Overview

Dataset statistics

Number of variables13
Number of observations702
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory73.5 KiB
Average record size in memory107.2 B

Variable types

Numeric2
Categorical3
Text8

Dataset

Description통계연도,자치구코드,자치구명,구분,구분명,소계,아동수_국공립,아동수_사회복지법인,아동수_법인단체등,아동수_민간,아동수_가정,아동수_부모협동,아동수_직장
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15460/S/1/datasetView.do

Alerts

구분명 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 구분명High correlation
자치구코드 is highly overall correlated with 자치구명High correlation
자치구명 is highly overall correlated with 자치구코드High correlation

Reproduction

Analysis started2024-04-06 11:43:42.415072
Analysis finished2024-04-06 11:43:44.734410
Duration2.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Real number (ℝ)

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-04-06T20:43:44.846744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32020
95-th percentile2022
Maximum2022
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5838299
Coefficient of variation (CV)0.0012803914
Kurtosis-1.2302099
Mean2018
Median Absolute Deviation (MAD)2
Skewness0
Sum1416636
Variance6.6761769
MonotonicityDecreasing
2024-04-06T20:43:45.094301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2022 78
11.1%
2021 78
11.1%
2020 78
11.1%
2019 78
11.1%
2018 78
11.1%
2017 78
11.1%
2016 78
11.1%
2015 78
11.1%
2014 78
11.1%
ValueCountFrequency (%)
2014 78
11.1%
2015 78
11.1%
2016 78
11.1%
2017 78
11.1%
2018 78
11.1%
2019 78
11.1%
2020 78
11.1%
2021 78
11.1%
2022 78
11.1%
ValueCountFrequency (%)
2022 78
11.1%
2021 78
11.1%
2020 78
11.1%
2019 78
11.1%
2018 78
11.1%
2017 78
11.1%
2016 78
11.1%
2015 78
11.1%
2014 78
11.1%

자치구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11400.577
Minimum11000
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.3 KiB
2024-04-06T20:43:45.370360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11000
5-th percentile11110
Q111230
median11395
Q311560
95-th percentile11710
Maximum11740
Range740
Interquartile range (IQR)330

Descriptive statistics

Standard deviation199.65766
Coefficient of variation (CV)0.017512944
Kurtosis-1.0049755
Mean11400.577
Median Absolute Deviation (MAD)165
Skewness-0.047642107
Sum8003205
Variance39863.183
MonotonicityNot monotonic
2024-04-06T20:43:45.585055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
11000 27
 
3.8%
11380 27
 
3.8%
11110 27
 
3.8%
11140 27
 
3.8%
11170 27
 
3.8%
11200 27
 
3.8%
11215 27
 
3.8%
11230 27
 
3.8%
11260 27
 
3.8%
11290 27
 
3.8%
Other values (16) 432
61.5%
ValueCountFrequency (%)
11000 27
3.8%
11110 27
3.8%
11140 27
3.8%
11170 27
3.8%
11200 27
3.8%
11215 27
3.8%
11230 27
3.8%
11260 27
3.8%
11290 27
3.8%
11305 27
3.8%
ValueCountFrequency (%)
11740 27
3.8%
11710 27
3.8%
11680 27
3.8%
11650 27
3.8%
11620 27
3.8%
11590 27
3.8%
11560 27
3.8%
11545 27
3.8%
11530 27
3.8%
11500 27
3.8%

자치구명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
계
 
27
강동
 
27
송파
 
27
강남
 
27
서초
 
27
Other values (21)
567 

Length

Max length3
Median length2
Mean length2.0769231
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row계
2nd row강동
3rd row강동
4th row송파
5th row송파

Common Values

ValueCountFrequency (%)
계 27
 
3.8%
강동 27
 
3.8%
송파 27
 
3.8%
강남 27
 
3.8%
서초 27
 
3.8%
관악 27
 
3.8%
동작 27
 
3.8%
영등포 27
 
3.8%
금천 27
 
3.8%
구로 27
 
3.8%
Other values (16) 432
61.5%

Length

2024-04-06T20:43:45.866385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
계 27
 
3.8%
강동 27
 
3.8%
중구 27
 
3.8%
용산 27
 
3.8%
성동 27
 
3.8%
광진 27
 
3.8%
동대문 27
 
3.8%
중랑 27
 
3.8%
성북 27
 
3.8%
강북 27
 
3.8%
Other values (16) 432
61.5%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
1
234 
2
234 
3
234 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row3
5th row2

