Overview

Dataset statistics

Number of variables15
Number of observations3638
Missing cells2915
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory451.3 KiB
Average record size in memory127.0 B

Variable types

Numeric7
Text3
DateTime4
Boolean1

Dataset

Description산림사업 용역업체에서 수행하는 임도 사방 등 산림토목사업의 계약정보로서 시행청코드, 업체명, 산림토목사업명 등 산림토목사업 정보로 구성됨
Author산림청
URLhttps://www.data.go.kr/data/15093783/fileData.do

Alerts

번호 is highly overall correlated with 계약보증금 and 2 other fieldsHigh correlation
계약금액 is highly overall correlated with 정산금액High correlation
정산금액 is highly overall correlated with 계약금액 and 1 other fieldsHigh correlation
계약보증금 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
지체상금율 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
사업기간 is highly overall correlated with 정산금액High correlation
하자보증금액 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
감리대상여부 is highly imbalanced (84.8%)Imbalance
사업종료일자 has 788 (21.7%) missing valuesMissing
준공일 has 1135 (31.2%) missing valuesMissing
감리대상여부 has 952 (26.2%) missing valuesMissing
하자보증금액 is highly skewed (γ1 = 56.51916125)Skewed
번호 has unique valuesUnique
산림토목사업ID has unique valuesUnique
계약금액 has 284 (7.8%) zerosZeros
정산금액 has 686 (18.9%) zerosZeros
계약보증금 has 2668 (73.3%) zerosZeros
지체상금율 has 2620 (72.0%) zerosZeros
사업기간 has 990 (27.2%) zerosZeros
하자보증금액 has 2930 (80.5%) zerosZeros

Reproduction

Analysis started2023-12-12 05:59:52.781634
Analysis finished2023-12-12 06:00:00.879293
Duration8.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3638
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1912.3749
Minimum1
Maximum3935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T15:00:00.953904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile182.85
Q1933.25
median1859.5
Q32895.75
95-th percentile3742.15
Maximum3935
Range3934
Interquartile range (IQR)1962.5

Descriptive statistics

Standard deviation1140.0538
Coefficient of variation (CV)0.59614556
Kurtosis-1.1867339
Mean1912.3749
Median Absolute Deviation (MAD)979
Skewness0.083954005
Sum6957220
Variance1299722.7
MonotonicityStrictly increasing
2023-12-12T15:00:01.141487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2542 1
 
< 0.1%
2509 1
 
< 0.1%
2510 1
 
< 0.1%
2513 1
 
< 0.1%
2515 1
 
< 0.1%
2517 1
 
< 0.1%
2518 1
 
< 0.1%
2521 1
 
< 0.1%
2523 1
 
< 0.1%
Other values (3628) 3628
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3935 1
< 0.1%
3934 1
< 0.1%
3933 1
< 0.1%
3932 1
< 0.1%
3931 1
< 0.1%
3930 1
< 0.1%
3929 1
< 0.1%
3928 1
< 0.1%
3927 1
< 0.1%
3926 1
< 0.1%

산림토목사업ID
Text

UNIQUE 

Distinct3638
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size28.6 KiB
2023-12-12T15:00:01.422216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters50932
Distinct characters19
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

Unique3638 ?
Unique (%)100.0%

Sample

1st rowBFN2015D100122
2nd rowBFN2015D100123
3rd rowBFN2015D100124
4th rowBFN2015D100125
5th rowBFN2015D100126
ValueCountFrequency (%)
bfn2015d100122 1
 
< 0.1%
ben2016c101011 1
 
< 0.1%
ben2016c100934 1
 
< 0.1%
ben2016c101003 1
 
< 0.1%
ben2016c100935 1
 
< 0.1%
ben2016c100937 1
 
< 0.1%
ben2016s100924 1
 
< 0.1%
ben2016c100940 1
 
< 0.1%
ben2016c100942 1
 
< 0.1%
ben2016c100945 1
 
< 0.1%
Other values (3628) 3628
99.7%
2023-12-12T15:00:01.827392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13829
27.2%
1 8051
15.8%
2 6074
11.9%
B 3638
 
