Overview

Dataset statistics

Number of variables10
Number of observations500
Missing cells493
Missing cells (%)9.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.6 KiB
Average record size in memory85.3 B

Variable types

Text2
DateTime1
Numeric5
Boolean1
Categorical1

Dataset

Description해당 파일은 신용보증기금이 운용하는 경영지원 서비스 중 경영컨설팅 신청에 관한 데이터로, 컨설팅비용, 지원금액 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15121505/fileData.do

Alerts

펌닥터여부 is highly overall correlated with 총컨설팅비용 and 1 other fieldsHigh correlation
우대기업상세내용 is highly overall correlated with 총컨설팅비용 and 5 other fieldsHigh correlation
총컨설팅비용 is highly overall correlated with 신용보증기금지원금액 and 3 other fieldsHigh correlation
신용보증기금부담비율 is highly overall correlated with 우대기업상세내용High correlation
신용보증기금지원금액 is highly overall correlated with 총컨설팅비용 and 2 other fieldsHigh correlation
컨설팅수행년도 is highly overall correlated with 우대기업상세내용High correlation
실제지급금액 is highly overall correlated with 총컨설팅비용 and 2 other fieldsHigh correlation
펌닥터여부 is highly imbalanced (88.2%)Imbalance
우대기업상세내용 is highly imbalanced (88.4%)Imbalance
기업특성상세내용 has 481 (96.2%) missing valuesMissing
컨설팅수행년도 has 12 (2.4%) missing valuesMissing
경영컨설팅신청ID has unique valuesUnique
총컨설팅비용 has 61 (12.2%) zerosZeros
신용보증기금부담비율 has 52 (10.4%) zerosZeros
신용보증기금지원금액 has 64 (12.8%) zerosZeros
실제지급금액 has 99 (19.8%) zerosZeros

Reproduction

Analysis started2023-12-12 14:34:05.834699
Analysis finished2023-12-12 14:34:09.017827
Duration3.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

경영컨설팅신청ID
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T23:34:09.283900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters5000
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st row9bSVsxNHFe
2nd row9b08XI1OJL
3rd row9cDXuwHwwD
4th row9bQhirQjyc
5th row9cHPXJEAsL
ValueCountFrequency (%)
9bsvsxnhfe 1
 
0.2%
9chxdvpuhw 1
 
0.2%
9clzrqfokp 1
 
0.2%
9cso1vryqr 1
 
0.2%
9ch4fxi1ek 1
 
0.2%
9cefoosoee 1
 
0.2%
9b81lcxyqd 1
 
0.2%
9b81k9s4vt 1
 
0.2%
9b81llbjqy 1
 
0.2%
9chw693auf 1
 
0.2%
Other values (490) 490
98.0%
2023-12-12T23:34:09.814900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 577
 
11.5%
c 412
 
8.2%
b 222
 
4.4%
g 87
 
1.7%
D 83
 
1.7%
E 80
 
1.6%
j 80
 
1.6%
a 79
 
1.6%
X 76
 
1.5%
Q 74
 
1.5%
Other values (52) 3230
64.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2152
43.0%
Uppercase Letter 1716
34.3%
Decimal Number 1132
22.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 412
19.1%
b 222
 
10.3%
g 87
 
4.0%
j 80
 
3.7%
a 79
 
3.7%
q 72
 
3.3%
w 71
 
3.3%
i 69
 
3.2%
s 69
 
3.2%
h 67
 
3.1%
Other values (16) 924
42.9%
Uppercase Letter
ValueCountFrequency (%)
D 83
 
4.8%
E 80
 
4.7%
X 76
 
4.4%
Q 74
 
4.3%
C 73
 
4.3%
K 73
 
4.3%
Z 73
 
4.3%
N 72
 
4.2%
J 72
 
4.2%
I 72
 
4.2%
Other values (16) 968
56.4%
Decimal Number
ValueCountFrequency (%)
9 577
51.0%
3 69
 
6.1%
8 68
 
6.0%
6 67
 
5.9%
1 65
 
5.7%
0 63
 
5.6%
2 60
 
5.3%
4 58
 
5.1%
7 57
 
5.0%
5 48
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 3868
77.4%
Common 1132
 
