Overview

Dataset statistics

Number of variables13
Number of observations1435
Missing cells644
Missing cells (%)3.5%
Duplicate rows59
Duplicate rows (%)4.1%
Total size in memory155.7 KiB
Average record size in memory111.1 B

Variable types

Numeric7
Categorical1
DateTime3
Text2

Dataset

Description산림사업용역관리시스템 내 사업수행업체의 조림, 숲가꾸기 사업의 계약체결 정보공사유림에 시행되는 조림, 숲가꾸기 정보
Author산림청
URLhttps://www.data.go.kr/data/15041978/fileData.do

Alerts

Dataset has 59 (4.1%) duplicate rowsDuplicates
용역사업번호 is highly overall correlated with 계약금액 and 1 other fieldsHigh correlation
계약금액 is highly overall correlated with 용역사업번호 and 1 other fieldsHigh correlation
계약보증금 is highly overall correlated with 용역사업번호 and 1 other fieldsHigh correlation
계약체결방법 is highly imbalanced (60.5%)Imbalance
생산재적 has 64 (4.5%) missing valuesMissing
용역사업낙착율 has 580 (40.4%) missing valuesMissing
총사업일수 has 15 (1.0%) zerosZeros
계약금액 has 93 (6.5%) zerosZeros
계획면적 has 102 (7.1%) zerosZeros
생산재적 has 834 (58.1%) zerosZeros

Reproduction

Analysis started2023-12-12 14:18:06.565282
Analysis finished2023-12-12 14:18:13.687921
Duration7.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

용역사업번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1059
Distinct (%)73.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.761376 × 108
Minimum1.2015 × 108
Maximum9.2019047 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-12T23:18:13.756710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2015 × 108
5-th percentile1.2019002 × 108
Q12.2017014 × 108
median2.2019005 × 108
Q39.2018035 × 108
95-th percentile9.2019025 × 108
Maximum9.2019047 × 108
Range8.0004046 × 108
Interquartile range (IQR)7.0001021 × 108

Descriptive statistics

Standard deviation3.4315441 × 108
Coefficient of variation (CV)0.72070429
Kurtosis-1.7267783
Mean4.761376 × 108
Median Absolute Deviation (MAD)39929
Skewness0.48865532
Sum6.8325745 × 1011
Variance1.1775495 × 1017
MonotonicityNot monotonic
2023-12-12T23:18:13.894406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220180175 11
 
0.8%
920180313 9
 
0.6%
220180222 9
 
0.6%
920180298 7
 
0.5%
220180279 7
 
0.5%
220180276 6
 
0.4%
920180257 6
 
0.4%
220190083 5
 
0.3%
920180050 5
 
0.3%
220180281 5
 
0.3%
Other values (1049) 1365
95.1%
ValueCountFrequency (%)
120150001 1
0.1%
120150002 1
0.1%
120150004 1
0.1%
120150008 1
0.1%
120150009 1
0.1%
120150010 1
0.1%
120150014 1
0.1%
120150016 1
0.1%
120150017 1
0.1%
120150020 1
0.1%
ValueCountFrequency (%)
920190466 1
0.1%
920190465 1
0.1%
920190448 1
0.1%
920190443 1
0.1%
920190442 1
0.1%
920190436 1
0.1%
920190435 1
0.1%
920190434 1
0.1%
920190433 1
0.1%
920190432 1
0.1%

계약체결방법
Categorical

IMBALANCE 

Distinct36
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
수의계약
806 
수의계약,국유림영림단
352 
수의계약,선택
100 
수의
 
52
수의계약,대리경영-국유림관리소장
 
35
Other values (31)
90 

Length

Max length17
Median length4
Mean length6.3825784
Min length2

Unique

Unique17 ?
Unique (%)1.2%

Sample

1st row수의계약,대리경영-국유림관리소장
2nd row수의계약,대리경영-임업인
3rd row수의계약,선택
4th row수의계약
5th row수의계약

