Overview

Dataset statistics

Number of variables17
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory70.9 KiB
Average record size in memory145.3 B

Variable types

Text2
Numeric9
Boolean2
Categorical4

Dataset

Description해당 파일 데이터는 신용보증기금의 보증고객 창업기업 성과공유금 폐지에 대해 확인하실 수 있는 자료이니 데이터 활용에 참고하여 주시기 바랍니다.
Author신용보증기금
URLhttps://www.data.go.kr/data/15093238/fileData.do

Alerts

4기성과공유금액대상여부 has constant value ""Constant
4기납부여부 has constant value ""Constant
5기성과공유금액대상여부 has constant value ""Constant
5기납부여부 has constant value ""Constant
유효개시일자 has constant value ""Constant
유효종료일자 has constant value ""Constant
4기설정금액 is highly overall correlated with 5기설정금액High correlation
4기건별발급합계금액 is highly overall correlated with 4기납부대상금액 and 2 other fieldsHigh correlation
4기납부대상금액 is highly overall correlated with 4기건별발급합계금액 and 2 other fieldsHigh correlation
5기설정금액 is highly overall correlated with 4기설정금액High correlation
5기건별발급합계금액 is highly overall correlated with 4기건별발급합계금액 and 2 other fieldsHigh correlation
5기납부대상금액 is highly overall correlated with 4기건별발급합계금액 and 2 other fieldsHigh correlation
4기건별발급합계금액 has 413 (82.6%) zerosZeros
4기납부대상금액 has 413 (82.6%) zerosZeros
5기건별발급합계금액 has 413 (82.6%) zerosZeros
5기납부대상금액 has 413 (82.6%) zerosZeros

Reproduction

Analysis started2023-12-12 11:48:16.406002
Analysis finished2023-12-12 11:48:27.068212
Duration10.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct331
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T20:48:27.312876image/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

Unique247 ?
Unique (%)49.4%

Sample

1st row9bsBkYGgH6
2nd row9bsBkYGgH6
3rd row9bv3YIys6C
4th row9bqa9z2lhu
5th row9bp1BBjC7z
ValueCountFrequency (%)
9bq3ddwuet 14
 
2.8%
9bsfmfrokv 9
 
1.8%
9bppb9hl4o 9
 
1.8%
9bujcec2mr 8
 
1.6%
9btmyvlyrb 7
 
1.4%
9bqdw0rlpg 6
 
1.2%
9bfckrnjdb 6
 
1.2%
9bmdlhzmpk 6
 
1.2%
9bwpqigjzv 5
 
1.0%
9bg9fsu9qq 5
 
1.0%
Other values (321) 425
85.0%
2023-12-12T20:48:27.863353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 573
 
11.5%
b 553
 
11.1%
q 129
 
2.6%
p 123
 
2.5%
u 116
 
2.3%
a 96
 
1.9%
o 94
 
1.9%
n 93
 
1.9%
t 90
 
1.8%
D 86
 
1.7%
Other values (52) 3047
60.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2455
49.1%
Uppercase Letter 1519
30.4%
Decimal Number 1026
20.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 553
22.5%
q 129
 
5.3%
p 123
 
5.0%
u 116
 
4.7%
a 96
 
3.9%
o 94
 
3.8%
n 93
 
3.8%
t 90
 
3.7%
s 85
 
3.5%
m 75
 
3.1%
Other values (16) 1001
40.8%
Uppercase Letter
ValueCountFrequency (%)
D 86
 
5.7%
F 76
 
5.0%
R 73
 
4.8%
K 70
 
4.6%
E 70
 
4.6%
X 69
 
4.5%
L 68
 
4.5%
J 68
 
4.5%
Q 65
 
4.3%
Z 61
 
4.0%
Other values (16) 813
53.5%
Decimal Number
ValueCountFrequency (%)
9 573
55.8%
2 70
 
6.8%
3 65
 
6.3%
6 63
 
6.1%
5 48
 
4.7%
4 46
 
4.5%
1 43
 
4.2%
7 43
 
4.2%
8 43
 
4.2%
0 32
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 3974
79.5%
Common 1026
 
20.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 553
 
13.9%
q 129
 
3.2%
p 123
 
3.1%
u 116
 
2.9%
a 96
 
2.4%
o 94
 
2.4%
n 93
 
2.3%
t 90
 
2.3%
D 86
 
2.2%
s 85
 
2.1%
Other values (42) 2509
63.1%
Common
ValueCountFrequency (%)
9 573
55.8%
2 70
 