7.1%
N 3121
 
6.1%
6 2875
 
5.6%
F 2034
 
4.0%
E 1604
 
3.1%
D 1366
 
2.7%
C 1244
 
2.4%
Other values (9) 7096
13.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36380
71.4%
Uppercase Letter 14552
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13829
38.0%
1 8051
22.1%
2 6074
16.7%
6 2875
 
7.9%
7 1190
 
3.3%
5 1147
 
3.2%
3 1080
 
3.0%
4 999
 
2.7%
8 583
 
1.6%
9 552
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
B 3638
25.0%
N 3121
21.4%
F 2034
14.0%
E 1604
11.0%
D 1366
 
9.4%
C 1244
 
8.5%
S 1027
 
7.1%
P 517
 
3.6%
I 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 36380
71.4%
Latin 14552
 
28.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13829
38.0%
1 8051
22.1%
2 6074
16.7%
6 2875
 
7.9%
7 1190
 
3.3%
5 1147
 
3.2%
3 1080
 
3.0%
4 999
 
2.7%
8 583
 
1.6%
9 552
 
1.5%
Latin
ValueCountFrequency (%)
B 3638
25.0%
N 3121
21.4%
F 2034
14.0%
E 1604
11.0%
D 1366
 
9.4%
C 1244
 
8.5%
S 1027
 
7.1%
P 517
 
3.6%
I 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13829
27.2%
1 8051
15.8%
2 6074
11.9%
B 3638
 
7.1%
N 3121
 
6.1%
6 2875
 
5.6%
F 2034
 
4.0%
E 1604
 
3.1%
D 1366
 
2.7%
C 1244
 
2.4%
Other values (9) 7096
13.9%

업체명
Text

Distinct790
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Memory size28.6 KiB
2023-12-12T15:00:02.171272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length19
Mean length8.8963716
Min length1

Characters and Unicode

Total characters32365
Distinct characters259
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

Unique318 ?
Unique (%)8.7%

Sample

1st row산림조합중앙회 ENG센터
2nd row산림조합중앙회 ENG센터
3rd row산림조합중앙회 ENG센터
4th row산림조합중앙회 ENG센터
5th row다산산림기술사사무소
ValueCountFrequency (%)
산림조합중앙회 397
 
8.4%
eng 141
 
3.0%
산림종합기술본부 124
 
2.6%
산림기술사사무소 117
 
2.5%
산림조합 87
 
1.8%
창송엔지니어링 84
 
1.8%
강원산림기술사사무소 76
 
1.6%
주식회사 74
 
1.6%
태성eng 69
 
1.5%
토목용역업체 60
 
1.3%
Other values (698) 3511
74.1%
2023-12-12T15:00:02.637446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 3087
 
9.5%
림 2934
 
9.1%
사 1757
 
5.4%
조 1369
 
4.2%
합 1340
 
4.1%
1229
 
3.8%
기 1055
 
3.3%
술 1005
 
3.1%
지 841
 
2.6%
부 829
 
2.6%
Other values (249) 16919
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29435
90.9%
Space Separator 1229
 
3.8%
Uppercase Letter 1019
 
3.1%
Other Symbol 253
 
0.8%
Open Punctuation 212
 
0.7%
Close Punctuation 211
 
0.7%
Lowercase Letter 3
 
< 0.1%
Connector Punctuation 2
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 3087
 
10.5%
림 2934
 
10.0%
사 1757
 
6.0%
조 1369
 
4.7%
합 1340
 
4.6%
기 1055
 
3.6%
술 1005
 
3.4%
지 841
 
2.9%
부 829
 
2.8%
본 648
 
2.2%
Other values (235) 14570
49.5%
Uppercase Letter
ValueCountFrequency (%)
E 327
32.1%
N 327
32.1%
G 327
32.1%
B 19
 
1.9%
M 19
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
n 1
33.3%
g 1
33.3%
e 1
33.3%
Space Separator
ValueCountFrequency (%)
1229
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 253
100.0%
Open Punctuation
ValueCountFrequency (%)
( 212
100.0%
Close Punctuation
ValueCountFrequency (%)
) 211
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Decimal Number
ValueCountFrequency (%)
5 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29688
91.7%
Common 1655
 
