Overview

Dataset statistics

Number of variables8
Number of observations25
Missing cells90
Missing cells (%)45.0%
Duplicate rows1
Duplicate rows (%)4.0%
Total size in memory1.9 KiB
Average record size in memory76.3 B

Variable types

Text1
Numeric7

Dataset

Description○ 충남도내(15시·군) 민생6대분야(원산지, 식품위생, 축산물위생, 공중위생, 청소년, 환경)단속대상 업소수 현황임
Author충청남도
URLhttps://www.data.go.kr/data/15019413/fileData.do

Alerts

Dataset has 1 (4.0%) duplicate rowsDuplicates
총 계 is highly overall correlated with 원산지식품위생 and 3 other fieldsHigh correlation
원산지식품위생 is highly overall correlated with 총 계 and 3 other fieldsHigh correlation
식품위생 is highly overall correlated with 총 계 and 2 other fieldsHigh correlation
축산물위생 is highly overall correlated with 총 계 and 2 other fieldsHigh correlation
청소년 is highly overall correlated with 총 계 and 1 other fieldsHigh correlation
시 군 has 10 (40.0%) missing valuesMissing
총 계 has 10 (40.0%) missing valuesMissing
원산지식품위생 has 11 (44.0%) missing valuesMissing
식품위생 has 10 (40.0%) missing valuesMissing
축산물위생 has 12 (48.0%) missing valuesMissing
환 경 has 11 (44.0%) missing valuesMissing
공중위생 has 13 (52.0%) missing valuesMissing
청소년 has 13 (52.0%) missing valuesMissing

Reproduction

Analysis started2024-04-06 08:18:23.340001
Analysis finished2024-04-06 08:18:34.180472
Duration10.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시 군
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing10
Missing (%)40.0%
Memory size332.0 B
2024-04-06T17:18:34.372400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)100.0%

Sample

1st row천안시
2nd row공주시
3rd row보령시
4th row아산시
5th row서산시
ValueCountFrequency (%)
천안시 1
 
6.7%
공주시 1
 
6.7%
보령시 1
 
6.7%
아산시 1
 
6.7%
서산시 1
 
6.7%
논산시 1
 
6.7%
계룡시 1
 
6.7%
당진시 1
 
6.7%
금산군 1
 
6.7%
부여군 1
 
6.7%
Other values (5) 5
33.3%
2024-04-06T17:18:35.137876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8
17.8%
7
15.6%
5
 
11.1%
2
 
4.4%
2
 
4.4%
2
 
4.4%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
Other values (15) 15
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8
17.8%
7
15.6%
5
 
11.1%
2
 
4.4%
2
 
4.4%
2
 
4.4%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
Other values (15) 15
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8
17.8%
7
15.6%
5
 
11.1%
2
 
4.4%
2
 
4.4%
2
 
4.4%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
Other values (15) 15
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8
17.8%
7
15.6%
5
 
11.1%
2
 
4.4%
2
 
4.4%
2
 
4.4%
1
 
2.2%
1
 
2.2%
1
 
2.2%
1
 
2.2%
Other values (15) 15
33.3%

총 계
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)100.0%
Missing10
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean349.53333
Minimum68
Maximum1148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:18:35.405726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile82.7
Q1135
median265
Q3427.5
95-th percentile939.4
Maximum1148
Range1080
Interquartile range (IQR)292.5

Descriptive statistics

Standard deviation306.76395
Coefficient of variation (CV)0.87763863
Kurtosis2.3367139
Mean349.53333
Median Absolute Deviation (MAD)147
Skewness1.6335437
Sum5243
Variance94104.124
MonotonicityNot monotonic
2024-04-06T17:18:35.692516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
850 1
 
4.0%
265 1
 
4.0%
325 1
 
4.0%
530 1
 
4.0%
574 1
 
4.0%
294 1
 
4.0%
89 1
 
4.0%
256 1
 
4.0%
118 1
 
4.0%
111 1
 
4.0%
Other values (5) 5
20.0%
(Missing) 10
40.0%
ValueCountFrequency (%)
68 1
4.0%
89 1
4.0%
111 1
4.0%
118 1
4.0%
152 1
4.0%
197 1
4.0%
256 1
4.0%
265 1
4.0%
266 1
4.0%
294 1
4.0%
ValueCountFrequency (%)
1148 1
4.0%
850 1
4.0%
574 1
4.0%
530 1
4.0%
325 1
4.0%
294 1
4.0%
266 1
4.0%
265 1
4.0%
256 1
4.0%
197 1
4.0%