22.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 412
 
10.7%
b 222
 
5.7%
g 87
 
2.2%
D 83
 
2.1%
E 80
 
2.1%
j 80
 
2.1%
a 79
 
2.0%
X 76
 
2.0%
Q 74
 
1.9%
C 73
 
1.9%
Other values (42) 2602
67.3%
Common
ValueCountFrequency (%)
9 577
51.0%
3 69
 
6.1%
8 68
 
6.0%
6 67
 
5.9%
1 65
 
5.7%
0 63
 
5.6%
2 60
 
5.3%
4 58
 
5.1%
7 57
 
5.0%
5 48
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 577
 
11.5%
c 412
 
8.2%
b 222
 
4.4%
g 87
 
1.7%
D 83
 
1.7%
E 80
 
1.6%
j 80
 
1.6%
a 79
 
1.6%
X 76
 
1.5%
Q 74
 
1.5%
Other values (52) 3230
64.6%

입력일자
Date

Distinct356
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Minimum2008-02-20 00:00:00
Maximum2018-12-26 00:00:00
2023-12-12T23:34:10.005021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:10.177197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

총컨설팅비용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2822540
Minimum0
Maximum15700000
Zeros61
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:34:10.326399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12400000
median3000000
Q33000000
95-th percentile4500000
Maximum15700000
Range15700000
Interquartile range (IQR)600000

Descriptive statistics

Standard deviation1905400.4
Coefficient of variation (CV)0.67506588
Kurtosis10.519446
Mean2822540
Median Absolute Deviation (MAD)0
Skewness2.1971013
Sum1.41127 × 109
Variance3.6305509 × 1012
MonotonicityNot monotonic
2023-12-12T23:34:10.489761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3000000 285
57.0%
0 61
 
12.2%
3900000 35
 
7.0%
2400000 33
 
6.6%
600000 20
 
4.0%
4500000 16
 
3.2%
2100000 8
 
1.6%
900000 8
 
1.6%
10000000 5
 
1.0%
6000000 5
 
1.0%
Other values (15) 24
 
4.8%
ValueCountFrequency (%)
0 61
 
12.2%
600000 20
 
4.0%
900000 8
 
1.6%
1100000 3
 
0.6%
1800000 3
 
0.6%
2100000 8
 
1.6%
2400000 33
 
6.6%
2700000 2
 
0.4%
2970000 1
 
0.2%
3000000 285
57.0%
ValueCountFrequency (%)
15700000 1
 
0.2%
12500000 1
 
0.2%
12300000 2
 
0.4%
11000000 1
 
0.2%
10200000 3
0.6%
10000000 5
1.0%
9900000 1
 
0.2%
8500000 1
 
0.2%
6500000 1
 
0.2%
6000000 5
1.0%

신용보증기금부담비율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.48
Minimum0
Maximum100
Zeros52
Zeros (%)10.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:34:10.957301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q190
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation30.923812
Coefficient of variation (CV)0.36176664
Kurtosis3.2062763
Mean85.48
Median Absolute Deviation (MAD)0
Skewness-2.1650982
Sum42740
Variance956.28216
MonotonicityNot monotonic
2023-12-12T23:34:11.112944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
100 370
74.0%
0 52
 
10.4%
80 35
 
7.0%
70 18
 
3.6%
60 16
 
3.2%
90 7
 
1.4%
40 1
 
0.2%
50 1
 
0.2%
ValueCountFrequency (%)
0 52
 
10.4%
40 1
 
0.2%
50 1
 
0.2%
60 16
 
3.2%
70 18
 
3.6%
80 35
 
7.0%
90 7
 
1.4%
100 370
74.0%
ValueCountFrequency (%)
100 370
74.0%
90 7
 
1.4%
80 35
 
7.0%
70 18
 
3.6%
60 16
 
3.2%
50 1
 
0.2%
40 1
 
0.2%
0 52
 
10.4%

신용보증기금지원금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2602140
Minimum0
Maximum10000000
Zeros64
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:34:11.261725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12100000
median3000000
Q33000000
95-th percentile3900000
Maximum10000000
Range10000000
Interquartile range (IQR)900000

