Overview

Dataset statistics

Number of variables17
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.0 KiB
Average record size in memory156.0 B

Variable types

Text1
Numeric14
Categorical2

Dataset

Description경기도 기업 SOS넷 처리 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=GJ1IYMQ29QRP23A45KK330338485&infSeq=1

Alerts

합계 is highly overall correlated with 공장설립건수 and 12 other fieldsHigh correlation
공장설립건수 is highly overall correlated with 합계 and 9 other fieldsHigh correlation
주변인프라건수 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
창업벤처건수 is highly overall correlated with 합계 and 7 other fieldsHigh correlation
자금지원건수 is highly overall correlated with 합계 and 12 other fieldsHigh correlation
판로수출건수 is highly overall correlated with 합계 and 10 other fieldsHigh correlation
기술인증건수 is highly overall correlated with 합계 and 10 other fieldsHigh correlation
인력교육건수 is highly overall correlated with 합계 and 8 other fieldsHigh correlation
인사노무건수 is highly overall correlated with 판로수출건수 and 2 other fieldsHigh correlation
마케팅건수 is highly overall correlated with 세무회계건수 and 1 other fieldsHigh correlation
법률건수 is highly overall correlated with 합계 and 12 other fieldsHigh correlation
특허건수 is highly overall correlated with 합계 and 10 other fieldsHigh correlation
지원사업건수 is highly overall correlated with 합계 and 10 other fieldsHigh correlation
기타건수 is highly overall correlated with 합계 and 11 other fieldsHigh correlation
세무회계건수 is highly overall correlated with 합계 and 14 other fieldsHigh correlation
불공정거래건수 is highly overall correlated with 합계 and 14 other fieldsHigh correlation
세무회계건수 is highly imbalanced (67.0%)Imbalance
불공정거래건수 is highly imbalanced (67.0%)Imbalance
소재지명 has unique valuesUnique
공장설립건수 has 17 (51.5%) zerosZeros
주변인프라건수 has 22 (66.7%) zerosZeros
창업벤처건수 has 10 (30.3%) zerosZeros
판로수출건수 has 1 (3.0%) zerosZeros
기술인증건수 has 4 (12.1%) zerosZeros
인력교육건수 has 23 (69.7%) zerosZeros
인사노무건수 has 13 (39.4%) zerosZeros
마케팅건수 has 17 (51.5%) zerosZeros
법률건수 has 12 (36.4%) zerosZeros
특허건수 has 11 (33.3%) zerosZeros
기타건수 has 1 (3.0%) zerosZeros

Reproduction

Analysis started2024-03-12 23:04:48.082318
Analysis finished2024-03-12 23:05:02.805204
Duration14.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재지명
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2024-03-13T08:05:02.921808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0606061
Min length1

Characters and Unicode

Total characters101
Distinct characters43
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

Unique33 ?
Unique (%)100.0%

Sample

1st row
2nd row광주시
3rd row남양주시
4th row고양시
5th row화성시
ValueCountFrequency (%)
1
 
3.0%
안산시 1
 
3.0%
과천시 1
 
3.0%
동두천시 1
 
3.0%
연천군 1
 
3.0%
가평군 1
 
3.0%
여주시 1
 
3.0%
오산시 1
 
3.0%
양평군 1
 
3.0%
구리시 1
 
3.0%
Other values (23) 23
69.7%
2024-03-13T08:05:03.185228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
28.7%
6
 
5.9%
5
 
5.0%
5
 
5.0%
4
 
4.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
Other values (33) 37
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 101
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
29
28.7%
6
 
5.9%
5
 
5.0%
5
 
5.0%
4
 
4.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
Other values (33) 37
36.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 101
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
29
28.7%
6
 
5.9%
5
 
5.0%
5
 
5.0%
4
 
4.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
Other values (33) 37
36.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 101
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
29
28.7%
6
 