원산지식품위생
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)85.7%
Missing11
Missing (%)44.0%
Infinite0
Infinite (%)0.0%
Mean78.857143
Minimum4
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:18:35.875141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.3
Q118.5
median49
Q389.5
95-th percentile238.15
Maximum400
Range396
Interquartile range (IQR)71

Descriptive statistics

Standard deviation103.22407
Coefficient of variation (CV)1.3090009
Kurtosis7.7878788
Mean78.857143
Median Absolute Deviation (MAD)37.5
Skewness2.6073893
Sum1104
Variance10655.209
MonotonicityNot monotonic
2024-04-06T17:18:36.120138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
49 2
 
8.0%
6 2
 
8.0%
97 1
 
4.0%
58 1
 
4.0%
134 1
 
4.0%
151 1
 
4.0%
67 1
 
4.0%
4 1
 
4.0%
43 1
 
4.0%
17 1
 
4.0%
Other values (2) 2
 
8.0%
(Missing) 11
44.0%
ValueCountFrequency (%)
4 1
4.0%
6 2
8.0%
17 1
4.0%
23 1
4.0%
43 1
4.0%
49 2
8.0%
58 1
4.0%
67 1
4.0%
97 1
4.0%
134 1
4.0%
ValueCountFrequency (%)
400 1
4.0%
151 1
4.0%
134 1
4.0%
97 1
4.0%
67 1
4.0%
58 1
4.0%
49 2
8.0%
43 1
4.0%
23 1
4.0%
17 1
4.0%

식품위생
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)100.0%
Missing10
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean139.93333
Minimum17
Maximum426
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:18:36.475696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile20.5
Q154
median98
Q3184.5
95-th percentile401.5
Maximum426
Range409
Interquartile range (IQR)130.5

Descriptive statistics

Standard deviation129.80175
Coefficient of variation (CV)0.9275971
Kurtosis0.7661828
Mean139.93333
Median Absolute Deviation (MAD)69
Skewness1.3015472
Sum2099
Variance16848.495
MonotonicityNot monotonic
2024-04-06T17:18:36.799920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
391 1
 
4.0%
63 1
 
4.0%
117 1
 
4.0%
270 1
 
4.0%
190 1
 
4.0%
98 1
 
4.0%
29 1
 
4.0%
123 1
 
4.0%
52 1
 
4.0%
56 1
 
4.0%
Other values (5) 5
20.0%
(Missing) 10
40.0%
ValueCountFrequency (%)
17 1
4.0%
22 1
4.0%
29 1
4.0%
52 1
4.0%
56 1
4.0%
63 1
4.0%
66 1
4.0%
98 1
4.0%
117 1
4.0%
123 1
4.0%
ValueCountFrequency (%)
426 1
4.0%
391 1
4.0%
270 1
4.0%
190 1
4.0%
179 1
4.0%
123 1
4.0%
117 1
4.0%
98 1
4.0%
66 1
4.0%
63 1
4.0%

축산물위생
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)84.6%
Missing12
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean31.153846
Minimum6
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:18:37.123085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6.6
Q18
median16
Q336
95-th percentile87
Maximum108
Range102
Interquartile range (IQR)28

Descriptive statistics

Standard deviation32.500888
Coefficient of variation (CV)1.0432384
Kurtosis1.2617056
Mean31.153846
Median Absolute Deviation (MAD)9
Skewness1.4574603
Sum405
Variance1056.3077
MonotonicityNot monotonic
2024-04-06T17:18:37.333826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8 3
 
