Overview

Dataset statistics

Number of variables19
Number of observations31
Missing cells62
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory174.3 B

Variable types

Unsupported2
Text1
Numeric16

Dataset

Description자동차매매·정비·폐차업 집계 현황
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=O4O4OZ591717OQ29W29412397335&infSeq=1

Alerts

종합정비업체수(개) is highly overall correlated with 종합정비종사자수(명) and 14 other fieldsHigh correlation
종합정비종사자수(명) is highly overall correlated with 종합정비업체수(개) and 13 other fieldsHigh correlation
소형정비업체수(개) is highly overall correlated with 종합정비업체수(개) and 12 other fieldsHigh correlation
소형정비종사자수(명) is highly overall correlated with 종합정비업체수(개) and 9 other fieldsHigh correlation
전문정비업체수(개) is highly overall correlated with 종합정비업체수(개) and 12 other fieldsHigh correlation
전문정비종사자수(명) is highly overall correlated with 종합정비업체수(개) and 10 other fieldsHigh correlation
원동기정비업체수(개) is highly overall correlated with 종합정비업체수(개) and 7 other fieldsHigh correlation
원동기정비종사자수(명) is highly overall correlated with 종합정비업체수(개) and 8 other fieldsHigh correlation
매매업체수(개) is highly overall correlated with 종합정비업체수(개) and 12 other fieldsHigh correlation
매매종사자수(명) is highly overall correlated with 종합정비업체수(개) and 8 other fieldsHigh correlation
폐차업체수(개) is highly overall correlated with 종합정비업체수(개) and 1 other fieldsHigh correlation
폐차종사자수(명) is highly overall correlated with 종합정비업체수(개) and 10 other fieldsHigh correlation
성능점검업체수(개) is highly overall correlated with 종합정비업체수(개) and 11 other fieldsHigh correlation
성능점검종사자수(명) is highly overall correlated with 종합정비업체수(개) and 10 other fieldsHigh correlation
택시미터업체수(개) is highly overall correlated with 종합정비업체수(개) and 7 other fieldsHigh correlation
택시미터종사자수(명) is highly overall correlated with 종합정비업체수(개) and 4 other fieldsHigh correlation
집계년도 has 31 (100.0%) missing valuesMissing
조사분기 has 31 (100.0%) missing valuesMissing
시군명 has unique valuesUnique
종합정비종사자수(명) has unique valuesUnique
전문정비업체수(개) has unique valuesUnique
집계년도 is an unsupported type, check if it needs cleaning or further analysisUnsupported
조사분기 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소형정비업체수(개) has 2 (6.5%) zerosZeros
소형정비종사자수(명) has 2 (6.5%) zerosZeros
원동기정비업체수(개) has 9 (29.0%) zerosZeros
원동기정비종사자수(명) has 9 (29.0%) zerosZeros
매매업체수(개) has 4 (12.9%) zerosZeros
매매종사자수(명) has 4 (12.9%) zerosZeros
폐차업체수(개) has 7 (22.6%) zerosZeros
폐차종사자수(명) has 7 (22.6%) zerosZeros
성능점검업체수(개) has 6 (19.4%) zerosZeros
성능점검종사자수(명) has 6 (19.4%) zerosZeros
택시미터업체수(개) has 5 (16.1%) zerosZeros
택시미터종사자수(명) has 5 (16.1%) zerosZeros

Reproduction

Analysis started2023-12-10 22:01:29.220387
Analysis finished2023-12-10 22:01:52.308499
Duration23.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing31
Missing (%)100.0%
Memory size411.0 B

시군명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-11T07:01:52.438102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Characters and Unicode

Total characters96
Distinct characters38
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

Unique31 ?
Unique (%)100.0%

Sample

1st row수원시
2nd row연천군
3rd row고양시
4th row화성시
5th row성남시
ValueCountFrequency (%)
수원시 1
 
3.2%
광명시 1
 
3.2%
가평군 1
 
3.2%
과천시 1
 
3.2%
동두천시 1
 
3.2%
여주시 1
 
3.2%
양평군 1
 
3.2%
포천시 1
 
3.2%
의왕시 1
 
3.2%
구리시 1
 
3.2%
Other values (21) 21
67.7%
2023-12-11T07:01:52.741091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
30.2%
6
 