5.1%
Latin 1022
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 3087
 
10.4%
림 2934
 
9.9%
사 1757
 
5.9%
조 1369
 
4.6%
합 1340
 
4.5%
기 1055
 
3.6%
술 1005
 
3.4%
지 841
 
2.8%
부 829
 
2.8%
본 648
 
2.2%
Other values (236) 14823
49.9%
Latin
ValueCountFrequency (%)
E 327
32.0%
N 327
32.0%
G 327
32.0%
B 19
 
1.9%
M 19
 
1.9%
n 1
 
0.1%
g 1
 
0.1%
e 1
 
0.1%
Common
ValueCountFrequency (%)
1229
74.3%
( 212
 
12.8%
) 211
 
12.7%
_ 2
 
0.1%
5 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29435
90.9%
ASCII 2677
 
8.3%
None 253
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 3087
 
10.5%
림 2934
 
10.0%
사 1757
 
6.0%
조 1369
 
4.7%
합 1340
 
4.6%
기 1055
 
3.6%
술 1005
 
3.4%
지 841
 
2.9%
부 829
 
2.8%
본 648
 
2.2%
Other values (235) 14570
49.5%
ASCII
ValueCountFrequency (%)
1229
45.9%
E 327
 
12.2%
N 327
 
12.2%
G 327
 
12.2%
( 212
 
7.9%
) 211
 
7.9%
B 19
 
0.7%
M 19
 
0.7%
_ 2
 
0.1%
n 1
 
< 0.1%
Other values (3) 3
 
0.1%
None
ValueCountFrequency (%)
㈜ 253
100.0%

산림토목사업명
Text

Distinct3028
Distinct (%)83.5%
Missing10
Missing (%)0.3%
Memory size28.6 KiB
2023-12-12T15:00:02.977862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length47
Mean length23.494763
Min length4

Characters and Unicode

Total characters85239
Distinct characters259
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2794 ?
Unique (%)77.0%

Sample

1st row북부청 2015년도 임도 설계사업(기번 22)
2nd row북부청 2015년도 임도 설계사업(기번 23)
3rd row북부청 2015년도 임도 설계사업(기번 24)
4th row북부청 2015년도 임도 설계사업(기번 25)
5th row북부청 2015년도 임도 설계사업(기번 26)
ValueCountFrequency (%)
2016년도 1720
 
10.4%
임도 1469
 
8.9%
사방 1159
 
7.0%
설계사업(기번 875
 
5.3%
시공사업(기번 870
 
5.2%
감리사업(기번 825
 
5.0%
2020년 652
 
3.9%
2017년도 549
 
3.3%
경북 516
 
3.1%
2015년도 301
 
1.8%
Other values (855) 7641
46.1%
2023-12-12T15:00:03.480735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12953
 
15.2%
2 5404
 
6.3%
사 4782
 
5.6%
도 4536
 
5.3%
0 4526
 
5.3%
1 4259
 
5.0%
년 3533
 
4.1%
업 3288
 
3.9%
( 3065
 
3.6%
) 3062
 
3.6%
Other values (249) 35831
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45138
53.0%
Decimal Number 19651
23.1%
Space Separator 12953
 
15.2%
Open Punctuation 3074
 
3.6%
Close Punctuation 3071
 
3.6%
Connector Punctuation 497
 
0.6%
Other Punctuation 417
 
0.5%
Lowercase Letter 284
 
0.3%
Dash Punctuation 148
 
0.2%
Letter Number 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 4782
 
10.6%
도 4536
 
10.0%
년 3533
 
7.8%
업 3288
 
7.3%
기 2653
 
5.9%
번 2633
 
5.8%
임 1934
 
4.3%
방 1770
 
3.9%
설 1305
 
2.9%
계 1296
 
2.9%
Other values (211) 17408
38.6%
Lowercase Letter
ValueCountFrequency (%)
m 127
44.7%
k 125
44.0%
r 6
 
2.1%
e 6
 
2.1%
s 5
 
1.8%
p 4
 
1.4%
a 4
 
1.4%
v 3
 
1.1%
o 2
 
0.7%
y 1
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 5404
27.5%
0 4526
23.0%
1 4259
21.7%
6 2106
 
