Overview

Dataset statistics

Number of variables8
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory74.1 B

Variable types

Numeric5
Text3

Dataset

Description전라남도에 소재한 자동차 운전전문학원 목록 현황(학원명, 주소, 부지면적, 교육생 정원 등)을 정리한 자료입니다.
Author경찰청 전라남도경찰청
URLhttps://www.data.go.kr/data/15113745/fileData.do

Alerts

수강료(1종보통) is highly overall correlated with 수강료(2종자동)High correlation
수강료(2종자동) is highly overall correlated with 수강료(1종보통)High correlation
연번 has unique valuesUnique
학원명 has unique valuesUnique
전화번호 has unique valuesUnique
주 소 has unique valuesUnique
총부지면적(제곱미터) has unique valuesUnique

Reproduction

Analysis started2024-05-11 07:49:23.374415
Analysis finished2024-05-11 07:49:29.579050
Duration6.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-05-11T16:49:29.735682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.25
Q17.25
median13.5
Q319.75
95-th percentile24.75
Maximum26
Range25
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation7.6485293
Coefficient of variation (CV)0.56655772
Kurtosis-1.2
Mean13.5
Median Absolute Deviation (MAD)6.5
Skewness0
Sum351
Variance58.5
MonotonicityStrictly increasing
2024-05-11T16:49:30.189526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 1
 
3.8%
15 1
 
3.8%
26 1
 
3.8%
25 1
 
3.8%
24 1
 
3.8%
23 1
 
3.8%
22 1
 
3.8%
21 1
 
3.8%
20 1
 
3.8%
19 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1 1
3.8%
2 1
3.8%
3 1
3.8%
4 1
3.8%
5 1
3.8%
6 1
3.8%
7 1
3.8%
8 1
3.8%
9 1
3.8%
10 1
3.8%
ValueCountFrequency (%)
26 1
3.8%
25 1
3.8%
24 1
3.8%
23 1
3.8%
22 1
3.8%
21 1
3.8%
20 1
3.8%
19 1
3.8%
18 1
3.8%
17 1
3.8%

학원명
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2024-05-11T16:49:30.505528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.7692308
Min length2

Characters and Unicode

Total characters98
Distinct characters48
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row(유)남경
2nd row무안
3rd row순천현대
4th row순천대우
5th row(주)영진
ValueCountFrequency (%)
신세계 2
 
7.1%
유)남경 1
 
3.6%
화순 1
 
3.6%
해남대흥 1
 
3.6%
우양 1
 
3.6%
나주 1
 
3.6%
영암 1
 
3.6%
주)구산 1
 
3.6%
새천년 1
 
3.6%
주)동광양 1
 
3.6%
Other values (17) 17
60.7%
2024-05-11T16:49:31.082437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 8
 
8.2%
) 8
 
8.2%
천 5
 
5.1%
유 5
 
5.1%
주 4
 
4.1%
아 4
 
4.1%
순 3
 
3.1%
대 3
 
3.1%
남 3
 
3.1%
영 3
 
3.1%
Other values (38) 52
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80
81.6%
Open Punctuation 8
 
8.2%
Close Punctuation 8
 
8.2%
Space Separator 2
 
2.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 5
 
6.2%
유 5
 
6.2%
주 4
 
5.0%
아 4
 
5.0%
순 3
 
3.8%
대 3
 
3.8%
남 3
 
3.8%
영 3
 
3.8%
양 3
 
3.8%
신 2
 
2.5%
Other values (35) 45
56.2%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80
81.6%
Common 18
 
18.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 5
 
6.2%
유 5
 
6.2%
주 4
 
5.0%
아 4
 
5.0%
순 3
 
3.8%
대 3
 
3.8%
남 3
 
3.8%
영 3
 
3.8%
양 3
 
3.8%
신 2
 
2.5%
Other values (35) 45
56.2%
Common
ValueCountFrequency (%)
( 8
44.4%
) 8
44.4%
2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80
81.6%
ASCII 18
 
18.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 8
44.4%
) 8
44.4%
2
 
11.1%
Hangul
ValueCountFrequency (%)
천 5
 
6.2%
유 5
 
6.2%
주 4
 
5.0%
아 4
 
5.0%
순 3
 
3.8%
대 3
 
3.8%
남 3
 
3.8%
영 3
 
3.8%
양 3
 
3.8%
신 2
 
2.5%
Other values (35) 45
56.2%

전화번호
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2024-05-11T16:49:31.547739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters312
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row061-282-5555
2nd row061-454-2508
3rd row061-721-0700
4th row061-741-3434
5th row061-651-6105
ValueCountFrequency (%)
061-282-5555 1
 