Common Values

ValueCountFrequency (%)
1 234
33.3%
2 234
33.3%
3 234
33.3%

Length

2024-04-06T20:43:46.111810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:43:46.359500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 234
33.3%
2 234
33.3%
3 234
33.3%

구분명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
아동정원수
234 
아동현원수
234 
정원충족률
234 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row아동정원수
2nd row아동현원수
3rd row아동정원수
4th row정원충족률
5th row아동현원수

Common Values

ValueCountFrequency (%)
아동정원수 234
33.3%
아동현원수 234
33.3%
정원충족률 234
33.3%

Length

2024-04-06T20:43:46.584042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:43:46.828925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아동정원수 234
33.3%
아동현원수 234
33.3%
정원충족률 234
33.3%

소계
Text

Distinct683
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-06T20:43:47.437137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9458689
Min length4

Characters and Unicode

Total characters3472
Distinct characters12
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

Unique665 ?
Unique (%)94.7%

Sample

1st row223628
2nd row9878
3rd row12454
4th row83.47%
5th row12990
ValueCountFrequency (%)
85.45 3
 
0.4%
88.81 2
 
0.3%
87.36 2
 
0.3%
8444 2
 
0.3%
87.21 2
 
0.3%
5554 2
 
0.3%
11123 2
 
0.3%
85.95 2
 
0.3%
88.95 2
 
0.3%
7005 2
 
0.3%
Other values (673) 681
97.0%
2024-04-06T20:43:48.582587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 420
12.1%
8 417
12.0%
7 345
9.9%
6 291
8.4%
9 291
8.4%
4 278
8.0%
3 250
7.2%
2 245
7.1%
5 244
7.0%
. 234
6.7%
Other values (2) 457
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3004
86.5%
Other Punctuation 468
 
13.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 420
14.0%
8 417
13.9%
7 345
11.5%
6 291
9.7%
9 291
9.7%
4 278
9.3%
3 250
8.3%
2 245
8.2%
5 244
8.1%
0 223
7.4%
Other Punctuation
ValueCountFrequency (%)
. 234
50.0%
% 234
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 420
12.1%
8 417
12.0%
7 345
9.9%
6 291
8.4%
9 291
8.4%
4 278
8.0%
3 250
7.2%
2 245
7.1%
5 244
7.0%
. 234
6.7%
Other values (2) 457
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 420
12.1%
8 417
12.0%
7 345
9.9%
6 291
8.4%
9 291
8.4%
4 278
8.0%
3 250
7.2%
2 245
7.1%
5 244
7.0%
. 234
6.7%
Other values (2) 457
13.2%

아동수_국공립
Text

Distinct658
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-06T20:43:49.288470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.6552707
Min length4

Characters and Unicode

Total characters3268
Distinct characters12
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

Unique614 ?
Unique (%)87.5%

Sample

1st row103678
2nd row4589
3rd row5405
4th row88.48%
5th row5823
ValueCountFrequency (%)
3791 2
 
0.3%
2216 2
 
0.3%
1737 2
 
0.3%
4467 2
 
0.3%
3703 2
 
0.3%
2515 2
 
0.3%
92 2
 
0.3%
2812 2
 
0.3%
1790 2
 
0.3%
87.93 2
 
0.3%
Other values (648) 682
97.2%
2024-04-06T20:43:50.210696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 367
11.2%
2 334
10.2%
3 329
10.1%
9 298
9.1%
4 295
9.0%
1 271
8.3%
5 243
7.4%
7 240
7.3%
. 234
7.2%
% 234
7.2%
Other values (2) 423
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2800
85.7%
Other Punctuation 468
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 367
13.1%
2 334
11.9%
3 329
11.8%
9 298
10.6%
4 295
10.5%
1 271
9.7%
5 243
8.7%
7 240
8.6%
6 224
8.0%
0 199
7.1%
Other Punctuation
ValueCountFrequency (%)
. 234
50.0%
% 234
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3268
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 367
11.2%
2 334
10.2%
3 329
10.1%
9 298
9.1%
4 295
9.0%
1 271
8.3%
5 243
7.4%
7 240
7.3%
. 234
7.2%
% 234
7.2%
Other values (2) 423
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 367
11.2%
2 334
10.2%
3 329
10.1%
9 298
9.1%
4 295
9.0%
1 271
8.3%
5 243
7.4%
7 240
7.3%
. 234
7.2%
% 234
7.2%
Other values (2) 423
12.9%