Descriptive statistics

Standard deviation1556380.1
Coefficient of variation (CV)0.59811544
Kurtosis5.1598957
Mean2602140
Median Absolute Deviation (MAD)0
Skewness1.0555159
Sum1.30107 × 109
Variance2.4223191 × 1012
MonotonicityNot monotonic
2023-12-12T23:34:11.439788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
3000000 252
50.4%
0 64
 
12.8%
2400000 39
 
7.8%
3900000 31
 
6.2%
600000 20
 
4.0%
2100000 16
 
3.2%
1800000 12
 
2.4%
2700000 10
 
2.0%
900000 8
 
1.6%
3600000 7
 
1.4%
Other values (23) 41
 
8.2%
ValueCountFrequency (%)
0 64
12.8%
600000 20
 
4.0%
900000 8
 
1.6%
1100000 3
 
0.6%
1200000 1
 
0.2%
1440000 1
 
0.2%
1500000 1
 
0.2%
1800000 12
 
2.4%
2100000 16
 
3.2%
2160000 1
 
0.2%
ValueCountFrequency (%)
10000000 1
 
0.2%
9900000 1
 
0.2%
9840000 2
 
0.4%
8160000 2
 
0.4%
8000000 7
1.4%
7920000 1
 
0.2%
5950000 1
 
0.2%
4800000 3
0.6%
4550000 1
 
0.2%
4500000 1
 
0.2%

펌닥터여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
492 
True
 
8
ValueCountFrequency (%)
False 492
98.4%
True 8
 
1.6%
2023-12-12T23:34:11.563261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

기업특성상세내용
Text

MISSING 

Distinct16
Distinct (%)84.2%
Missing481
Missing (%)96.2%
Memory size4.0 KiB
2023-12-12T23:34:11.759724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.2631579
Min length2

Characters and Unicode

Total characters100
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)73.7%

Sample

1st row경영혁신
2nd row투자목적
3rd row수출중소기업
4th row경영혁신
5th row창업
ValueCountFrequency (%)
유망창업기업 3
15.8%
경영혁신 2
 
10.5%
우망창업 1
 
5.3%
개성공단기업 1
 
5.3%
경쟁력향상 1
 
5.3%
경영혁신형중소기업 1
 
5.3%
창업기업 1
 
5.3%
가젤기업 1
 
5.3%
청년창업기업 1
 
5.3%
예비재창업 1
 
5.3%
Other values (6) 6
31.6%
2023-12-12T23:34:12.168992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 19
19.0%
기 11
 
11.0%
창 9
 
9.0%
경 5
 
5.0%
망 5
 
5.0%
유 4
 
4.0%
영 3
 
3.0%
혁 3
 
3.0%
신 3
 
3.0%
쟁 2
 
2.0%
Other values (31) 36
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 100
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 19
19.0%
기 11
 
11.0%
창 9
 
9.0%
경 5
 
5.0%
망 5
 
5.0%
유 4
 
4.0%
영 3
 
3.0%
혁 3
 
3.0%
신 3
 
3.0%
쟁 2
 
2.0%
Other values (31) 36
36.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 100
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 19
19.0%
기 11
 
11.0%
창 9
 
9.0%
경 5
 
5.0%
망 5
 
5.0%
유 4
 
4.0%
영 3
 
3.0%
혁 3
 
3.0%
신 3
 
3.0%
쟁 2
 
2.0%
Other values (31) 36
36.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 100
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 19
19.0%
기 11
 
11.0%
창 9
 
9.0%
경 5
 
5.0%
망 5
 
5.0%
유 4
 
4.0%
영 3
 
3.0%
혁 3
 
3.0%
신 3
 
3.0%
쟁 2
 
2.0%
Other values (31) 36
36.0%

우대기업상세내용
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
<NA>
475 
재기지원
 
18
지역주력산업
 
1
수출진입기업
 
1
지식기반
 
1
Other values (4)
 
4

Length

Max length10
Median length4
Mean length4.034
Min length4

Unique

Unique7 ?
Unique (%)1.4%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 475
95.0%
재기지원 18
 