3.8%
061-454-2508 1
 
3.8%
061-535-3441 1
 
3.8%
061-381-0381 1
 
3.8%
061-336-6611 1
 
3.8%
061-472-9700 1
 
3.8%
061-683-2266 1
 
3.8%
061-337-8332 1
 
3.8%
061-794-8088 1
 
3.8%
061-454-1183 1
 
3.8%
Other values (16) 16
61.5%
2024-05-11T16:49:32.381766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 52
16.7%
0 46
14.7%
1 44
14.1%
6 42
13.5%
3 39
12.5%
2 20
 
6.4%
7 19
 
6.1%
5 17
 
5.4%
8 16
 
5.1%
4 15
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260
83.3%
Dash Punctuation 52
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46
17.7%
1 44
16.9%
6 42
16.2%
3 39
15.0%
2 20
7.7%
7 19
7.3%
5 17
 
6.5%
8 16
 
6.2%
4 15
 
5.8%
9 2
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 312
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 52
16.7%
0 46
14.7%
1 44
14.1%
6 42
13.5%
3 39
12.5%
2 20
 
6.4%
7 19
 
6.1%
5 17
 
5.4%
8 16
 
5.1%
4 15
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 52
16.7%
0 46
14.7%
1 44
14.1%
6 42
13.5%
3 39
12.5%
2 20
 
6.4%
7 19
 
6.1%
5 17
 
5.4%
8 16
 
5.1%
4 15
 
4.8%

주 소
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2024-05-11T16:49:32.852629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length17.884615
Min length13

Characters and Unicode

Total characters465
Distinct characters89
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row전남 목포시 선곡로 20
2nd row전남 무안군 망운면 현해로 195
3rd row전남 순천시 해룡면 순광로 234-16
4th row전남 순천시 녹색로 1462
5th row전남 여수시 만성로 231
ValueCountFrequency (%)
전남 26
 
21.3%
나주시 3
 
2.5%
순천시 3
 
2.5%
여수시 3
 
2.5%
목포시 2
 
1.6%
무안군 2
 
1.6%
담양군 2
 
1.6%
39 1
 
0.8%
왕곡면 1
 
0.8%
89 1
 
0.8%
Other values (78) 78
63.9%
2024-05-11T16:49:33.616950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
96
20.6%
남 28
 
6.0%
전 27
 
5.8%
로 18
 
3.9%
2 17
 
3.7%
1 16
 
3.4%
군 13
 
2.8%
시 13
 
2.8%
3 12
 
2.6%
면 12
 
2.6%
Other values (79) 213
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 269
57.8%
Space Separator 96
 
20.6%
Decimal Number 91
 
19.6%
Dash Punctuation 9
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 28
 
10.4%
전 27
 
10.0%
로 18
 
6.7%
군 13
 
4.8%
시 13
 
4.8%
면 12
 
4.5%
길 8
 
3.0%
곡 7
 
2.6%
읍 7
 
2.6%
순 6
 
2.2%
Other values (67) 130
48.3%
Decimal Number
ValueCountFrequency (%)
2 17
18.7%
1 16
17.6%
3 12
13.2%
7 9
9.9%
4 9
9.9%
9 8
8.8%
6 7
7.7%
0 6
 
6.6%
5 4
 
4.4%
8 3
 
3.3%
Space Separator
ValueCountFrequency (%)
96
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 269
57.8%
Common 196
42.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 28
 
10.4%
전 27
 
10.0%
로 18
 
6.7%
군 13
 
4.8%
시 13
 
4.8%
면 12
 
4.5%
길 8
 
3.0%
곡 7
 
2.6%
읍 7
 
2.6%
순 6
 
2.2%
Other values (67) 130
48.3%
Common
ValueCountFrequency (%)
96
49.0%
2 17
 
8.7%
1 16
 
8.2%
3 12
 
6.1%
7 9
 
4.6%
4 9
 
4.6%
- 9
 
4.6%
9 8
 
4.1%
6 7
 
3.6%
0 6
 
3.1%
Other values (2) 7
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 269
57.8%
ASCII 196
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
96
49.0%
2 17
 
8.7%
1 16
 
8.2%
3 12
 
6.1%
7 9
 
4.6%
4 9
 
4.6%
- 9
 
4.6%
9 8
 
4.1%
6 7
 
3.6%
0 6
 
3.1%
Other values (2) 7
 
3.6%
Hangul
ValueCountFrequency (%)
남 28
 
10.4%
전 27
 
10.0%
로 18
 
6.7%
군 13
 
4.8%
시 13
 
4.8%
면 12
 
4.5%
길 8
 
3.0%
곡 7
 
2.6%
읍 7
 
2.6%
순 6
 
2.2%
Other values (67) 130
48.3%

총부지면적(제곱미터)
Real number (ℝ)