10.7%
7 923
 
4.7%
5 724
 
3.7%
3 590
 
3.0%
4 507
 
2.6%
8 334
 
1.7%
9 278
 
1.4%
Other Punctuation
ValueCountFrequency (%)
. 300
71.9%
/ 41
 
9.8%
, 35
 
8.4%
# 34
 
8.2%
; 4
 
1.0%
& 2
 
0.5%
! 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 3065
99.7%
[ 9
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 3062
99.7%
] 9
 
0.3%
Letter Number
ValueCountFrequency (%)
Ⅱ 2
50.0%
Ⅰ 2
50.0%
Space Separator
ValueCountFrequency (%)
12953
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 497
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%
Uppercase Letter
ValueCountFrequency (%)
I 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45138
53.0%
Common 39811
46.7%
Latin 290
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 4782
 
10.6%
도 4536
 
10.0%
년 3533
 
7.8%
업 3288
 
7.3%
기 2653
 
5.9%
번 2633
 
5.8%
임 1934
 
4.3%
방 1770
 
3.9%
설 1305
 
2.9%
계 1296
 
2.9%
Other values (211) 17408
38.6%
Common
ValueCountFrequency (%)
12953
32.5%
2 5404
13.6%
0 4526
 
11.4%
1 4259
 
10.7%
( 3065
 
7.7%
) 3062
 
7.7%
6 2106
 
5.3%
7 923
 
2.3%
5 724
 
1.8%
3 590
 
1.5%
Other values (14) 2199
 
5.5%
Latin
ValueCountFrequency (%)
m 127
43.8%
k 125
43.1%
r 6
 
2.1%
e 6
 
2.1%
s 5
 
1.7%
p 4
 
1.4%
a 4
 
1.4%
v 3
 
1.0%
I 2
 
0.7%
Ⅱ 2
 
0.7%
Other values (4) 6
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45138
53.0%
ASCII 40097
47.0%
Number Forms 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12953
32.3%
2 5404
13.5%
0 4526
 
11.3%
1 4259
 
10.6%
( 3065
 
7.6%
) 3062
 
7.6%
6 2106
 
5.3%
7 923
 
2.3%
5 724
 
1.8%
3 590
 
1.5%
Other values (26) 2485
 
6.2%
Hangul
ValueCountFrequency (%)
사 4782
 
10.6%
도 4536
 
10.0%
년 3533
 
7.8%
업 3288
 
7.3%
기 2653
 
5.9%
번 2633
 
5.8%
임 1934
 
4.3%
방 1770
 
3.9%
설 1305
 
2.9%
계 1296
 
2.9%
Other values (211) 17408
38.6%
Number Forms
ValueCountFrequency (%)
Ⅱ 2
50.0%
Ⅰ 2
50.0%

계약일
Date

Distinct551
Distinct (%)15.2%
Missing15
Missing (%)0.4%
Memory size28.6 KiB
Minimum2010-01-15 00:00:00
Maximum2021-08-11 00:00:00
2023-12-12T15:00:03.652066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:03.794876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

계약금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2543
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7856776 × 109
Minimum0
Maximum2.38585 × 1011
Zeros284
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T15:00:04.260464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14606750
median12375000
Q31.3279853 × 108
95-th percentile4.7620665 × 108
Maximum2.38585 × 1011
Range2.38585 × 1011
Interquartile range (IQR)1.2819178 × 108

Descriptive statistics

Standard deviation1.7139441 × 1010
Coefficient of variation (CV)9.5982842
Kurtosis112.56129
Mean1.7856776 × 109
Median Absolute Deviation (MAD)9879500
Skewness10.514941
Sum6.496295 × 1012
Variance2.9376043 × 1020
MonotonicityNot monotonic
2023-12-12T15:00:04.406716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 284
 