Common Values

ValueCountFrequency (%)
수의계약 806
56.2%
수의계약,국유림영림단 352
24.5%
수의계약,선택 100
 
7.0%
수의 52
 
3.6%
수의계약,대리경영-국유림관리소장 35
 
2.4%
공개경쟁입찰 22
 
1.5%
수의계약,대리경영-임업인 11
 
0.8%
수의계약,산림조합 9
 
0.6%
g2b 7
 
0.5%
G2B전자계약 4
 
0.3%
Other values (26) 37
 
2.6%

Length

2023-12-12T23:18:14.019774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수의계약 809
56.2%
수의계약,국유림영림단 352
24.4%
수의계약,선택 100
 
6.9%
수의 52
 
3.6%
수의계약,대리경영-국유림관리소장 35
 
2.4%
공개경쟁입찰 22
 
1.5%
g2b 12
 
0.8%
수의계약,대리경영-임업인 11
 
0.8%
수의계약,산림조합 9
 
0.6%
g2b전자계약 6
 
0.4%
Other values (21) 32
 
2.2%

계약일
Date

Distinct472
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
Minimum2014-12-03 00:00:00
Maximum2019-09-11 00:00:00
2023-12-12T23:18:14.119318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:14.230069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사업시작일
Date

Distinct448
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
Minimum2014-08-20 00:00:00
Maximum2019-09-16 00:00:00
2023-12-12T23:18:14.342630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:14.452444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사업종료일
Date

Distinct533
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
Minimum2014-09-28 00:00:00
Maximum2019-10-21 00:00:00
2023-12-12T23:18:14.562887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:14.672975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

총사업일수
Real number (ℝ)

ZEROS 

Distinct121
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.910105
Minimum0
Maximum163
Zeros15
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-12T23:18:14.782535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q115
median25
Q335
95-th percentile94
Maximum163
Range163
Interquartile range (IQR)20

Descriptive statistics

Standard deviation26.844604
Coefficient of variation (CV)0.84125717
Kurtosis5.0968828
Mean31.910105
Median Absolute Deviation (MAD)10
Skewness2.1721709
Sum45791
Variance720.63278
MonotonicityNot monotonic
2023-12-12T23:18:14.895330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 159
 
11.1%
30 152
 
10.6%
20 150
 
10.5%
25 125
 
8.7%
10 59
 
4.1%
40 47
 
3.3%
21 44
 
3.1%
7 43
 
3.0%
14 35
 
2.4%
60 34
 
2.4%
Other values (111) 587
40.9%
ValueCountFrequency (%)
0 15
 
1.0%
1 11
 
0.8%
2 5
 
0.3%
3 4
 
0.3%
4 3
 
0.2%
5 7
 
0.5%
6 6
 
0.4%
7 43
3.0%
8 9
 
0.6%
9 7
 
0.5%
ValueCountFrequency (%)
163 1
 
0.1%
155 1
 
0.1%
154 1
 
0.1%
151 3
0.2%
148 1
 
0.1%
145 1
 
0.1%
141 2
0.1%
138 1
 
0.1%
136 1
 
0.1%
134 2
0.1%

전자계약번호
Text

Distinct1100
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
2023-12-12T23:18:15.085055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length13.269686
Min length6

Characters and Unicode

Total characters19042
Distinct characters40
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique888 ?
Unique (%)61.9%

Sample

1st row2018050EDC6-03
2nd row20180803c79-02
3rd row2018060CF64-05
4th row201810068C1-00
5th row201810068C1-02
ValueCountFrequency (%)
2.02e+09 13
 
0.9%
20180505f97-00 11
 
0.8%
201809028d8-01 8
 
0.6%
20181205a0e-00 6
 
0.4%
20150321568 6
 
0.4%
20181202c78-00 5
 
0.3%
20181202433-00 5
 
0.3%
2019010f467-00 5
 
0.3%
20190505425-00 5
 
0.3%
201811121b4-00 5
 
0.3%
Other values (1091) 1369
95.2%
2023-12-12T23:18:15.450938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6082
31.9%
1 2761
14.5%
2 2121
 
11.1%
- 1180
 
6.2%
8 1173
 
6.2%
9 880
 
4.6%
5 829
 
4.4%
6 649
 
3.4%
3 588
 
3.1%
7 536
 
2.8%
Other values (30) 2243
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16143
84.8%
Uppercase Letter 1565
 