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12982.692
Minimum8261
Maximum21400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-05-11T16:49:34.326818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8261
5-th percentile8672.5
Q19904
median12315
Q315240.5
95-th percentile17785.75
Maximum21400
Range13139
Interquartile range (IQR)5336.5

Descriptive statistics

Standard deviation3455.2543
Coefficient of variation (CV)0.26614313
Kurtosis-0.35806291
Mean12982.692
Median Absolute Deviation (MAD)2569
Skewness0.594891
Sum337550
Variance11938782
MonotonicityNot monotonic
2024-05-11T16:49:34.624678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
15290 1
 
3.8%
10224 1
 
3.8%
11982 1
 
3.8%
17497 1
 
3.8%
11644 1
 
3.8%
9900 1
 
3.8%
13859 1
 
3.8%
9803 1
 
3.8%
9689 1
 
3.8%
10569 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
8261 1
3.8%
8425 1
3.8%
9415 1
3.8%
9650 1
3.8%
9689 1
3.8%
9803 1
3.8%
9900 1
3.8%
9916 1
3.8%
10224 1
3.8%
10569 1
3.8%
ValueCountFrequency (%)
21400 1
3.8%
17868 1
3.8%
17539 1
3.8%
17497 1
3.8%
16885 1
3.8%
15971 1
3.8%
15290 1
3.8%
15092 1
3.8%
14664 1
3.8%
13978 1
3.8%

교육생정원
Real number (ℝ)

Distinct22
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.92308
Minimum56
Maximum455
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-05-11T16:49:34.876729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile114
Q1150
median194
Q3262
95-th percentile430.5
Maximum455
Range399
Interquartile range (IQR)112

Descriptive statistics

Standard deviation96.683783
Coefficient of variation (CV)0.44776957
Kurtosis1.2360615
Mean215.92308
Median Absolute Deviation (MAD)47
Skewness1.1065736
Sum5614
Variance9347.7538
MonotonicityNot monotonic
2024-05-11T16:49:35.291396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
182 2
 
7.7%
192 2
 
7.7%
150 2
 
7.7%
210 2
 
7.7%
378 1
 
3.8%
238 1
 
3.8%
270 1
 
3.8%
144 1
 
3.8%
224 1
 
3.8%
120 1
 
3.8%
Other values (12) 12
46.2%
ValueCountFrequency (%)
56 1
3.8%
112 1
3.8%
120 1
3.8%
126 1
3.8%
140 1
3.8%
144 1
3.8%
150 2
7.7%
168 1
3.8%
182 2
7.7%
192 2
7.7%
ValueCountFrequency (%)
455 1
3.8%
448 1
3.8%
378 1
3.8%
294 1
3.8%
280 1
3.8%
272 1
3.8%
270 1
3.8%
238 1
3.8%
225 1
3.8%
224 1
3.8%

수강료(1종보통)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean608273.42
Minimum476500
Maximum704000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-05-11T16:49:35.545328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum476500
5-th percentile508000
Q1572500
median606454.5
Q3663750
95-th percentile694700
Maximum704000
Range227500
Interquartile range (IQR)91250

Descriptive statistics

Standard deviation60026.98
Coefficient of variation (CV)0.098684207
Kurtosis-0.4118881
Mean608273.42
Median Absolute Deviation (MAD)50204.5
Skewness-0.2552608
Sum15815109
Variance3.6032383 × 109
MonotonicityNot monotonic
2024-05-11T16:49:35.798088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
680000 3
 
11.5%
580000 3
 
11.5%
665000 2
 
7.7%
630000 1
 
3.8%
621000 1
 
3.8%
704000 1
 
3.8%
498000 1
 
3.8%
615000 1
 
3.8%
570000 1
 
3.8%
476500 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
476500 1
 
3.8%
498000 1
 
3.8%
538000 1
 
3.8%
555000 1
 
3.8%
555500 1
 
3.8%
557000 1
 
3.8%
570000 1
 
3.8%
580000 3
11.5%
590000 1
 
3.8%
598000 1
 
3.8%
ValueCountFrequency (%)
704000 1
 
3.8%
699600 1
 
3.8%
680000 3
11.5%
665000 2
7.7%
660000 1
 
3.8%
630000 1
 
3.8%
624600 1
 
3.8%
621000 1
 
3.8%
615000 1
 
3.8%
612000 1
 
3.8%

수강료(2종자동)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean608273.42
Minimum476500
Maximum704000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-05-11T16:49:36.089386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum476500
5-th percentile508000
Q1572500
median606454.5
Q3663750
95-th percentile694700
Maximum704000
Range227500
Interquartile range (IQR)91250

