Overview

Dataset statistics

Number of variables11
Number of observations2451
Missing cells30
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory229.9 KiB
Average record size in memory96.1 B

Variable types

Numeric6
Categorical3
Text1
DateTime1

Dataset

Description인천광역시 중구 관내에 위치한 쓰레기종량제봉투 LOT정보에 대한 데이터 입니다. 파일명 인천광역시_중구_쓰레기종량제봉투_LOT정보 파일내용 인천광역시 중구 코드, 제작업체코드, 봉투종류 등
URLhttps://www.data.go.kr/data/15060077/fileData.do

Alerts

시작번호 has constant value ""Constant
데이터기준일자 has constant value ""Constant
판매가 is highly overall correlated with 도매가High correlation
도매가 is highly overall correlated with 판매가High correlation
연번 has unique valuesUnique
LOT코드 has unique valuesUnique
제작업체코드 has 72 (2.9%) zerosZeros

Reproduction

Analysis started2023-12-11 23:01:11.772254
Analysis finished2023-12-11 23:01:16.288208
Duration4.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct2451
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1226
Minimum1
Maximum2451
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.7 KiB
2023-12-12T08:01:16.380789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile123.5
Q1613.5
median1226
Q31838.5
95-th percentile2328.5
Maximum2451
Range2450
Interquartile range (IQR)1225

Descriptive statistics

Standard deviation707.68708
Coefficient of variation (CV)0.57723253
Kurtosis-1.2
Mean1226
Median Absolute Deviation (MAD)613
Skewness0
Sum3004926
Variance500821
MonotonicityStrictly increasing
2023-12-12T08:01:16.516663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1639 1
 
< 0.1%
1632 1
 
< 0.1%
1633 1
 
< 0.1%
1634 1
 
< 0.1%
1635 1
 
< 0.1%
1636 1
 
< 0.1%
1637 1
 
< 0.1%
1638 1
 
< 0.1%
1640 1
 
< 0.1%
Other values (2441) 2441
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2451 1
< 0.1%
2450 1
< 0.1%
2449 1
< 0.1%
2448 1
< 0.1%
2447 1
< 0.1%
2446 1
< 0.1%
2445 1
< 0.1%
2444 1
< 0.1%
2443 1
< 0.1%
2442 1
< 0.1%

LOT코드
Real number (ℝ)

UNIQUE 

Distinct2451
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43511.481
Minimum3
Maximum99998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.7 KiB
2023-12-12T08:01:16.649515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile448
Q113710.5
median41986
Q370723.5
95-th percentile96890.5
Maximum99998
Range99995
Interquartile range (IQR)57013

Descriptive statistics

Standard deviation31639.276
Coefficient of variation (CV)0.72714775
Kurtosis-1.2440424
Mean43511.481
Median Absolute Deviation (MAD)28695
Skewness0.20063027
Sum1.0664664 × 108
Variance1.0010438 × 109
MonotonicityNot monotonic
2023-12-12T08:01:16.790962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99903 1
 
< 0.1%
35820 1
 
< 0.1%
97097 1
 
< 0.1%
20877 1
 
< 0.1%
20878 1
 
< 0.1%
20879 1
 
< 0.1%
32607 1
 
< 0.1%
32608 1
 
< 0.1%
32609 1
 
< 0.1%
35821 1
 
< 0.1%
Other values (2441) 2441
99.6%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
ValueCountFrequency (%)
99998 1
< 0.1%
99997 1
< 0.1%
99996 1
< 0.1%
99995 1
< 0.1%
99994 1
< 0.1%
99993 1
< 0.1%
99992 1
< 0.1%
99991 1
< 0.1%
99990 1
< 0.1%
99989 1
< 0.1%

제작업체코드
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.375765
Minimum0
Maximum26
Zeros72
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size21.7 KiB
2023-12-12T08:01:16.938296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median11
Q314
95-th percentile15
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.3370695
Coefficient of variation (CV)0.41799997
Kurtosis1.3807249
Mean10.375765
Median Absolute Deviation (MAD)2
Skewness-0.59335087
Sum25431
Variance18.810172
MonotonicityNot monotonic
2023-12-12T08:01:17.104846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
11 1098
44.8%
14 501
20.4%
3 177
 