아동수_사회복지법인
Text

Distinct258
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-06T20:43:50.931630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.8760684
Min length1

Characters and Unicode

Total characters2019
Distinct characters12
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

Unique190 ?
Unique (%)27.1%

Sample

1st row1533
2nd row68
3rd row87
4th row55.14%
5th row59
ValueCountFrequency (%)
0 275
39.2%
89 12
 
1.7%
223 10
 
1.4%
87 9
 
1.3%
33 9
 
1.3%
123 9
 
1.3%
79 9
 
1.3%
74 8
 
1.1%
82 8
 
1.1%
103 7
 
1.0%
Other values (241) 346
49.3%
2024-04-06T20:43:51.778517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 349
17.3%
. 234
11.6%
% 234
11.6%
1 183
9.1%
9 163
8.1%
2 163
8.1%
7 163
8.1%
8 160
7.9%
3 122
 
6.0%
6 100
 
5.0%
Other values (2) 148
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1551
76.8%
Other Punctuation 468
 
23.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 349
22.5%
1 183
11.8%
9 163
10.5%
2 163
10.5%
7 163
10.5%
8 160
10.3%
3 122
 
7.9%
6 100
 
6.4%
4 83
 
5.4%
5 65
 
4.2%
Other Punctuation
ValueCountFrequency (%)
. 234
50.0%
% 234
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2019
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 349
17.3%
. 234
11.6%
% 234
11.6%
1 183
9.1%
9 163
8.1%
2 163
8.1%
7 163
8.1%
8 160
7.9%
3 122
 
6.0%
6 100
 
5.0%
Other values (2) 148
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 349
17.3%
. 234
11.6%
% 234
11.6%
1 183
9.1%
9 163
8.1%
2 163
8.1%
7 163
8.1%
8 160
7.9%
3 122
 
6.0%
6 100
 
5.0%
Other values (2) 148
7.3%

아동수_법인단체등
Text

Distinct440
Distinct (%)62.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-06T20:43:52.441064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.6980057
Min length1

Characters and Unicode

Total characters2596
Distinct characters12
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

Unique346 ?
Unique (%)49.3%

Sample

1st row4954
2nd row73
3rd row76
4th row78.45%
5th row91
ValueCountFrequency (%)
0 51
 
7.3%
20 11
 
1.6%
101 10
 
1.4%
202 9
 
1.3%
237 9
 
1.3%
204 9
 
1.3%
156 7
 
1.0%
149 7
 
1.0%
100 7
 
1.0%
276 7
 
1.0%
Other values (429) 575
81.9%
2024-04-06T20:43:53.291610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 277
10.7%
7 255
9.8%
2 242
9.3%
. 234
9.0%
% 234
9.0%
8 219
8.4%
0 204
7.9%
4 192
7.4%
6 190
7.3%
3 186
7.2%
Other values (2) 363
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2128
82.0%
Other Punctuation 468
 
18.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 277
13.0%
7 255
12.0%
2 242
11.4%
8 219
10.3%
0 204
9.6%
4 192
9.0%
6 190
8.9%
3 186
8.7%
5 182
8.6%
9 181
8.5%
Other Punctuation
ValueCountFrequency (%)
. 234
50.0%
% 234
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2596
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 277
10.7%
7 255
9.8%
2 242
9.3%
. 234
9.0%
% 234
9.0%
8 219
8.4%
0 204
7.9%
4 192
7.4%
6 190
7.3%
3 186
7.2%
Other values (2) 363
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 277
10.7%
7 255
9.8%
2 242
9.3%
. 234
9.0%
% 234
9.0%
8 219
8.4%
0 204
7.9%
4 192
7.4%
6 190
7.3%
3 186
7.2%
Other values (2) 363
14.0%

아동수_민간
Text

Distinct668
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-06T20:43:54.024243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.6253561
Min length3

Characters and Unicode

Total characters3247
Distinct characters12
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

Unique637 ?
Unique (%)90.7%

Sample

1st row65662
2nd row3585
3rd row4957
4th row80.25%
5th row3945
ValueCountFrequency (%)
237 3
 
