Overview

Dataset statistics

Number of variables19
Number of observations124
Missing cells1483
Missing cells (%)62.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.6 KiB
Average record size in memory170.1 B

Variable types

Categorical4
Numeric8
Unsupported7

Dataset

Description종량제봉투(가정및사업장용)판매가격 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=BU8K5EE20U45LH6LIY4P20641884&infSeq=2

Alerts

100ℓ(원/매) is highly overall correlated with 3ℓ(원/매) and 6 other fieldsHigh correlation
구분명 is highly overall correlated with 5ℓ(원/매) and 2 other fieldsHigh correlation
시군명 is highly overall correlated with 100ℓ(원/매)High correlation
2ℓ(원/매) is highly overall correlated with 3ℓ(원/매) and 4 other fieldsHigh correlation
3ℓ(원/매) is highly overall correlated with 2ℓ(원/매) and 6 other fieldsHigh correlation
5ℓ(원/매) is highly overall correlated with 2ℓ(원/매) and 8 other fieldsHigh correlation
10ℓ(원/매) is highly overall correlated with 2ℓ(원/매) and 7 other fieldsHigh correlation
20ℓ(원/매) is highly overall correlated with 2ℓ(원/매) and 7 other fieldsHigh correlation
30ℓ(원/매) is highly overall correlated with 2ℓ(원/매) and 5 other fieldsHigh correlation
50ℓ(원/매) is highly overall correlated with 3ℓ(원/매) and 6 other fieldsHigh correlation
75ℓ(원/매) is highly overall correlated with 3ℓ(원/매) and 6 other fieldsHigh correlation
100ℓ(원/매) is highly imbalanced (89.9%)Imbalance
120ℓ(원/매) is highly imbalanced (93.2%)Imbalance
2ℓ(원/매) has 104 (83.9%) missing valuesMissing
3ℓ(원/매) has 86 (69.4%) missing valuesMissing
5ℓ(원/매) has 64 (51.6%) missing valuesMissing
10ℓ(원/매) has 55 (44.4%) missing valuesMissing
20ℓ(원/매) has 41 (33.1%) missing valuesMissing
30ℓ(원/매) has 112 (90.3%) missing valuesMissing
40ℓ(원/매) has 124 (100.0%) missing valuesMissing
50ℓ(원/매) has 68 (54.8%) missing valuesMissing
60ℓ(원/매) has 124 (100.0%) missing valuesMissing
75ℓ(원/매) has 85 (68.5%) missing valuesMissing
125ℓ(원/매) has 124 (100.0%) missing valuesMissing
50kg(원/매) has 124 (100.0%) missing valuesMissing
80kg(원/매) has 124 (100.0%) missing valuesMissing
롤봉투37ℓ(원/매) has 124 (100.0%) missing valuesMissing
압축100ℓ(원/매) has 124 (100.0%) missing valuesMissing
40ℓ(원/매) is an unsupported type, check if it needs cleaning or further analysisUnsupported
60ℓ(원/매) is an unsupported type, check if it needs cleaning or further analysisUnsupported
125ℓ(원/매) is an unsupported type, check if it needs cleaning or further analysisUnsupported
50kg(원/매) is an unsupported type, check if it needs cleaning or further analysisUnsupported
80kg(원/매) is an unsupported type, check if it needs cleaning or further analysisUnsupported
롤봉투37ℓ(원/매) is an unsupported type, check if it needs cleaning or further analysisUnsupported
압축100ℓ(원/매) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 22:28:54.268085
Analysis finished2023-12-10 22:29:01.543796
Duration7.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
가평군
 
4
고양시
 
4
과천시
 
4
광명시
 
4
광주시
 
4
Other values (26)
104 

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가평군
2nd row가평군
3rd row가평군
4th row가평군
5th row고양시

Common Values

ValueCountFrequency (%)
가평군 4
 
3.2%
고양시 4
 
3.2%
과천시 4
 
3.2%
광명시 4
 
3.2%
광주시 4
 
3.2%
구리시 4
 
3.2%
군포시 4
 
3.2%
김포시 4
 
3.2%
남양주시 4
 
3.2%
동두천시 4
 
3.2%
Other values (21) 84
67.7%

Length

2023-12-11T07:29:01.619002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가평군 4
 
