Overview

Dataset statistics

Number of variables14
Number of observations72
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.8 KiB
Average record size in memory125.8 B

Variable types

DateTime1
Categorical2
Numeric11

Dataset

Description중소, 벤처기업 보유 기술을 평가한 월별 정보건수 정보 제공- 업종별 현황(기준년월, 기업형태구분(개인,주식,유한,합자,합명,기타), 기계, 재료금속, 전기전자, 정보통신, 화공, 건설, 기타제조, 사업서비스, 섬유, 환경, 농업, 기타)
Author기술보증기금
URLhttps://www.data.go.kr/data/15086031/fileData.do

Alerts

기계 is highly overall correlated with 재료금속 and 9 other fieldsHigh correlation
재료금속 is highly overall correlated with 기계 and 9 other fieldsHigh correlation
전기전자 is highly overall correlated with 기계 and 10 other fieldsHigh correlation
정보통신 is highly overall correlated with 기계 and 10 other fieldsHigh correlation
화공 is highly overall correlated with 기계 and 10 other fieldsHigh correlation
건설 is highly overall correlated with 기계 and 9 other fieldsHigh correlation
기타제조 is highly overall correlated with 기계 and 10 other fieldsHigh correlation
사업서비스 is highly overall correlated with 기계 and 11 other fieldsHigh correlation
섬유 is highly overall correlated with 기계 and 10 other fieldsHigh correlation
농업 is highly overall correlated with 전기전자 and 6 other fieldsHigh correlation
기타 is highly overall correlated with 기계 and 10 other fieldsHigh correlation
기업형태구분 is highly overall correlated with 사업서비스High correlation
환경 is highly overall correlated with 기계 and 9 other fieldsHigh correlation
환경 is highly imbalanced (64.4%)Imbalance
기계 has 33 (45.8%) zerosZeros
재료금속 has 34 (47.2%) zerosZeros
전기전자 has 34 (47.2%) zerosZeros
정보통신 has 38 (52.8%) zerosZeros
화공 has 27 (37.5%) zerosZeros
건설 has 44 (61.1%) zerosZeros
기타제조 has 25 (34.7%) zerosZeros
사업서비스 has 28 (38.9%) zerosZeros
섬유 has 45 (62.5%) zerosZeros
농업 has 40 (55.6%) zerosZeros
기타 has 34 (47.2%) zerosZeros

Reproduction

Analysis started2024-03-15 01:21:15.161604
Analysis finished2024-03-15 01:21:46.140250
Duration30.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct12
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size704.0 B
Minimum2023-01-23 00:00:00
Maximum2023-12-23 00:00:00
2024-03-15T10:21:46.270121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:46.604228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)

기업형태구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size704.0 B
개인
12 
주식
12 
유한
12 
합자
12 
합명
12 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row주식
3rd row유한
4th row합자
5th row합명

Common Values

ValueCountFrequency (%)
개인 12
16.7%
주식 12
16.7%
유한 12
16.7%
합자 12
16.7%
합명 12
16.7%
기타 12
16.7%

Length

2024-03-15T10:21:47.005226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:21:47.359111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 12
16.7%
주식 12
16.7%
유한 12
16.7%
합자 12
16.7%
합명 12
16.7%
기타 12
16.7%

기계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.194444
Minimum0
Maximum526
Zeros33
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-15T10:21:47.728773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q393
95-th percentile343.75
Maximum526
Range526
Interquartile range (IQR)93

Descriptive statistics

Standard deviation128.53362
Coefficient of variation (CV)1.780381
Kurtosis3.0261447
Mean72.194444
Median Absolute Deviation (MAD)1
Skewness1.9459136
Sum5198
Variance16520.891
MonotonicityNot monotonic
2024-03-15T10:21:47.958049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 33
45.8%
1 4
 
