Overview

Dataset statistics

Number of variables20
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory175.3 B

Variable types

Numeric9
Categorical1
Text10

Dataset

Description에너지다소비사업자의 에너지사용량신고에 대한 데이터로 지역(지자체)별, 연도별, 부문별 다소비사업자의 ‘보일러’설비 에너지사용량에 대한 데이터 개방
Author한국에너지공단
URLhttps://www.data.go.kr/data/15086732/fileData.do

Alerts

경기 is highly overall correlated with 경남 and 7 other fieldsHigh correlation
경남 is highly overall correlated with 경기 and 6 other fieldsHigh correlation
광주 is highly overall correlated with 경기 and 7 other fieldsHigh correlation
대전 is highly overall correlated with 경기 and 7 other fieldsHigh correlation
부산 is highly overall correlated with 경기 and 6 other fieldsHigh correlation
서울 is highly overall correlated with 경기 and 4 other fieldsHigh correlation
인천 is highly overall correlated with 경기 and 6 other fieldsHigh correlation
총합계 is highly overall correlated with 경기 and 7 other fieldsHigh correlation
부문 is highly overall correlated with 경기 and 7 other fieldsHigh correlation
경기 has unique valuesUnique
대전 has unique valuesUnique
서울 has unique valuesUnique
총합계 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:20:39.277013
Analysis finished2023-12-12 09:20:50.812903
Duration11.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T18:20:50.880730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2019
Q32021
95-th percentile2022
Maximum2022
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0493902
Coefficient of variation (CV)0.0010150521
Kurtosis-1.2573099
Mean2019
Median Absolute Deviation (MAD)2
Skewness0
Sum42399
Variance4.2
MonotonicityIncreasing
2023-12-12T18:20:51.004568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2016 3
14.3%
2017 3
14.3%
2018 3
14.3%
2019 3
14.3%
2020 3
14.3%
2021 3
14.3%
2022 3
14.3%
ValueCountFrequency (%)
2016 3
14.3%
2017 3
14.3%
2018 3
14.3%
2019 3
14.3%
2020 3
14.3%
2021 3
14.3%
2022 3
14.3%
ValueCountFrequency (%)
2022 3
14.3%
2021 3
14.3%
2020 3
14.3%
2019 3
14.3%
2018 3
14.3%
2017 3
14.3%
2016 3
14.3%

부문
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
산업
건물
수송

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row산업
2nd row건물
3rd row수송
4th row산업
5th row건물

Common Values

ValueCountFrequency (%)
산업 7
33.3%
건물 7
33.3%
수송 7
33.3%

Length

2023-12-12T18:20:51.123074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:20:51.235885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
산업 7
33.3%
건물 7
33.3%
수송 7
33.3%

강원
Text

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T18:20:51.400597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.5714286
Min length2

Characters and Unicode

Total characters96
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)71.4%

Sample

1st row35848
2nd row43824
3rd row10
4th row39191
5th row43757
ValueCountFrequency (%)
해당없음 6
28.6%
35848 1
 
4.8%
43824 1
 
4.8%
10 1
 
4.8%
39191 1
 
4.8%
43757 1
 
4.8%
40296 1
 
4.8%
51180 1
 
4.8%
40618 1
 
4.8%
50469 1
 
4.8%
Other values (6) 6
28.6%
2023-12-12T18:20:51.715074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 14
14.6%
1 11
11.5%
9 9
9.4%
3 7
 
7.3%
0 7
 
7.3%
6
 
6.2%
6
 
6.2%
6
 
6.2%
6
 
6.2%
8 6
 
6.2%
Other values (4) 18
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72
75.0%
Other Letter 24
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 14
19.4%
1 11
15.3%
9 9
12.5%
3 7
9.7%
0 7
9.7%
8 6
8.3%
7 5
 
6.9%
6 5
 
6.9%
5 4
 
5.6%
2 4
 
5.6%
Other Letter
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
75.0%
Hangul 24
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 14
19.4%
1 11
15.3%
9 9
12.5%
3 7
9.7%
0 7
9.7%
8 6
8.3%
7 5
 
6.9%
6 5
 
6.9%
5 4
 
5.6%
2 4
 
5.6%
Hangul
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
75.0%
Hangul 24
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 14
19.4%
1 11
15.3%
9 9
12.5%
3 7
9.7%
0 7
9.7%
8 6
8.3%
7 5
 
6.9%
6 5
 
6.9%
5 4
 
5.6%
2 4
 
5.6%
Hangul
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%

경기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean477736.33
Minimum159
Maximum1617812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T18:20:51.845252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum159
5-th percentile165
Q1682
median88281
Q31156356
95-th percentile1538001
Maximum1617812
Range1617653
Interquartile range (IQR)1155674