7.2%
6 135
 
5.5%
15 132
 
5.4%
13 120
 
4.9%
1 102
 
4.2%
0 72
 
2.9%
8 57
 
2.3%
26 24
 
1.0%
Other values (3) 33
 
1.3%
ValueCountFrequency (%)
0 72
 
2.9%
1 102
 
4.2%
3 177
 
7.2%
4 6
 
0.2%
6 135
 
5.5%
7 6
 
0.2%
8 57
 
2.3%
10 21
 
0.9%
11 1098
44.8%
13 120
 
4.9%
ValueCountFrequency (%)
26 24
 
1.0%
15 132
 
5.4%
14 501
20.4%
13 120
 
4.9%
11 1098
44.8%
10 21
 
0.9%
8 57
 
2.3%
7 6
 
0.2%
6 135
 
5.5%
4 6
 
0.2%

봉투단위
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
1
817 
2
817 
3
817 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 817
33.3%
2 817
33.3%
3 817
33.3%

Length

2023-12-12T08:01:17.246150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:17.390920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 817
33.3%
2 817
33.3%
3 817
33.3%

봉투종류
Text

Distinct59
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
2023-12-12T08:01:17.597561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length8.1578947
Min length6

Characters and Unicode

Total characters19995
Distinct characters68
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반용 5L
2nd row일반용 5L
3rd row일반용 5L
4th row일반용 10L
5th row일반용 10L
ValueCountFrequency (%)
일반용 1026
20.3%
음식물 861
17.0%
20l 489
9.7%
10l 351
 
6.9%
50l 318
 
6.3%
100l 303
 
6.0%
5l 273
 
5.4%
2l 216
 
4.3%
3l 195
 
3.9%
재사용 156
 
3.1%
Other values (34) 867
17.2%
2023-12-12T08:01:17.916040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2604
13.0%
0 2355
11.8%
L 2301
11.5%
용 1533
 
7.7%
일 1026
 
5.1%
반 1026
 
5.1%
물 1011
 
5.1%
음 861
 
4.3%
식 861
 
4.3%
1 789
 
3.9%
Other values (58) 5628
28.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9843
49.2%
Decimal Number 4854
24.3%
Space Separator 2604
 
13.0%
Uppercase Letter 2346
 
11.7%
Close Punctuation 159
 
0.8%
Open Punctuation 159
 
0.8%
Other Punctuation 30
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 1533
15.6%
일 1026
 
10.4%
반 1026
 
10.4%
물 1011
 
10.3%
음 861
 
8.7%
식 861
 
8.7%
사 333
 
3.4%
재 219
 
2.2%
계 180
 
1.8%
기 177
 
1.8%
Other values (43) 2616
26.6%
Decimal Number
ValueCountFrequency (%)
0 2355
48.5%
1 789
 
16.3%
5 720
 
14.8%
2 717
 
14.8%
3 210
 
4.3%
7 39
 
0.8%
6 24
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
L 2301
98.1%
P 15
 
0.6%
E 15
 
0.6%
T 15
 
0.6%
Space Separator
ValueCountFrequency (%)
2604
100.0%
Close Punctuation
ValueCountFrequency (%)
) 159
100.0%
Open Punctuation
ValueCountFrequency (%)
( 159
100.0%
Other Punctuation
ValueCountFrequency (%)
, 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9843
49.2%
Common 7806
39.0%
Latin 2346
 
11.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 1533
15.6%
일 1026
 
10.4%
반 1026
 
10.4%
물 1011
 
10.3%
음 861
 
8.7%
식 861
 
8.7%
사 333
 
3.4%
재 219
 
2.2%
계 180
 
1.8%
기 177
 
1.8%
Other values (43) 2616
26.6%
Common
ValueCountFrequency (%)
2604
33.4%
0 2355
30.2%
1 789
 
10.1%
5 720
 
9.2%
2 717
 
9.2%
3 210
 
2.7%
) 159
 
2.0%
( 159
 
2.0%
7 39
 
0.5%
, 30
 
0.4%
Latin
ValueCountFrequency (%)
L 2301
98.1%
P 15
 
0.6%
E 15
 
0.6%
T 15
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10152
50.8%
Hangul 9843
49.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2604
25.7%
0 2355
23.2%
L 2301
22.7%
1 789
 
7.8%
5 720
 
7.1%
2 717
 
7.1%
3 210
 
2.1%
) 159
 
1.6%
( 159
 
1.6%
7 39
 
0.4%
Other values (5) 99
 
1.0%
Hangul
ValueCountFrequency (%)
용 1533
15.6%
일 1026
 
10.4%
반 1026
 
10.4%
물 1011
 
10.3%
음 861
 
8.7%
식 861
 
8.7%
사 333
 
3.4%
재 219
 
2.2%
계 180
 
1.8%
기 177
 
1.8%
Other values (43) 2616
26.6%

시작번호
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
1
2451 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 2451
100.0%