8.2%
Dash Punctuation 1180
 
6.2%
Lowercase Letter 80
 
0.4%
Other Punctuation 26
 
0.1%
Math Symbol 26
 
0.1%
Other Letter 19
 
0.1%
Space Separator 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ㄹ 2
10.5%
전 2
10.5%
자 2
10.5%
계 2
10.5%
약 2
10.5%
번 2
10.5%
호 2
10.5%
ㅊ 1
5.3%
정 1
5.3%
선 1
5.3%
Other values (2) 2
10.5%
Decimal Number
ValueCountFrequency (%)
0 6082
37.7%
1 2761
17.1%
2 2121
 
13.1%
8 1173
 
7.3%
9 880
 
5.5%
5 829
 
5.1%
6 649
 
4.0%
3 588
 
3.6%
7 536
 
3.3%
4 524
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
C 298
19.0%
F 284
18.1%
E 251
16.0%
A 248
15.8%
B 247
15.8%
D 233
14.9%
O 3
 
0.2%
S 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
f 18
22.5%
c 18
22.5%
d 14
17.5%
e 13
16.2%
a 9
11.2%
b 8
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 1180
100.0%
Other Punctuation
ValueCountFrequency (%)
. 26
100.0%
Math Symbol
ValueCountFrequency (%)
+ 26
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17378
91.3%
Latin 1645
 
8.6%
Hangul 19
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6082
35.0%
1 2761
15.9%
2 2121
 
12.2%
- 1180
 
6.8%
8 1173
 
6.7%
9 880
 
5.1%
5 829
 
4.8%
6 649
 
3.7%
3 588
 
3.4%
7 536
 
3.1%
Other values (4) 579
 
3.3%
Latin
ValueCountFrequency (%)
C 298
18.1%
F 284
17.3%
E 251
15.3%
A 248
15.1%
B 247
15.0%
D 233
14.2%
f 18
 
1.1%
c 18
 
1.1%
d 14
 
0.9%
e 13
 
0.8%
Other values (4) 21
 
1.3%
Hangul
ValueCountFrequency (%)
ㄹ 2
10.5%
전 2
10.5%
자 2
10.5%
계 2
10.5%
약 2
10.5%
번 2
10.5%
호 2
10.5%
ㅊ 1
5.3%
정 1
5.3%
선 1
5.3%
Other values (2) 2
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19023
99.9%
Hangul 16
 
0.1%
Compat Jamo 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6082
32.0%
1 2761
14.5%
2 2121
 
11.1%
- 1180
 
6.2%
8 1173
 
6.2%
9 880
 
4.6%
5 829
 
4.4%
6 649
 
3.4%
3 588
 
3.1%
7 536
 
2.8%
Other values (18) 2224
 
11.7%
Compat Jamo
ValueCountFrequency (%)
ㄹ 2
66.7%
ㅊ 1
33.3%
Hangul
ValueCountFrequency (%)
전 2
12.5%
자 2
12.5%
계 2
12.5%
약 2
12.5%
번 2
12.5%
호 2
12.5%
정 1
6.2%
선 1
6.2%
공 1
6.2%
사 1
6.2%

계약자명
Text

Distinct161
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
2023-12-12T23:18:15.787217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length7.6682927
Min length4

Characters and Unicode

Total characters11004
Distinct characters173
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)2.7%

Sample

1st row오안기계화영림단
2nd row주천영림단
3rd row상진임업
4th row(주) 한숲
5th row(주) 한숲
ValueCountFrequency (%)
주 97
 
5.4%
한숲 97
 
5.4%
산림기술사사무소 72
 
4.0%
용역업체 65
 
3.6%
주식회사 49
 
2.7%
주)만송엔지니어링 45
 
2.5%
대림산림기술사사무소 45
 
2.5%
숲사랑 41
 
2.3%
eng 41
 
2.3%
산하산림기술사사무소 40
 
2.2%
Other values (164) 1201
67.0%
2023-12-12T23:18:16.295126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
림 984
 
8.9%
사 833
 
7.6%
산 588
 
5.3%
기 533
 
4.8%
술 368
 
3.3%
영 366
 
3.3%
358
 
3.3%
단 344
 
3.1%
주 340
 
3.1%
무 340
 
3.1%
Other values (163) 5950
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9812
89.2%
Space Separator 358
 