5.6%
3 3
 
4.2%
86 2
 
2.8%
8 2
 
2.8%
4 2
 
2.8%
6 2
 
2.8%
124 1
 
1.4%
208 1
 
1.4%
127 1
 
1.4%
Other values (21) 21
29.2%
ValueCountFrequency (%)
0 33
45.8%
1 4
 
5.6%
2 1
 
1.4%
3 3
 
4.2%
4 2
 
2.8%
6 2
 
2.8%
8 2
 
2.8%
11 1
 
1.4%
34 1
 
1.4%
65 1
 
1.4%
ValueCountFrequency (%)
526 1
1.4%
462 1
1.4%
442 1
1.4%
374 1
1.4%
319 1
1.4%
317 1
1.4%
302 1
1.4%
295 1
1.4%
294 1
1.4%
211 1
1.4%

재료금속
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)40.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.013889
Minimum0
Maximum245
Zeros34
Zeros (%)47.2%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-15T10:21:48.291443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q379
95-th percentile175.25
Maximum245
Range245
Interquartile range (IQR)79

Descriptive statistics

Standard deviation67.200036
Coefficient of variation (CV)1.5994719
Kurtosis0.91947575
Mean42.013889
Median Absolute Deviation (MAD)1
Skewness1.4441874
Sum3025
Variance4515.8449
MonotonicityNot monotonic
2024-03-15T10:21:48.709564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 34
47.2%
1 5
 
6.9%
2 4
 
5.6%
146 2
 
2.8%
5 2
 
2.8%
82 2
 
2.8%
165 1
 
1.4%
147 1
 
1.4%
4 1
 
1.4%
78 1
 
1.4%
Other values (19) 19
26.4%
ValueCountFrequency (%)
0 34
47.2%
1 5
 
6.9%
2 4
 
5.6%
3 1
 
1.4%
4 1
 
1.4%
5 2
 
2.8%
6 1
 
1.4%
30 1
 
1.4%
44 1
 
1.4%
63 1
 
1.4%
ValueCountFrequency (%)
245 1
1.4%
222 1
1.4%
216 1
1.4%
178 1
1.4%
173 1
1.4%
165 1
1.4%
154 1
1.4%
147 1
1.4%
146 2
2.8%
133 1
1.4%

전기전자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.736111
Minimum0
Maximum295
Zeros34
Zeros (%)47.2%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-15T10:21:49.021163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q318.75
95-th percentile203.4
Maximum295
Range295
Interquartile range (IQR)18.75

Descriptive statistics

Standard deviation72.764717
Coefficient of variation (CV)2.0361677
Kurtosis3.6469581
Mean35.736111
Median Absolute Deviation (MAD)1
Skewness2.1675182
Sum2573
Variance5294.704
MonotonicityNot monotonic
2024-03-15T10:21:49.371331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 34
47.2%
1 6
 
8.3%
2 4
 
5.6%
15 3
 
4.2%
3 3
 
4.2%
16 1
 
1.4%
94 1
 
1.4%
12 1
 
1.4%
165 1
 
1.4%
11 1
 
1.4%
Other values (17) 17
23.6%
ValueCountFrequency (%)
0 34
47.2%
1 6
 
8.3%
2 4
 
5.6%
3 3
 
4.2%
4 1
 
1.4%
11 1
 
1.4%
12 1
 
1.4%
15 3
 
4.2%
16 1
 
1.4%
27 1
 
1.4%
ValueCountFrequency (%)
295 1
1.4%
266 1
1.4%
243 1
1.4%
221 1
1.4%
189 1
1.4%
181 1
1.4%
165 1
1.4%
160 1
1.4%
159 1
1.4%
141 1
1.4%

정보통신
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.208333
Minimum0
Maximum419
Zeros38
Zeros (%)52.8%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-15T10:21:49.961380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326.25
95-th percentile285.25
Maximum419
Range419
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation104.22915
Coefficient of variation (CV)2.1620568
Kurtosis4.3190965
Mean48.208333
Median Absolute Deviation (MAD)0
Skewness2.2989606
Sum3471
Variance10863.717
MonotonicityNot monotonic
2024-03-15T10:21:50.193740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 38
52.8%
1 6
 
