Overview

Dataset statistics

Number of variables9
Number of observations1422
Missing cells5
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory100.1 KiB
Average record size in memory72.1 B

Variable types

Categorical3
Text3
Unsupported3

Dataset

Description평가인증어린이집현황20148
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=201973

Alerts

Unnamed: 4 is highly overall correlated with 2014. 평가인증 어린이집 현황 and 1 other fieldsHigh correlation
Unnamed: 1 is highly overall correlated with Unnamed: 4High correlation
2014. 평가인증 어린이집 현황 is highly overall correlated with Unnamed: 4High correlation
Unnamed: 4 is highly imbalanced (98.9%)Imbalance
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 00:30:44.704506
Analysis finished2024-03-14 00:30:45.457127
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2014. 평가인증 어린이집 현황
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
전주시 완산구
348 
전주시 덕진구
280 
익산시
231 
군산시
207 
정읍시
87 
Other values (12)
269 

Length

Max length7
Median length3
Mean length4.7672293
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 완산구 348
24.5%
전주시 덕진구 280
19.7%
익산시 231
16.2%
군산시 207
14.6%
정읍시 87
 
6.1%
완주군 64
 
4.5%
남원시 64
 
4.5%
김제시 58
 
4.1%
부안군 22
 
1.5%
고창군 20
 
1.4%
Other values (7) 41
 
2.9%

Length

2024-03-14T09:30:45.514554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 628
30.6%
완산구 348
17.0%
덕진구 280
13.7%
익산시 231
 
11.3%
군산시 207
 
10.1%
정읍시 87
 
4.2%
완주군 64
 
3.1%
남원시 64
 
3.1%
김제시 58
 
2.8%
부안군 22
 
1.1%
Other values (8) 61
 
3.0%

Unnamed: 1
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
가정
716 
민간
419 
사회복지법인
138 
법인·단체등
92 
국공립
 
50
Other values (3)
 
7

Length

Max length6
Median length2
Mean length2.6863572
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
가정 716
50.4%
민간 419
29.5%
사회복지법인 138
 
9.7%
법인·단체등 92
 
6.5%
국공립 50
 
3.5%
직장 5
 
0.4%
<NA> 1
 
0.1%
어린이집유형 1
 
0.1%

Length

2024-03-14T09:30:45.627928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:30:45.729740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가정 716
50.4%
민간 419
29.5%
사회복지법인 138
 
9.7%
법인·단체등 92
 
6.5%
국공립 50
 
3.5%
직장 5
 
0.4%
na 1
 
0.1%
어린이집유형 1
 
0.1%

Unnamed: 2
Text

Distinct1159
Distinct (%)81.6%
Missing1
Missing (%)0.1%
Memory size11.2 KiB
2024-03-14T09:30:45.919927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length7.4053483
Min length4

Characters and Unicode

Total characters10523
Distinct characters468
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique983 ?
Unique (%)69.2%

Sample

1st row어린이집
2nd rowECG우미어린이집
3rd rowNQ보듬이어린이집
4th rowe편한세상어린이집
5th row가람어린이집
ValueCountFrequency (%)
어린이집 59
 
4.0%
해바라기어린이집 6
 
0.4%
행복한어린이집 5
 
0.3%
해맑은어린이집 5
 
0.3%
솔로몬어린이집 5
 
0.3%
아기별어린이집 5
 
0.3%
동화나라어린이집 5
 
0.3%
뽀뽀뽀어린이집 4
 
0.3%
이화어린이집 4
 
0.3%
천사어린이집 4
 
0.3%
Other values (1162) 1391
93.2%
2024-03-14T09:30:46.215654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 1566
 
14.9%
린 1442
 
13.7%
어 1425
 
13.5%
집 1421
 
13.5%
아 183
 
1.7%
리 101
 
1.0%
사 100
 
1.0%
랑 97
 
0.9%
나 96
 
0.9%
마 93
 
0.9%
Other values (458) 3999
38.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10402
98.9%
Space Separator 72
 
0.7%
Uppercase Letter 28
 
0.3%
Decimal Number 10
 
0.1%
Lowercase Letter 3
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 1566
15.1%
린 1442
 
13.9%
어 1425
 
13.7%
집 1421
 
13.7%
아 183
 
1.8%
리 101
 
1.0%
사 100
 
1.0%
랑 97
 
0.9%
나 96
 
0.9%
마 93
 
0.9%
Other values (435) 3878
37.3%
Uppercase Letter
ValueCountFrequency (%)
C 5
17.9%
A 4
14.3%
E 4
14.3%
Q 4
14.3%
W 2
 