3.3%
Uppercase Letter 267
 
2.4%
Open Punctuation 265
 
2.4%
Close Punctuation 265
 
2.4%
Decimal Number 34
 
0.3%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
림 984
 
10.0%
사 833
 
8.5%
산 588
 
6.0%
기 533
 
5.4%
술 368
 
3.8%
영 366
 
3.7%
단 344
 
3.5%
주 340
 
3.5%
무 340
 
3.5%
소 328
 
3.3%
Other values (148) 4788
48.8%
Decimal Number
ValueCountFrequency (%)
2 12
35.3%
3 7
20.6%
4 6
17.6%
5 5
14.7%
7 2
 
5.9%
1 2
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
E 89
33.3%
N 89
33.3%
G 89
33.3%
Lowercase Letter
ValueCountFrequency (%)
g 1
33.3%
e 1
33.3%
n 1
33.3%
Space Separator
ValueCountFrequency (%)
358
100.0%
Open Punctuation
ValueCountFrequency (%)
( 265
100.0%
Close Punctuation
ValueCountFrequency (%)
) 265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9812
89.2%
Common 922
 
8.4%
Latin 270
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
림 984
 
10.0%
사 833
 
8.5%
산 588
 
6.0%
기 533
 
5.4%
술 368
 
3.8%
영 366
 
3.7%
단 344
 
3.5%
주 340
 
3.5%
무 340
 
3.5%
소 328
 
3.3%
Other values (148) 4788
48.8%
Common
ValueCountFrequency (%)
358
38.8%
( 265
28.7%
) 265
28.7%
2 12
 
1.3%
3 7
 
0.8%
4 6
 
0.7%
5 5
 
0.5%
7 2
 
0.2%
1 2
 
0.2%
Latin
ValueCountFrequency (%)
E 89
33.0%
N 89
33.0%
G 89
33.0%
g 1
 
0.4%
e 1
 
0.4%
n 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9812
89.2%
ASCII 1192
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
림 984
 
10.0%
사 833
 
8.5%
산 588
 
6.0%
기 533
 
5.4%
술 368
 
3.8%
영 366
 
3.7%
단 344
 
3.5%
주 340
 
3.5%
무 340
 
3.5%
소 328
 
3.3%
Other values (148) 4788
48.8%
ASCII
ValueCountFrequency (%)
358
30.0%
( 265
22.2%
) 265
22.2%
E 89
 
7.5%
N 89
 
7.5%
G 89
 
7.5%
2 12
 
1.0%
3 7
 
0.6%
4 6
 
0.5%
5 5
 
0.4%
Other values (5) 7
 
0.6%

계약금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct991
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33791292
Minimum0
Maximum5 × 108
Zeros93
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-12T23:18:16.840475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16545000
median12370000
Q344356000
95-th percentile1.362717 × 108
Maximum5 × 108
Range5 × 108
Interquartile range (IQR)37811000

Descriptive statistics

Standard deviation46275817
Coefficient of variation (CV)1.3694598
Kurtosis9.9274059
Mean33791292
Median Absolute Deviation (MAD)7680000
Skewness2.4421359
Sum4.8490505 × 1010
Variance2.1414513 × 1015
MonotonicityNot monotonic
2023-12-12T23:18:17.001215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93
 
6.5%
5000000 12
 
0.8%
48100000 7
 
0.5%
10000000 7
 
0.5%
100000000 7
 
0.5%
5500000 6
 
0.4%
6200000 6
 
0.4%
6000000 6
 
0.4%
87400000 6
 
0.4%
12200000 6
 
0.4%
Other values (981) 1279
89.1%
ValueCountFrequency (%)
0 93
6.5%
100 1
 
0.1%
3000 1
 
0.1%
10000 1
 
0.1%
12356 1
 
0.1%
500000 1
 
0.1%
690000 2
 
0.1%
805000 2
 
0.1%
1000000 2
 
0.1%
1230000 2
 
0.1%
ValueCountFrequency (%)
500000000 1
 
0.1%
290800000 1
 
0.1%
264930000 1
 
0.1%
248380000 1
 
0.1%
247870000 1
 
0.1%
227588000 1
 
0.1%
220400000 2
0.1%
196170000 1
 
0.1%
192430000 3
0.2%
191750000 1
 
0.1%

계약보증금
Real number (ℝ)