3.2%
안양시 4
 
3.2%
하남시 4
 
3.2%
포천시 4
 
3.2%
평택시 4
 
3.2%
파주시 4
 
3.2%
이천시 4
 
3.2%
의정부시 4
 
3.2%
의왕시 4
 
3.2%
용인시 4
 
3.2%
Other values (21) 84
67.7%

구분명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
특수규격 봉투(PP마대 등)
31 
사업장 생활폐기물용 봉투
31 
가정용 봉투
31 
음식물용(전용봉투)
31 

Length

Max length15
Median length11.5
Mean length11
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row특수규격 봉투(PP마대 등)
2nd row사업장 생활폐기물용 봉투
3rd row가정용 봉투
4th row음식물용(전용봉투)
5th row가정용 봉투

Common Values

ValueCountFrequency (%)
특수규격 봉투(PP마대 등) 31
25.0%
사업장 생활폐기물용 봉투 31
25.0%
가정용 봉투 31
25.0%
음식물용(전용봉투) 31
25.0%

Length

2023-12-11T07:29:01.727345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:29:01.828225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
봉투 62
22.2%
특수규격 31
11.1%
봉투(pp마대 31
11.1%
등 31
11.1%
사업장 31
11.1%
생활폐기물용 31
11.1%
가정용 31
11.1%
음식물용(전용봉투 31
11.1%

2ℓ(원/매)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)30.0%
Missing104
Missing (%)83.9%
Infinite0
Infinite (%)0.0%
Mean71
Minimum50
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T07:29:01.934148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile50
Q150
median60
Q382.5
95-th percentile103
Maximum160
Range110
Interquartile range (IQR)32.5

Descriptive statistics

Standard deviation27.890764
Coefficient of variation (CV)0.39282767
Kurtosis4.3944086
Mean71
Median Absolute Deviation (MAD)10
Skewness1.9235427
Sum1420
Variance777.89474
MonotonicityNot monotonic
2023-12-11T07:29:02.109570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
50 7
 
5.6%
60 6
 
4.8%
100 3
 
2.4%
80 2
 
1.6%
90 1
 
0.8%
160 1
 
0.8%
(Missing) 104
83.9%
ValueCountFrequency (%)
50 7
5.6%
60 6
4.8%
80 2
 
1.6%
90 1
 
0.8%
100 3
2.4%
160 1
 
0.8%
ValueCountFrequency (%)
160 1
 
0.8%
100 3
2.4%
90 1
 
0.8%
80 2
 
1.6%
60 6
4.8%
50 7
5.6%

3ℓ(원/매)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)31.6%
Missing86
Missing (%)69.4%
Infinite0
Infinite (%)0.0%
Mean105.26316
Minimum70
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T07:29:02.238511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile70
Q180
median90
Q3120
95-th percentile163
Maximum240
Range170
Interquartile range (IQR)40

Descriptive statistics

Standard deviation36.148107
Coefficient of variation (CV)0.34340702
Kurtosis4.045444
Mean105.26316
Median Absolute Deviation (MAD)20
Skewness1.7292438
Sum4000
Variance1306.6856
MonotonicityNot monotonic
2023-12-11T07:29:02.352078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
90 9
 
7.3%
70 7
 
5.6%
80 5
 
4.0%
120 3
 
2.4%
140 3
 
2.4%
110 3
 
2.4%
100 2
 
1.6%
130 2
 
1.6%
150 1
 
0.8%
160 1
 
0.8%
Other values (2) 2
 
1.6%
(Missing) 86
69.4%
ValueCountFrequency (%)
70 7
5.6%
80 5
4.0%
90 9
7.3%
100 2
 
1.6%
110 3
 
2.4%
120 3
 
2.4%
130 2
 
1.6%
140 3
 
2.4%
150 1
 
0.8%
160 1
 
0.8%
ValueCountFrequency (%)
240 1
 
0.8%
180 1
 
0.8%
160 1
 
0.8%
150 1
 
0.8%
140 3
 
2.4%
130 2
 
1.6%
120 3
 
2.4%
110 3
 
2.4%
100 2
 
1.6%
90 9
7.3%

5ℓ(원/매)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)30.0%
Missing64
Missing (%)51.6%
Infinite0
Infinite (%)0.0%
Mean201.83333
Minimum120
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T07:29:02.460324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile120
Q1137.5
median150
Q3192.5
95-th percentile292.5
Maximum2000
Range1880
Interquartile range (IQR)55