6.2%
5
 
5.2%
5
 
5.2%
4
 
4.2%
3
 
3.1%
3
 
3.1%
3
 
3.1%
3
 
3.1%
3
 
3.1%
Other values (28) 32
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
29
30.2%
6
 
6.2%
5
 
5.2%
5
 
5.2%
4
 
4.2%
3
 
3.1%
3
 
3.1%
3
 
3.1%
3
 
3.1%
3
 
3.1%
Other values (28) 32
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
29
30.2%
6
 
6.2%
5
 
5.2%
5
 
5.2%
4
 
4.2%
3
 
3.1%
3
 
3.1%
3
 
3.1%
3
 
3.1%
3
 
3.1%
Other values (28) 32
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
29
30.2%
6
 
6.2%
5
 
5.2%
5
 
5.2%
4
 
4.2%
3
 
3.1%
3
 
3.1%
3
 
3.1%
3
 
3.1%
3
 
3.1%
Other values (28) 32
33.3%

조사분기
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing31
Missing (%)100.0%
Memory size411.0 B

종합정비업체수(개)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.806452
Minimum1
Maximum117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:53.105827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q111.5
median27
Q357.5
95-th percentile90.5
Maximum117
Range116
Interquartile range (IQR)46

Descriptive statistics

Standard deviation31.620056
Coefficient of variation (CV)0.83636668
Kurtosis-0.23100476
Mean37.806452
Median Absolute Deviation (MAD)18
Skewness0.88925732
Sum1172
Variance999.82796
MonotonicityNot monotonic
2023-12-11T07:01:53.212190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
78 2
 
6.5%
86 2
 
6.5%
10 2
 
6.5%
42 1
 
3.2%
95 1
 
3.2%
7 1
 
3.2%
1 1
 
3.2%
23 1
 
3.2%
13 1
 
3.2%
44 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
1 1
3.2%
5 1
3.2%
7 1
3.2%
8 1
3.2%
9 1
3.2%
10 2
6.5%
11 1
3.2%
12 1
3.2%
13 1
3.2%
15 1
3.2%
ValueCountFrequency (%)
117 1
3.2%
95 1
3.2%
86 2
6.5%
78 2
6.5%
68 1
3.2%
63 1
3.2%
52 1
3.2%
47 1
3.2%
45 1
3.2%
44 1
3.2%

종합정비종사자수(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean339.06452
Minimum15
Maximum1137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:53.339624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile66
Q1152.5
median240
Q3473
95-th percentile848.5
Maximum1137
Range1122
Interquartile range (IQR)320.5

Descriptive statistics

Standard deviation270.2928
Coefficient of variation (CV)0.79717217
Kurtosis1.8987231
Mean339.06452
Median Absolute Deviation (MAD)152
Skewness1.377628
Sum10511
Variance73058.196
MonotonicityNot monotonic
2023-12-11T07:01:53.447033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1013 1
 
3.2%
71 1
 
3.2%
684 1
 
3.2%
62 1
 
3.2%
15 1
 
3.2%
70 1
 
3.2%
175 1
 
3.2%
136 1
 
3.2%
195 1
 
3.2%
205 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
15 1
3.2%
62 1
3.2%
70 1
3.2%
71 1
3.2%
101 1
3.2%
104 1
3.2%
136 1
3.2%
144 1
3.2%
161 1
3.2%
175 1
3.2%
ValueCountFrequency (%)
1137 1
3.2%
1013 1
3.2%
684 1
3.2%
659 1
3.2%
588 1
3.2%
559 1
3.2%
515 1
3.2%
482 1
3.2%
464 1
3.2%
407 1
3.2%

소형정비업체수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.741935
Minimum0
Maximum84
Zeros2
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:53.552593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q13.5
median15
Q330.5
95-th percentile53
Maximum84
Range84
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.159312
Coefficient of variation (CV)0.97191083
Kurtosis1.8542064
Mean20.741935
Median Absolute Deviation (MAD)12
Skewness1.3440493
Sum643
Variance406.39785
MonotonicityNot monotonic
2023-12-11T07:01:53.654628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 3
 