HIGH CORRELATION 

Distinct839
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5560178.1
Minimum0
Maximum2.0145 × 108
Zeros10
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-12T23:18:17.165867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile412500
Q11077000
median1944000
Q36783000
95-th percentile20790000
Maximum2.0145 × 108
Range2.0145 × 108
Interquartile range (IQR)5706000

Descriptive statistics

Standard deviation10107526
Coefficient of variation (CV)1.8178421
Kurtosis125.60832
Mean5560178.1
Median Absolute Deviation (MAD)1119000
Skewness8.4709551
Sum7.9788556 × 109
Variance1.0216208 × 1014
MonotonicityNot monotonic
2023-12-12T23:18:17.386406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500000 12
 
0.8%
1350000 11
 
0.8%
2346000 11
 
0.8%
0 10
 
0.7%
7215000 9
 
0.6%
1032000 9
 
0.6%
13110000 7
 
0.5%
10000000 7
 
0.5%
2055000 7
 
0.5%
1869000 7
 
0.5%
Other values (829) 1345
93.7%
ValueCountFrequency (%)
0 10
0.7%
1 3
 
0.2%
4640 1
 
0.1%
50000 1
 
0.1%
69000 2
 
0.1%
76000 3
 
0.2%
100000 2
 
0.1%
120750 2
 
0.1%
123456 1
 
0.1%
132500 1
 
0.1%
ValueCountFrequency (%)
201450000 1
 
0.1%
124334000 1
 
0.1%
100810950 2
0.1%
90934500 1
 
0.1%
50000000 2
0.1%
43620000 1
 
0.1%
39739500 1
 
0.1%
37257000 1
 
0.1%
37180500 1
 
0.1%
35000000 4
0.3%

계획면적
Real number (ℝ)

ZEROS 

Distinct656
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2173.2606
Minimum0
Maximum482000
Zeros102
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-12T23:18:17.596068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127.8
median60.2
Q3115
95-th percentile237.99
Maximum482000
Range482000
Interquartile range (IQR)87.2

Descriptive statistics

Standard deviation21840.114
Coefficient of variation (CV)10.049468
Kurtosis281.69095
Mean2173.2606
Median Absolute Deviation (MAD)38.1
Skewness15.416222
Sum3118628.9
Variance4.7699057 × 108
MonotonicityNot monotonic
2023-12-12T23:18:17.803375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 102
 
7.1%
20.0 14
 
1.0%
26.0 12
 
0.8%
58.0 12
 
0.8%
18.0 11
 
0.8%
24.0 11
 
0.8%
21.0 11
 
0.8%
3000.0 9
 
0.6%
38.0 9
 
0.6%
17.0 9
 
0.6%
Other values (646) 1235
86.1%
ValueCountFrequency (%)
0.0 102
7.1%
1.6 1
 
0.1%
2.0 2
 
0.1%
3.0 2
 
0.1%
4.1 2
 
0.1%
4.8 1
 
0.1%
5.0 5
 
0.3%
6.0 1
 
0.1%
6.8 2
 
0.1%
7.2 3
 
0.2%
ValueCountFrequency (%)
482000.0 1
 
0.1%
418699.99 1
 
0.1%
268000.0 1
 
0.1%
190500.0 2
0.1%
170700.0 1
 
0.1%
119500.0 1
 
0.1%
115100.0 2
0.1%
114500.0 1
 
0.1%
108000.0 1
 
0.1%
96000.0 3
0.2%

생산재적
Real number (ℝ)

MISSING  ZEROS 

Distinct335
Distinct (%)24.4%
Missing64
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean91.871444
Minimum0
Maximum1666.08
Zeros834
Zeros (%)58.1%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-12T23:18:17.979840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q340.85
95-th percentile727.615
Maximum1666.08
Range1666.08
Interquartile range (IQR)40.85

Descriptive statistics

Standard deviation238.19891
Coefficient of variation (CV)2.5927415
Kurtosis12.087616
Mean91.871444
Median Absolute Deviation (MAD)0
Skewness3.3975393
Sum125955.75
Variance56738.718
MonotonicityNot monotonic
2023-12-12T23:18:18.156712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 834
58.1%
20.0 10
 