Length

2023-12-12T08:01:18.154023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:18.244092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2451
100.0%

종료번호
Real number (ℝ)

Distinct256
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30927.512
Minimum1
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.7 KiB
2023-12-12T08:01:18.345929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q1100
median1000
Q330000
95-th percentile199900
Maximum1000000
Range999999
Interquartile range (IQR)29900

Descriptive statistics

Standard deviation70294.899
Coefficient of variation (CV)2.2728921
Kurtosis31.332626
Mean30927.512
Median Absolute Deviation (MAD)980
Skewness4.4013679
Sum75803333
Variance4.9413729 × 109
MonotonicityNot monotonic
2023-12-12T08:01:18.478644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 150
 
6.1%
1000 132
 
5.4%
100000 121
 
4.9%
200 111
 
4.5%
50000 103
 
4.2%
500 102
 
4.2%
50 102
 
4.2%
1 86
 
3.5%
300 76
 
3.1%
2000 67
 
2.7%
Other values (246) 1401
57.2%
ValueCountFrequency (%)
1 86
3.5%
2 2
 
0.1%
5 12
 
0.5%
6 5
 
0.2%
8 3
 
0.1%
9 2
 
0.1%
10 49
2.0%
14 2
 
0.1%
15 12
 
0.5%
16 3
 
0.1%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
700000 1
 
< 0.1%
650000 1
 
< 0.1%
600000 2
0.1%
550000 1
 
< 0.1%
500000 4
0.2%
438000 1
 
< 0.1%
409800 1
 
< 0.1%
400000 2
0.1%
340000 1
 
< 0.1%

판매가
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)1.8%
Missing15
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1487.1158
Minimum1
Maximum43600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.7 KiB
2023-12-12T08:01:18.630646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile70
Q1160
median620
Q31540
95-th percentile5290
Maximum43600
Range43599
Interquartile range (IQR)1380

Descriptive statistics

Standard deviation2824.7398
Coefficient of variation (CV)1.8994754
Kurtosis82.906802
Mean1487.1158
Median Absolute Deviation (MAD)460
Skewness7.161285
Sum3622614
Variance7979155
MonotonicityNot monotonic
2023-12-12T08:01:18.809842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
620 444
18.1%
310 315
12.9%
1540 240
9.8%
160 222
9.1%
3060 189
 
7.7%
70 126
 
5.1%
100 114
 
4.7%
120 90
 
3.7%
180 78
 
3.2%
1000 78
 
3.2%
Other values (33) 540
22.0%
ValueCountFrequency (%)
1 63
 
2.6%
70 126
5.1%
100 114
4.7%
120 90
3.7%
130 3
 
0.1%
160 222
9.1%
180 78
 
3.2%
232 3
 
0.1%
250 3
 
0.1%
300 48
 
2.0%
ValueCountFrequency (%)
43600 3
 
0.1%
32700 3
 
0.1%
22222 3
 
0.1%
13880 3
 
0.1%
10400 51
2.1%
10130 3
 
0.1%
7200 6
 
0.2%
6500 3
 
0.1%
5290 57
2.3%
5000 72
2.9%

도매가
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)1.7%
Missing15
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1368.4175
Minimum1
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size21.7 KiB
2023-12-12T08:01:18.964388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile60
Q1150
median570
Q31418
95-th percentile4850
Maximum40000
Range39999
Interquartile range (IQR)1268

Descriptive statistics

Standard deviation2607.7237
Coefficient of variation (CV)1.9056492
Kurtosis82.298313
Mean1368.4175
Median Absolute Deviation (MAD)420
Skewness7.1744593
Sum3333465
Variance6800222.8
MonotonicityNot monotonic
2023-12-12T08:01:19.102409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
570 444
18.1%
289 315
12.9%
1418 240
9.8%
150 222
9.1%
2821 189
 