8.3%
2 3
 
4.2%
16 2
 
2.8%
29 2
 
2.8%
180 1
 
1.4%
132 1
 
1.4%
7 1
 
1.4%
227 1
 
1.4%
27 1
 
1.4%
Other values (16) 16
22.2%
ValueCountFrequency (%)
0 38
52.8%
1 6
 
8.3%
2 3
 
4.2%
3 1
 
1.4%
7 1
 
1.4%
15 1
 
1.4%
16 2
 
2.8%
20 1
 
1.4%
26 1
 
1.4%
27 1
 
1.4%
ValueCountFrequency (%)
419 1
1.4%
401 1
1.4%
375 1
1.4%
299 1
1.4%
274 1
1.4%
240 1
1.4%
227 1
1.4%
222 1
1.4%
208 1
1.4%
195 1
1.4%

화공
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.805556
Minimum0
Maximum262
Zeros27
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-15T10:21:50.563809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q335.25
95-th percentile179.5
Maximum262
Range262
Interquartile range (IQR)35.25

Descriptive statistics

Standard deviation64.631924
Coefficient of variation (CV)1.9118729
Kurtosis3.8923382
Mean33.805556
Median Absolute Deviation (MAD)2
Skewness2.169855
Sum2434
Variance4177.2856
MonotonicityNot monotonic
2024-03-15T10:21:50.914403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 27
37.5%
2 7
 
9.7%
1 7
 
9.7%
3 5
 
6.9%
150 2
 
2.8%
4 2
 
2.8%
45 2
 
2.8%
24 1
 
1.4%
36 1
 
1.4%
117 1
 
1.4%
Other values (17) 17
23.6%
ValueCountFrequency (%)
0 27
37.5%
1 7
 
9.7%
2 7
 
9.7%
3 5
 
6.9%
4 2
 
2.8%
17 1
 
1.4%
20 1
 
1.4%
21 1
 
1.4%
24 1
 
1.4%
26 1
 
1.4%
ValueCountFrequency (%)
262 1
1.4%
253 1
1.4%
220 1
1.4%
196 1
1.4%
166 1
1.4%
150 2
2.8%
144 1
1.4%
142 1
1.4%
117 1
1.4%
98 1
1.4%

건설
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8194444
Minimum0
Maximum53
Zeros44
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-15T10:21:51.117335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile44.15
Maximum53
Range53
Interquartile range (IQR)1

Descriptive statistics

Standard deviation15.158623
Coefficient of variation (CV)2.222853
Kurtosis2.8772066
Mean6.8194444
Median Absolute Deviation (MAD)0
Skewness2.1016583
Sum491
Variance229.78384
MonotonicityNot monotonic
2024-03-15T10:21:51.378823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 44
61.1%
1 12
 
16.7%
2 3
 
4.2%
40 2
 
2.8%
22 1
 
1.4%
52 1
 
1.4%
49 1
 
1.4%
53 1
 
1.4%
48 1
 
1.4%
36 1
 
1.4%
Other values (5) 5
 
6.9%
ValueCountFrequency (%)
0 44
61.1%
1 12
 
16.7%
2 3
 
4.2%
4 1
 
1.4%
20 1
 
1.4%
22 1
 
1.4%
30 1
 
1.4%
36 1
 
1.4%
38 1
 
1.4%
40 2
 
2.8%
ValueCountFrequency (%)
53 1
1.4%
52 1
1.4%
49 1
1.4%
48 1
1.4%
41 1
1.4%
40 2
2.8%
38 1
1.4%
36 1
1.4%
30 1
1.4%
22 1
1.4%

기타제조
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.041667
Minimum0
Maximum264
Zeros25
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-15T10:21:51.762700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9.5
Q347.25
95-th percentile172.5
Maximum264
Range264
Interquartile range (IQR)47.25

Descriptive statistics

Standard deviation64.550763
Coefficient of variation (CV)1.6120898
Kurtosis3.6496417
Mean40.041667
Median Absolute Deviation (MAD)9.5
Skewness2.0430569
Sum2883
Variance4166.8011
MonotonicityNot monotonic
2024-03-15T10:21:52.206790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0 25
34.7%
1 3
 