5.9%
5
 
5.0%
5
 
5.0%
4
 
4.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
3
 
3.0%
Other values (33) 37
36.6%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean352.18182
Minimum15
Maximum5811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:03.285633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile21
Q146
median122
Q3263
95-th percentile651.4
Maximum5811
Range5796
Interquartile range (IQR)217

Descriptive statistics

Standard deviation996.67517
Coefficient of variation (CV)2.8300018
Kurtosis30.630412
Mean352.18182
Median Absolute Deviation (MAD)81
Skewness5.452737
Sum11622
Variance993361.4
MonotonicityNot monotonic
2024-03-13T08:05:03.384379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
21 2
 
6.1%
41 2
 
6.1%
5811 1
 
3.0%
802 1
 
3.0%
15 1
 
3.0%
32 1
 
3.0%
33 1
 
3.0%
45 1
 
3.0%
46 1
 
3.0%
55 1
 
3.0%
Other values (21) 21
63.6%
ValueCountFrequency (%)
15 1
3.0%
21 2
6.1%
32 1
3.0%
33 1
3.0%
41 2
6.1%
45 1
3.0%
46 1
3.0%
55 1
3.0%
76 1
3.0%
77 1
3.0%
ValueCountFrequency (%)
5811 1
3.0%
802 1
3.0%
551 1
3.0%
510 1
3.0%
423 1
3.0%
397 1
3.0%
328 1
3.0%
312 1
3.0%
263 1
3.0%
234 1
3.0%

공장설립건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6060606
Minimum0
Maximum142
Zeros17
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:03.467407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile53
Maximum142
Range142
Interquartile range (IQR)1

Descriptive statistics

Standard deviation28.234442
Coefficient of variation (CV)3.2807626
Kurtosis17.058511
Mean8.6060606
Median Absolute Deviation (MAD)0
Skewness4.0500293
Sum284
Variance797.18371
MonotonicityNot monotonic
2024-03-13T08:05:03.552033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 17
51.5%
1 9
27.3%
2 3
 
9.1%
142 1
 
3.0%
35 1
 
3.0%
80 1
 
3.0%
12 1
 
3.0%
ValueCountFrequency (%)
0 17
51.5%
1 9
27.3%
2 3
 
9.1%
12 1
 
3.0%
35 1
 
3.0%
80 1
 
3.0%
142 1
 
3.0%
ValueCountFrequency (%)
142 1
 
3.0%
80 1
 
3.0%
35 1
 
3.0%
12 1
 
3.0%
2 3
 
9.1%
1 9
27.3%
0 17
51.5%

주변인프라건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.666667
Minimum0
Maximum209
Zeros22
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:03.651823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile71.2
Maximum209
Range209
Interquartile range (IQR)3

Descriptive statistics

Standard deviation42.583496
Coefficient of variation (CV)3.361855
Kurtosis16.393507
Mean12.666667
Median Absolute Deviation (MAD)0
Skewness4.0486447
Sum418
Variance1813.3542
MonotonicityNot monotonic
2024-03-13T08:05:03.747302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 22
66.7%
1 2
 
6.1%
209 1
 
3.0%
136 1
 
3.0%
28 1
 
3.0%
4 1
 
3.0%
8 1
 
3.0%
7 1
 
3.0%
10 1
 
3.0%
3 1
 
3.0%
ValueCountFrequency (%)
0 22
66.7%
1 2
 
6.1%
3 1
 
3.0%
4 1
 
3.0%
7 1
 
3.0%
8 1
 
3.0%
10 1
 
3.0%
11 1
 
3.0%
28 1
 
3.0%
136 1
 
3.0%
ValueCountFrequency (%)
209 1
3.0%
136 1
3.0%
28 1
3.0%
11 1
3.0%
10 1
3.0%
8 1
3.0%
7 1
3.0%
4 1
3.0%
3 1
3.0%
1 2
6.1%