7.7%
60 126
 
5.1%
90 114
 
4.7%
110 90
 
3.7%
910 78
 
3.2%
165 78
 
3.2%
Other values (32) 540
22.0%
ValueCountFrequency (%)
1 63
 
2.6%
60 126
5.1%
90 114
4.7%
110 90
3.7%
121 3
 
0.1%
150 222
9.1%
165 78
 
3.2%
231 3
 
0.1%
250 3
 
0.1%
275 48
 
2.0%
ValueCountFrequency (%)
40000 3
 
0.1%
30000 3
 
0.1%
22222 3
 
0.1%
12740 3
 
0.1%
9540 51
2.1%
9300 3
 
0.1%
6600 6
 
0.2%
5960 3
 
0.1%
4850 57
2.3%
4550 72
2.9%

LOT발생일
Date

Distinct229
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
Minimum2000-11-11 00:00:00
Maximum2022-08-23 00:00:00
2023-12-12T08:01:19.263419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:19.453551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.3 KiB
2023-08-08
2451 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-08
2nd row2023-08-08
3rd row2023-08-08
4th row2023-08-08
5th row2023-08-08

Common Values

ValueCountFrequency (%)
2023-08-08 2451
100.0%

Length

2023-12-12T08:01:19.616609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:01:19.727645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-08 2451
100.0%

Interactions

2023-12-12T08:01:15.206969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:12.301808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:12.750058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:13.204015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:13.777698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:14.607459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:15.319618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:12.377499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:12.825201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:13.279816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:13.886314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:14.690152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:15.422501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:12.448152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:12.900037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:13.400860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:13.996257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:14.781616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:15.516247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:12.521323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:12.978062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:13.488531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:14.080541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:14.882097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:15.621994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:12.596506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:13.054387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:13.568801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:14.166431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:14.980999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:15.739528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:12.666013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:13.122730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:13.652863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:14.246049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:01:15.087789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:01:19.812742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번LOT코드제작업체코드봉투단위봉투종류종료번호판매가도매가
연번1.0000.3770.7670.0000.6630.1190.2090.209
LOT코드0.3771.0000.3800.0000.5860.0540.1770.177
제작업체코드0.7670.3801.0000.0000.8890.0780.5520.552
봉투단위0.0000.0000.0001.0000.0000.4030.0000.000
봉투종류0.6630.5860.8890.0001.0000.0000.9890.989
종료번호0.1190.0540.0780.4030.0001.0000.0000.000
판매가0.2090.1770.5520.0000.9890.0001.0001.000
도매가0.2090.1770.5520.0000.9890.0001.0001.000
2023-12-12T08:01:19.972962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번LOT코드제작업체코드종료번호판매가도매가봉투단위
연번1.0000.0300.4670.0080.0390.0390.000
LOT코드0.0301.000-0.019-0.1110.0110.0110.000
제작업체코드0.467-0.0191.0000.0660.0420.0430.000
종료번호0.008-0.1110.0661.000-0.052-0.0520.280
판매가0.0390.0110.042-0.0521.0001.0000.000
도매가0.0390.0110.043-0.0521.0001.0000.000
봉투단위0.0000.0000.0000.2800.0000.0001.000

Missing values

2023-12-12T08:01:15.907404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:01:16.089683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T08:01:16.216509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번LOT코드제작업체코드봉투단위봉투종류시작번호종료번호판매가도매가LOT발생일데이터기준일자
0199903261일반용 5L111601502000-11-112023-08-08
1299904262일반용 5L1201601502000-11-112023-08-08
2399905263일반용 5L11001601502000-11-112023-08-08
3499906261일반용 10L112322502000-11-112023-08-08
4599907262일반용 10L1102322502000-11-112023-08-08
5699908263일반용 10L11002322502000-11-112023-08-08
6799912261일반용 20L114625002000-11-112023-08-08
7899913262일반용 20L1104625002000-11-112023-08-08
8999914263일반용 20L11004625002000-11-112023-08-08
91099915261일반용 50L11113412302000-11-112023-08-08
연번LOT코드제작업체코드봉투단위봉투종류시작번호종료번호판매가도매가LOT발생일데이터기준일자
2441244234733113음식물 5L11000003002752022-08-232023-08-08
2442244343194111음식물 2L11001201102022-08-232023-08-08
2443244443195112음식물 2L110001201102022-08-232023-08-08
2444244543196113음식물 2L11000001201102022-08-232023-08-08
2445244649782151음식물 필증 2L151201102022-08-232023-08-08
2446244749783152음식물 필증 2L15001201102022-08-232023-08-08
2447244849784153음식물 필증 2L1500001201102022-08-232023-08-08
2448244985845151음식물 필증 60L125360033002022-08-232023-08-08
2449245085846152음식물 필증 60L12500360033002022-08-232023-08-08
2450245185847153음식물 필증 60L150000360033002022-08-232023-08-08