7.8%
12375000 37
 
1.0%
14599000 23
 
0.6%
13184000 15
 
0.4%
13425000 14
 
0.4%
7800000 13
 
0.4%
8800000 12
 
0.3%
9336000 10
 
0.3%
5750000 9
 
0.2%
7920000 9
 
0.2%
Other values (2533) 3212
88.3%
ValueCountFrequency (%)
0 284
7.8%
332 1
 
< 0.1%
12000 1
 
< 0.1%
93000 2
 
0.1%
100000 1
 
< 0.1%
124000 1
 
< 0.1%
197000 1
 
< 0.1%
204000 1
 
< 0.1%
237000 1
 
< 0.1%
285000 1
 
< 0.1%
ValueCountFrequency (%)
238585000000 1
< 0.1%
221522000000 1
< 0.1%
211199000000 2
0.1%
209716000000 1
< 0.1%
202040000000 1
< 0.1%
200706000000 1
< 0.1%
198999000000 1
< 0.1%
195943000000 1
< 0.1%
194410000000 1
< 0.1%
192731000000 1
< 0.1%

정산금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2233
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7754184 × 109
Minimum0
Maximum2.38585 × 1011
Zeros686
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T15:00:04.546704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12861500
median9400000
Q375040750
95-th percentile4.333226 × 108
Maximum2.38585 × 1011
Range2.38585 × 1011
Interquartile range (IQR)72179250

Descriptive statistics

Standard deviation1.7143667 × 1010
Coefficient of variation (CV)9.6561275
Kurtosis112.47296
Mean1.7754184 × 109
Median Absolute Deviation (MAD)9400000
Skewness10.509294
Sum6.4589722 × 1012
Variance2.9390531 × 1020
MonotonicityNot monotonic
2023-12-12T15:00:04.704835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 686
 
18.9%
12375000 38
 
1.0%
14599000 23
 
0.6%
13425000 14
 
0.4%
7800000 12
 
0.3%
13184000 11
 
0.3%
9336000 10
 
0.3%
8800000 10
 
0.3%
5750000 9
 
0.2%
7920000 9
 
0.2%
Other values (2223) 2816
77.4%
ValueCountFrequency (%)
0 686
18.9%
332 1
 
< 0.1%
12000 1
 
< 0.1%
93000 2
 
0.1%
124000 1
 
< 0.1%
197000 1
 
< 0.1%
204000 1
 
< 0.1%
237000 1
 
< 0.1%
285000 1
 
< 0.1%
325824 1
 
< 0.1%
ValueCountFrequency (%)
238585000000 1
< 0.1%
221522000000 1
< 0.1%
211199000000 2
0.1%
209716000000 1
< 0.1%
202040000000 1
< 0.1%
200706000000 1
< 0.1%
198999000000 1
< 0.1%
195943000000 1
< 0.1%
194410000000 1
< 0.1%
192731000000 1
< 0.1%

계약보증금
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct789
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3500971.1
Minimum0
Maximum1.8281445 × 108
Zeros2668
Zeros (%)73.3%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T15:00:04.877406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3312075
95-th percentile24528477
Maximum1.8281445 × 108
Range1.8281445 × 108
Interquartile range (IQR)312075

Descriptive statistics

Standard deviation12797005
Coefficient of variation (CV)3.655273
Kurtosis38.484603
Mean3500971.1
Median Absolute Deviation (MAD)0
Skewness5.4801435
Sum1.2736533 × 1010
Variance1.6376334 × 1014
MonotonicityNot monotonic
2023-12-12T15:00:05.050958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2668
73.3%
2189850 18
 
0.5%
4620000 9
 
0.2%
3283200 8
 
0.2%
500000 7
 
0.2%
1680900 6
 
0.2%
168000 5
 
0.1%
1425750 5
 
0.1%
2046000 5
 
0.1%
50000 4
 
0.1%
Other values (779) 903
 
24.8%
ValueCountFrequency (%)
0 2668
73.3%
1 1
 
< 0.1%
10 1
 
< 0.1%
100 1
 
< 0.1%
230 1
 
< 0.1%
1800 1
 
< 0.1%
1910 1
 
< 0.1%
15340 1
 
< 0.1%
20000 1
 
< 0.1%
35550 1
 
< 0.1%
ValueCountFrequency (%)
182814450 1
< 0.1%
156333092 1
< 0.1%
121692935 1
< 0.1%
121425000 1
< 0.1%
118366500 1
< 0.1%
111000000 1
< 0.1%
105300000 1
< 0.1%
97500000 1
< 0.1%
95010000 1
< 0.1%
94695000 1
< 0.1%