창업벤처건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum0
Maximum66
Zeros10
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:03.833212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile12.6
Maximum66
Range66
Interquartile range (IQR)2

Descriptive statistics

Standard deviation11.594719
Coefficient of variation (CV)2.8986797
Kurtosis27.563609
Mean4
Median Absolute Deviation (MAD)1
Skewness5.1018174
Sum132
Variance134.4375
MonotonicityNot monotonic
2024-03-13T08:05:03.909414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 11
33.3%
0 10
30.3%
2 4
 
12.1%
3 3
 
9.1%
66 1
 
3.0%
11 1
 
3.0%
5 1
 
3.0%
15 1
 
3.0%
7 1
 
3.0%
ValueCountFrequency (%)
0 10
30.3%
1 11
33.3%
2 4
 
12.1%
3 3
 
9.1%
5 1
 
3.0%
7 1
 
3.0%
11 1
 
3.0%
15 1
 
3.0%
66 1
 
3.0%
ValueCountFrequency (%)
66 1
 
3.0%
15 1
 
3.0%
11 1
 
3.0%
7 1
 
3.0%
5 1
 
3.0%
3 3
 
9.1%
2 4
 
12.1%
1 11
33.3%
0 10
30.3%

자금지원건수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)60.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.606061
Minimum2
Maximum934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:03.996507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median10
Q326
95-th percentile171.8
Maximum934
Range932
Interquartile range (IQR)22

Descriptive statistics

Standard deviation165.26644
Coefficient of variation (CV)2.9195891
Kurtosis26.682384
Mean56.606061
Median Absolute Deviation (MAD)7
Skewness4.9893874
Sum1868
Variance27312.996
MonotonicityNot monotonic
2024-03-13T08:05:04.077870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 5
15.2%
4 3
 
9.1%
5 3
 
9.1%
3 3
 
9.1%
11 2
 
6.1%
26 2
 
6.1%
6 2
 
6.1%
934 1
 
3.0%
13 1
 
3.0%
14 1
 
3.0%
Other values (10) 10
30.3%
ValueCountFrequency (%)
2 5
15.2%
3 3
9.1%
4 3
9.1%
5 3
9.1%
6 2
 
6.1%
10 1
 
3.0%
11 2
 
6.1%
13 1
 
3.0%
14 1
 
3.0%
16 1
 
3.0%
ValueCountFrequency (%)
934 1
3.0%
194 1
3.0%
157 1
3.0%
141 1
3.0%
134 1
3.0%
56 1
3.0%
30 1
3.0%
26 2
6.1%
19 1
3.0%
18 1
3.0%

판로수출건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.666667
Minimum0
Maximum671
Zeros1
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:04.161180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median18
Q338
95-th percentile50.6
Maximum671
Range671
Interquartile range (IQR)32

Descriptive statistics

Standard deviation114.31116
Coefficient of variation (CV)2.8109302
Kurtosis31.558841
Mean40.666667
Median Absolute Deviation (MAD)15
Skewness5.5624
Sum1342
Variance13067.042
MonotonicityNot monotonic
2024-03-13T08:05:04.253234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
34 3
 
9.1%
38 2
 
6.1%
17 2
 
6.1%
1 2
 
6.1%
20 2
 
6.1%
14 2
 
6.1%
3 2
 
6.1%
671 1
 
3.0%
18 1
 
3.0%
0 1
 
3.0%
Other values (15) 15
45.5%
ValueCountFrequency (%)
0 1
3.0%
1 2
6.1%
2 1
3.0%
3 2
6.1%
4 1
3.0%
5 1
3.0%
6 1
3.0%
8 1
3.0%
10 1
3.0%
11 1
3.0%
ValueCountFrequency (%)
671 1
 