Descriptive statistics

Standard deviation641826.24
Coefficient of variation (CV)1.3434738
Kurtosis-1.2051254
Mean477736.33
Median Absolute Deviation (MAD)88087
Skewness0.86529975
Sum10032463
Variance4.1194093 × 1011
MonotonicityNot monotonic
2023-12-12T18:20:52.014316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1053509 1
 
4.8%
89335 1
 
4.8%
652 1
 
4.8%
83474 1
 
4.8%
1617812 1
 
4.8%
745 1
 
4.8%
77989 1
 
4.8%
1538001 1
 
4.8%
194 1
 
4.8%
80015 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
159 1
4.8%
165 1
4.8%
169 1
4.8%
194 1
4.8%
652 1
4.8%
682 1
4.8%
745 1
4.8%
77989 1
4.8%
80015 1
4.8%
83474 1
4.8%
ValueCountFrequency (%)
1617812 1
4.8%
1538001 1
4.8%
1457711 1
4.8%
1431859 1
4.8%
1172520 1
4.8%
1156356 1
4.8%
1053509 1
4.8%
91480 1
4.8%
91355 1
4.8%
89335 1
4.8%

경남
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103712.52
Minimum51
Maximum301174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T18:20:52.175847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile52
Q159
median15926
Q3289011
95-th percentile299025
Maximum301174
Range301123
Interquartile range (IQR)288952

Descriptive statistics

Standard deviation139009.74
Coefficient of variation (CV)1.3403371
Kurtosis-1.5743056
Mean103712.52
Median Absolute Deviation (MAD)15871
Skewness0.75624463
Sum2177963
Variance1.9323709 × 1010
MonotonicityNot monotonic
2023-12-12T18:20:52.346473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
52 2
 
9.5%
301174 1
 
4.8%
51 1
 
4.8%
212 1
 
4.8%
16812 1
 
4.8%
295558 1
 
4.8%
12491 1
 
4.8%
298942 1
 
4.8%
15124 1
 
4.8%
287737 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
51 1
4.8%
52 2
9.5%
55 1
4.8%
56 1
4.8%
59 1
4.8%
212 1
4.8%
12491 1
4.8%
13931 1
4.8%
15124 1
4.8%
15926 1
4.8%
ValueCountFrequency (%)
301174 1
4.8%
299025 1
4.8%
298942 1
4.8%
295684 1
4.8%
295558 1
4.8%
289011 1
4.8%
287737 1
4.8%
18747 1
4.8%
17264 1
4.8%
16812 1
4.8%

경북
Text

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T18:20:52.542710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.5714286
Min length1

Characters and Unicode

Total characters96
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)81.0%

Sample

1st row759785
2nd row15833
3rd row해당없음
4th row756600
5th row16266
ValueCountFrequency (%)
해당없음 4
19.0%
759785 1
 
4.8%
8 1
 
4.8%
780256 1
 
4.8%
12125 1
 
4.8%
798209 1
 
4.8%
5 1
 
4.8%
11467 1
 
4.8%
739163 1
 
4.8%
14335 1
 
4.8%
Other values (8) 8
38.1%
2023-12-12T18:20:52.919090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
14.6%
7 11
11.5%
5 11
11.5%
6 10
10.4%
3 9
9.4%
2 8
8.3%
9 5
 
5.2%
8 5
 
5.2%
4
 
4.2%
4
 
4.2%
Other values (4) 15
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
83.3%
Other Letter 16
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
17.5%
7 11
13.8%
5 11
13.8%
6 10
12.5%
3 9
11.2%
2 8
10.0%
9 5
 
6.2%
8 5
 
6.2%
0 4
 
5.0%
4 3
 
3.8%
Other Letter
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80
83.3%
Hangul 16
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
17.5%
7 11
13.8%
5 11
13.8%
6 10
12.5%
3 9
11.2%
2 8
10.0%
9 5
 
6.2%
8 5
 
6.2%
0 4
 
5.0%
4 3
 
3.8%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80
83.3%
Hangul 16
 
16.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
17.5%
7 11
13.8%
5 11
13.8%
6 10
12.5%
3 9
11.2%
2 8
10.0%
9 5
 
6.2%
8 5
 
6.2%
0 4
 
5.0%
4 3
 
3.8%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%

광주
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28539.571
Minimum17
Maximum87195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T18:20:53.097010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile23
Q133
median6892
Q373678
95-th percentile81204
Maximum87195
Range87178
Interquartile range (IQR)73645

Descriptive statistics

Standard deviation36316.71
Coefficient of variation (CV)1.2725037
Kurtosis-1.4931363
Mean28539.571
Median Absolute Deviation (MAD)6863
Skewness0.76382773
Sum599331
Variance1.3189034 × 109
MonotonicityNot monotonic
2023-12-12T18:20:53.254305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
29 3
 