4.2%
27 2
 
2.8%
14 2
 
2.8%
146 2
 
2.8%
6 2
 
2.8%
3 2
 
2.8%
16 2
 
2.8%
99 1
 
1.4%
19 1
 
1.4%
Other values (30) 30
41.7%
ValueCountFrequency (%)
0 25
34.7%
1 3
 
4.2%
2 1
 
1.4%
3 2
 
2.8%
4 1
 
1.4%
6 2
 
2.8%
7 1
 
1.4%
9 1
 
1.4%
10 1
 
1.4%
14 2
 
2.8%
ValueCountFrequency (%)
264 1
1.4%
252 1
1.4%
244 1
1.4%
200 1
1.4%
150 1
1.4%
146 2
2.8%
142 1
1.4%
137 1
1.4%
118 1
1.4%
109 1
1.4%

사업서비스
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.875
Minimum0
Maximum252
Zeros28
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-15T10:21:52.639873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q334.25
95-th percentile185.45
Maximum252
Range252
Interquartile range (IQR)34.25

Descriptive statistics

Standard deviation65.83696
Coefficient of variation (CV)1.8877981
Kurtosis3.1981542
Mean34.875
Median Absolute Deviation (MAD)1.5
Skewness2.0635542
Sum2511
Variance4334.5053
MonotonicityNot monotonic
2024-03-15T10:21:53.142195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 28
38.9%
1 8
 
11.1%
2 4
 
5.6%
3 3
 
4.2%
5 3
 
4.2%
4 2
 
2.8%
25 2
 
2.8%
38 2
 
2.8%
149 1
 
1.4%
127 1
 
1.4%
Other values (18) 18
25.0%
ValueCountFrequency (%)
0 28
38.9%
1 8
 
11.1%
2 4
 
5.6%
3 3
 
4.2%
4 2
 
2.8%
5 3
 
4.2%
17 1
 
1.4%
25 2
 
2.8%
26 1
 
1.4%
30 1
 
1.4%
ValueCountFrequency (%)
252 1
1.4%
245 1
1.4%
230 1
1.4%
197 1
1.4%
176 1
1.4%
171 1
1.4%
156 1
1.4%
149 1
1.4%
133 1
1.4%
132 1
1.4%

섬유
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5138889
Minimum0
Maximum66
Zeros45
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-15T10:21:53.517313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile41.25
Maximum66
Range66
Interquartile range (IQR)12

Descriptive statistics

Standard deviation15.620493
Coefficient of variation (CV)1.8347072
Kurtosis4.3660281
Mean8.5138889
Median Absolute Deviation (MAD)0
Skewness2.1570584
Sum613
Variance243.9998
MonotonicityNot monotonic
2024-03-15T10:21:53.906316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 45
62.5%
27 2
 
2.8%
2 2
 
2.8%
10 2
 
2.8%
12 2
 
2.8%
24 2
 
2.8%
18 2
 
2.8%
11 1
 
1.4%
15 1
 
1.4%
4 1
 
1.4%
Other values (12) 12
 
16.7%
ValueCountFrequency (%)
0 45
62.5%
1 1
 
1.4%
2 2
 
2.8%
3 1
 
1.4%
4 1
 
1.4%
10 2
 
2.8%
11 1
 
1.4%
12 2
 
2.8%
13 1
 
1.4%
15 1
 
1.4%
ValueCountFrequency (%)
66 1
1.4%
64 1
1.4%
55 1
1.4%
44 1
1.4%
39 1
1.4%
36 1
1.4%
33 1
1.4%
27 2
2.8%
24 2
2.8%
23 1
1.4%

환경
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size704.0 B
0
63 
1
 
5
2
 
3
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
0 63
87.5%
1 5
 
6.9%
2 3
 
4.2%
3 1
 
1.4%

Length

2024-03-15T10:21:54.319240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:21:54.661361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 63
87.5%
1 5
 
6.9%
2 3
 
4.2%
3 1
 
1.4%

농업
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum0
Maximum9
Zeros40
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-15T10:21:54.974193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6529956
Coefficient of variation (CV)1.6529956
Kurtosis7.712245
Mean1
Median Absolute Deviation (MAD)0
Skewness2.5018176
Sum72
Variance2.7323944
MonotonicityNot monotonic
2024-03-15T10:21:55.317653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 40
55.6%
1 15
 