3.0%
59 1
 
3.0%
45 1
 
3.0%
43 1
 
3.0%
42 1
 
3.0%
41 1
 
3.0%
39 1
 
3.0%
38 2
6.1%
34 3
9.1%
31 1
 
3.0%

기술인증건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.060606
Minimum0
Maximum199
Zeros4
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:04.595952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile21.4
Maximum199
Range199
Interquartile range (IQR)6

Descriptive statistics

Standard deviation34.090816
Coefficient of variation (CV)2.8266254
Kurtosis30.792226
Mean12.060606
Median Absolute Deviation (MAD)3
Skewness5.4709874
Sum398
Variance1162.1837
MonotonicityNot monotonic
2024-03-13T08:05:04.696290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 4
12.1%
3 4
12.1%
1 3
9.1%
5 3
9.1%
4 3
9.1%
8 2
 
6.1%
7 2
 
6.1%
6 2
 
6.1%
2 2
 
6.1%
199 1
 
3.0%
Other values (7) 7
21.2%
ValueCountFrequency (%)
0 4
12.1%
1 3
9.1%
2 2
6.1%
3 4
12.1%
4 3
9.1%
5 3
9.1%
6 2
6.1%
7 2
6.1%
8 2
6.1%
9 1
 
3.0%
ValueCountFrequency (%)
199 1
3.0%
25 1
3.0%
19 1
3.0%
17 1
3.0%
15 1
3.0%
14 1
3.0%
12 1
3.0%
9 1
3.0%
8 2
6.1%
7 2
6.1%

세무회계건수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
30 
1
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.0%

Sample

1st row2
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 30
90.9%
1 2
 
6.1%
2 1
 
3.0%

Length

2024-03-13T08:05:04.795556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:05:04.876234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
90.9%
1 2
 
6.1%
2 1
 
3.0%

인력교육건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.757576
Minimum0
Maximum260
Zeros23
Zeros (%)69.7%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:04.949978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile100.8
Maximum260
Range260
Interquartile range (IQR)1

Descriptive statistics

Standard deviation58.740973
Coefficient of variation (CV)3.7277925
Kurtosis14.04096
Mean15.757576
Median Absolute Deviation (MAD)0
Skewness3.8773181
Sum520
Variance3450.5019
MonotonicityNot monotonic
2024-03-13T08:05:05.031814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 23
69.7%
1 3
 
9.1%
3 2
 
6.1%
260 1
 
3.0%
225 1
 
3.0%
18 1
 
3.0%
6 1
 
3.0%
2 1
 
3.0%
ValueCountFrequency (%)
0 23
69.7%
1 3
 
9.1%
2 1
 
3.0%
3 2
 
6.1%
6 1
 
3.0%
18 1
 
3.0%
225 1
 
3.0%
260 1
 
3.0%
ValueCountFrequency (%)
260 1
 
3.0%
225 1
 
3.0%
18 1
 
3.0%
6 1
 
3.0%
3 2
 
6.1%
2 1
 
3.0%
1 3
 
9.1%
0 23
69.7%

인사노무건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3636364
Minimum0
Maximum105
Zeros13
Zeros (%)39.4%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:05.115555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile20.8
Maximum105
Range105
Interquartile range (IQR)4

Descriptive statistics

Standard deviation19.065378
Coefficient of variation (CV)2.995988
Kurtosis24.079857
Mean6.3636364
Median Absolute Deviation (MAD)1
Skewness4.7676769
Sum210
Variance363.48864
MonotonicityNot monotonic
2024-03-13T08:05:05.197891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 13
39.4%
1 5
 