14.3%
33 2
 
9.5%
79681 1
 
4.8%
7608 1
 
4.8%
17 1
 
4.8%
6892 1
 
4.8%
87195 1
 
4.8%
23 1
 
4.8%
8339 1
 
4.8%
81204 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
17 1
 
4.8%
23 1
 
4.8%
29 3
14.3%
33 2
9.5%
6056 1
 
4.8%
6643 1
 
4.8%
6785 1
 
4.8%
6892 1
 
4.8%
7608 1
 
4.8%
8339 1
 
4.8%
ValueCountFrequency (%)
87195 1
4.8%
81204 1
4.8%
80155 1
4.8%
79681 1
4.8%
77025 1
4.8%
73678 1
4.8%
69074 1
4.8%
8803 1
4.8%
8339 1
4.8%
7608 1
4.8%

대구
Text

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T18:20:53.454950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters105
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)66.7%

Sample

1st row356549
2nd row13896
3rd row해당없음
4th row334833
5th row13118
ValueCountFrequency (%)
해당없음 7
33.3%
356549 1
 
4.8%
13896 1
 
4.8%
334833 1
 
4.8%
13118 1
 
4.8%
306467 1
 
4.8%
14873 1
 
4.8%
283807 1
 
4.8%
14963 1
 
4.8%
261068 1
 
4.8%
Other values (5) 5
23.8%
2023-12-12T18:20:53.862542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 13
12.4%
8 13
12.4%
1 10
9.5%
6 9
8.6%
2 8
7.6%
7
 
6.7%
7
 
6.7%
7
 
6.7%
7
 
6.7%
4 6
 
5.7%
Other values (4) 18
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77
73.3%
Other Letter 28
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 13
16.9%
8 13
16.9%
1 10
13.0%
6 9
11.7%
2 8
10.4%
4 6
7.8%
9 6
7.8%
0 5
 
6.5%
7 5
 
6.5%
5 2
 
2.6%
Other Letter
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77
73.3%
Hangul 28
 
26.7%

Most frequent character per script

Common
ValueCountFrequency (%)
3 13
16.9%
8 13
16.9%
1 10
13.0%
6 9
11.7%
2 8
10.4%
4 6
7.8%
9 6
7.8%
0 5
 
6.5%
7 5
 
6.5%
5 2
 
2.6%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77
73.3%
Hangul 28
 
26.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 13
16.9%
8 13
16.9%
1 10
13.0%
6 9
11.7%
2 8
10.4%
4 6
7.8%
9 6
7.8%
0 5
 
6.5%
7 5
 
6.5%
5 2
 
2.6%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

대전
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62690.952
Minimum3
Maximum148806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T18:20:54.034092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile261
Q1360
median51288
Q3132126
95-th percentile143803
Maximum148806
Range148803
Interquartile range (IQR)131766

Descriptive statistics

Standard deviation57778.41
Coefficient of variation (CV)0.92163874
Kurtosis-1.5367708
Mean62690.952
Median Absolute Deviation (MAD)51004
Skewness0.33387751
Sum1316510
Variance3.3383447 × 109
MonotonicityNot monotonic
2023-12-12T18:20:54.202841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
132126 1
 
4.8%
56792 1
 
4.8%
261 1
 
4.8%
42858 1
 
4.8%
135864 1
 
4.8%
284 1
 
4.8%
48972 1
 
4.8%
148806 1
 
4.8%
278 1
 
4.8%
46151 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
3 1
4.8%
261 1
4.8%
278 1
4.8%
284 1
4.8%
315 1
4.8%
360 1
4.8%
382 1
4.8%
42858 1
4.8%
46151 1
4.8%
48972 1
4.8%
ValueCountFrequency (%)
148806 1
4.8%
143803 1
4.8%
135864 1
4.8%
135619 1
4.8%
133512 1
4.8%
132126 1
4.8%
125102 1
4.8%
58239 1
4.8%
56792 1
4.8%
55495 1
4.8%

부산
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58072.238
Minimum9
Maximum155976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T18:20:54.374708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile165
Q1222
median36313
Q3123161
95-th percentile149211
Maximum155976
Range155967
Interquartile range (IQR)122939

Descriptive statistics

Standard deviation60073.436
Coefficient of variation (CV)1.0344605
Kurtosis-1.4131429
Mean58072.238
Median Absolute Deviation (MAD)36139
Skewness0.59964135
Sum1219517
Variance3.6088177 × 109
MonotonicityNot monotonic
2023-12-12T18:20:54.540011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
174 2
 
9.5%
149211 1
 
4.8%
222 1
 
4.8%
304 1
 
4.8%
37761 1
 
4.8%
121000 1
 
4.8%
31938 1
 
4.8%
127527 1
 
4.8%
196 1
 
4.8%
31956 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
9 1
4.8%
165 1
4.8%
174 2
9.5%
196 1
4.8%
222 1
4.8%
304 1
4.8%
31938 1
4.8%
31956 1
4.8%
34096 1
4.8%
36313 1
4.8%
ValueCountFrequency (%)
155976 1
4.8%
149211 1
4.8%
148367 1
4.8%
137277 1
4.8%
127527 1
4.8%
123161 1
4.8%
121000 1
4.8%
45231 1
4.8%
38459 1
4.8%
37761 1
4.8%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130410.48
Minimum1129
Maximum234310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T18:20:54.705453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1129
5-th percentile1164
Q11905
median164452
Q3211703
95-th percentile228816
Maximum234310
Range233181
Interquartile range (IQR)209798