Descriptive statistics

Standard deviation242.44861
Coefficient of variation (CV)1.2012318
Kurtosis53.632411
Mean201.83333
Median Absolute Deviation (MAD)25
Skewness7.1545086
Sum12110
Variance58781.328
MonotonicityNot monotonic
2023-12-11T07:29:02.568034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
140 10
 
8.1%
150 9
 
7.3%
130 8
 
6.5%
120 7
 
5.6%
180 6
 
4.8%
210 3
 
2.4%
220 3
 
2.4%
190 2
 
1.6%
240 2
 
1.6%
170 2
 
1.6%
Other values (8) 8
 
6.5%
(Missing) 64
51.6%
ValueCountFrequency (%)
120 7
5.6%
130 8
6.5%
140 10
8.1%
150 9
7.3%
160 1
 
0.8%
170 2
 
1.6%
180 6
4.8%
190 2
 
1.6%
200 1
 
0.8%
210 3
 
2.4%
ValueCountFrequency (%)
2000 1
 
0.8%
400 1
 
0.8%
340 1
 
0.8%
290 1
 
0.8%
270 1
 
0.8%
250 1
 
0.8%
240 2
1.6%
220 3
2.4%
210 3
2.4%
200 1
 
0.8%

10ℓ(원/매)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)36.2%
Missing55
Missing (%)44.4%
Infinite0
Infinite (%)0.0%
Mean407.3913
Minimum230
Maximum3000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T07:29:02.677473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum230
5-th percentile238
Q1260
median300
Q3410
95-th percentile800
Maximum3000
Range2770
Interquartile range (IQR)150

Descriptive statistics

Standard deviation354.02581
Coefficient of variation (CV)0.86900678
Kurtosis43.220096
Mean407.3913
Median Absolute Deviation (MAD)50
Skewness6.0382598
Sum28110
Variance125334.27
MonotonicityNot monotonic
2023-12-11T07:29:02.786209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
300 10
 
8.1%
250 9
 
7.3%
280 6
 
4.8%
360 6
 
4.8%
260 6
 
4.8%
230 4
 
3.2%
800 4
 
3.2%
350 3
 
2.4%
290 2
 
1.6%
410 2
 
1.6%
Other values (15) 17
 
13.7%
(Missing) 55
44.4%
ValueCountFrequency (%)
230 4
 
3.2%
250 9
7.3%
260 6
4.8%
280 6
4.8%
290 2
 
1.6%
300 10
8.1%
310 1
 
0.8%
330 2
 
1.6%
350 3
 
2.4%
360 6
4.8%
ValueCountFrequency (%)
3000 1
 
0.8%
800 4
3.2%
790 1
 
0.8%
770 1
 
0.8%
660 1
 
0.8%
580 1
 
0.8%
550 1
 
0.8%
500 2
1.6%
490 1
 
0.8%
450 1
 
0.8%

20ℓ(원/매)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)42.2%
Missing41
Missing (%)33.1%
Infinite0
Infinite (%)0.0%
Mean945.3012
Minimum440
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T07:29:02.927613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum440
5-th percentile482
Q1555
median700
Q31135
95-th percentile2000
Maximum5000
Range4560
Interquartile range (IQR)580

Descriptive statistics

Standard deviation677.8685
Coefficient of variation (CV)0.7170926
Kurtosis15.411456
Mean945.3012
Median Absolute Deviation (MAD)200
Skewness3.2675594
Sum78460
Variance459505.7
MonotonicityNot monotonic
2023-12-11T07:29:03.068494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
500 10
 
8.1%
560 9
 
7.3%
600 7
 
5.6%
1000 5
 
4.0%
2000 5
 
4.0%
550 4
 
3.2%
800 3
 
2.4%
1600 3
 
2.4%
700 3
 
2.4%
1500 3
 
2.4%
Other values (25) 31
25.0%
(Missing) 41
33.1%
ValueCountFrequency (%)
440 2
 
1.6%
460 2
 
1.6%
480 1
 
0.8%
500 10
8.1%
510 1
 
0.8%
520 1
 
0.8%
550 4
 
3.2%
560 9
7.3%
600 7
5.6%
640 1
 
0.8%
ValueCountFrequency (%)
5000 1
 
0.8%
3000 1
 
0.8%
2070 1
 
0.8%
2000 5
4.0%
1600 3
2.4%
1550 1
 
0.8%
1500 3
2.4%
1250 1
 
0.8%
1240 1
 
0.8%
1200 3
2.4%

30ℓ(원/매)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)75.0%
Missing112
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean1443.3333
Minimum600
Maximum4000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T07:29:03.211930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600
5-th percentile666
Q1825
median870
Q31267.5
95-th percentile4000
Maximum4000
Range3400
Interquartile range (IQR)442.5