15.2%
7 3
 
9.1%
2 3
 
9.1%
3 3
 
9.1%
6 2
 
6.1%
105 1
 
3.0%
40 1
 
3.0%
8 1
 
3.0%
4 1
 
3.0%
ValueCountFrequency (%)
0 13
39.4%
1 5
 
15.2%
2 3
 
9.1%
3 3
 
9.1%
4 1
 
3.0%
6 2
 
6.1%
7 3
 
9.1%
8 1
 
3.0%
40 1
 
3.0%
105 1
 
3.0%
ValueCountFrequency (%)
105 1
 
3.0%
40 1
 
3.0%
8 1
 
3.0%
7 3
 
9.1%
6 2
 
6.1%
4 1
 
3.0%
3 3
 
9.1%
2 3
 
9.1%
1 5
 
15.2%
0 13
39.4%

마케팅건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6060606
Minimum0
Maximum109
Zeros17
Zeros (%)51.5%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:05.299556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile30.4
Maximum109
Range109
Interquartile range (IQR)4

Descriptive statistics

Standard deviation20.429665
Coefficient of variation (CV)3.092564
Kurtosis21.198665
Mean6.6060606
Median Absolute Deviation (MAD)0
Skewness4.4682192
Sum218
Variance417.37121
MonotonicityNot monotonic
2024-03-13T08:05:05.393344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 17
51.5%
1 5
 
15.2%
5 4
 
12.1%
4 2
 
6.1%
109 1
 
3.0%
2 1
 
3.0%
49 1
 
3.0%
18 1
 
3.0%
7 1
 
3.0%
ValueCountFrequency (%)
0 17
51.5%
1 5
 
15.2%
2 1
 
3.0%
4 2
 
6.1%
5 4
 
12.1%
7 1
 
3.0%
18 1
 
3.0%
49 1
 
3.0%
109 1
 
3.0%
ValueCountFrequency (%)
109 1
 
3.0%
49 1
 
3.0%
18 1
 
3.0%
7 1
 
3.0%
5 4
 
12.1%
4 2
 
6.1%
2 1
 
3.0%
1 5
 
15.2%
0 17
51.5%

법률건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9393939
Minimum0
Maximum65
Zeros12
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:05.468042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile8.8
Maximum65
Range65
Interquartile range (IQR)3

Descriptive statistics

Standard deviation11.26926
Coefficient of variation (CV)2.8606582
Kurtosis29.204513
Mean3.9393939
Median Absolute Deviation (MAD)1
Skewness5.2772164
Sum130
Variance126.99621
MonotonicityNot monotonic
2024-03-13T08:05:05.548160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 12
36.4%
1 8
24.2%
3 4
 
12.1%
5 2
 
6.1%
2 2
 
6.1%
65 1
 
3.0%
10 1
 
3.0%
7 1
 
3.0%
6 1
 
3.0%
8 1
 
3.0%
ValueCountFrequency (%)
0 12
36.4%
1 8
24.2%
2 2
 
6.1%
3 4
 
12.1%
5 2
 
6.1%
6 1
 
3.0%
7 1
 
3.0%
8 1
 
3.0%
10 1
 
3.0%
65 1
 
3.0%
ValueCountFrequency (%)
65 1
 
3.0%
10 1
 
3.0%
8 1
 
3.0%
7 1
 
3.0%
6 1
 
3.0%
5 2
 
6.1%
3 4
 
12.1%
2 2
 
6.1%
1 8
24.2%
0 12
36.4%

특허건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5151515
Minimum0
Maximum58
Zeros11
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:05.629116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5.4
Maximum58
Range58
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.9502551
Coefficient of variation (CV)2.830676
Kurtosis30.598714
Mean3.5151515
Median Absolute Deviation (MAD)1
Skewness5.4427936
Sum116
Variance99.007576
MonotonicityNot monotonic
2024-03-13T08:05:05.705624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 11
33.3%
3 6
18.2%
1 6
18.2%
5 4
 
12.1%
2 4
 
12.1%
58 1
 
3.0%
6 1
 
3.0%
ValueCountFrequency (%)
0 11
33.3%
1 6
18.2%
2 4
 
12.1%
3 6
18.2%
5 4
 
12.1%
6 1
 
3.0%
58 1
 
3.0%
ValueCountFrequency (%)
58 1
 
3.0%
6 1
 
3.0%
5 4
 
12.1%
3 6
18.2%
2 4
 
12.1%
1 6
18.2%
0 11
33.3%

불공정거래건수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
0
30 
1
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.0%