6.8%
3 65
 
6.3%
6 63
 
6.1%
5 48
 
4.7%
4 46
 
4.5%
1 43
 
4.2%
7 43
 
4.2%
8 43
 
4.2%
0 32
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9 573
 
11.5%
b 553
 
11.1%
q 129
 
2.6%
p 123
 
2.5%
u 116
 
2.3%
a 96
 
1.9%
o 94
 
1.9%
n 93
 
1.9%
t 90
 
1.8%
D 86
 
1.7%
Other values (52) 3047
60.9%

이력일련번호
Real number (ℝ)

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.064
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:48:28.048921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum14
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1082679
Coefficient of variation (CV)1.0214476
Kurtosis7.4399759
Mean2.064
Median Absolute Deviation (MAD)0
Skewness2.6221064
Sum1032
Variance4.4447936
MonotonicityNot monotonic
2023-12-12T20:48:28.215724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 331
66.2%
2 57
 
11.4%
3 41
 
8.2%
5 17
 
3.4%
4 13
 
2.6%
6 13
 
2.6%
7 9
 
1.8%
9 6
 
1.2%
8 5
 
1.0%
11 3
 
0.6%
Other values (4) 5
 
1.0%
ValueCountFrequency (%)
1 331
66.2%
2 57
 
11.4%
3 41
 
8.2%
4 13
 
2.6%
5 17
 
3.4%
6 13
 
2.6%
7 9
 
1.8%
8 5
 
1.0%
9 6
 
1.2%
10 2
 
0.4%
ValueCountFrequency (%)
14 1
 
0.2%
13 1
 
0.2%
12 1
 
0.2%
11 3
 
0.6%
10 2
 
0.4%
9 6
 
1.2%
8 5
 
1.0%
7 9
1.8%
6 13
2.6%
5 17
3.4%
Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-12T20:48:28.357959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

4기설정금액
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5859200
Minimum600000
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:48:28.496188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600000
5-th percentile2000000
Q14000000
median6000000
Q36000000
95-th percentile10000000
Maximum10000000
Range9400000
Interquartile range (IQR)2000000

Descriptive statistics

Standard deviation2331619.2
Coefficient of variation (CV)0.39794157
Kurtosis-0.16434394
Mean5859200
Median Absolute Deviation (MAD)300000
Skewness0.25647557
Sum2.9296 × 109
Variance5.4364483 × 1012
MonotonicityNot monotonic
2023-12-12T20:48:28.664021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
6000000 239
47.8%
10000000 83
 
16.6%
4000000 64
 
12.8%
3000000 22
 
4.4%
2000000 16
 
3.2%
5000000 15
 
3.0%
6300000 14
 
2.8%
1000000 7
 
1.4%
1600000 6
 
1.2%
2400000 6
 
1.2%
Other values (15) 28
 
5.6%
ValueCountFrequency (%)
600000 1
 
0.2%
800000 1
 
0.2%
1000000 7
1.4%
1200000 1
 
0.2%
1300000 1
 
0.2%
1400000 1
 
0.2%
1600000 6
 
1.2%
1800000 4
 
0.8%
2000000 16
3.2%
2400000 6
 
1.2%
ValueCountFrequency (%)
10000000 83
 
16.6%
9000000 2
 
0.4%
8000000 4
 
0.8%
7000000 2
 
0.4%
6300000 14
 
2.8%
6000000 239
47.8%
5500000 2
 
0.4%
5000000 15
 
3.0%
4500000 1
 
0.2%
4400000 1
 
0.2%

4기건별발급합계금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408086
Minimum0
Maximum10800000
Zeros413
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:48:28.862517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2241000
Maximum10800000
Range10800000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1253099
Coefficient of variation (CV)3.0706737
Kurtosis25.156406
Mean408086
Median Absolute Deviation (MAD)0
Skewness4.5612751
Sum2.04043 × 108
Variance1.570257 × 1012
MonotonicityNot monotonic
2023-12-12T20:48:29.056677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 413
82.6%
1800000 10
 