12.0%
108 1
 
4.0%
31 1
 
4.0%
7 1
 
4.0%
13 1
 
4.0%
73 1
 
4.0%
36 1
 
4.0%
16 1
 
4.0%
6 1
 
4.0%
21 1
 
4.0%
(Missing) 12
48.0%
ValueCountFrequency (%)
6 1
 
4.0%
7 1
 
4.0%
8 3
12.0%
13 1
 
4.0%
16 1
 
4.0%
21 1
 
4.0%
31 1
 
4.0%
36 1
 
4.0%
70 1
 
4.0%
73 1
 
4.0%
ValueCountFrequency (%)
108 1
 
4.0%
73 1
 
4.0%
70 1
 
4.0%
36 1
 
4.0%
31 1
 
4.0%
21 1
 
4.0%
16 1
 
4.0%
13 1
 
4.0%
8 3
12.0%
7 1
 
4.0%

환 경
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)92.9%
Missing11
Missing (%)44.0%
Infinite0
Infinite (%)0.0%
Mean58.214286
Minimum8
Maximum192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:18:37.535673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.65
Q121.25
median43.5
Q359.75
95-th percentile177.7
Maximum192
Range184
Interquartile range (IQR)38.5

Descriptive statistics

Standard deviation55.6696
Coefficient of variation (CV)0.95628761
Kurtosis2.4627197
Mean58.214286
Median Absolute Deviation (MAD)21
Skewness1.761628
Sum815
Variance3099.1044
MonotonicityNot monotonic
2024-04-06T17:18:37.752603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
18 2
 
8.0%
170 1
 
4.0%
44 1
 
4.0%
192 1
 
4.0%
60 1
 
4.0%
59 1
 
4.0%
9 1
 
4.0%
72 1
 
4.0%
49 1
 
4.0%
31 1
 
4.0%
Other values (3) 3
 
12.0%
(Missing) 11
44.0%
ValueCountFrequency (%)
8 1
4.0%
9 1
4.0%
18 2
8.0%
31 1
4.0%
42 1
4.0%
43 1
4.0%
44 1
4.0%
49 1
4.0%
59 1
4.0%
60 1
4.0%
ValueCountFrequency (%)
192 1
4.0%
170 1
4.0%
72 1
4.0%
60 1
4.0%
59 1
4.0%
49 1
4.0%
44 1
4.0%
43 1
4.0%
42 1
4.0%
31 1
4.0%

공중위생
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)75.0%
Missing13
Missing (%)52.0%
Infinite0
Infinite (%)0.0%
Mean19.666667
Minimum3
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:18:37.982299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median9
Q321
95-th percentile65.35
Maximum89
Range86
Interquartile range (IQR)16

Descriptive statistics

Standard deviation25.356847
Coefficient of variation (CV)1.2893312
Kurtosis5.0848065
Mean19.666667
Median Absolute Deviation (MAD)5.5
Skewness2.216488
Sum236
Variance642.9697
MonotonicityNot monotonic
2024-04-06T17:18:38.209029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 3
 
12.0%
3 2
 
8.0%
14 1
 
4.0%
8 1
 
4.0%
30 1
 
4.0%
10 1
 
4.0%
18 1
 
4.0%
46 1
 
4.0%
89 1
 
4.0%
(Missing) 13
52.0%
ValueCountFrequency (%)
3 2
8.0%
5 3
12.0%
8 1
 
4.0%
10 1
 
4.0%
14 1
 
4.0%
18 1
 
4.0%
30 1
 
4.0%
46 1
 
4.0%
89 1
 
4.0%
ValueCountFrequency (%)
89 1
 
4.0%
46 1
 
4.0%
30 1
 
4.0%
18 1
 
4.0%
14 1
 
4.0%
10 1
 
4.0%
8 1
 
4.0%
5 3
12.0%
3 2
8.0%

청소년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing13
Missing (%)52.0%
Infinite0
Infinite (%)0.0%
Mean48.666667
Minimum1
Maximum198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-04-06T17:18:38.445775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.65
Q17.75
median36.5
Q361
95-th percentile139.7
Maximum198
Range197
Interquartile range (IQR)53.25

Descriptive statistics

Standard deviation55.488465
Coefficient of variation (CV)1.1401739
Kurtosis4.5636364
Mean48.666667
Median Absolute Deviation (MAD)29
Skewness1.9652389
Sum584
Variance3078.9697
MonotonicityNot monotonic
2024-04-06T17:18:38.678204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
79 1
 