Sample

1st row2
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
90.9%
1 2
 
6.1%
2 1
 
3.0%

Length

2024-03-13T08:05:05.796147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:05:05.868685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
90.9%
1 2
 
6.1%
2 1
 
3.0%

지원사업건수
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146.42424
Minimum2
Maximum2416
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:05.945097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10.8
Q128
median61
Q3111
95-th percentile242.8
Maximum2416
Range2414
Interquartile range (IQR)83

Descriptive statistics

Standard deviation412.2616
Coefficient of variation (CV)2.8155283
Kurtosis31.331575
Mean146.42424
Median Absolute Deviation (MAD)39
Skewness5.5376104
Sum4832
Variance169959.63
MonotonicityNot monotonic
2024-03-13T08:05:06.037995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
39 2
 
6.1%
61 2
 
6.1%
2416 1
 
3.0%
3 1
 
3.0%
2 1
 
3.0%
16 1
 
3.0%
18 1
 
3.0%
26 1
 
3.0%
19 1
 
3.0%
33 1
 
3.0%
Other values (21) 21
63.6%
ValueCountFrequency (%)
2 1
3.0%
3 1
3.0%
16 1
3.0%
18 1
3.0%
19 1
3.0%
20 1
3.0%
23 1
3.0%
26 1
3.0%
28 1
3.0%
31 1
3.0%
ValueCountFrequency (%)
2416 1
3.0%
289 1
3.0%
212 1
3.0%
163 1
3.0%
155 1
3.0%
127 1
3.0%
118 1
3.0%
114 1
3.0%
111 1
3.0%
108 1
3.0%

기타건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.727273
Minimum0
Maximum573
Zeros1
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-03-13T08:05:06.121537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median13
Q323
95-th percentile80
Maximum573
Range573
Interquartile range (IQR)20

Descriptive statistics

Standard deviation99.278105
Coefficient of variation (CV)2.8587936
Kurtosis29.292156
Mean34.727273
Median Absolute Deviation (MAD)10
Skewness5.2946558
Sum1146
Variance9856.142
MonotonicityNot monotonic
2024-03-13T08:05:06.205688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 4
 
12.1%
4 3
 
9.1%
3 2
 
6.1%
14 2
 
6.1%
6 2
 
6.1%
66 2
 
6.1%
13 2
 
6.1%
17 2
 
6.1%
2 2
 
6.1%
573 1
 
3.0%
Other values (11) 11
33.3%
ValueCountFrequency (%)
0 1
 
3.0%
1 4
12.1%
2 2
6.1%
3 2
6.1%
4 3
9.1%
5 1
 
3.0%
6 2
6.1%
7 1
 
3.0%
13 2
6.1%
14 2
6.1%
ValueCountFrequency (%)
573 1
3.0%
101 1
3.0%
66 2
6.1%
44 1
3.0%
37 1
3.0%
33 1
3.0%
29 1
3.0%
23 1
3.0%
20 1
3.0%
17 2
6.1%