3.6%
지역주력산업 1
 
0.2%
수출진입기업 1
 
0.2%
지식기반 1
 
0.2%
유망창업기업 1
 
0.2%
청년창업 1
 
0.2%
특허실용신안의장권 1
 
0.2%
유망창업지식재산보증 1
 
0.2%

Length

2023-12-12T23:34:12.338481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:34:12.461684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 475
95.0%
재기지원 18
 
3.6%
지역주력산업 1
 
0.2%
수출진입기업 1
 
0.2%
지식기반 1
 
0.2%
유망창업기업 1
 
0.2%
청년창업 1
 
0.2%
특허실용신안의장권 1
 
0.2%
유망창업지식재산보증 1
 
0.2%

컨설팅수행년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)2.5%
Missing12
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean2014.0184
Minimum2008
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:34:12.573437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2010
Q12013
median2014
Q32016
95-th percentile2018
Maximum2019
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4101232
Coefficient of variation (CV)0.0011966739
Kurtosis-0.45765237
Mean2014.0184
Median Absolute Deviation (MAD)1
Skewness-0.20376104
Sum982841
Variance5.8086941
MonotonicityNot monotonic
2023-12-12T23:34:12.708549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2014 101
20.2%
2013 99
19.8%
2016 49
9.8%
2015 49
9.8%
2017 48
9.6%
2018 40
 
8.0%
2010 36
 
7.2%
2011 28
 
5.6%
2012 20
 
4.0%
2009 11
 
2.2%
Other values (2) 7
 
1.4%
(Missing) 12
 
2.4%
ValueCountFrequency (%)
2008 5
 
1.0%
2009 11
 
2.2%
2010 36
 
7.2%
2011 28
 
5.6%
2012 20
 
4.0%
2013 99
19.8%
2014 101
20.2%
2015 49
9.8%
2016 49
9.8%
2017 48
9.6%
ValueCountFrequency (%)
2019 2
 
0.4%
2018 40
 
8.0%
2017 48
9.6%
2016 49
9.8%
2015 49
9.8%
2014 101
20.2%
2013 99
19.8%
2012 20
 
4.0%
2011 28
 
5.6%
2010 36
 
7.2%

실제지급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2398860
Minimum0
Maximum10000000
Zeros99
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T23:34:12.847265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1900000
median3000000
Q33000000
95-th percentile3900000
Maximum10000000
Range10000000
Interquartile range (IQR)2100000

Descriptive statistics

Standard deviation1674830.3
Coefficient of variation (CV)0.6981776
Kurtosis3.8239789
Mean2398860
Median Absolute Deviation (MAD)555000
Skewness0.92907645
Sum1.19943 × 109
Variance2.8050566 × 1012
MonotonicityNot monotonic
2023-12-12T23:34:12.968233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3000000 232
46.4%
0 99
19.8%
2400000 41
 