9.7%
47 2
 
6.5%
2 2
 
6.5%
11 2
 
6.5%
0 2
 
6.5%
20 2
 
6.5%
46 1
 
3.2%
59 1
 
3.2%
13 1
 
3.2%
7 1
 
3.2%
Other values (14) 14
45.2%
ValueCountFrequency (%)
0 2
6.5%
1 1
 
3.2%
2 2
6.5%
3 3
9.7%
4 1
 
3.2%
7 1
 
3.2%
10 1
 
3.2%
11 2
6.5%
12 1
 
3.2%
13 1
 
3.2%
ValueCountFrequency (%)
84 1
3.2%
59 1
3.2%
47 2
6.5%
46 1
3.2%
42 1
3.2%
35 1
3.2%
33 1
3.2%
28 1
3.2%
23 1
3.2%
22 1
3.2%

소형정비종사자수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.03226
Minimum0
Maximum653
Zeros2
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:53.753597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q122.5
median92
Q3194.5
95-th percentile484.5
Maximum653
Range653
Interquartile range (IQR)172

Descriptive statistics

Standard deviation165.25122
Coefficient of variation (CV)1.1800939
Kurtosis3.2859722
Mean140.03226
Median Absolute Deviation (MAD)72
Skewness1.8547877
Sum4341
Variance27307.966
MonotonicityNot monotonic
2023-12-11T07:01:53.857057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 2
 
6.5%
20 2
 
6.5%
373 1
 
3.2%
4 1
 
3.2%
596 1
 
3.2%
11 1
 
3.2%
10 1
 
3.2%
15 1
 
3.2%
42 1
 
3.2%
25 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
0 2
6.5%
4 1
3.2%
10 1
3.2%
11 1
3.2%
15 1
3.2%
20 2
6.5%
25 1
3.2%
33 1
3.2%
42 1
3.2%
59 1
3.2%
ValueCountFrequency (%)
653 1
3.2%
596 1
3.2%
373 1
3.2%
355 1
3.2%
272 1
3.2%
260 1
3.2%
235 1
3.2%
234 1
3.2%
155 1
3.2%
119 1
3.2%

전문정비업체수(개)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.70968
Minimum37
Maximum626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:53.963112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile48
Q195
median201
Q3325
95-th percentile570.5
Maximum626
Range589
Interquartile range (IQR)230

Descriptive statistics

Standard deviation171.44877
Coefficient of variation (CV)0.71523506
Kurtosis-0.24650451
Mean239.70968
Median Absolute Deviation (MAD)115
Skewness0.81074728
Sum7431
Variance29394.68
MonotonicityNot monotonic
2023-12-11T07:01:54.067920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
528 1
 
3.2%
37 1
 
3.2%
577 1
 
3.2%
55 1
 
3.2%
45 1
 
3.2%
60 1
 
3.2%
85 1
 
3.2%
51 1
 
3.2%
199 1
 
3.2%
63 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
37 1
3.2%
45 1
3.2%
51 1
3.2%
55 1
3.2%
60 1
3.2%
63 1
3.2%
85 1
3.2%
86 1
3.2%
104 1
3.2%
121 1
3.2%
ValueCountFrequency (%)
626 1
3.2%
577 1
3.2%
564 1
3.2%
528 1
3.2%
441 1
3.2%
394 1
3.2%
359 1
3.2%
347 1
3.2%
303 1
3.2%
297 1
3.2%

전문정비종사자수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean368.64516
Minimum67
Maximum1041
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:54.173880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile90.5
Q1206
median319
Q3453.5
95-th percentile895
Maximum1041
Range974
Interquartile range (IQR)247.5

Descriptive statistics

Standard deviation260.59324
Coefficient of variation (CV)0.70689451
Kurtosis0.58646455
Mean368.64516
Median Absolute Deviation (MAD)131
Skewness1.125943
Sum11428
Variance67908.837
MonotonicityNot monotonic
2023-12-11T07:01:54.286556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
355 2
 
6.5%
697 1
 
3.2%
227 1
 
3.2%
859 1
 
3.2%
86 1
 
3.2%
143 1
 
3.2%
98 1
 
3.2%
95 1
 
3.2%
105 1
 
3.2%
214 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
67 1
3.2%
86 1
3.2%
95 1
3.2%
98 1
3.2%
104 1
3.2%
105 1
3.2%
143 1
3.2%
200 1
3.2%
212 1
3.2%
214 1
3.2%
ValueCountFrequency (%)
1041 1
3.2%
931 1
3.2%
859 1
3.2%
715 1
3.2%
710 1
3.2%
697 1
3.2%
470 1
3.2%
457 1
3.2%
450 1
3.2%
392 1
3.2%