Descriptive statistics

Standard deviation60026.98
Coefficient of variation (CV)0.098684207
Kurtosis-0.4118881
Mean608273.42
Median Absolute Deviation (MAD)50204.5
Skewness-0.2552608
Sum15815109
Variance3.6032383 × 109
MonotonicityNot monotonic
2024-05-11T16:49:36.461149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
680000 3
 
11.5%
580000 3
 
11.5%
665000 2
 
7.7%
630000 1
 
3.8%
621000 1
 
3.8%
704000 1
 
3.8%
498000 1
 
3.8%
615000 1
 
3.8%
570000 1
 
3.8%
476500 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
476500 1
 
3.8%
498000 1
 
3.8%
538000 1
 
3.8%
555000 1
 
3.8%
555500 1
 
3.8%
557000 1
 
3.8%
570000 1
 
3.8%
580000 3
11.5%
590000 1
 
3.8%
598000 1
 
3.8%
ValueCountFrequency (%)
704000 1
 
3.8%
699600 1
 
3.8%
680000 3
11.5%
665000 2
7.7%
660000 1
 
3.8%
630000 1
 
3.8%
624600 1
 
3.8%
621000 1
 
3.8%
615000 1
 
3.8%
612000 1
 
3.8%

Interactions

2024-05-11T16:49:28.041122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:24.053088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:25.051888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:25.962538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:27.002948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:28.225250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:24.229801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:25.232131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:26.137207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:27.174931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:28.476251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:24.468465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:25.421348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:26.328668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:27.434965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:28.666317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:24.665516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:25.604525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:26.592661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:27.653636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:28.850140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:24.899804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:25.790923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:26.796913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:27.836167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T16:49:36.730447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번학원명전화번호주 소총부지면적(제곱미터)교육생정원수강료(1종보통)수강료(2종자동)
연번1.0001.0001.0001.0000.4440.0000.3210.321
학원명1.0001.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.000
주 소1.0001.0001.0001.0001.0001.0001.0001.000
총부지면적(제곱미터)0.4441.0001.0001.0001.0000.2930.3250.325
교육생정원0.0001.0001.0001.0000.2931.0000.3460.346
수강료(1종보통)0.3211.0001.0001.0000.3250.3461.0001.000
수강료(2종자동)0.3211.0001.0001.0000.3250.3461.0001.000
2024-05-11T16:49:37.006684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번총부지면적(제곱미터)교육생정원수강료(1종보통)수강료(2종자동)
연번1.000-0.1810.1120.1240.124
총부지면적(제곱미터)-0.1811.0000.1040.3300.330
교육생정원0.1120.1041.000-0.249-0.249
수강료(1종보통)0.1240.330-0.2491.0001.000
수강료(2종자동)0.1240.330-0.2491.0001.000

Missing values

2024-05-11T16:49:29.219521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T16:49:29.477793image/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

연번학원명전화번호주 소총부지면적(제곱미터)교육생정원수강료(1종보통)수강료(2종자동)
01(유)남경061-282-5555전남 목포시 선곡로 2015290378630000630000
12무안061-454-2508전남 무안군 망운면 현해로 195842556580000580000
23순천현대061-721-0700전남 순천시 해룡면 순광로 234-1621400448598000598000
34순천대우061-741-3434전남 순천시 녹색로 14629916182557000557000
45(주)영진061-651-6105전남 여수시 만성로 23117539182680000680000
56담양아시아061-383-5777전남 담양군 담양읍 운교길 439650192538000538000
67(유)강진061-433-4070전남 강진군 성전면 629-2112445140580000580000
78영광061-351-4033전남 영광군 영광읍 함영로 346213399112660000660000
89동성061-643-2222전남 여수시 신월로 496-1217868126680000680000
910고흥061-835-3161전남 고흥군 고흥읍 송곡길 4-912185150612000612000
연번학원명전화번호주 소총부지면적(제곱미터)교육생정원수강료(1종보통)수강료(2종자동)
1617(유)호남061-277-1002전남 목포시 대양로 109번길 3915971210699600699600
1718목포061-454-1183전남 무안군 청계면 영산로 1743-279415238580000580000
1819(주)동광양061-794-8088전남 광양시 도이1길 8910569192665000665000
1920새천년061-337-8332전남 나주시 왕곡면 고분로 14659689280476500476500
2021(주)구산061-683-2266전남 여수 소라면 서부로347-59803168680000680000
2122영암 신세계061-472-9700전남 영암군 신북면 신북공단로 2313859294570000570000
2223나주061-336-6611전남 나주시 다시면 구진포로 289900120615000615000
2324우양061-381-0381전남 담양군 봉산면 죽향대로 729-211644224498000498000
2425해남대흥061-535-3441전남 해남군 북평면 와룡길 3717497144704000704000
2526천일061-363-2200전남 곡성군 곡성읍 곡고로 27711982270665000665000