2.0%
1260000 8
 
1.6%
1980000 7
 
1.4%
900000 7
 
1.4%
1955000 4
 
0.8%
360000 4
 
0.8%
540000 4
 
0.8%
1170000 3
 
0.6%
630000 2
 
0.4%
Other values (32) 38
 
7.6%
ValueCountFrequency (%)
0 413
82.6%
255000 1
 
0.2%
340000 1
 
0.2%
360000 4
 
0.8%
540000 4
 
0.8%
630000 2
 
0.4%
680000 1
 
0.2%
799000 1
 
0.2%
900000 7
 
1.4%
986000 1
 
0.2%
ValueCountFrequency (%)
10800000 1
0.2%
9820000 1
0.2%
7920000 2
0.4%
7560000 1
0.2%
6250000 2
0.4%
5800000 1
0.2%
4700000 1
0.2%
4680000 1
0.2%
4400000 1
0.2%
4000000 1
0.2%

4기납부대상금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean406486
Minimum0
Maximum10000000
Zeros413
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:48:29.232702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2241000
Maximum10000000
Range10000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1240248.5
Coefficient of variation (CV)3.0511468
Kurtosis23.624732
Mean406486
Median Absolute Deviation (MAD)0
Skewness4.4555712
Sum2.03243 × 108
Variance1.5382162 × 1012
MonotonicityNot monotonic
2023-12-12T20:48:29.422360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 413
82.6%
1800000 10
 
2.0%
1260000 8
 
1.6%
1980000 7
 
1.4%
900000 7
 
1.4%
1955000 4
 
0.8%
360000 4
 
0.8%
540000 4
 
0.8%
1170000 3
 
0.6%
630000 2
 
0.4%
Other values (32) 38
 
7.6%
ValueCountFrequency (%)
0 413
82.6%
255000 1
 
0.2%
340000 1
 
0.2%
360000 4
 
0.8%
540000 4
 
0.8%
630000 2
 
0.4%
680000 1
 
0.2%
799000 1
 
0.2%
900000 7
 
1.4%
986000 1
 
0.2%
ValueCountFrequency (%)
10000000 1
0.2%
9820000 1
0.2%
7920000 2
0.4%
7560000 1
0.2%
6250000 2
0.4%
5800000 1
0.2%
4700000 1
0.2%
4680000 1
0.2%
4400000 1
0.2%
4000000 1
0.2%

4기납부여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
500
100.0%

Length

2023-12-12T20:48:29.632631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:48:29.755612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.
Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size632.0 B
False
500 
ValueCountFrequency (%)
False 500
100.0%
2023-12-12T20:48:29.855829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

5기설정금액
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5859200
Minimum600000
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:48:29.975780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600000
5-th percentile2000000
Q14000000
median6000000
Q36000000
95-th percentile10000000
Maximum10000000
Range9400000
Interquartile range (IQR)2000000

Descriptive statistics

Standard deviation2331619.2
Coefficient of variation (CV)0.39794157
Kurtosis-0.16434394
Mean5859200
Median Absolute Deviation (MAD)300000
Skewness0.25647557
Sum2.9296 × 109
Variance5.4364483 × 1012
MonotonicityNot monotonic
2023-12-12T20:48:30.121054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
6000000 239
47.8%
10000000 83
 
16.6%
4000000 64
 
12.8%
3000000 22
 
4.4%
2000000 16
 
3.2%
5000000 15
 
3.0%
6300000 14
 
2.8%
1000000 7
 
1.4%
1600000 6
 
1.2%
2400000 6
 
1.2%
Other values (15) 28
 
5.6%
ValueCountFrequency (%)
600000 1
 
0.2%
800000 1
 
0.2%
1000000 7
1.4%
1200000 1
 
0.2%
1300000 1
 
0.2%
1400000 1
 
0.2%
1600000 6
 
1.2%
1800000 4
 
0.8%
2000000 16
3.2%
2400000 6
 
1.2%
ValueCountFrequency (%)
10000000 83
 
16.6%
9000000 2
 
0.4%
8000000 4
 
0.8%
7000000 2
 
0.4%
6300000 14
 
2.8%
6000000 239
47.8%
5500000 2
 
0.4%
5000000 15
 
3.0%
4500000 1
 
0.2%
4400000 1
 
0.2%

5기건별발급합계금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408086
Minimum0
Maximum10800000
Zeros413
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:48:30.297652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2241000
Maximum10800000
Range10800000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1253099
Coefficient of variation (CV)3.0706737
Kurtosis25.156406
Mean408086
Median Absolute Deviation (MAD)0
Skewness4.5612751
Sum2.04043 × 108
Variance1.570257 × 1012
MonotonicityNot monotonic
2023-12-12T20:48:30.495432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 413
82.6%
1800000 10
 