지체상금율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026973062
Minimum0
Maximum0.25
Zeros2620
Zeros (%)72.0%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T15:00:05.174109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.05
95-th percentile0.125
Maximum0.25
Range0.25
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.047702916
Coefficient of variation (CV)1.7685391
Kurtosis0.53343062
Mean0.026973062
Median Absolute Deviation (MAD)0
Skewness1.4614187
Sum98.128
Variance0.0022755681
MonotonicityNot monotonic
2023-12-12T15:00:05.282108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 2620
72.0%
0.125 587
 
16.1%
0.05 377
 
10.4%
0.12 20
 
0.5%
0.1 16
 
0.4%
0.2 7
 
0.2%
0.01 5
 
0.1%
0.005 2
 
0.1%
0.123 1
 
< 0.1%
0.25 1
 
< 0.1%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
0.0 2620
72.0%
0.005 2
 
0.1%
0.01 5
 
0.1%
0.03 1
 
< 0.1%
0.04 1
 
< 0.1%
0.05 377
 
10.4%
0.1 16
 
0.4%
0.12 20
 
0.5%
0.123 1
 
< 0.1%
0.125 587
 
16.1%
ValueCountFrequency (%)
0.25 1
 
< 0.1%
0.2 7
 
0.2%
0.125 587
16.1%
0.123 1
 
< 0.1%
0.12 20
 
0.5%
0.1 16
 
0.4%
0.05 377
10.4%
0.04 1
 
< 0.1%
0.03 1
 
< 0.1%
0.01 5
 
0.1%

사업기간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct263
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.08961
Minimum0
Maximum1146
Zeros990
Zeros (%)27.2%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T15:00:05.405169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40
Q396
95-th percentile212
Maximum1146
Range1146
Interquartile range (IQR)96

Descriptive statistics

Standard deviation77.709549
Coefficient of variation (CV)1.1582948
Kurtosis16.025241
Mean67.08961
Median Absolute Deviation (MAD)40
Skewness2.3996749
Sum244072
Variance6038.7739
MonotonicityNot monotonic
2023-12-12T15:00:05.547734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 990
27.2%
40 179
 
4.9%
29 131
 
3.6%
90 106
 
2.9%
30 98
 
2.7%
180 89
 
2.4%
89 63
 
1.7%
20 59
 
1.6%
26 50
 
1.4%
39 49
 
1.3%
Other values (253) 1824
50.1%
ValueCountFrequency (%)
0 990
27.2%
1 22
 
0.6%
2 3
 
0.1%
3 2
 
0.1%
4 1
 
< 0.1%
6 2
 
0.1%
7 3
 
0.1%
8 5
 
0.1%
9 3
 
0.1%
10 7
 
0.2%
ValueCountFrequency (%)
1146 1
 
< 0.1%
771 1
 
< 0.1%
761 2
0.1%
427 2
0.1%
416 1
 
< 0.1%
403 1
 
< 0.1%
400 1
 
< 0.1%
399 1
 
< 0.1%
396 2
0.1%
395 3
0.1%

사업시작일자
Date

Distinct568
Distinct (%)15.7%
Missing15
Missing (%)0.4%
Memory size28.6 KiB
Minimum2010-01-15 00:00:00
Maximum2021-11-18 00:00:00
2023-12-12T15:00:05.697810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:05.854901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사업종료일자
Date

MISSING 

Distinct620
Distinct (%)21.8%
Missing788
Missing (%)21.7%
Memory size28.6 KiB
Minimum2010-02-22 00:00:00
Maximum2021-12-21 00:00:00
2023-12-12T15:00:05.985519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:06.116489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

준공일
Date

MISSING 

Distinct553
Distinct (%)22.1%
Missing1135
Missing (%)31.2%
Memory size28.6 KiB
Minimum2010-02-22 00:00:00
Maximum2021-12-09 00:00:00
2023-12-12T15:00:06.257074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:06.401221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