원동기정비업체수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1290323
Minimum0
Maximum8
Zeros9
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:54.383957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32.5
95-th percentile7.5
Maximum8
Range8
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.5395792
Coefficient of variation (CV)1.1928326
Kurtosis0.58618103
Mean2.1290323
Median Absolute Deviation (MAD)1
Skewness1.3467078
Sum66
Variance6.4494624
MonotonicityNot monotonic
2023-12-11T07:01:54.480455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 9
29.0%
0 9
29.0%
2 5
16.1%
8 2
 
6.5%
7 2
 
6.5%
3 2
 
6.5%
5 1
 
3.2%
6 1
 
3.2%
ValueCountFrequency (%)
0 9
29.0%
1 9
29.0%
2 5
16.1%
3 2
 
6.5%
5 1
 
3.2%
6 1
 
3.2%
7 2
 
6.5%
8 2
 
6.5%
ValueCountFrequency (%)
8 2
 
6.5%
7 2
 
6.5%
6 1
 
3.2%
5 1
 
3.2%
3 2
 
6.5%
2 5
16.1%
1 9
29.0%
0 9
29.0%

원동기정비종사자수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7096774
Minimum0
Maximum27
Zeros9
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:54.581572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q310
95-th percentile24
Maximum27
Range27
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.2227066
Coefficient of variation (CV)1.0665435
Kurtosis0.12217616
Mean7.7096774
Median Absolute Deviation (MAD)5
Skewness1.1071373
Sum239
Variance67.612903
MonotonicityNot monotonic
2023-12-11T07:01:54.685408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 9
29.0%
6 4
12.9%
5 3
 
9.7%
10 3
 
9.7%
4 3
 
9.7%
24 2
 
6.5%
19 2
 
6.5%
22 1
 
3.2%
27 1
 
3.2%
12 1
 
3.2%
Other values (2) 2
 
6.5%
ValueCountFrequency (%)
0 9
29.0%
3 1
 
3.2%
4 3
 
9.7%
5 3
 
9.7%
6 4
12.9%
8 1
 
3.2%
10 3
 
9.7%
12 1
 
3.2%
19 2
 
6.5%
22 1
 
3.2%
ValueCountFrequency (%)
27 1
 
3.2%
24 2
6.5%
22 1
 
3.2%
19 2
6.5%
12 1
 
3.2%
10 3
9.7%
8 1
 
3.2%
6 4
12.9%
5 3
9.7%
4 3
9.7%

매매업체수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.387097
Minimum0
Maximum293
Zeros4
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:54.791666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median24
Q363.5
95-th percentile135.5
Maximum293
Range293
Interquartile range (IQR)57.5

Descriptive statistics

Standard deviation60.607853
Coefficient of variation (CV)1.3969096
Kurtosis8.9763374
Mean43.387097
Median Absolute Deviation (MAD)20
Skewness2.6611452
Sum1345
Variance3673.3118
MonotonicityNot monotonic
2023-12-11T07:01:54.912005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 4
 
12.9%
11 3
 
9.7%
6 2
 
6.5%
29 2
 
6.5%
293 1
 
3.2%
24 1
 
3.2%
133 1
 
3.2%
8 1
 
3.2%
2 1
 
3.2%
17 1
 
3.2%
Other values (14) 14
45.2%
ValueCountFrequency (%)
0 4
12.9%
1 1
 
3.2%
2 1
 
3.2%
4 1
 
3.2%
6 2
6.5%
8 1
 
3.2%
11 3
9.7%
12 1
 
3.2%
17 1
 
3.2%
24 1
 
3.2%
ValueCountFrequency (%)
293 1
3.2%
138 1
3.2%
133 1
3.2%
94 1
3.2%
91 1
3.2%
85 1
3.2%
73 1
3.2%
71 1
3.2%
56 1
3.2%
38 1
3.2%

매매종사자수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean427.96774
Minimum0
Maximum5240
Zeros4
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:55.022624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q132
median107
Q3434
95-th percentile1410
Maximum5240
Range5240
Interquartile range (IQR)402

Descriptive statistics

Standard deviation972.1069
Coefficient of variation (CV)2.271449
Kurtosis21.23679
Mean427.96774
Median Absolute Deviation (MAD)98
Skewness4.3631575
Sum13267
Variance944991.83
MonotonicityNot monotonic
2023-12-11T07:01:55.134995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 4
 