2.0%
1260000 8
 
1.6%
1980000 7
 
1.4%
900000 7
 
1.4%
1955000 4
 
0.8%
360000 4
 
0.8%
540000 4
 
0.8%
1170000 3
 
0.6%
630000 2
 
0.4%
Other values (32) 38
 
7.6%
ValueCountFrequency (%)
0 413
82.6%
255000 1
 
0.2%
340000 1
 
0.2%
360000 4
 
0.8%
540000 4
 
0.8%
630000 2
 
0.4%
680000 1
 
0.2%
799000 1
 
0.2%
900000 7
 
1.4%
986000 1
 
0.2%
ValueCountFrequency (%)
10800000 1
0.2%
9820000 1
0.2%
7920000 2
0.4%
7560000 1
0.2%
6250000 2
0.4%
5800000 1
0.2%
4700000 1
0.2%
4680000 1
0.2%
4400000 1
0.2%
4000000 1
0.2%

5기납부대상금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean406486
Minimum0
Maximum10000000
Zeros413
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:48:30.716371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2241000
Maximum10000000
Range10000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1240248.5
Coefficient of variation (CV)3.0511468
Kurtosis23.624732
Mean406486
Median Absolute Deviation (MAD)0
Skewness4.4555712
Sum2.03243 × 108
Variance1.5382162 × 1012
MonotonicityNot monotonic
2023-12-12T20:48:30.897842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 413
82.6%
1800000 10
 
2.0%
1260000 8
 
1.6%
1980000 7
 
1.4%
900000 7
 
1.4%
1955000 4
 
0.8%
360000 4
 
0.8%
540000 4
 
0.8%
1170000 3
 
0.6%
630000 2
 
0.4%
Other values (32) 38
 
7.6%
ValueCountFrequency (%)
0 413
82.6%
255000 1
 
0.2%
340000 1
 
0.2%
360000 4
 
0.8%
540000 4
 
0.8%
630000 2
 
0.4%
680000 1
 
0.2%
799000 1
 
0.2%
900000 7
 
1.4%
986000 1
 
0.2%
ValueCountFrequency (%)
10000000 1
0.2%
9820000 1
0.2%
7920000 2
0.4%
7560000 1
0.2%
6250000 2
0.4%
5800000 1
0.2%
4700000 1
0.2%
4680000 1
0.2%
4400000 1
0.2%
4000000 1
0.2%

5기납부여부
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
500 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
500
100.0%

Length

2023-12-12T20:48:31.079001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:48:31.178005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

유효개시일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T20:48:31.290700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:48:31.392120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

유효종료일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
00:00.0
500 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00.0
2nd row00:00.0
3rd row00:00.0
4th row00:00.0
5th row00:00.0

Common Values

ValueCountFrequency (%)
00:00.0 500
100.0%

Length

2023-12-12T20:48:31.510053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:48:31.632534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
00:00.0 500
100.0%

최종수정수
Real number (ℝ)

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.238
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:48:31.726367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum14
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.1490912
Coefficient of variation (CV)0.96027312
Kurtosis6.3196425
Mean2.238
Median Absolute Deviation (MAD)0
Skewness2.4123969
Sum1119
Variance4.6185932
MonotonicityNot monotonic
2023-12-12T20:48:31.854184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 281
56.2%
2 98
 
19.6%
3 36
 
7.2%
4 23
 
4.6%
5 17
 
3.4%
6 14
 
2.8%
7 10
 
2.0%
8 7
 
1.4%
9 5
 
1.0%
11 3
 
0.6%
Other values (4) 6
 
1.2%
ValueCountFrequency (%)
1 281
56.2%
2 98
 
19.6%
3 36
 
7.2%
4 23
 
4.6%
5 17
 
3.4%
6 14
 
2.8%
7 10
 
2.0%
8 7
 
1.4%
9 5
 
1.0%
10 3
 
0.6%
ValueCountFrequency (%)
14 1
 
0.2%
13 1
 
0.2%
12 1
 
0.2%
11 3
 
0.6%
10 3
 
0.6%
9 5
 
1.0%
8 7
1.4%
7 10
2.0%
6 14
2.8%
5 17
3.4%
Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-12T20:48:32.249184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3500
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique498 ?
Unique (%)99.6%

Sample

1st row03:10.8
2nd row06:34.1
3rd row04:00.0
4th row07:45.6
5th row34:08.3
ValueCountFrequency (%)
32:33.7 2
 