하자보증금액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct574
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean990235.44
Minimum0
Maximum8.17673 × 108
Zeros2930
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size32.1 KiB
2023-12-12T15:00:06.554388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5881450
Maximum8.17673 × 108
Range8.17673 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13844226
Coefficient of variation (CV)13.980742
Kurtosis3332.1882
Mean990235.44
Median Absolute Deviation (MAD)0
Skewness56.519161
Sum3.6024765 × 109
Variance1.916626 × 1014
MonotonicityNot monotonic
2023-12-12T15:00:06.710262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2930
80.5%
583960 23
 
0.6%
656640 8
 
0.2%
448240 6
 
0.2%
630000 6
 
0.2%
380200 6
 
0.2%
676020 4
 
0.1%
409200 4
 
0.1%
190080 4
 
0.1%
179280 4
 
0.1%
Other values (564) 643
 
17.7%
ValueCountFrequency (%)
0 2930
80.5%
9 1
 
< 0.1%
2790 2
 
0.1%
9000 1
 
< 0.1%
9480 1
 
< 0.1%
9774 1
 
< 0.1%
11400 1
 
< 0.1%
11809 1
 
< 0.1%
13000 1
 
< 0.1%
13904 1
 
< 0.1%
ValueCountFrequency (%)
817673000 1
< 0.1%
36691000 1
< 0.1%
32622000 1
< 0.1%
31838000 1
< 0.1%
27669000 1
< 0.1%
26251000 1
< 0.1%
25055000 1
< 0.1%
23337000 1
< 0.1%
23286000 1
< 0.1%
21764000 1
< 0.1%

감리대상여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing952
Missing (%)26.2%
Memory size7.2 KiB
True
2627 
False
 
59
(Missing)
952 
ValueCountFrequency (%)
True 2627
72.2%
False 59
 
1.6%
(Missing) 952
 
26.2%
2023-12-12T15:00:06.855474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-12T14:59:59.506438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:54.022572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:54.993563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:55.827341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:56.905456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:57.716019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:58.633047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:59.629018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:54.150209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:55.127226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:55.929985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:57.008342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:57.836341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:58.770435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:59.756297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:54.273756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:55.218356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:56.051703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:57.119889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:57.950161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:58.889981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:59.870898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:54.406256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:55.338256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:56.162886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:57.255477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:58.062696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:59.040841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:59.992988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:54.535403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:55.470767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:56.294172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:57.388780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:58.205538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:59.188860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:00.108408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:54.685086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:55.601593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:56.677833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:57.496225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:58.372350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:59.286321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:00:00.215067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:54.861039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:55.715568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:56.779399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:57.613781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:58.513005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:59:59.397362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:00:06.929851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호계약금액정산금액계약보증금지체상금율사업기간하자보증금액감리대상여부
번호1.0000.1490.1490.2860.6340.4000.0240.328
계약금액0.1491.0001.0000.0000.0000.1650.0000.000
정산금액0.1491.0001.0000.0000.0000.1650.0000.000
계약보증금0.2860.0000.0001.0000.4990.3830.0880.000
지체상금율0.6340.0000.0000.4991.0000.2290.0000.044
사업기간0.4000.1650.1650.3830.2291.0000.0000.083
하자보증금액0.0240.0000.0000.0880.0000.0001.0000.000
감리대상여부0.3280.0000.0000.0000.0440.0830.0001.000
2023-12-12T15:00:07.050245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호계약금액정산금액계약보증금지체상금율사업기간하자보증금액감리대상여부
번호1.000-0.0460.0570.7290.754-0.1060.5720.252
계약금액-0.0461.0000.7420.2020.0780.4850.2360.000
정산금액0.0570.7421.0000.2270.0850.7260.3400.000
계약보증금0.7290.2020.2271.0000.9070.1010.7610.000
지체상금율0.7540.0780.0850.9071.000-0.0250.6760.050
사업기간-0.1060.4850.7260.101-0.0251.0000.2030.059
하자보증금액0.5720.2360.3400.7610.6760.2031.0000.000
감리대상여부0.2520.0000.0000.0000.0500.0590.0001.000

Missing values

2023-12-12T15:00:00.403276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:00:00.641976image/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.
2023-12-12T15:00:00.792997image/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