Descriptive statistics

Standard deviation96440.757
Coefficient of variation (CV)0.73951695
Kurtosis-1.598131
Mean130410.48
Median Absolute Deviation (MAD)51677
Skewness-0.56506768
Sum2738620
Variance9.3008196 × 109
MonotonicityNot monotonic
2023-12-12T18:20:54.874585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
161044 1
 
4.8%
234310 1
 
4.8%
1320 1
 
4.8%
211703 1
 
4.8%
214757 1
 
4.8%
1164 1
 
4.8%
202482 1
 
4.8%
175171 1
 
4.8%
1129 1
 
4.8%
201324 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1129 1
4.8%
1164 1
4.8%
1320 1
4.8%
1355 1
4.8%
1379 1
4.8%
1905 1
4.8%
2025 1
4.8%
139052 1
4.8%
161044 1
4.8%
163295 1
4.8%
ValueCountFrequency (%)
234310 1
4.8%
228816 1
4.8%
225126 1
4.8%
216129 1
4.8%
214757 1
4.8%
211703 1
4.8%
202482 1
4.8%
201324 1
4.8%
190682 1
4.8%
175171 1
4.8%

세종
Text

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T18:20:55.069266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4
Min length3

Characters and Unicode

Total characters84
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)66.7%

Sample

1st row32015
2nd row382
3rd row해당없음
4th row25420
5th row389
ValueCountFrequency (%)
해당없음 7
33.3%
32015 1
 
4.8%
382 1
 
4.8%
25420 1
 
4.8%
389 1
 
4.8%
25708 1
 
4.8%
401 1
 
4.8%
35401 1
 
4.8%
398 1
 
4.8%
28190 1
 
4.8%
Other values (5) 5
23.8%
2023-12-12T18:20:55.491883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10
11.9%
3 8
9.5%
7
8.3%
7
8.3%
7
8.3%
7
8.3%
0 7
8.3%
8 7
8.3%
5 6
7.1%
9 6
7.1%
Other values (4) 12
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56
66.7%
Other Letter 28
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10
17.9%
3 8
14.3%
0 7
12.5%
8 7
12.5%
5 6
10.7%
9 6
10.7%
1 4
 
7.1%
4 3
 
5.4%
7 3
 
5.4%
6 2
 
3.6%
Other Letter
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56
66.7%
Hangul 28
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10
17.9%
3 8
14.3%
0 7
12.5%
8 7
12.5%
5 6
10.7%
9 6
10.7%
1 4
 
7.1%
4 3
 
5.4%
7 3
 
5.4%
6 2
 
3.6%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56
66.7%
Hangul 28
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10
17.9%
3 8
14.3%
0 7
12.5%
8 7
12.5%
5 6
10.7%
9 6
10.7%
1 4
 
7.1%
4 3
 
5.4%
7 3
 
5.4%
6 2
 
3.6%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

울산
Text

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T18:20:55.711996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length5
Min length4

Characters and Unicode

Total characters105
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)66.7%

Sample

1st row2362614
2nd row3871
3rd row해당없음
4th row2289725
5th row3964
ValueCountFrequency (%)
해당없음 7
33.3%
2362614 1
 
4.8%
3871 1
 
4.8%
2289725 1
 
4.8%
3964 1
 
4.8%
2465650 1
 
4.8%
3890 1
 
4.8%
2480999 1
 
4.8%
3956 1
 
4.8%
2338013 1
 
4.8%
Other values (5) 5
23.8%
2023-12-12T18:20:56.131725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 15
14.3%
2 12
11.4%
5 9
8.6%
9 8
7.6%
7
 
6.7%
7
 
6.7%
7
 
6.7%
7
 
6.7%
6 7
 
6.7%
8 7
 
6.7%
Other values (4) 19
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77
73.3%
Other Letter 28
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 15
19.5%
2 12
15.6%
5 9
11.7%
9 8
10.4%
6 7
9.1%
8 7
9.1%
1 6
 
7.8%
4 5
 
6.5%
0 5
 
6.5%
7 3
 
3.9%
Other Letter
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77
73.3%
Hangul 28
 
26.7%

Most frequent character per script

Common
ValueCountFrequency (%)
3 15
19.5%
2 12
15.6%
5 9
11.7%
9 8
10.4%
6 7
9.1%
8 7
9.1%
1 6
 
7.8%
4 5
 
6.5%
0 5
 
6.5%
7 3
 
3.9%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77
73.3%
Hangul 28
 
26.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 15
19.5%
2 12
15.6%
5 9
11.7%
9 8
10.4%
6 7
9.1%
8 7
9.1%
1 6
 