0.4%
06:15.7 1
 
0.2%
44:42.6 1
 
0.2%
33:00.2 1
 
0.2%
43:51.6 1
 
0.2%
49:17.6 1
 
0.2%
00:24.4 1
 
0.2%
32:27.1 1
 
0.2%
21:44.4 1
 
0.2%
21:51.5 1
 
0.2%
Other values (489) 489
97.8%
2023-12-12T20:48:32.878421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 351
10.0%
2 339
9.7%
1 321
9.2%
0 319
9.1%
4 315
9.0%
3 296
8.5%
9 149
 
4.3%
7 143
 
4.1%
Other values (2) 267
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2500
71.4%
Other Punctuation 1000
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 351
14.0%
2 339
13.6%
1 321
12.8%
0 319
12.8%
4 315
12.6%
3 296
11.8%
9 149
6.0%
7 143
5.7%
6 138
 
5.5%
8 129
 
5.2%
Other Punctuation
ValueCountFrequency (%)
: 500
50.0%
. 500
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 351
10.0%
2 339
9.7%
1 321
9.2%
0 319
9.1%
4 315
9.0%
3 296
8.5%
9 149
 
4.3%
7 143
 
4.1%
Other values (2) 267
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 500
14.3%
. 500
14.3%
5 351
10.0%
2 339
9.7%
1 321
9.2%
0 319
9.1%
4 315
9.0%
3 296
8.5%
9 149
 
4.3%
7 143
 
4.1%
Other values (2) 267
7.6%

처리직원번호
Real number (ℝ)

Distinct254
Distinct (%)50.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3901.106
Minimum1865
Maximum4805
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-12T20:48:33.068793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1865
5-th percentile2770
Q13437.5
median3984
Q34453.75
95-th percentile4761
Maximum4805
Range2940
Interquartile range (IQR)1016.25

Descriptive statistics

Standard deviation661.84413
Coefficient of variation (CV)0.16965551
Kurtosis-0.15148227
Mean3901.106
Median Absolute Deviation (MAD)523
Skewness-0.64541876
Sum1950553
Variance438037.65
MonotonicityNot monotonic
2023-12-12T20:48:33.248412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4761 14
 
2.8%
4530 10
 
2.0%
3465 9
 
1.8%
3461 9
 
1.8%
4285 8
 
1.6%
4456 8
 
1.6%
4784 7
 
1.4%
3291 7
 
1.4%
3504 7
 
1.4%
3360 7
 
1.4%
Other values (244) 414
82.8%
ValueCountFrequency (%)
1865 2
0.4%
1875 1
0.2%
1916 2
0.4%
2074 2
0.4%
2082 1
0.2%
2111 1
0.2%
2253 1
0.2%
2345 1
0.2%
2394 1
0.2%
2450 1
0.2%
ValueCountFrequency (%)
4805 1
 