20.8%
2 9
 
12.5%
5 4
 
5.6%
3 2
 
2.8%
9 1
 
1.4%
4 1
 
1.4%
ValueCountFrequency (%)
0 40
55.6%
1 15
 
20.8%
2 9
 
12.5%
3 2
 
2.8%
4 1
 
1.4%
5 4
 
5.6%
9 1
 
1.4%
ValueCountFrequency (%)
9 1
 
1.4%
5 4
 
5.6%
4 1
 
1.4%
3 2
 
2.8%
2 9
 
12.5%
1 15
 
20.8%
0 40
55.6%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)31.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.069444
Minimum0
Maximum110
Zeros34
Zeros (%)47.2%
Negative0
Negative (%)0.0%
Memory size776.0 B
2024-03-15T10:21:55.675123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile71.25
Maximum110
Range110
Interquartile range (IQR)8

Descriptive statistics

Standard deviation26.312492
Coefficient of variation (CV)2.0132831
Kurtosis4.2494411
Mean13.069444
Median Absolute Deviation (MAD)1
Skewness2.2631571
Sum941
Variance692.34722
MonotonicityNot monotonic
2024-03-15T10:21:56.057405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 34
47.2%
1 8
 
11.1%
2 6
 
8.3%
8 3
 
4.2%
7 2
 
2.8%
20 2
 
2.8%
36 1
 
1.4%
34 1
 
1.4%
69 1
 
1.4%
11 1
 
1.4%
Other values (13) 13
 
18.1%
ValueCountFrequency (%)
0 34
47.2%
1 8
 
11.1%
2 6
 
8.3%
3 1
 
1.4%
5 1
 
1.4%
7 2
 
2.8%
8 3
 
4.2%
10 1
 
1.4%
11 1
 
1.4%
13 1
 
1.4%
ValueCountFrequency (%)
110 1
1.4%
97 1
1.4%
95 1
1.4%
74 1
1.4%
69 1
1.4%
66 1
1.4%
63 1
1.4%
61 1
1.4%
52 1
1.4%
44 1
1.4%