Descriptive statistics

Standard deviation1217.7948
Coefficient of variation (CV)0.84373776
Kurtosis2.1946625
Mean1443.3333
Median Absolute Deviation (MAD)195
Skewness1.8895021
Sum17320
Variance1483024.2
MonotonicityNot monotonic
2023-12-11T07:29:03.319127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
840 3
 
2.4%
4000 2
 
1.6%
1110 1
 
0.8%
600 1
 
0.8%
900 1
 
0.8%
1500 1
 
0.8%
720 1
 
0.8%
1190 1
 
0.8%
780 1
 
0.8%
(Missing) 112
90.3%
ValueCountFrequency (%)
600 1
 
0.8%
720 1
 
0.8%
780 1
 
0.8%
840 3
2.4%
900 1
 
0.8%
1110 1
 
0.8%
1190 1
 
0.8%
1500 1
 
0.8%
4000 2
1.6%
ValueCountFrequency (%)
4000 2
1.6%
1500 1
 
0.8%
1190 1
 
0.8%
1110 1
 
0.8%
900 1
 
0.8%
840 3
2.4%
780 1
 
0.8%
720 1
 
0.8%
600 1
 
0.8%

40ℓ(원/매)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

50ℓ(원/매)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)58.9%
Missing68
Missing (%)54.8%
Infinite0
Infinite (%)0.0%
Mean2089.4643
Minimum1100
Maximum6000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T07:29:03.442956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile1150
Q11330
median1750
Q32425
95-th percentile5000
Maximum6000
Range4900
Interquartile range (IQR)1095

Descriptive statistics

Standard deviation1117.8509
Coefficient of variation (CV)0.53499402
Kurtosis3.125142
Mean2089.4643
Median Absolute Deviation (MAD)500
Skewness1.8354055
Sum117010
Variance1249590.6
MonotonicityNot monotonic
2023-12-11T07:29:03.569118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1400 6
 
4.8%
1250 5
 
4.0%
1500 4
 
3.2%
2500 3
 
2.4%
1750 2
 
1.6%
5000 2
 
1.6%
2000 2
 
1.6%
1150 2
 
1.6%
1800 2
 
1.6%
1950 2
 
1.6%
Other values (23) 26
 
21.0%
(Missing) 68
54.8%
ValueCountFrequency (%)
1100 2
 
1.6%
1150 2
 
1.6%
1200 2
 
1.6%
1240 2
 
1.6%
1250 5
4.0%
1300 1
 
0.8%
1340 1
 
0.8%
1400 6
4.8%
1500 4
3.2%
1580 1
 
0.8%
ValueCountFrequency (%)
6000 1
0.8%
5050 1
0.8%
5000 2
1.6%
3750 1
0.8%
3600 1
0.8%
3330 1
0.8%
3300 1
0.8%
3200 1
0.8%
3000 1
0.8%
2530 1
0.8%

60ℓ(원/매)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

75ℓ(원/매)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)66.7%
Missing85
Missing (%)68.5%
Infinite0
Infinite (%)0.0%
Mean2661.2821
Minimum1700
Maximum5570
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T07:29:03.706031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1700
5-th percentile1838
Q12025
median2250
Q32955
95-th percentile4550
Maximum5570
Range3870
Interquartile range (IQR)930

Descriptive statistics

Standard deviation941.54459
Coefficient of variation (CV)0.35379361
Kurtosis1.7652008
Mean2661.2821
Median Absolute Deviation (MAD)390
Skewness1.482964
Sum103790
Variance886506.21
MonotonicityNot monotonic
2023-12-11T07:29:03.849826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2100 6
 
4.8%
2250 4
 
3.2%
2000 2
 
1.6%
2630 2
 
1.6%
1880 2
 
1.6%
2700 2
 
1.6%
1860 2
 
1.6%
2360 1
 
0.8%
3500 1
 
0.8%
5570 1
 
0.8%
Other values (16) 16
 
12.9%
(Missing) 85
68.5%
ValueCountFrequency (%)
1700 1
 
0.8%
1730 1
 
0.8%
1850 1
 
0.8%
1860 2
 
1.6%
1870 1
 
0.8%
1880 2
 
1.6%
2000 2
 
1.6%
2050 1
 
0.8%
2100 6
4.8%
2250 4
3.2%
ValueCountFrequency (%)
5570 1
0.8%
5000 1
0.8%
4500 1
0.8%
4200 1
0.8%
3800 1
0.8%
3660 1
0.8%
3600 1
0.8%
3500 1
0.8%
3330 1
0.8%
3000 1
0.8%