Interactions

2024-03-13T08:05:01.605915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:48.523216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.462023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.555379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.701831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.670029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.740457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.696583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.843398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.750468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.668143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.598168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.541276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.678650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.667930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:48.586341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.539939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.623010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.766371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.730789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.824070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.764688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.910216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.811359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.730236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.662680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.604499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.748362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.732360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:48.651472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.613144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.700349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.840928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.798383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.896160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.840340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.990124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.892952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.794093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.732074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.674640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.831177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.793526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:48.712212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.673194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.764941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.924145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.854575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.959730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.901297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.051594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.952921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.850359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.791935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.733404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.888417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.860529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:48.775977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.743319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.831877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.997911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.932629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.029779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.969048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.115718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.020660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.929577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.868356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.795136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.954897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.919112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:48.833600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.811355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.898897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.082629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.999203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.092415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.029216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.173628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.079453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.001091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.929418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.058464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.012028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.984998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:48.901749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.882797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.977871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.154582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.068446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.171903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.098204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.250008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.147927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.071800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.995987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.126667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.078579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:02.053830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:48.975663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.966473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.060440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.223685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.150923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.244002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.167938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.330613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.214065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.161679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.066483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.202505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.146381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:02.119919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.035115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.054786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.122717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.284228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.219654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.309147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.231466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.388471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.273371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.228227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.133304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.282478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.211715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:02.184281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.097332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.143063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.178595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.347345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.289543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.372196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.295408