유림제7영림단2016-06-232016-08-21수의계약2016-06-2348.7679.3560
722춘천2012D2132012112603323200000348000000한얼영림단 대표 김화2012-11-202012-12-08수의계약2012-11-1931.50.019
4582무주2015D0612015101679740940000614100000임실국유림영림단2015-10-122015-11-05수의계약2015-10-1233.50.025
12581인제2019D045201906008A8-001905000002857500000<NA>2019-06-032019-09-09수의계약,국유림영림단2019-06-03122.50.099
3747무주2014S04620140641289-0057200000858000000반딧불기계화영림단2014-06-272014-07-25수의계약2014-06-2717.0802.7229
사업번호전자계약번호계약금액계약보증금선급금금회집행금액계약자명사업시작일사업종료일계약체결방법계약일계획면적생산재적총사업일수
8443충주2014C0722014122494111800000177000000산림조합중앙회 충북본부 류재철2014-12-152014-12-20G2B전자계약(수의)2014-12-159.00.06
2976영덕2013D06620130313014-0018500000277500000세다임업2013-03-112013-03-26수의2013-03-0718.00.016
10760영덕2018C0012018010A1EB-009400000141000000영덕2기계화영림단 강성일2018-01-252018-02-08수의계약2018-01-235.60.015
7429태백2017D04020170606753-0036240000543600000동산임업2017-06-152017-07-09수의계약2017-06-1532.00.025
6198부여2017D0342017060E3F0-002550000003825000000하나기계화영림단장 이태호2017-06-272017-08-27수의계약2017-06-27224.60.062
9548양산2015D00320150214138-00672230001005000000양산기계화영림단 대표 오주찬2015-02-112015-03-11수의계약2015-02-1010.0903.8929
700양양2012D0042.01E+1246980000469000000탁주덕2012-05-312012-06-22전자계약2012-05-2922.2296.723
3076정선2014D094정선공사-348050000720750000임계영림단 대표 홍순학2014-01-182014-02-12전자계약2014-01-2133.085.826
1439춘천2015C0922015092535135360000530400000명산기계화임업영림단 대표 이명식2015-09-162015-10-09수의계약2015-09-1619.20.024
5260영덕2015D01220150121301-002143600003215400000영덕3기계화영림단 박성호2015-01-232015-05-08수의계약2015-01-22101.01132.08106

Duplicate rows

Most frequently occurring

사업번호전자계약번호계약금액계약보증금선급금금회집행금액계약자명사업시작일사업종료일계약체결방법계약일계획면적생산재적총사업일수# duplicates
23평창2013D05820130343911-0281900000843000000진부임업 김영규2013-03-252013-06-03수의계약2013-03-2517.01504.23713
0단양2018D00320180200421-01945000001417500000삼봉기계화영림단2018-02-012018-03-23수의계약,국유림영림단2018-02-0142.0922.13512
1무주2013D05920131220090781570001431800000무주기계화영림단2013-11-142013-12-27수의계약2013-11-1413.01004.05442
2무주2013D06020131120112777410001413600000임실기능인영림단2013-11-142013-12-27수의계약2013-11-1412.4923.9442
3무주2014C0052014034334434900000698000000임실기능인영림단 대표 서인수2014-03-242014-04-17수의계약2014-03-2621.50.0252
4수원2012D00512-Jan26220000524400000양평7영림단(대표 조길현)2012-01-112012-01-31수의계약2012-01-1019.00.0212
5수원2012D04320120227348-0045500000682500000양평2영림단2012-02-172012-03-16전자공개 수의계약2012-02-1717.0459.51292
6수원2012D04420120443026-0048300000724500000양평3영림단2012-04-232012-05-26전자공개 수의계약2012-04-2316.04503.98342
7수원2012D06720120628146-0049900000748500000지귀현2012-06-202012-07-26전자계약2012-06-2054.90.0372
8수원2012D08920120838508-0038630000579450000지귀현2012-08-312012-09-20전자계약2012-08-3120.0370.72212