7.8%
4 5
 
6.5%
0 5
 
6.5%
7 3
 
3.9%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

인천
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97366.048
Minimum16
Maximum387843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T18:20:56.316652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile18
Q123
median14491
Q3220724
95-th percentile322586
Maximum387843
Range387827
Interquartile range (IQR)220701

Descriptive statistics

Standard deviation134255.28
Coefficient of variation (CV)1.3788717
Kurtosis-0.70815381
Mean97366.048
Median Absolute Deviation (MAD)14471
Skewness0.98724032
Sum2044687
Variance1.8024481 × 1010
MonotonicityNot monotonic
2023-12-12T18:20:56.492959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
23 2
 
9.5%
20 2
 
9.5%
322586 1
 
4.8%
19 1
 
4.8%
14153 1
 
4.8%
216496 1
 
4.8%
13957 1
 
4.8%
220724 1
 
4.8%
18 1
 
4.8%
14491 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
16 1
4.8%
18 1
4.8%
19 1
4.8%
20 2
9.5%
23 2
9.5%
13957 1
4.8%
14153 1
4.8%
14296 1
4.8%
14491 1
4.8%
14900 1
4.8%
ValueCountFrequency (%)
387843 1
4.8%
322586 1
4.8%
288138 1
4.8%
250952 1
4.8%
248510 1
4.8%
220724 1
4.8%
216496 1
4.8%
20605 1
4.8%
16897 1
4.8%
14900 1
4.8%

전남
Text

Distinct17
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T18:20:56.707946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.952381
Min length3

Characters and Unicode

Total characters104
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)76.2%

Sample

1st row2607205
2nd row2634
3rd row해당없음
4th row2869300
5th row3320
ValueCountFrequency (%)
해당없음 5
23.8%
2607205 1
 
4.8%
2873794 1
 
4.8%
4240 1
 
4.8%
3169880 1
 
4.8%
616 1
 
4.8%
4121 1
 
4.8%
3110240 1
 
4.8%
3477 1
 
4.8%
3495 1
 
4.8%
Other values (7) 7
33.3%
2023-12-12T18:20:57.145689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 13
12.5%
2 12
11.5%
0 11
10.6%
1 9
8.7%
4 8
7.7%
6 8
7.7%
7 8
7.7%
9 7
 
6.7%
5
 
4.8%
5
 
4.8%
Other values (4) 18
17.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84
80.8%
Other Letter 20
 
19.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 13
15.5%
2 12
14.3%
0 11
13.1%
1 9
10.7%
4 8
9.5%
6 8
9.5%
7 8
9.5%
9 7
8.3%
8 5
 
6.0%
5 3
 
3.6%
Other Letter
ValueCountFrequency (%)
5
25.0%
5
25.0%
5
25.0%
5
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84
80.8%
Hangul 20
 
19.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 13
15.5%
2 12
14.3%
0 11
13.1%
1 9
10.7%
4 8
9.5%
6 8
9.5%
7 8
9.5%
9 7
8.3%
8 5
 
6.0%
5 3
 
3.6%
Hangul
ValueCountFrequency (%)
5
25.0%
5
25.0%
5
25.0%
5
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84
80.8%
Hangul 20
 
19.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 13
15.5%
2 12
14.3%
0 11
13.1%
1 9
10.7%
4 8
9.5%
6 8
9.5%
7 8
9.5%
9 7
8.3%
8 5
 
6.0%
5 3
 
3.6%
Hangul
ValueCountFrequency (%)
5
25.0%
5
25.0%
5
25.0%
5
25.0%

전북
Text

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T18:20:57.344421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.3333333
Min length4

Characters and Unicode

Total characters112
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)66.7%

Sample

1st row1801152
2nd row10483
3rd row해당없음
4th row1783515
5th row10959
ValueCountFrequency (%)
해당없음 7
33.3%
1801152 1
 
4.8%
10483 1
 
4.8%
1783515 1
 
4.8%
10959 1
 
4.8%
1688865 1
 
4.8%
10975 1
 
4.8%
1615947 1
 
4.8%
11744 1
 
4.8%
1595203 1
 
4.8%
Other values (5) 5
23.8%
2023-12-12T18:20:57.737774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 22
19.6%
5 12
10.7%
8 10
8.9%
0 9
8.0%
7
 
6.2%
7
 
6.2%
7
 
6.2%
7
 
6.2%
7 7
 
6.2%
4 6
 
5.4%
Other values (4) 18
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84
75.0%
Other Letter 28
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
26.2%
5 12
14.3%
8 10
11.9%
0 9
10.7%
7 7
 
8.3%
4 6
 
7.1%
9 6
 
7.1%
6 6
 
7.1%
2 3
 
3.6%
3 3
 
3.6%
Other Letter
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84
75.0%
Hangul 28
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22
26.2%
5 12
14.3%
8 10
11.9%
0 9
10.7%
7 7
 