Interactions

2024-03-15T10:21:42.321650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:16.237014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:18.920830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:21.145463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:23.344762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:26.136585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:28.785815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:31.355790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:34.136441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:36.665823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:39.571532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:42.607394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:16.476560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:19.123140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:21.287339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:23.785523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:26.372309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:29.031404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:31.649916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:34.389382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:37.026907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:39.826978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:42.947407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:16.718196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:19.261003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:21.437306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:24.017979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:26.611709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:29.271453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:31.917891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:34.650584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:37.210585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:40.073768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:43.198600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:16.963759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:19.414476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:21.586776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:24.260259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:26.862615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:29.519827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:32.179753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:34.881085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:37.492426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:40.327106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:43.468090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:17.213838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:19.650284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:21.774743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:24.489375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:27.097045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:29.722031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:32.652796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:35.040960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:37.734818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:40.566496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:43.731187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:17.451125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:19.889472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:22.019223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:24.727899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:27.333371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:29.902918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:32.903576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:35.322313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:37.981667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:40.808041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:43.972633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:17.700922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:20.125622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:22.169030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:24.960765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:27.575831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:30.141246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:33.047116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:35.580333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:38.224273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:41.052469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:44.215849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:17.932627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:20.363208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:22.494069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:25.195115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:27.824636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:30.383878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:33.191743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:35.822401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:38.555717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:41.338339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:44.453358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:18.177958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:20.606528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:22.638446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:25.429846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:28.067139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:30.628985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:33.335026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:36.063941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:38.805792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:41.591621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:44.702778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:18.432809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:20.845622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:22.856523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:25.674748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:28.312732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:30.882368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:33.560405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:36.311072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:39.070730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:41.850468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:44.950844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:18.679355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:20.978684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:23.098743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:25.902413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:28.547667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:31.114072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:33.836622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:36.525836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:39.315713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:21:42.025034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:21:56.298789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년월기업형태구분기계재료금속전기전자정보통신화공건설기타제조사업서비스섬유환경농업기타
기준년월1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
기업형태구분0.0001.0000.7190.6990.6890.5400.6910.5370.7080.7300.7010.5120.4580.581
기계0.0000.7191.0000.9640.9000.8940.9700.8900.9260.9120.9010.9140.5700.897
재료금속0.0000.6990.9641.0000.9050.8910.9700.8370.8680.8750.9140.8260.5680.828
전기전자0.0000.6890.9000.9051.0000.9690.9520.8880.9160.9370.9690.8100.5370.899
정보통신0.0000.5400.8940.8910.9691.0000.9400.8980.9720.9760.9140.9470.4040.974
화공0.0000.6910.9700.9700.9520.9401.0000.9060.9230.9550.9440.8640.4210.897
건설0.0000.5370.8900.8370.8880.8980.9061.0000.8650.8730.8810.7270.2190.853
기타제조0.0000.7080.9260.8680.9160.9720.9230.8651.0000.9880.8550.8680.5510.929
사업서비스0.0000.7300.9120.8750.9370.9760.9550.8730.9881.0000.8810.9300.4080.939
섬유0.0000.7010.9010.9140.9690.9140.9440.8810.8550.8811.0000.7740.4870.845
환경0.0000.5120.9140.8260.8100.9470.8640.7270.8680.9300.7741.0000.4090.984
농업0.0000.4580.5700.5680.5370.4040.4210.2190.5510.4080.4870.4091.0000.486
기타0.0000.5810.8970.8280.8990.9740.8970.8530.9290.9390.8450.9840.4861.000
2024-03-15T10:21:56.542277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환경기업형태구분
환경1.0000.348
기업형태구분0.3481.000
2024-03-15T10:21:56.816955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기계재료금속전기전자정보통신화공건설기타제조사업서비스섬유농업기타기업형태구분환경
기계1.0000.9380.9480.8130.9180.7490.8260.8670.8940.4710.7520.4680.770
재료금속0.9381.0000.9000.8320.9100.7170.8130.8300.8830.4220.7370.4470.630
전기전자0.9480.9001.0000.8070.9390.7460.8570.8630.8920.5230.8070.4150.650
정보통신0.8130.8320.8071.0000.8240.6460.8520.8550.8790.5650.8080.3300.677
화공0.9180.9100.9390.8241.0000.6990.9040.9100.8680.5980.8190.4390.687
건설0.7490.7170.7460.6460.6991.0000.6170.6690.6790.2310.5940.3520.586
기타제조0.8260.8130.8570.8520.9040.6171.0000.9280.8200.6800.9150.4860.533
사업서비스0.8670.8300.8630.8550.9100.6690.9281.0000.8560.6330.8880.5110.637
섬유0.8940.8830.8920.8790.8680.6790.8200.8561.0000.5020.7970.4270.601
농업0.4710.4220.5230.5650.5980.2310.6800.6330.5021.0000.5820.2900.284
기타0.7520.7370.8070.8080.8190.5940.9150.8880.7970.5821.0000.3590.804
기업형태구분0.4680.4470.4150.3300.4390.3520.4860.5110.4270.2900.3591.0000.348
환경0.7700.6300.6500.6770.6870.5860.5330.6370.6010.2840.8040.3481.000

Missing values

2024-03-15T10:21:45.318224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:21:45.904689image/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

기준년월기업형태구분기계재료금속전기전자정보통신화공건설기타제조사업서비스섬유환경농업기타
02023-01-23개인65441516240272611007
12023-01-23주식21111015919514422118132240074
22023-01-23유한121020100010
32023-01-23합자000000000000
42023-01-23합명000000000000
52023-01-23기타0001101600050
62023-02-23개인1591334629400554123025
72023-02-23주식37417322129919640200197361166
82023-02-23유한832032600000
92023-02-23합자000000000001
기준년월기업형태구분기계재료금속전기전자정보통신화공건설기타제조사업서비스섬유환경농업기타
622023-11-23유한320010110000
632023-11-23합자000001000000
642023-11-23합명000000000000
652023-11-23기타0001001950012
662023-12-23개인343012721114174018
672023-12-23주식141829413283209976181134
682023-12-23유한310010320000
692023-12-23합자000000000000
702023-12-23합명000000000000
712023-12-23기타0002101410002