12.9%
9 2
 
6.5%
5240 1
 
3.2%
1031 1
 
3.2%
51 1
 
3.2%
41 1
 
3.2%
169 1
 
3.2%
23 1
 
3.2%
90 1
 
3.2%
120 1
 
3.2%
Other values (17) 17
54.8%
ValueCountFrequency (%)
0 4
12.9%
4 1
 
3.2%
9 2
6.5%
23 1
 
3.2%
41 1
 
3.2%
51 1
 
3.2%
55 1
 
3.2%
70 1
 
3.2%
73 1
 
3.2%
90 1
 
3.2%
ValueCountFrequency (%)
5240 1
3.2%
1460 1
3.2%
1360 1
3.2%
1031 1
3.2%
590 1
3.2%
568 1
3.2%
485 1
3.2%
477 1
3.2%
391 1
3.2%
268 1
3.2%

폐차업체수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9677419
Minimum0
Maximum24
Zeros7
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:55.256949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q38.5
95-th percentile18
Maximum24
Range24
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation6.4626304
Coefficient of variation (CV)1.3009191
Kurtosis2.1574725
Mean4.9677419
Median Absolute Deviation (MAD)1
Skewness1.5960628
Sum154
Variance41.765591
MonotonicityNot monotonic
2023-12-11T07:01:55.410882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 9
29.0%
0 7
22.6%
2 3
 
9.7%
11 2
 
6.5%
8 1
 
3.2%
7 1
 
3.2%
22 1
 
3.2%
10 1
 
3.2%
12 1
 
3.2%
24 1
 
3.2%
Other values (4) 4
12.9%
ValueCountFrequency (%)
0 7
22.6%
1 9
29.0%
2 3
 
9.7%
5 1
 
3.2%
6 1
 
3.2%
7 1
 
3.2%
8 1
 
3.2%
9 1
 
3.2%
10 1
 
3.2%
11 2
 
6.5%
ValueCountFrequency (%)
24 1
3.2%
22 1
3.2%
14 1
3.2%
12 1
3.2%
11 2
6.5%
10 1
3.2%
9 1
3.2%
8 1
3.2%
7 1
3.2%
6 1
3.2%

폐차종사자수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.709677
Minimum0
Maximum161
Zeros7
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:55.510223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median20
Q346.5
95-th percentile104.5
Maximum161
Range161
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation38.683497
Coefficient of variation (CV)1.1826316
Kurtosis2.9391223
Mean32.709677
Median Absolute Deviation (MAD)20
Skewness1.6768221
Sum1014
Variance1496.4129
MonotonicityNot monotonic
2023-12-11T07:01:55.615124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 7
22.6%
20 2
 
6.5%
25 1
 
3.2%
27 1
 
3.2%
82 1
 
3.2%
5 1
 
3.2%
13 1
 
3.2%
18 1
 
3.2%
7 1
 
3.2%
46 1
 
3.2%
Other values (14) 14
45.2%
ValueCountFrequency (%)
0 7
22.6%
3 1
 
3.2%
5 1
 
3.2%
7 1
 
3.2%
9 1
 
3.2%
11 1
 
3.2%
13 1
 
3.2%
17 1
 
3.2%
18 1
 
3.2%
20 2
 
6.5%
ValueCountFrequency (%)
161 1
3.2%
108 1
3.2%
101 1
3.2%
82 1
3.2%
67 1
3.2%
65 1
3.2%
60 1
3.2%
47 1
3.2%
46 1
3.2%
44 1
3.2%

성능점검업체수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6129032
Minimum0
Maximum23
Zeros6
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:55.732123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35.5
95-th percentile10.5
Maximum23
Range23
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation4.7023215
Coefficient of variation (CV)1.3015354
Kurtosis8.9912085
Mean3.6129032
Median Absolute Deviation (MAD)2
Skewness2.6374167
Sum112
Variance22.111828
MonotonicityNot monotonic
2023-12-11T07:01:55.824489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 8
25.8%
0 6
19.4%
1 6
19.4%
6 3
 