7.1%
Y 2
 
7.1%
B 2
 
7.1%
N 2
 
7.1%
K 1
 
3.6%
O 1
 
3.6%
Decimal Number
ValueCountFrequency (%)
2 3
30.0%
1 2
20.0%
3 2
20.0%
4 2
20.0%
5 1
 
10.0%
Lowercase Letter
ValueCountFrequency (%)
i 2
66.7%
e 1
33.3%
Space Separator
ValueCountFrequency (%)
72
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10402
98.9%
Common 90
 
0.9%
Latin 31
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 1566
15.1%
린 1442
 
13.9%
어 1425
 
13.7%
집 1421
 
13.7%
아 183
 
1.8%
리 101
 
1.0%
사 100
 
1.0%
랑 97
 
0.9%
나 96
 
0.9%
마 93
 
0.9%
Other values (435) 3878
37.3%
Latin
ValueCountFrequency (%)
C 5
16.1%
A 4
12.9%
E 4
12.9%
Q 4
12.9%
W 2
 
6.5%
Y 2
 
6.5%
i 2
 
6.5%
B 2
 
6.5%
N 2
 
6.5%
K 1
 
3.2%
Other values (3) 3
9.7%
Common
ValueCountFrequency (%)
72
80.0%
2 3
 
3.3%
1 2
 
2.2%
3 2
 
2.2%
- 2
 
2.2%
) 2
 
2.2%
( 2
 
2.2%
. 2
 
2.2%
4 2
 
2.2%
5 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10402
98.9%
ASCII 121
 
1.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 1566
15.1%
린 1442
 
13.9%
어 1425
 
13.7%
집 1421
 
13.7%
아 183
 
1.8%
리 101
 
1.0%
사 100
 
1.0%
랑 97
 
0.9%
나 96
 
0.9%
마 93
 
0.9%
Other values (435) 3878
37.3%
ASCII
ValueCountFrequency (%)
72
59.5%
C 5
 
4.1%
A 4
 
3.3%
E 4
 
3.3%
Q 4
 
3.3%
2 3
 
2.5%
1 2
 
1.7%
W 2
 
1.7%
Y 2
 
1.7%
3 2
 
1.7%
Other values (13) 21
 
17.4%

Unnamed: 3
Text

Distinct159
Distinct (%)11.2%
Missing1
Missing (%)0.1%
Memory size11.2 KiB
2024-03-14T09:30:46.438689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.4679803
Min length2

Characters and Unicode

Total characters4928
Distinct characters125
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

Unique46 ?
Unique (%)3.2%

Sample

1st row행정동
2nd row평화2동
3rd row효자4동
4th row서신동
5th row삼천3동
ValueCountFrequency (%)
평화2동 75
 
5.3%
송천1동 57
 
4.0%
효자4동 51
 
3.6%
인후3동 50
 
3.5%
수송동 47
 
3.3%
서신동 46
 
3.2%
삼천3동 42
 
3.0%
나운3동 40
 
2.8%
봉동읍 33
 
2.3%
동산동 33
 
2.3%
Other values (149) 947
66.6%
2024-03-14T09:30:46.798676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1289
26.2%
2 219
 
4.4%
1 179
 
3.6%
천 151
 
3.1%
3 147
 
3.0%
송 141
 
2.9%
화 130
 
2.6%
삼 116
 
2.4%
면 113
 
2.3%
산 107
 
2.2%
Other values (115) 2336
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4332
87.9%
Decimal Number 596
 
12.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1289
29.8%
천 151
 
3.5%
송 141
 
3.3%
화 130
 
3.0%
삼 116
 
2.7%
면 113
 
2.6%
산 107
 
2.5%
읍 104
 
2.4%
성 101
 
2.3%
인 93
 
2.1%
Other values (111) 1987
45.9%
Decimal Number
ValueCountFrequency (%)
2 219
36.7%
1 179
30.0%
3 147
24.7%
4 51
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4332
87.9%
Common 596
 
12.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1289
29.8%
천 151
 
3.5%
송 141
 
3.3%
화 130
 
3.0%
삼 116
 
2.7%
면 113
 
2.6%
산 107
 
2.5%
읍 104
 
2.4%
성 101
 
2.3%
인 93
 
2.1%
Other values (111) 1987
45.9%
Common
ValueCountFrequency (%)
2 219
36.7%
1 179
30.0%
3 147
24.7%
4 51
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4332
87.9%
ASCII 596
 
12.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1289
29.8%
천 151
 
3.5%
송 141
 
3.3%
화 130
 
3.0%
삼 116
 
2.7%
면 113
 
2.6%
산 107
 
2.5%
읍 104
 
2.4%
성 101
 
2.3%
인 93
 
2.1%
Other values (111) 1987
45.9%
ASCII
ValueCountFrequency (%)
2 219
36.7%
1 179
30.0%
3 147
24.7%
4 51
 