0.4%
81.92 3
 
0.4%
458 3
 
0.4%
5140 2
 
0.3%
82.6 2
 
0.3%
89.59 2
 
0.3%
84.94 2
 
0.3%
344 2
 
0.3%
84.42 2
 
0.3%
2499 2
 
0.3%
Other values (658) 679
96.7%
2024-04-06T20:43:55.094336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 348
10.7%
2 303
9.3%
7 302
9.3%
4 299
9.2%
5 290
8.9%
1 284
8.7%
3 273
8.4%
6 265
8.2%
9 254
7.8%
. 234
7.2%
Other values (2) 395
12.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2779
85.6%
Other Punctuation 468
 
14.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 348
12.5%
2 303
10.9%
7 302
10.9%
4 299
10.8%
5 290
10.4%
1 284
10.2%
3 273
9.8%
6 265
9.5%
9 254
9.1%
0 161
5.8%
Other Punctuation
ValueCountFrequency (%)
. 234
50.0%
% 234
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 348
10.7%
2 303
9.3%
7 302
9.3%
4 299
9.2%
5 290
8.9%
1 284
8.7%
3 273
8.4%
6 265
8.2%
9 254
7.8%
. 234
7.2%
Other values (2) 395
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 348
10.7%
2 303
9.3%
7 302
9.3%
4 299
9.2%
5 290
8.9%
1 284
8.7%
3 273
8.4%
6 265
8.2%
9 254
7.8%
. 234
7.2%
Other values (2) 395
12.2%

아동수_가정
Text

Distinct644
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-06T20:43:55.954793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.4301994
Min length2

Characters and Unicode

Total characters3110
Distinct characters12
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

Unique591 ?
Unique (%)84.2%

Sample

1st row25770
2nd row1295
3rd row1529
4th row87.24%
5th row1996
ValueCountFrequency (%)
89.47 3
 
0.4%
979 3
 
0.4%
1634 3
 
0.4%
88.76 3
 
0.4%
977 3
 
0.4%
2182 2
 
0.3%
2853 2
 
0.3%
1035 2
 
0.3%
2827 2
 
0.3%
83.28 2
 
0.3%
Other values (633) 677
96.4%
2024-04-06T20:43:57.075021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 379
12.2%
8 330
10.6%
9 309
9.9%
2 301
9.7%
3 257
8.3%
7 247
7.9%
. 234
7.5%
% 234
7.5%
4 217
7.0%
5 214
6.9%
Other values (2) 388
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2642
85.0%
Other Punctuation 468
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 379
14.3%
8 330
12.5%
9 309
11.7%
2 301
11.4%
3 257
9.7%
7 247
9.3%
4 217
8.2%
5 214
8.1%
6 201
7.6%
0 187
7.1%
Other Punctuation
ValueCountFrequency (%)
. 234
50.0%
% 234
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3110
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 379
12.2%
8 330
10.6%
9 309
9.9%
2 301
9.7%
3 257
8.3%
7 247
7.9%
. 234
7.5%
% 234
7.5%
4 217
7.0%
5 214
6.9%
Other values (2) 388
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 379
12.2%
8 330
10.6%
9 309
9.9%
2 301
9.7%
3 257
8.3%
7 247
7.9%
. 234
7.5%
% 234
7.5%
4 217
7.0%
5 214
6.9%
Other values (2) 388
12.5%

아동수_부모협동
Text

Distinct217
Distinct (%)30.9%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-06T20:43:57.707979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.8247863
Min length1

Characters and Unicode

Total characters1983
Distinct characters12
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

Unique145 ?
Unique (%)20.7%

Sample

1st row805
2nd row17
3rd row20
4th row62.5%
5th row30
ValueCountFrequency (%)
0 201
28.6%
16 21
 
3.0%
100 19
 
2.7%
20 18
 
2.6%
49 17
 
2.4%
30 14
 
2.0%
29 14
 
2.0%
27 12
 
1.7%
37 10
 
1.4%
39 10
 
1.4%
Other values (200) 366
52.1%
2024-04-06T20:43:58.621892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 319
16.1%
. 234
11.8%
% 234
11.8%
1 168
8.5%
3 146
7.4%
7 140
7.1%
2 139
7.0%
9 133
6.7%
8 129
6.5%
6 123
 
6.2%
Other values (2) 218
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1515
76.4%
Other Punctuation 468
 
23.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 319
21.1%
1 168
11.1%
3 146
9.6%
7 140
9.2%
2 139
9.2%
9 133
8.8%
8 129
8.5%
6 123
 