9.7%
5 2
 
6.5%
23 1
 
3.2%
8 1
 
3.2%
7 1
 
3.2%
3 1
 
3.2%
10 1
 
3.2%
ValueCountFrequency (%)
0 6
19.4%
1 6
19.4%
2 8
25.8%
3 1
 
3.2%
5 2
 
6.5%
6 3
 
9.7%
7 1
 
3.2%
8 1
 
3.2%
10 1
 
3.2%
11 1
 
3.2%
ValueCountFrequency (%)
23 1
 
3.2%
11 1
 
3.2%
10 1
 
3.2%
8 1
 
3.2%
7 1
 
3.2%
6 3
 
9.7%
5 2
 
6.5%
3 1
 
3.2%
2 8
25.8%
1 6
19.4%

성능점검종사자수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4516129
Minimum0
Maximum38
Zeros6
Zeros (%)19.4%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:55.922883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q314
95-th percentile27.5
Maximum38
Range38
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.8821007
Coefficient of variation (CV)1.1692562
Kurtosis1.5610951
Mean8.4516129
Median Absolute Deviation (MAD)4
Skewness1.4099305
Sum262
Variance97.655914
MonotonicityNot monotonic
2023-12-11T07:01:56.032518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 6
19.4%
2 4
12.9%
4 3
9.7%
1 3
9.7%
14 2
 
6.5%
8 2
 
6.5%
30 1
 
3.2%
25 1
 
3.2%
3 1
 
3.2%
10 1
 
3.2%
Other values (7) 7
22.6%
ValueCountFrequency (%)
0 6
19.4%
1 3
9.7%
2 4
12.9%
3 1
 
3.2%
4 3
9.7%
5 1
 
3.2%
8 2
 
6.5%
10 1
 
3.2%
12 1
 
3.2%
14 2
 
6.5%
ValueCountFrequency (%)
38 1
3.2%
30 1
3.2%
25 1
3.2%
20 1
3.2%
19 1
3.2%
18 1
3.2%
15 1
3.2%
14 2
6.5%
12 1
3.2%
10 1
3.2%

택시미터업체수(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9032258
Minimum0
Maximum29
Zeros5
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:56.138780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q314.5
95-th percentile23
Maximum29
Range29
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation8.1949368
Coefficient of variation (CV)0.9204458
Kurtosis-0.33082426
Mean8.9032258
Median Absolute Deviation (MAD)6
Skewness0.741973
Sum276
Variance67.156989
MonotonicityNot monotonic
2023-12-11T07:01:56.244531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 5
16.1%
2 5
16.1%
4 3
 
9.7%
9 2
 
6.5%
11 2
 
6.5%
25 1
 
3.2%
17 1
 
3.2%
18 1
 
3.2%
16 1
 
3.2%
14 1
 
3.2%
Other values (9) 9
29.0%
ValueCountFrequency (%)
0 5
16.1%
1 1
 
3.2%
2 5
16.1%
4 3
9.7%
5 1
 
3.2%
8 1
 
3.2%
9 2
 
6.5%
11 2
 
6.5%
12 1
 
3.2%
13 1
 
3.2%
ValueCountFrequency (%)
29 1
3.2%
25 1
3.2%
21 1
3.2%
20 1
3.2%
18 1
3.2%
17 1
3.2%
16 1
3.2%
15 1
3.2%
14 1
3.2%
13 1
3.2%

택시미터종사자수(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.483871
Minimum0
Maximum241
Zeros5
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-11T07:01:56.352583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median19
Q356
95-th percentile90
Maximum241
Range241
Interquartile range (IQR)50

Descriptive statistics

Standard deviation47.471304
Coefficient of variation (CV)1.3766234
Kurtosis11.550969
Mean34.483871
Median Absolute Deviation (MAD)18
Skewness2.9606822
Sum1069
Variance2253.5247
MonotonicityNot monotonic
2023-12-11T07:01:56.464958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 5
 
16.1%
90 2
 
6.5%
20 2
 
6.5%
68 1
 
3.2%
1 1
 
3.2%
9 1
 
3.2%
8 1
 
3.2%
18 1
 
3.2%
28 1
 
3.2%
33 1
 
3.2%
Other values (15) 15
48.4%
ValueCountFrequency (%)
0 5
16.1%
1 1
 
3.2%
3 1
 
3.2%
5 1
 
3.2%
7 1
 
3.2%
8 1
 
3.2%
9 1
 
3.2%
10 1
 
3.2%
11 1
 
3.2%
13 1
 
3.2%
ValueCountFrequency (%)
241 1
3.2%
90 2
6.5%
70 1
3.2%
68 1
3.2%
63 1
3.2%
61 1
3.2%
60 1
3.2%
52 1
3.2%
47 1
3.2%
33 1
3.2%