100ℓ(원/매)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
121 
2200
 
1
2470
 
1
5300
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique3 ?
Unique (%)2.4%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 121
97.6%
2200 1
 
0.8%
2470 1
 
0.8%
5300 1
 
0.8%

Length

2023-12-11T07:29:03.978237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:29:04.097270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 121
97.6%
2200 1
 
0.8%
2470 1
 
0.8%
5300 1
 
0.8%

120ℓ(원/매)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
123 
4000
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 123
99.2%
4000 1
 
0.8%

Length

2023-12-11T07:29:04.207679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:29:04.320394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 123
99.2%
4000 1
 
0.8%

125ℓ(원/매)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

50kg(원/매)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

80kg(원/매)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

롤봉투37ℓ(원/매)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

압축100ℓ(원/매)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing124
Missing (%)100.0%
Memory size1.2 KiB

Interactions

2023-12-11T07:29:00.297132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:54.857221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.511095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:56.254223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:57.087217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:57.844635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:58.572332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:59.299021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:00.361135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:54.938869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.589255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:56.350711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:57.195215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:57.923064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:58.660831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:59.381000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:00.444039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.018315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.679934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:56.456266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:57.292131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:58.007073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:58.775604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:59.481032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:00.526045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.093843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.776013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:56.566389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:57.384669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:58.092731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:58.863021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:59.585609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:00.603368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.178884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.865005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:56.667932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:57.467255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:58.193261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:58.938071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:59.677426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:00.681447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.259766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.961448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:56.760361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:57.563169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:58.267731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:59.039604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:00.038868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:00.750182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.344249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:56.050628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:56.835462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:57.657084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:58.360568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:59.123747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:00.146709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:00.818696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:55.434772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:56.135425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:56.945972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:57.755885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:58.473873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:28:59.205830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:29:00.222103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:29:04.394446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명구분명2ℓ(원/매)3ℓ(원/매)5ℓ(원/매)10ℓ(원/매)20ℓ(원/매)30ℓ(원/매)50ℓ(원/매)75ℓ(원/매)100ℓ(원/매)
시군명1.0000.0001.0000.9610.0000.0000.0000.8700.0000.0001.000
구분명0.0001.0000.0000.0000.8890.7310.6140.7000.6390.9021.000
2ℓ(원/매)1.0000.0001.0000.9121.0000.5810.6070.0000.000NaNNaN
3ℓ(원/매)0.9610.0000.9121.0001.0001.0000.9371.0001.0000.9760.000
5ℓ(원/매)0.0000.8891.0001.0001.0000.7700.343NaN1.000NaNNaN
10ℓ(원/매)0.0000.7310.5811.0000.7701.0000.966NaN0.743NaNNaN
20ℓ(원/매)0.0000.6140.6070.9370.3430.9661.0000.9620.9951.0000.000
30ℓ(원/매)0.8700.7000.0001.000NaNNaN0.9621.0000.7821.000NaN
50ℓ(원/매)0.0000.6390.0001.0001.0000.7430.9950.7821.0000.9730.000
75ℓ(원/매)0.0000.902NaN0.976NaNNaN1.0001.0000.9731.000NaN
100ℓ(원/매)1.0001.000NaN0.000NaNNaN0.000NaN0.000NaN1.000
2023-12-11T07:29:04.524137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
100ℓ(원/매)120ℓ(원/매)구분명시군명
100ℓ(원/매)1.000NaN1.0001.000
120ℓ(원/매)NaN1.000NaNNaN
구분명1.000NaN1.0000.000
시군명1.000NaN0.0001.000
2023-12-11T07:29:04.657820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2ℓ(원/매)3ℓ(원/매)5ℓ(원/매)10ℓ(원/매)20ℓ(원/매)30ℓ(원/매)50ℓ(원/매)75ℓ(원/매)시군명구분명100ℓ(원/매)120ℓ(원/매)
2ℓ(원/매)1.0000.7900.7670.7370.6891.0000.000NaN0.4850.000NaN0.000
3ℓ(원/매)0.7901.0000.9670.9250.8900.4000.8590.8510.4350.0001.000NaN
5ℓ(원/매)0.7670.9671.0000.9840.9500.8970.9530.9370.0000.5941.000NaN
10ℓ(원/매)0.7370.9250.9841.0000.9810.7750.9750.9540.0000.3721.000NaN
20ℓ(원/매)0.6890.8900.9500.9811.0000.9980.9900.9680.0000.4401.000NaN
30ℓ(원/매)1.0000.4000.8970.7750.9981.0001.0001.0000.4770.4990.0000.000
50ℓ(원/매)0.0000.8590.9530.9750.9901.0001.0000.9870.0000.4461.0000.000
75ℓ(원/매)NaN0.8510.9370.9540.9681.0000.9871.0000.0000.768NaN0.000
시군명0.4850.4350.0000.0000.0000.4770.0000.0001.0000.0001.000NaN
구분명0.0000.0000.5940.3720.4400.4990.4460.7680.0001.0001.000NaN
100ℓ(원/매)NaN1.0001.0001.0001.0000.0001.000NaN1.0001.0001.0000.000
120ℓ(원/매)0.000NaNNaNNaNNaN0.0000.0000.000NaNNaN0.0001.000