8.2%
3900000 30
 
6.0%
600000 19
 
3.8%
2100000 13
 
2.6%
2700000 9
 
1.8%
900000 8
 
1.6%
1800000 7
 
1.4%
8000000 7
 
1.4%
Other values (20) 35
 
7.0%
ValueCountFrequency (%)
0 99
19.8%
300000 1
 
0.2%
600000 19
 
3.8%
900000 8
 
1.6%
1100000 3
 
0.6%
1200000 1
 
0.2%
1440000 1
 
0.2%
1800000 7
 
1.4%
2100000 13
 
2.6%
2160000 1
 
0.2%
ValueCountFrequency (%)
10000000 1
 
0.2%
9900000 1
 
0.2%
9840000 2
 
0.4%
8160000 2
 
0.4%
8000000 7
1.4%
7920000 1
 
0.2%
4800000 3
0.6%
4500000 1
 
0.2%
4200000 1
 
0.2%
4000000 1
 
0.2%

Interactions

2023-12-12T23:34:08.009315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:06.178167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:06.622569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:07.136343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:07.540069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:08.136085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:06.276537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:06.730033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:07.228267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:07.629251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:08.296816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:06.369123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:06.824284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:07.304164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:07.713716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:08.384389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:06.449528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:06.913211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:07.379442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:07.800966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:08.477641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:06.535271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:07.027291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:07.458238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:07.892845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:34:13.057928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총컨설팅비용신용보증기금부담비율신용보증기금지원금액펌닥터여부기업특성상세내용우대기업상세내용컨설팅수행년도실제지급금액
총컨설팅비용1.0000.6240.9890.5100.6731.0000.4730.909
신용보증기금부담비율0.6241.0000.7030.3930.6581.0000.4180.772
신용보증기금지원금액0.9890.7031.0000.4300.6731.0000.4690.976
펌닥터여부0.5100.3930.4301.0001.000NaN0.3310.575
기업특성상세내용0.6730.6580.6731.0001.0001.0001.0000.673
우대기업상세내용1.0001.0001.000NaN1.0001.0001.0001.000
컨설팅수행년도0.4730.4180.4690.3311.0001.0001.0000.463
실제지급금액0.9090.7720.9760.5750.6731.0000.4631.000
2023-12-12T23:34:13.186209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
펌닥터여부우대기업상세내용
펌닥터여부1.0001.000
우대기업상세내용1.0001.000
2023-12-12T23:34:13.284352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총컨설팅비용신용보증기금부담비율신용보증기금지원금액컨설팅수행년도실제지급금액펌닥터여부우대기업상세내용
총컨설팅비용1.0000.1150.913-0.1040.7680.5080.879
신용보증기금부담비율0.1151.0000.350-0.0550.3570.2940.860
신용보증기금지원금액0.9130.3501.000-0.0810.8590.4270.879
컨설팅수행년도-0.104-0.055-0.0811.0000.0380.2520.900
실제지급금액0.7680.3570.8590.0381.0000.4320.879
펌닥터여부0.5080.2940.4270.2520.4321.0001.000
우대기업상세내용0.8790.8600.8790.9000.8791.0001.000

Missing values

2023-12-12T23:34:08.631071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:34:08.815957image/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.
2023-12-12T23:34:08.950875image/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

경영컨설팅신청ID입력일자총컨설팅비용신용보증기금부담비율신용보증기금지원금액펌닥터여부기업특성상세내용우대기업상세내용컨설팅수행년도실제지급금액
09bSVsxNHFe2011-08-113000000601800000N<NA><NA>20111800000
19b08XI1OJL2012-07-053000000601800000N<NA><NA>20121800000
29cDXuwHwwD2016-10-060700N<NA><NA><NA>0
39bQhirQjyc2011-04-2730000001003000000N<NA><NA>20110
49cHPXJEAsL2017-03-0910000000808000000Y<NA><NA>20178000000
59clkH99AoV2014-09-222400000601440000N<NA><NA>20141440000
69cMjU9n6UE2017-09-063900000803120000N경영혁신<NA>20173120000
79cB86A4uNo2016-07-25125000008010000000Y<NA><NA>201610000000
89b6RfVyIB42013-02-2110000000808000000N<NA><NA>20138000000
99csa5NGpec2015-06-224500000803600000Y<NA><NA>20153600000
경영컨설팅신청ID입력일자총컨설팅비용신용보증기금부담비율신용보증기금지원금액펌닥터여부기업특성상세내용우대기업상세내용컨설팅수행년도실제지급금액
4909bQLlDshnU2011-05-1630000001003000000N<NA><NA>20113000000
4919bXfnaqWVW2012-02-013000000601800000N<NA><NA>20120
4929bMhrg2wxr2010-11-1930000001003000000N<NA><NA>20113000000
4939bH10XBqgZ2010-06-0130000001003000000N<NA><NA>20103000000
4949bIbbukvTC2010-06-0730000001003000000N<NA><NA>20103000000
4959bJEyNcJGs2010-08-0530000001003000000N<NA><NA>20103000000
4969cDh1yJ3di2016-09-1001000N<NA><NA><NA>0
4979bJ09huVKC2010-08-2030000001003000000N<NA><NA>20103000000
4989bHxIqrtjH2010-05-1230000001003000000N<NA><NA>20103000000
4999bGatvH6od2010-03-1830000001003000000N<NA><NA>20103000000