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.449086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.332668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.290751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.198951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.349609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.287689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:02.251246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.158626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.222976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.435439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.407182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.366822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.434964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.360537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.507527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.391438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.350971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.272356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.424383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.349267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:02.335474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.234055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.322406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.498434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.475182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.465273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.498271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.425680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.569762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.453990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.411747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.351702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.489693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.411021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:02.403278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.319490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.420410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.571001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.541489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.564152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.563648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.497508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.630677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.522318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.473632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.417328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.552227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.474565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:02.475416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:49.389916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:50.489613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:51.635239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:52.606129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:53.660829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:54.627936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:55.772918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:56.688592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:57.598308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:58.535625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:04:59.477474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:00.615094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:05:01.534013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:05:06.279332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지명합계공장설립건수주변인프라건수창업벤처건수자금지원건수판로수출건수기술인증건수세무회계건수인력교육건수인사노무건수마케팅건수법률건수특허건수불공정거래건수지원사업건수기타건수
소재지명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
합계1.0001.0001.0001.0000.6481.0001.0000.9330.9341.0000.9330.6480.9730.9330.9860.9330.934
공장설립건수1.0001.0001.0001.0000.8600.9901.0000.6480.6481.0000.6480.8590.7420.6480.7870.6480.649
주변인프라건수1.0001.0001.0001.0000.8600.9901.0000.6480.6481.0000.6480.8590.7420.6480.7870.6480.649
창업벤처건수1.0000.6480.8600.8601.0000.8621.0000.6480.6490.6480.6480.8600.7421.0000.6490.6480.689
자금지원건수1.0001.0000.9900.9900.8621.0001.0000.7410.6891.0000.7410.9160.7430.6480.7880.6480.670
판로수출건수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기술인증건수1.0000.9330.6480.6480.6480.7411.0001.0000.9860.9331.0001.0000.9340.9330.9340.9330.934
세무회계건수1.0000.9340.6480.6480.6490.6891.0000.9861.0000.9340.9860.7870.9340.9340.9340.9340.953
인력교육건수1.0001.0001.0001.0000.6481.0001.0000.9330.9341.0000.9330.6480.9730.9330.9860.9330.934
인사노무건수1.0000.9330.6480.6480.6480.7411.0001.0000.9860.9331.0001.0000.9340.9330.9340.9330.934
마케팅건수1.0000.6480.8590.8590.8600.9161.0001.0000.7870.6481.0001.0000.6490.6480.6480.6480.649
법률건수1.0000.9730.7420.7420.7420.7431.0000.9340.9340.9730.9340.6491.0000.9730.9530.9730.935
특허건수1.0000.9330.6480.6481.0000.6481.0000.9330.9340.9330.9330.6480.9731.0000.9340.9330.934
불공정거래건수1.0000.9860.7870.7870.6490.7881.0000.9340.9340.9860.9340.6480.9530.9341.0000.9340.934
지원사업건수1.0000.9330.6480.6480.6480.6481.0000.9330.9340.9330.9330.6480.9730.9330.9341.0000.934
기타건수1.0000.9340.6490.6490.6890.6701.0000.9340.9530.9340.9340.6490.9350.9340.9340.9341.000
2024-03-13T08:05:06.395266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
불공정거래건수세무회계건수
불공정거래건수1.0000.685
세무회계건수0.6851.000
2024-03-13T08:05:06.467081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계공장설립건수주변인프라건수창업벤처건수자금지원건수판로수출건수기술인증건수인력교육건수인사노무건수마케팅건수법률건수특허건수지원사업건수기타건수세무회계건수불공정거래건수
합계1.0000.6630.5970.5570.8890.8550.8930.5880.4150.3180.7330.7340.9430.8510.6840.851
공장설립건수0.6631.0000.6960.5550.7380.5740.4820.4750.1990.2530.5130.3560.5330.6100.6600.832
주변인프라건수0.5970.6961.0000.4050.6350.3100.5830.6010.0030.3700.5000.2940.5150.4360.6600.832
창업벤처건수0.5570.5550.4051.0000.5770.3760.4640.3410.261-0.0310.6890.5180.4750.6530.6610.661
자금지원건수0.8890.7380.6350.5771.0000.6930.7310.5360.4070.1540.6030.6620.7990.8210.7090.832
판로수출건수0.8550.5740.3100.3760.6931.0000.7800.4360.5840.4480.5380.5610.8100.6890.9840.984
기술인증건수0.8930.4820.5830.4640.7310.7801.0000.5610.3940.3970.7270.6490.8570.6970.8510.684
인력교육건수0.5880.4750.6010.3410.5360.4360.5611.0000.2740.4260.5090.5110.4510.5670.6840.851
인사노무건수0.4150.1990.0030.2610.4070.5840.3940.2741.0000.0220.2490.4540.4310.4180.8510.684
마케팅건수0.3180.2530.370-0.0310.1540.4480.3970.4260.0221.0000.1740.0620.2310.0660.8320.660
법률건수0.7330.5130.5000.6890.6030.5380.7270.5090.2490.1741.0000.6330.6550.7260.6860.732
특허건수0.7340.3560.2940.5180.6620.5610.6490.5110.4540.0620.6331.0000.6860.7590.6840.684
지원사업건수0.9430.5330.5150.4750.7990.8100.8570.4510.4310.2310.6550.6861.0000.7530.6840.684
기타건수0.8510.6100.4360.6530.8210.6890.6970.5670.4180.0660.7260.7590.7531.0000.7320.686
세무회계건수0.6840.6600.6600.6610.7090.9840.8510.6840.8510.8320.6860.6840.6840.7321.0000.685
불공정거래건수0.8510.8320.8320.6610.8320.9840.6840.8510.6840.6600.7320.6840.6840.6860.6851.000

Missing values

2024-03-13T08:05:02.586570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:05:02.743489image/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.

Sample

소재지명합계공장설립건수주변인프라건수창업벤처건수자금지원건수판로수출건수기술인증건수세무회계건수인력교육건수인사노무건수마케팅건수법률건수특허건수불공정거래건수지원사업건수기타건수
05811142209669346711992260105109655822416573
1광주시8023513631943115022505103110044
2남양주시55180283157428001211021216
3고양시5102411345925118404953015514
4화성시42310230387007075028937
5안양시3971283141176060035095101
6수원시328001126397006055016366
7용인시31227556348137063011466
8성남시2631015264117008086010833
9파주시23411011634140321813011813
소재지명합계공장설립건수주변인프라건수창업벤처건수자금지원건수판로수출건수기술인증건수세무회계건수인력교육건수인사노무건수마케팅건수법률건수특허건수불공정거래건수지원사업건수기타건수
23의정부시5511114530001300206
24구리시460015420001020283
25양평군450014630011000236
26오산시410012210000010331
27여주시4100061020030000191
28가평군330005100000000261
29연천군320002800004000180
30동두천시210003000000000162
31과천시15001211006001021
32경기도외21107230000010034