8.3%
4 6
 
7.1%
9 6
 
7.1%
6 6
 
7.1%
2 3
 
3.6%
3 3
 
3.6%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84
75.0%
Hangul 28
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22
26.2%
5 12
14.3%
8 10
11.9%
0 9
10.7%
7 7
 
8.3%
4 6
 
7.1%
9 6
 
7.1%
6 6
 
7.1%
2 3
 
3.6%
3 3
 
3.6%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

제주
Text

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T18:20:57.913978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.4761905
Min length1

Characters and Unicode

Total characters73
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)66.7%

Sample

1st row548
2nd row6607
3rd row해당없음
4th row638
5th row7114
ValueCountFrequency (%)
해당없음 5
23.8%
0 2
 
9.5%
548 1
 
4.8%
6607 1
 
4.8%
638 1
 
4.8%
7114 1
 
4.8%
522 1
 
4.8%
8556 1
 
4.8%
453 1
 
4.8%
8658 1
 
4.8%
Other values (6) 6
28.6%
2023-12-12T18:20:58.575071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 9
12.3%
6 8
11.0%
8 6
8.2%
7 6
8.2%
1 6
8.2%
5
 
6.8%
5
 
6.8%
5
 
6.8%
5
 
6.8%
0 5
 
6.8%
Other values (3) 13
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53
72.6%
Other Letter 20
 
27.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 9
17.0%
6 8
15.1%
8 6
11.3%
7 6
11.3%
1 6
11.3%
0 5
9.4%
4 5
9.4%
3 5
9.4%
2 3
 
5.7%
Other Letter
ValueCountFrequency (%)
5
25.0%
5
25.0%
5
25.0%
5
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53
72.6%
Hangul 20
 
27.4%

Most frequent character per script

Common
ValueCountFrequency (%)
5 9
17.0%
6 8
15.1%
8 6
11.3%
7 6
11.3%
1 6
11.3%
0 5
9.4%
4 5
9.4%
3 5
9.4%
2 3
 
5.7%
Hangul
ValueCountFrequency (%)
5
25.0%
5
25.0%
5
25.0%
5
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53
72.6%
Hangul 20
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 9
17.0%
6 8
15.1%
8 6
11.3%
7 6
11.3%
1 6
11.3%
0 5
9.4%
4 5
9.4%
3 5
9.4%
2 3
 
5.7%
Hangul
ValueCountFrequency (%)
5
25.0%
5
25.0%
5
25.0%
5
25.0%

충남
Text

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T18:20:58.740108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.1428571
Min length1

Characters and Unicode

Total characters108
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)71.4%

Sample

1st row969840
2nd row18469
3rd row6
4th row1184724
5th row15458
ValueCountFrequency (%)
해당없음 6
28.6%
969840 1
 
4.8%
18469 1
 
4.8%
6 1
 
4.8%
1184724 1
 
4.8%
15458 1
 
4.8%
1233484 1
 
4.8%
16624 1
 
4.8%
1335789 1
 
4.8%
14718 1
 
4.8%
Other values (6) 6
28.6%
2023-12-12T18:20:59.060754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18
16.7%
4 11
10.2%
3 11
10.2%
9 10
9.3%
8 10
9.3%
2 7
 
6.5%
6
 
5.6%
6
 
5.6%
6
 
5.6%
6
 
5.6%
Other values (4) 17
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84
77.8%
Other Letter 24
 
22.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18
21.4%
4 11
13.1%
3 11
13.1%
9 10
11.9%
8 10
11.9%
2 7
 
8.3%
6 5
 
6.0%
7 5
 
6.0%
5 5
 
6.0%
0 2
 
2.4%
Other Letter
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84
77.8%
Hangul 24
 
22.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18
21.4%
4 11
13.1%
3 11
13.1%
9 10
11.9%
8 10
11.9%
2 7
 
8.3%
6 5
 
6.0%
7 5
 
6.0%
5 5
 
6.0%
0 2
 
2.4%
Hangul
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84
77.8%
Hangul 24
 
22.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18
21.4%
4 11
13.1%
3 11
13.1%
9 10
11.9%
8 10
11.9%
2 7
 
8.3%
6 5
 
6.0%
7 5
 
6.0%
5 5
 
6.0%
0 2
 
2.4%
Hangul
ValueCountFrequency (%)
6
25.0%
6
25.0%
6
25.0%
6
25.0%

충북
Text

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T18:20:59.248458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5
Min length4

Characters and Unicode

Total characters105
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)66.7%

Sample

1st row337795
2nd row13769
3rd row해당없음
4th row334387
5th row14367
ValueCountFrequency (%)
해당없음 7
33.3%
337795 1
 