0.7%
36.0 7
 
0.5%
287.63 7
 
0.5%
21.0 6
 
0.4%
27.0 6
 
0.4%
32.0 6
 
0.4%
637.13 6
 
0.4%
10.0 6
 
0.4%
38.0 5
 
0.3%
Other values (325) 478
33.3%
(Missing) 64
 
4.5%
ValueCountFrequency (%)
0.0 834
58.1%
2.0 4
 
0.3%
2.5 2
 
0.1%
3.0 1
 
0.1%
3.6 1
 
0.1%
4.5 2
 
0.1%
5.0 5
 
0.3%
5.8 2
 
0.1%
6.0 5
 
0.3%
6.9 2
 
0.1%
ValueCountFrequency (%)
1666.08 1
 
0.1%
1585.62 2
0.1%
1509.6 3
0.2%
1292.23 2
0.1%
1195.58 1
 
0.1%
1184.05 1
 
0.1%
1177.23 1
 
0.1%
1155.71 1
 
0.1%
1126.71 1
 
0.1%
1116.8 1
 
0.1%

용역사업낙착율
Real number (ℝ)

MISSING 

Distinct520
Distinct (%)60.8%
Missing580
Missing (%)40.4%
Infinite0
Infinite (%)0.0%
Mean95.993037
Minimum0
Maximum1003.225
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-12T23:18:18.314048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile80.3203
Q188.636
median89.217
Q389.927
95-th percentile112.402
Maximum1003.225
Range1003.225
Interquartile range (IQR)1.291

Descriptive statistics

Standard deviation53.694974
Coefficient of variation (CV)0.55936321
Kurtosis121.83785
Mean95.993037
Median Absolute Deviation (MAD)0.681
Skewness9.4804566
Sum82074.047
Variance2883.1502
MonotonicityNot monotonic
2023-12-12T23:18:18.493485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 16
 
1.1%
90.409 11
 
0.8%
88.636 9
 
0.6%
88.892 8
 
0.6%
89.0 7
 
0.5%
89.091 7
 
0.5%
89.18 6
 
0.4%
88.991 6
 
0.4%
88.93 6
 
0.4%
88.996 6
 
0.4%
Other values (510) 773
53.9%
(Missing) 580
40.4%
ValueCountFrequency (%)
0.0 1
 
0.1%
1.039 1
 
0.1%
7.32 1
 
0.1%
7.581 1
 
0.1%
8.994 1
 
0.1%
13.484 2
0.1%
31.297 1
 
0.1%
35.301 1
 
0.1%
42.838 4
0.3%
46.397 3
0.2%
ValueCountFrequency (%)
1003.225 1
0.1%
614.609 1
0.1%
517.826 1
0.1%
515.42 1
0.1%
498.162 1
0.1%
433.546 1
0.1%
415.672 1
0.1%
366.166 1
0.1%
363.007 1
0.1%
280.246 1
0.1%

Interactions

2023-12-12T23:18:12.408128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:07.120603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:07.922938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:08.822525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:09.655487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:10.830019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:11.615732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:12.517875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:07.217704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:08.116687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:08.929877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:09.778308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:10.945634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:11.741451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:12.663634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:07.324452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:08.266377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:09.034199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:09.902739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:11.050853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:11.869283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:12.790158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:07.445029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:08.385091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:09.177847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:10.009143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:11.153812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:11.995593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:12.898315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:07.569276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:08.506326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:09.288582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:10.113989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:11.303186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:12.107160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:13.026700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:07.677217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:08.604522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:09.398702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:10.237702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:11.423240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:12.206402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:13.144545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:07.796172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:08.708075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:09.532092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:10.692694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:11.518441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:18:12.308935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:18:18.590129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용역사업번호계약체결방법총사업일수계약금액계약보증금계획면적생산재적용역사업낙착율
용역사업번호1.0000.7710.4910.4680.2040.0800.3820.199
계약체결방법0.7711.0000.5130.5400.3470.7580.4890.562
총사업일수0.4910.5131.0000.5560.3850.0000.5360.096
계약금액0.4680.5400.5561.0000.8190.0000.732NaN
계약보증금0.2040.3470.3850.8191.0000.0000.405NaN
계획면적0.0800.7580.0000.0000.0001.000NaNNaN
생산재적0.3820.4890.5360.7320.405NaN1.0000.000
용역사업낙착율0.1990.5620.096NaNNaNNaN0.0001.000
2023-12-12T23:18:18.715622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용역사업번호총사업일수계약금액계약보증금계획면적생산재적용역사업낙착율계약체결방법
용역사업번호1.0000.3600.5920.593-0.3980.0440.0240.429
총사업일수0.3601.0000.4690.466-0.0010.4030.0610.204
계약금액0.5920.4691.0000.8960.0890.1960.0170.251
계약보증금0.5930.4660.8961.000-0.0490.130-0.0140.147
계획면적-0.398-0.0010.089-0.0491.000-0.018-0.1280.426
생산재적0.0440.4030.1960.130-0.0181.0000.0190.203
용역사업낙착율0.0240.0610.017-0.014-0.1280.0191.0000.231
계약체결방법0.4290.2040.2510.1470.4260.2030.2311.000