Interactions

2023-12-11T07:01:50.538123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:29.737401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:30.998188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:32.430773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:33.795558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:35.128680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:36.382402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:37.752408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:39.063974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:40.457592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:41.731033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:43.218154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:44.572428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:46.069421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:47.591967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:49.078165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:50.618718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:29.818752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:31.073721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:32.508228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:33.888595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:35.218803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:36.460588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:37.826674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:39.161129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:40.534567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:41.811905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:43.306703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:44.653718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:46.156595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:47.686874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:49.170077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:50.705202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:29.899654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:31.155187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:32.578217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:33.968845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:35.295218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:36.529486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:37.901616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:39.248990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:40.615410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:42.119147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:43.381791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:44.735306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:46.228242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:47.790288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T07:01:37.681662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:38.976243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:40.361339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:41.655371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:43.123929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:44.498711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:45.991006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:47.516008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:49.000834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:01:50.443522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:01:56.564359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명종합정비업체수(개)종합정비종사자수(명)소형정비업체수(개)소형정비종사자수(명)전문정비업체수(개)전문정비종사자수(명)원동기정비업체수(개)원동기정비종사자수(명)매매업체수(개)매매종사자수(명)폐차업체수(개)폐차종사자수(명)성능점검업체수(개)성능점검종사자수(명)택시미터업체수(개)택시미터종사자수(명)
시군명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
종합정비업체수(개)1.0001.0000.7630.7830.6760.7370.8220.7210.5720.7090.6880.6180.8660.7410.7140.9050.825
종합정비종사자수(명)1.0000.7631.0000.8520.7050.6330.7450.8340.6680.8300.9230.0000.6110.6110.6910.7260.344
소형정비업체수(개)1.0000.7830.8521.0000.8350.8240.9260.7960.6660.7100.8250.5050.8760.6400.8260.6070.448
소형정비종사자수(명)1.0000.6760.7050.8351.0000.7050.8130.7520.6890.6470.7040.4370.3440.5680.8190.4510.000
전문정비업체수(개)1.0000.7370.6330.8240.7051.0000.8060.5550.7350.7230.6670.0000.5140.6460.8630.6480.000
전문정비종사자수(명)1.0000.8220.7450.9260.8130.8061.0000.8500.7570.6390.8590.5900.8600.6520.7260.6580.000
원동기정비업체수(개)1.0000.7210.8340.7960.7520.5550.8501.0000.9800.5680.6420.6650.8670.2250.5660.5500.670
원동기정비종사자수(명)1.0000.5720.6680.6660.6890.7350.7570.9801.0000.5480.5660.5680.6590.0000.4740.7010.457
매매업체수(개)1.0000.7090.8300.7100.6470.7230.6390.5680.5481.0000.9350.3790.6730.9310.9100.7320.000
매매종사자수(명)1.0000.6880.9230.8250.7040.6670.8590.6420.5660.9351.0000.0000.7400.8380.8690.6960.000
폐차업체수(개)1.0000.6180.0000.5050.4370.0000.5900.6650.5680.3790.0001.0000.8310.7580.5390.2520.261
폐차종사자수(명)1.0000.8660.6110.8760.3440.5140.8600.8670.6590.6730.7400.8311.0000.7470.6800.6050.623
성능점검업체수(개)1.0000.7410.6110.6400.5680.6460.6520.2250.0000.9310.8380.7580.7471.0000.9090.7030.000
성능점검종사자수(명)1.0000.7140.6910.8260.8190.8630.7260.5660.4740.9100.8690.5390.6800.9091.0000.5190.704
택시미터업체수(개)1.0000.9050.7260.6070.4510.6480.6580.5500.7010.7320.6960.2520.6050.7030.5191.0000.522
택시미터종사자수(명)1.0000.8250.3440.4480.0000.0000.0000.6700.4570.0000.0000.2610.6230.0000.7040.5221.000
2023-12-11T07:01:56.771325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종합정비업체수(개)종합정비종사자수(명)소형정비업체수(개)소형정비종사자수(명)전문정비업체수(개)전문정비종사자수(명)원동기정비업체수(개)원동기정비종사자수(명)매매업체수(개)매매종사자수(명)폐차업체수(개)폐차종사자수(명)성능점검업체수(개)성능점검종사자수(명)택시미터업체수(개)택시미터종사자수(명)
종합정비업체수(개)1.0000.8680.7800.6070.7730.7320.7460.7310.7220.6530.6390.7640.7060.6020.7790.548
종합정비종사자수(명)0.8681.0000.8490.7810.7850.7710.7730.7820.6770.6850.2730.5080.6890.6360.6470.577
소형정비업체수(개)0.7800.8491.0000.9250.8920.8770.6260.6290.7200.7330.3030.5210.7160.7000.5570.406
소형정비종사자수(명)0.6070.7810.9251.0000.8000.8090.4900.5200.5950.6600.0310.2760.6350.6480.4190.400
전문정비업체수(개)0.7730.7850.8920.8001.0000.9670.6400.6220.7700.7820.2980.5270.7750.7200.5320.381
전문정비종사자수(명)0.7320.7710.8770.8090.9671.0000.6040.5960.7040.7090.2190.4700.7680.7030.4830.360
원동기정비업체수(개)0.7460.7730.6260.4900.6400.6041.0000.9600.5310.5000.3710.5640.4590.3920.4710.372
원동기정비종사자수(명)0.7310.7820.6290.5200.6220.5960.9601.0000.5100.4900.2880.5190.4440.3750.4960.408
매매업체수(개)0.7220.6770.7200.5950.7700.7040.5310.5101.0000.9670.4350.6230.8590.7910.5230.374
매매종사자수(명)0.6530.6850.7330.6600.7820.7090.5000.4900.9671.0000.2860.4860.8110.7710.4650.334
폐차업체수(개)0.6390.2730.3030.0310.2980.2190.3710.2880.4350.2861.0000.8910.3760.3020.5000.226
폐차종사자수(명)0.7640.5080.5210.2760.5270.4700.5640.5190.6230.4860.8911.0000.6210.5330.5660.365
성능점검업체수(개)0.7060.6890.7160.6350.7750.7680.4590.4440.8590.8110.3760.6211.0000.9520.5420.552
성능점검종사자수(명)0.6020.6360.7000.6480.7200.7030.3920.3750.7910.7710.3020.5330.9521.0000.4330.523
택시미터업체수(개)0.7790.6470.5570.4190.5320.4830.4710.4960.5230.4650.5000.5660.5420.4331.0000.743
택시미터종사자수(명)0.5480.5770.4060.4000.3810.3600.3720.4080.3740.3340.2260.3650.5520.5230.7431.000