번호산림토목사업ID업체명산림토목사업명계약일계약금액정산금액계약보증금지체상금율사업기간사업시작일자사업종료일자준공일하자보증금액감리대상여부
01BFN2015D100122산림조합중앙회 ENG센터북부청 2015년도 임도 설계사업(기번 22)2011-01-136263000626300000.0302011-01-132011-02-122011-02-120Y
12BFN2015D100123산림조합중앙회 ENG센터북부청 2015년도 임도 설계사업(기번 23)2014-11-103406000340600000.0382014-11-102014-12-182014-12-180Y
23BFN2015D100124산림조합중앙회 ENG센터북부청 2015년도 임도 설계사업(기번 24)2014-11-10115070001150600000.0382014-11-102014-12-182014-12-180Y
34BFN2015D100125산림조합중앙회 ENG센터북부청 2015년도 임도 설계사업(기번 25)2014-11-104603000460300000.0382014-11-102014-12-182014-12-180Y
45BFN2015D100126다산산림기술사사무소북부청 2015년도 임도 설계사업(기번 26)2014-11-10138940001389400000.0382014-11-102014-12-182014-12-180Y
56BFN2015D100201푸른산림기술사동부청 2015년도 임도 설계사업(기번 1)2014-11-06182400001824000000.0402014-11-062014-12-162014-12-160Y
67BFN2015D100202사람과 숲 산림기술사동부청 2015년도 임도 설계사업(기번 2)2014-11-06106800001068000000.0402014-11-062014-12-162014-12-160Y
78BFN2015D100203창송엔지니어링동부청 2015년도 임도 설계사업(기번 3)2014-11-06120000001200000000.0402014-11-062014-12-162014-12-160Y
89BFN2015D100204창송엔지니어링동부청 2015년도 임도 설계사업(기번 4)2015-01-23183300001833000000.0242015-01-232015-02-162015-02-160Y
910BFN2015D100205창송엔지니어링동부청 2015년도 임도 설계사업(기번 5)2015-01-23183300001833000000.0242015-01-232015-02-162015-02-160Y
번호산림토목사업ID업체명산림토목사업명계약일계약금액정산금액계약보증금지체상금율사업기간사업시작일자사업종료일자준공일하자보증금액감리대상여부
36283926BFN2021C000708화란봉영농조합법인춘천2021년도 임도시공 추진계획2021-03-29330327880330327880247745900.052402021-04-022021-11-27<NA>0Y
36293927BFN2021C000711춘천시산림조합춘천2021년도 임도시공 추진계획2021-03-17304000000304000000228000000.052412021-03-222021-11-17<NA>0Y
36303928BFN2021C000709가평군산림춘천2021년도 임도시공 추진계획2021-03-17192600000192600000144450000.051802021-03-222021-09-17<NA>0Y
36313929BFN2021C000710화천군산림(조)춘천2021년도 임도시공 추진계획2021-03-17345850000345850000259387500.052402021-03-222021-11-16<NA>0Y
36323930BEN2021C004701정선군산림2021년 사방사업 추진계획(사방댐_중동골)2021-03-04164580000137265000151799250.05902021-03-082021-06-052021-06-165490600Y
36333931BEN2021C004801정선군산림2021년 사방사업 추진계획(계류보전_제당골)2021-03-04230300000118347000345450000.05902021-03-082021-06-052021-06-234733880Y
36343932BEN2021C004901화양기업2021년 사방사업 추진계획(산지사방_눈꽃마을)2021-03-17136336000130623000204504000.05902021-03-222021-06-192021-06-235453440Y
36353933BFN2021C000702산림조합중앙회산림사업본부춘천2021년도 임도시공 추진계획2021-03-17364890000364890000273667500.051802021-03-222021-09-17<NA>0Y
36363934BEN2021C002801화천군산림(조)2021년 사방사업(사방댐 외1_구운)2021-03-15200690000243694000301035000.05922021-03-152021-06-142021-06-237915360Y
36373935BEN2021S001701창송엔지니어링2021년 사방사업 시공감리용역2021-03-15447000006705000.12502021-03-162021-06-26<NA>0<NA>