4.8%
13769 1
 
4.8%
334387 1
 
4.8%
14367 1
 
4.8%
379991 1
 
4.8%
15204 1
 
4.8%
391971 1
 
4.8%
14023 1
 
4.8%
417986 1
 
4.8%
Other values (5) 5
23.8%
2023-12-12T18:20:59.603298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
13.3%
3 11
10.5%
9 10
9.5%
4 10
9.5%
7 9
8.6%
7
6.7%
7
6.7%
7
6.7%
7
6.7%
6 7
6.7%
Other values (4) 16
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77
73.3%
Other Letter 28
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
18.2%
3 11
14.3%
9 10
13.0%
4 10
13.0%
7 9
11.7%
6 7
9.1%
5 5
 
6.5%
2 4
 
5.2%
0 4
 
5.2%
8 3
 
3.9%
Other Letter
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77
73.3%
Hangul 28
 
26.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
18.2%
3 11
14.3%
9 10
13.0%
4 10
13.0%
7 9
11.7%
6 7
9.1%
5 5
 
6.5%
2 4
 
5.2%
0 4
 
5.2%
8 3
 
3.9%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77
73.3%
Hangul 28
 
26.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
18.2%
3 11
14.3%
9 10
13.0%
4 10
13.0%
7 9
11.7%
6 7
9.1%
5 5
 
6.5%
2 4
 
5.2%
0 4
 
5.2%
8 3
 
3.9%
Hangul
ValueCountFrequency (%)
7
25.0%
7
25.0%
7
25.0%
7
25.0%

총합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4272072.1
Minimum1901
Maximum12948970
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T18:20:59.740905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1901
5-th percentile2180
Q13077
median566843
Q311892529
95-th percentile12757963
Maximum12948970
Range12947069
Interquartile range (IQR)11889452

Descriptive statistics

Standard deviation5793455.1
Coefficient of variation (CV)1.356123
Kurtosis-1.5525665
Mean4272072.1
Median Absolute Deviation (MAD)564160
Skewness0.76494382
Sum89713514
Variance3.3564122 × 1013
MonotonicityNot monotonic
2023-12-12T18:20:59.905629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
11462682 1
 
4.8%
597062 1
 
4.8%
3808 1
 
4.8%
553283 1
 
4.8%
12948970 1
 
4.8%
3077 1
 
4.8%
525535 1
 
4.8%
12757963 1
 
4.8%
1901 1
 
4.8%
513844 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1901 1
4.8%
2180 1
4.8%
2327 1
4.8%
2683 1
4.8%
2736 1
4.8%
3077 1
4.8%
3808 1
4.8%
513844 1
4.8%
525535 1
4.8%
553283 1
4.8%
ValueCountFrequency (%)
12948970 1
4.8%
12757963 1
4.8%
12337690 1
4.8%
12309185 1
4.8%
12055294 1
4.8%
11892529 1
4.8%
11462682 1
4.8%
599043 1
4.8%
597062 1
4.8%
574879 1
4.8%