Missing values

2023-12-11T07:01:51.973154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:01:52.224510image/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

집계년도시군명조사분기종합정비업체수(개)종합정비종사자수(명)소형정비업체수(개)소형정비종사자수(명)전문정비업체수(개)전문정비종사자수(명)원동기정비업체수(개)원동기정비종사자수(명)매매업체수(개)매매종사자수(명)폐차업체수(개)폐차종사자수(명)성능점검업체수(개)성능점검종사자수(명)택시미터업체수(개)택시미터종사자수(명)
0<NA>수원시<NA>7810134737352869715293524012523382568
1<NA>연천군<NA>117114376700498200011
2<NA>고양시<NA>8611378465356493182473477747619210
3<NA>화성시<NA>1174072883626104172271180221618152960
4<NA>성남시<NA>17221332722973780065513212913
5<NA>부천시<NA>47659473553597105271381360144681519
6<NA>남양주시<NA>523771160347470151170220222063
7<NA>안산시<NA>27392422604417151585146011752023
8<NA>평택시<NA>864823515539445731094590106771847
9<NA>안양시<NA>2855922234201355141216000241147
집계년도시군명조사분기종합정비업체수(개)종합정비종사자수(명)소형정비업체수(개)소형정비종사자수(명)전문정비업체수(개)전문정비종사자수(명)원동기정비업체수(개)원동기정비종사자수(명)매매업체수(개)매매종사자수(명)폐차업체수(개)폐차종사자수(명)성능점검업체수(개)성능점검종사자수(명)택시미터업체수(개)택시미터종사자수(명)
21<NA>안성시<NA>453942010315026626179014101241633
22<NA>구리시<NA>9177008621214223001100
23<NA>의왕시<NA>122053256310428000000228
24<NA>포천시<NA>441951342199214210291691146121818
25<NA>양평군<NA>13136315511050069171248
26<NA>여주시<NA>2317521085951311416180099
27<NA>동두천시<NA>10703116098008512131200
28<NA>과천시<NA>11500451430000000000
29<NA>가평군<NA>76222055860000150000
30<NA>용인시<NA>9568459596577859824133103158211301790