Missing values

2023-12-11T07:29:00.998699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:29:01.253083image/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.
2023-12-11T07:29:01.428695image/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

시군명구분명2ℓ(원/매)3ℓ(원/매)5ℓ(원/매)10ℓ(원/매)20ℓ(원/매)30ℓ(원/매)40ℓ(원/매)50ℓ(원/매)60ℓ(원/매)75ℓ(원/매)100ℓ(원/매)120ℓ(원/매)125ℓ(원/매)50kg(원/매)80kg(원/매)롤봉투37ℓ(원/매)압축100ℓ(원/매)
0가평군특수규격 봉투(PP마대 등)<NA><NA><NA><NA><NA><NA><NA>3750<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1가평군사업장 생활폐기물용 봉투<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2가평군가정용 봉투<NA><NA><NA>250500<NA><NA>1250<NA>1870<NA><NA><NA><NA><NA><NA><NA>
3가평군음식물용(전용봉투)5070120250500<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4고양시가정용 봉투<NA>120180360710<NA><NA>1760<NA>2640<NA><NA><NA><NA><NA><NA><NA>
5고양시사업장 생활폐기물용 봉투<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
6고양시음식물용(전용봉투)90120180360710<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7고양시특수규격 봉투(PP마대 등)<NA><NA><NA>7701240<NA><NA>2260<NA><NA><NA><NA><NA><NA><NA><NA><NA>
8과천시가정용 봉투6070120230440<NA><NA>1100<NA><NA>2200<NA><NA><NA><NA><NA><NA>
9과천시특수규격 봉투(PP마대 등)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
시군명구분명2ℓ(원/매)3ℓ(원/매)5ℓ(원/매)10ℓ(원/매)20ℓ(원/매)30ℓ(원/매)40ℓ(원/매)50ℓ(원/매)60ℓ(원/매)75ℓ(원/매)100ℓ(원/매)120ℓ(원/매)125ℓ(원/매)50kg(원/매)80kg(원/매)롤봉투37ℓ(원/매)압축100ℓ(원/매)
114포천시특수규격 봉투(PP마대 등)<NA><NA><NA><NA>560<NA><NA>1400<NA><NA><NA><NA><NA><NA><NA><NA><NA>
115포천시음식물용(전용봉투)5070130260520780<NA>1300<NA><NA><NA><NA><NA><NA><NA><NA><NA>
116하남시가정용 봉투<NA>80130260500<NA><NA>1240<NA>1860<NA><NA><NA><NA><NA><NA><NA>
117하남시사업장 생활폐기물용 봉투<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
118하남시음식물용(전용봉투)<NA>110180360700<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
119하남시특수규격 봉투(PP마대 등)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
120화성시가정용 봉투<NA><NA>150300600<NA><NA>1500<NA>2250<NA><NA><NA><NA><NA><NA><NA>
121화성시사업장 생활폐기물용 봉투<NA><NA><NA><NA><NA><NA><NA>2530<NA>3800<NA><NA><NA><NA><NA><NA><NA>
122화성시특수규격 봉투(PP마대 등)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
123화성시음식물용(전용봉투)<NA>1802905801170<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>