Interactions

2023-12-12T18:20:49.302040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:39.986332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:41.423668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:42.618742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:43.580352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:44.649539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:45.784833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:46.737647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:47.805463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:49.432327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:40.100428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:41.524455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:42.737015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:43.685423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:44.783407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:45.894325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:46.854824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:47.933949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:49.537020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:40.222077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:41.663183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:42.858615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:43.820837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:44.919592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:46.004746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:46.951731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:48.073918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:49.663551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:40.333262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:41.798709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:42.960056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:43.918888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:45.058527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:46.120936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:47.060403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:48.215506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:49.758349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:40.482194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:41.911594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:43.046584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:44.008232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:45.191013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:46.214853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:47.175738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:48.329174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:49.883451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:40.607846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:42.057107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:43.150269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:44.180593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:45.303314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:46.324303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:47.273457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:48.798251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:49.989685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:40.739753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:42.219444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:43.265359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:44.312347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:45.422552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:46.433546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:47.404202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:48.925054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:50.085876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:40.851345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:42.347540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:43.365539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:44.404842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:45.545123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:46.528595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:47.571352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:49.039608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:50.203652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:40.969061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:42.492394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:43.483310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:44.528896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:45.670334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:46.627759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:47.685300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:20:49.169773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:21:00.053267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도부문강원경기경남경북광주대구대전부산서울세종울산인천전남전북제주충남충북총합계
연도1.0000.0000.0000.0000.0000.7170.0000.0000.0000.0000.0000.0000.0000.0000.5810.0000.3840.0000.0000.000
부문0.0001.0001.0000.6471.0001.0000.6801.0001.0001.0000.9841.0001.0000.8791.0001.0001.0001.0001.0000.926
강원0.0001.0001.0001.0001.0000.9771.0001.0001.0001.0001.0001.0001.0001.0000.9821.0000.9971.0001.0001.000
경기0.0000.6471.0001.0001.0001.0000.8941.0000.7810.9520.8111.0001.0000.9221.0001.0001.0001.0001.0001.000
경남0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9201.0001.0001.0001.0001.0001.0001.0001.0001.000
경북0.7171.0000.9771.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9421.0000.9880.9771.0001.000
광주0.0000.6801.0000.8941.0001.0001.0001.0000.8480.8610.6661.0001.0000.7731.0001.0001.0001.0001.0000.693
대구0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대전0.0001.0001.0000.7811.0001.0000.8481.0001.0000.9530.7501.0001.0000.6381.0001.0001.0001.0001.0000.725
부산0.0001.0001.0000.9521.0001.0000.8611.0000.9531.0000.8371.0001.0000.7191.0001.0001.0001.0001.0000.725
서울0.0000.9841.0000.8110.9201.0000.6661.0000.7500.8371.0001.0001.0000.8871.0001.0001.0001.0001.0000.891
세종0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
울산0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
인천0.0000.8791.0000.9221.0001.0000.7731.0000.6380.7190.8871.0001.0001.0001.0001.0001.0001.0001.0001.000
전남0.5811.0000.9821.0001.0000.9421.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9900.9821.0001.000
전북0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
제주0.3841.0000.9971.0001.0000.9881.0001.0001.0001.0001.0001.0001.0001.0000.9901.0001.0000.9971.0001.000
충남0.0001.0001.0001.0001.0000.9771.0001.0001.0001.0001.0001.0001.0001.0000.9821.0000.9971.0001.0001.000
충북0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
총합계0.0000.9261.0001.0001.0001.0000.6931.0000.7250.7250.8911.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T18:21:00.263641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도경기경남광주대전부산서울인천총합계부문
연도1.0000.094-0.053-0.061-0.055-0.083-0.106-0.1340.0670.000
경기0.0941.0000.8630.8670.9260.9050.5180.8750.9700.577
경남-0.0530.8631.0000.8710.8920.9150.4960.9470.9100.973
광주-0.0610.8670.8711.0000.9040.8630.5180.8520.8770.613
대전-0.0550.9260.8920.9041.0000.8850.5400.9140.9450.943
부산-0.0830.9050.9150.8630.8851.0000.4560.9580.9090.943
서울-0.1060.5180.4960.5180.5400.4561.0000.4780.5470.772
인천-0.1340.8750.9470.8520.9140.9580.4781.0000.9160.527
총합계0.0670.9700.9100.8770.9450.9090.5470.9161.0000.667
부문0.0000.5770.9730.6130.9430.9430.7720.5270.6671.000

Missing values

2023-12-12T18:20:50.402072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:20:50.705020image/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

연도부문강원경기경남경북광주대구대전부산서울세종울산인천전남전북제주충남충북총합계
02016산업358481053509301174759785796813565491321261492111610443201523626143225862607205180115254896984033779511462682
12016건물43824893351592615833880313896567924523123431038238711689726341048366071846913769597062
22016수송1016956해당없음33해당없음392025해당없음해당없음16해당없음해당없음해당없음6해당없음2327
32017산업3919111725202890117566008015533483312510215597616329525420228972528813828693001783515638118472433438711892529
42017건물43757914801393116266678513118582393631322512638939641429633201095971141545814367574879
52017수송해당없음16555해당없음33해당없음3821741905해당없음해당없음23해당없음해당없음해당없음해당없음해당없음2736
62018산업4029611563562990257775637702530646714380314836719068225708246565038784329875371688865522123348437999112309185
72018건물51180913551726416231605614873554953845922881640138902060530621097585561662415204599043
82018수송해당없음15959929해당없음3601651379해당없음해당없음20해당없음해당없음해당없음해당없음해당없음2180
92019산업4061814318592956847524137367828380713561913727713905235401248099925095229361731615947453133578939197112337690
연도부문강원경기경남경북광주대구대전부산서울세종울산인천전남전북제주충남충북총합계
112019수송해당없음68251829해당없음3152221355해당없음해당없음19해당없음해당없음해당없음해당없음해당없음2683
122020산업4191714577112877377391636907426106813351212316116445228190233801324851028737941595203410127539441798612055294
132020건물39472800151512411467760816280461513195620132463635121449134771060977381091713066513844
142020수송해당없음19452529해당없음2781961129해당없음해당없음18해당없음해당없음0해당없음해당없음1901
152021산업4429115380012989427982098120428892714880612752717517127532233141822072431102401721841527139439145021212757963
162021건물419907798912491121258339172804897231938202482890365013957412110760116651233114554525535
172021수송해당없음74552해당없음23해당없음2841741164해당없음해당없음20616해당없음0해당없음해당없음3077
182022산업4661716178122955587802568719528643913586412100021475732795253792521649631698801555678504138899546119712948970
192022건물480338347416812115216892183294285837761211703892358314153424011488137361282314986553283
202022수송해당없음652212해당없음17해당없음2613041320해당없음해당없음231019해당없음해당없음해당없음해당없음3808