Missing values

2023-12-12T23:18:13.288701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:18:13.520653image/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.
2023-12-12T23:18:13.630487image/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

용역사업번호계약체결방법계약일사업시작일사업종료일총사업일수전자계약번호계약자명계약금액계약보증금계획면적생산재적용역사업낙착율
0920180170수의계약,대리경영-국유림관리소장2018-05-252018-05-292018-09-271222018050EDC6-03오안기계화영림단606610001043700032.7143.2<NA>
1920180450수의계약,대리경영-임업인2018-08-132018-08-142018-09-092720180803c79-02주천영림단20046000303900030.70.0<NA>
2920180298수의계약,선택2018-06-262018-06-262018-11-061342018060CF64-05상진임업1005110001311000018.0735.87<NA>
3220180247수의계약2018-10-122018-10-122018-11-0525201810068C1-00(주) 한숲148450002226750150.4104.389.898
4220180247수의계약2018-10-122018-10-122018-11-0525201810068C1-02(주) 한숲154990002226750162.370.389.898
5220180238수의계약2018-10-192018-10-222018-11-30402018100B650-01주식회사임하8125000127950089.20.089.95
6920180666수의계약,국유림영림단2018-10-252018-10-252018-10-2952018100F01F-00양록기계화영림단890000013350008.70.0<NA>
7920180637수의계약,국유림영림단2018-11-272018-11-272018-12-162020181110AC6무주기계화국유림영림단37113000556695023.00.0<NA>
8920180355수의계약,국유림영림단2018-06-122018-06-142018-08-12602018060584D-00오안기계화영림단15345000023017500114.90.0<NA>
9920180673수의계약,산림조합2018-07-062018-07-062018-09-036020180702AC1산림조합중앙회서울인천경기지역본부11740000017610000130.00.0<NA>
용역사업번호계약체결방법계약일사업시작일사업종료일총사업일수전자계약번호계약자명계약금액계약보증금계획면적생산재적용역사업낙착율
1425920190269수의계약,선택2019-06-212019-06-242019-09-03722019060CBFC-00건국임업50980000764700027.1147.6<NA>
1426920190217수의계약,대리경영-국유림관리소장2019-06-052019-06-052019-06-151120190500430-01주천영림단692310001030200038.00.0<NA>
1427920190214수의계약,선택2019-04-302019-04-302019-06-25572019041805D-01영월영림단781200001162500037.30.0<NA>
1428220190231수의계약2019-08-302019-09-022019-09-27251231312-00용역업체3000000030000004000.0<NA><NA>
1429920190389수의계약,국유림영림단2019-06-072019-06-102019-08-08602019060384C-00대교영림19617000029425500140.30.0<NA>
1430220190232수의계약2019-08-302019-09-022019-09-2725023123123-00용역업체4000000040000003000.0<NA><NA>
1431220190233수의계약2019-08-302019-09-022019-09-272512312315-00용역업체1000000010000007000.0<NA><NA>
1432220190250공개경쟁입찰2019-08-012019-08-012019-08-313020190801-021용역업체50000005000005000.0<NA><NA>
1433220190251공개경쟁입찰2019-08-012019-08-012019-08-313020190801-022용역업체50000005000005000.0<NA><NA>
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Duplicate rows

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