0.2%
4803 1
 
0.2%
4798 2
 
0.4%
4795 1
 
0.2%
4784 7
1.4%
4780 1
 
0.2%
4765 2
 
0.4%
4761 14
2.8%
4759 3
 
0.6%
4756 1
 
0.2%

Interactions

2023-12-12T20:48:25.266252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:16.953603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:17.991381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:19.095288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:20.482608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:21.437199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:22.379123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:23.254176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:24.304260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:25.376004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:17.051533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:18.101034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:19.219222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:20.600069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:21.556317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:22.493047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:23.399700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:24.412688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:25.469391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:17.174146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:18.221864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:19.337158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:20.717466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:21.655937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:22.590076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:23.540031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:24.521754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:25.582885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:17.310377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:18.358158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:19.467048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:20.811666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:21.765475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:22.681319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:23.642168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:24.629012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:25.687199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:17.424248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:18.504390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:19.880803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:20.926726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:21.872099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:22.769820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:23.734856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:24.734218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:25.784436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:17.519223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:18.619887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:19.999861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:21.018751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:21.976023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:22.853751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:23.851701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:24.845572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:25.910567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:17.624601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:18.734869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:20.109496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:21.114616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:22.063865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:22.944409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:23.963284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:24.938524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:26.043233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:17.753814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:18.849239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:20.218290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:21.214009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:22.153114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:23.040388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:24.081772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:25.032135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:26.163970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:17.875174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:18.986942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:20.361445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:21.328810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:22.261856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:23.142692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:24.190681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:48:25.152101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:48:33.376833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호4기설정금액4기건별발급합계금액4기납부대상금액5기설정금액5기건별발급합계금액5기납부대상금액최종수정수처리직원번호
이력일련번호1.0000.3920.0000.0000.3920.0000.0000.9570.000
4기설정금액0.3921.0000.1890.2031.0000.1890.2030.4050.469
4기건별발급합계금액0.0000.1891.0000.9980.1891.0000.9980.0000.207
4기납부대상금액0.0000.2030.9981.0000.2030.9981.0000.0000.361
5기설정금액0.3921.0000.1890.2031.0000.1890.2030.4050.469
5기건별발급합계금액0.0000.1891.0000.9980.1891.0000.9980.0000.207
5기납부대상금액0.0000.2030.9981.0000.2030.9981.0000.0000.361
최종수정수0.9570.4050.0000.0000.4050.0000.0001.0000.000
처리직원번호0.0000.4690.2070.3610.4690.2070.3610.0001.000
2023-12-12T20:48:33.537514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이력일련번호4기설정금액4기건별발급합계금액4기납부대상금액5기설정금액5기건별발급합계금액5기납부대상금액최종수정수처리직원번호
이력일련번호1.0000.067-0.082-0.0820.067-0.082-0.0820.4760.201
4기설정금액0.0671.0000.0900.0901.0000.0900.0900.068-0.067
4기건별발급합계금액-0.0820.0901.0001.0000.0901.0001.000-0.1070.040
4기납부대상금액-0.0820.0901.0001.0000.0901.0001.000-0.1070.040
5기설정금액0.0671.0000.0900.0901.0000.0900.0900.068-0.067
5기건별발급합계금액-0.0820.0901.0001.0000.0901.0001.000-0.1070.040
5기납부대상금액-0.0820.0901.0001.0000.0901.0001.000-0.1070.040
최종수정수0.4760.068-0.107-0.1070.068-0.107-0.1071.0000.191
처리직원번호0.201-0.0670.0400.040-0.0670.0400.0400.1911.000

Missing values

2023-12-12T20:48:26.344802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:48:26.949341image/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

기업고객ID이력일련번호4기성과공유금액대상여부4기설정금액4기건별발급합계금액4기납부대상금액4기납부여부5기성과공유금액대상여부5기설정금액5기건별발급합계금액5기납부대상금액5기납부여부유효개시일자유효종료일자최종수정수처리시각처리직원번호
09bsBkYGgH61N600000000N60000000000:00.000:00.0303:10.84153
19bsBkYGgH63N600000000N60000000000:00.000:00.0206:34.14153
29bv3YIys6C1N600000000N60000000000:00.000:00.0104:00.03543
39bqa9z2lhu1N60000000N6000000000:00.000:00.0107:45.64334
49bp1BBjC7z1N600000000N60000000000:00.000:00.0134:08.34269
59bth6yOZSq1N600000000N60000000000:00.000:00.0119:51.14160
69bpJjjl7JK1N500000000N50000000000:00.000:00.0332:24.52111
79bvWwFOUFK1N240000000N24000000000:00.000:00.0137:56.13163
89bxVESED4M1N600000000N60000000000:00.000:00.0126:28.33082
99bt2QVEPha1N1000000000N100000000000:00.000:00.0101:36.84587
기업고객ID이력일련번호4기성과공유금액대상여부4기설정금액4기건별발급합계금액4기납부대상금액4기납부여부5기성과공유금액대상여부5기설정금액5기건별발급합계금액5기납부대상금액5기납부여부유효개시일자유효종료일자최종수정수처리시각처리직원번호
4909bitebRV451N1000000075600007560000N100000007560000756000000:00.000:00.0202:22.03676
4919bsCRHQ7es1N1000000000N100000000000:00.000:00.0156:53.32966
4929boKFV2xQe1N600000000N60000000000:00.000:00.0127:57.14380
4939a78HW89OP1N600000000N60000000000:00.000:00.0144:20.64428
4949bghuGWjFr1N1000000079200007920000N100000007920000792000000:00.000:00.0113:50.94406
4959a6DKaEtma1N600000000N60000000000:00.000:00.0148:47.24626
4969bq2cF4LJF1N200000000N20000000000:00.000:00.0149:45.94037
4979bqXbeEK9J1N600000000N60000000000:00.000:00.0223:45.53940
4989bqXbeEK9J2N600000000N60000000000:00.000:00.0120:59.73940
4999bsZBZujRX1N1000000000N100000000000:00.000:00.0133:29.53032