8.6%

Unnamed: 4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Y
1420 
<NA>
 
1
평가인증여부
 
1

Length

Max length6
Median length1
Mean length1.0056259
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
Y 1420
99.9%
<NA> 1
 
0.1%
평가인증여부 1
 
0.1%

Length

2024-03-14T09:30:47.208645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:30:47.318842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 1420
99.9%
na 1
 
0.1%
평가인증여부 1
 
0.1%

Unnamed: 5
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.1%
Memory size11.2 KiB

Unnamed: 6
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.1%
Memory size11.2 KiB

Unnamed: 7
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)0.1%
Memory size11.2 KiB

Unnamed: 8
Text

Distinct1421
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2024-03-14T09:30:47.498791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.007032
Min length4

Characters and Unicode

Total characters17074
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1420 ?
Unique (%)99.9%

Sample

1st row 2014.8.7기준
2nd row전화번호
3rd row063-228-1834
4th row070-7797-9811
5th row063-251-2131
ValueCountFrequency (%)
063-625-0151 2
 
0.1%
063-842-4543 1
 
0.1%
063-856-1008 1
 
0.1%
063-834-4116 1
 
0.1%
063-842-2548 1
 
0.1%
063-834-0636 1
 
0.1%
063-834-7448 1
 
0.1%
063-835-1856 1
 
0.1%
063-834-3252 1
 
0.1%
070-4312-1114 1
 
0.1%
Other values (1411) 1411
99.2%
2024-03-14T09:30:47.806905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2840
16.6%
3 2492
14.6%
6 2354
13.8%
0 2245
13.1%
2 1631
9.6%
5 1168
6.8%
4 1055
 
6.2%
8 917
 
5.4%
1 882
 
5.2%
7 837
 
4.9%
Other values (9) 653
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14222
83.3%
Dash Punctuation 2840
 
16.6%
Other Letter 6
 
< 0.1%
Space Separator 4
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2492
17.5%
6 2354
16.6%
0 2245
15.8%
2 1631
11.5%
5 1168
8.2%
4 1055
7.4%
8 917
 
6.4%
1 882
 
6.2%
7 837
 
5.9%
9 641
 
4.5%
Other Letter
ValueCountFrequency (%)
전 1
16.7%
화 1
16.7%
번 1
16.7%
호 1
16.7%
기 1
16.7%
준 1
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 2840
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17068
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2840
16.6%
3 2492
14.6%
6 2354
13.8%
0 2245
13.2%
2 1631
9.6%
5 1168
6.8%
4 1055
 
6.2%
8 917
 
5.4%
1 882
 
5.2%
7 837
 
4.9%
Other values (3) 647
 
3.8%
Hangul
ValueCountFrequency (%)
전 1
16.7%
화 1
16.7%
번 1
16.7%
호 1
16.7%
기 1
16.7%
준 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17068
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2840
16.6%
3 2492
14.6%
6 2354
13.8%
0 2245
13.2%
2 1631
9.6%
5 1168
6.8%
4 1055
 
6.2%
8 917
 
5.4%
1 882
 
5.2%
7 837
 
4.9%
Other values (3) 647
 
3.8%
Hangul
ValueCountFrequency (%)
전 1
16.7%
화 1
16.7%
번 1
16.7%
호 1
16.7%
기 1
16.7%
준 1
16.7%

Correlations

2024-03-14T09:30:47.901051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2014. 평가인증 어린이집 현황Unnamed: 1Unnamed: 4
2014. 평가인증 어린이집 현황1.0000.7311.000
Unnamed: 10.7311.0001.000
Unnamed: 41.0001.0001.000
2024-03-14T09:30:47.992382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 12014. 평가인증 어린이집 현황
Unnamed: 41.0000.9980.995
Unnamed: 10.9981.0000.445
2014. 평가인증 어린이집 현황0.9950.4451.000
2024-03-14T09:30:48.068995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2014. 평가인증 어린이집 현황Unnamed: 1Unnamed: 4
2014. 평가인증 어린이집 현황1.0000.4450.995
Unnamed: 10.4451.0000.998
Unnamed: 40.9950.9981.000

Missing values

2024-03-14T09:30:45.166147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:30:45.276671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-14T09:30:45.381390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

2014. 평가인증 어린이집 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
0<NA><NA><NA><NA><NA>NaNNaNNaN2014.8.7기준
1시군구어린이집유형어린이집행정동평가인증여부정원현원교직원전화번호
2전주시 완산구민간ECG우미어린이집평화2동Y32267063-228-1834
3전주시 완산구가정NQ보듬이어린이집효자4동Y19194070-7797-9811
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2014. 평가인증 어린이집 현황Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
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