4.0%
55 1
 
4.0%
46 1
 
4.0%
1 1
 
4.0%
92 1
 
4.0%
4 1
 
4.0%
21 1
 
4.0%
7 1
 
4.0%
35 1
 
4.0%
8 1
 
4.0%
Other values (2) 2
 
8.0%
(Missing) 13
52.0%
ValueCountFrequency (%)
1 1
4.0%
4 1
4.0%
7 1
4.0%
8 1
4.0%
21 1
4.0%
35 1
4.0%
38 1
4.0%
46 1
4.0%
55 1
4.0%
79 1
4.0%
ValueCountFrequency (%)
198 1
4.0%
92 1
4.0%
79 1
4.0%
55 1
4.0%
46 1
4.0%
38 1
4.0%
35 1
4.0%
21 1
4.0%
8 1
4.0%
7 1
4.0%

Interactions

2024-04-06T17:18:31.708325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:23.749191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:25.090660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:26.566959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:27.883183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:29.174838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:30.534633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:31.865845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:23.927234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:25.264322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:26.739512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:28.036701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:29.338545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:30.713032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:32.034620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:24.140805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:25.440407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:26.934716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:28.261221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:29.572461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:30.932373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:32.220073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:24.337353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:25.644689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:27.129497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:28.422679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:29.842538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:31.105248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:32.371650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:24.518239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:25.816194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:27.348005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:28.564869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:30.059526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:31.246785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:32.554116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:24.742160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:26.072330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:27.536387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:28.744855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:30.228011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:31.413441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:32.756547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:24.929541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:26.361542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:27.690929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:28.949333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:30.370684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:18:31.553184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:18:38.872577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시 군총 계원산지식품위생식품위생축산물위생환 경공중위생청소년
시 군1.0001.0001.0001.0001.0001.0001.0001.000
총 계1.0001.0000.7980.7830.6270.7320.7060.904
원산지식품위생1.0000.7981.0000.6340.9000.4380.6210.804
식품위생1.0000.7830.6341.0000.5220.6210.0000.840
축산물위생1.0000.6270.9000.5221.0000.4750.5220.688
환 경1.0000.7320.4380.6210.4751.0000.0000.527
공중위생1.0000.7060.6210.0000.5220.0001.0000.243
청소년1.0000.9040.8040.8400.6880.5270.2431.000
2024-04-06T17:18:39.125193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총 계원산지식품위생식품위생축산물위생환 경공중위생청소년
총 계1.0000.8810.8680.5080.2950.1200.545
원산지식품위생0.8811.0000.7160.5570.0910.1110.676
식품위생0.8680.7161.0000.5140.2620.1200.434
축산물위생0.5080.5570.5141.0000.2960.3570.444
환 경0.2950.0910.2620.2961.000-0.138-0.191
공중위생0.1200.1110.1200.357-0.1381.0000.201
청소년0.5450.6760.4340.444-0.1910.2011.000

Missing values

2024-04-06T17:18:33.031126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:18:33.282932image/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.
2024-04-06T17:18:33.621758image/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

시 군총 계원산지식품위생식품위생축산물위생환 경공중위생청소년
0천안시85097391108170579
1공주시265586331441455
2보령시325134117718346
3아산시530492701319251
4서산시5741511907360892
5논산시29467983659304
6계룡시894291691021
7당진시25643123<NA>7218<NA>
8금산군1186526495<NA>
9부여군11165683137
시 군총 계원산지식품위생식품위생축산물위생환 경공중위생청소년
15<NA><NA><NA><NA><NA><NA><NA><NA>
16<NA><NA><NA><NA><NA><NA><NA><NA>
17<NA><NA><NA><NA><NA><NA><NA><NA>
18<NA><NA><NA><NA><NA><NA><NA><NA>
19<NA><NA><NA><NA><NA><NA><NA><NA>
20<NA><NA><NA><NA><NA><NA><NA><NA>
21<NA><NA><NA><NA><NA><NA><NA><NA>
22<NA><NA><NA><NA><NA><NA><NA><NA>
23<NA><NA><NA><NA><NA><NA><NA><NA>
24<NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

시 군총 계원산지식품위생식품위생축산물위생환 경공중위생청소년# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA>10