8.1%
5 115
 
7.6%
4 103
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 234
50.0%
% 234
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1983
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 319
16.1%
. 234
11.8%
% 234
11.8%
1 168
8.5%
3 146
7.4%
7 140
7.1%
2 139
7.0%
9 133
6.7%
8 129
6.5%
6 123
 
6.2%
Other values (2) 218
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 319
16.1%
. 234
11.8%
% 234
11.8%
1 168
8.5%
3 146
7.4%
7 140
7.1%
2 139
7.0%
9 133
6.7%
8 129
6.5%
6 123
 
6.2%
Other values (2) 218
11.0%

아동수_직장
Text

Distinct549
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Memory size5.6 KiB
2024-04-06T20:43:59.347625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.1153846
Min length2

Characters and Unicode

Total characters2889
Distinct characters12
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

Unique464 ?
Unique (%)66.1%

Sample

1st row21226
2nd row251
3rd row380
4th row69.41%
5th row1046
ValueCountFrequency (%)
174 7
 
1.0%
360 7
 
1.0%
101 7
 
1.0%
132 7
 
1.0%
264 7
 
1.0%
524 7
 
1.0%
384 5
 
0.7%
158 4
 
0.6%
326 4
 
0.6%
270 4
 
0.6%
Other values (539) 643
91.6%
2024-04-06T20:44:00.282627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 350
12.1%
7 285
9.9%
2 281
9.7%
6 266
9.2%
8 238
8.2%
. 234
8.1%
% 234
8.1%
3 226
7.8%
4 221
7.6%
5 218
7.5%
Other values (2) 336
11.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2421
83.8%
Other Punctuation 468
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 350
14.5%
7 285
11.8%
2 281
11.6%
6 266
11.0%
8 238
9.8%
3 226
9.3%
4 221
9.1%
5 218
9.0%
9 174
7.2%
0 162
6.7%
Other Punctuation
ValueCountFrequency (%)
. 234
50.0%
% 234
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2889
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 350
12.1%
7 285
9.9%
2 281
9.7%
6 266
9.2%
8 238
8.2%
. 234
8.1%
% 234
8.1%
3 226
7.8%
4 221
7.6%
5 218
7.5%
Other values (2) 336
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2889
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 350
12.1%
7 285
9.9%
2 281
9.7%
6 266
9.2%
8 238
8.2%
. 234
8.1%
% 234
8.1%
3 226
7.8%
4 221
7.6%
5 218
7.5%
Other values (2) 336
11.6%

Interactions

2024-04-06T20:43:43.545518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:43:43.125243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:43:43.810984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T20:43:43.305754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T20:44:00.466801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도자치구코드자치구명구분구분명
통계연도1.0000.0000.0000.0000.000
자치구코드0.0001.0001.0000.0000.000
자치구명0.0001.0001.0000.0000.000
구분0.0000.0000.0001.0001.000
구분명0.0000.0000.0001.0001.000
2024-04-06T20:44:00.612675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분명구분자치구명
구분명1.0001.0000.000
구분1.0001.0000.000
자치구명0.0000.0001.000
2024-04-06T20:44:00.772061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도자치구코드자치구명구분구분명
통계연도1.0000.0000.0000.0000.000
자치구코드0.0001.0000.9880.0000.000
자치구명0.0000.9881.0000.0000.000
구분0.0000.0000.0001.0001.000
구분명0.0000.0000.0001.0001.000

Missing values

2024-04-06T20:43:44.078606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T20:43:44.553615image/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

통계연도자치구코드자치구명구분구분명소계아동수_국공립아동수_사회복지법인아동수_법인단체등아동수_민간아동수_가정아동수_부모협동아동수_직장
0202211000계1아동정원수22362810367815334954656622577080521226
1202211740강동2아동현원수9878458968733585129517251
2202211740강동1아동정원수12454540587764957152920380
3202211710송파3정원충족률83.47%88.48%55.14%78.45%80.25%87.24%62.5%69.41%
4202211710송파2아동현원수129905823599139451996301046
5202211710송파1아동정원수15563658110711649162288481507
6202211680강남3정원충족률63.83%69.24%0.%0.%65.74%83.46%69.57%44.11%
7202211680강남2아동현원수68463566001625570481037
8202211680강남1아동정원수107255150002472683692351
9202211650서초3정원충족률70.66%75.41%0.%73.42%73.69%88.24%77.03%51.81%
통계연도자치구코드자치구명구분구분명소계아동수_국공립아동수_사회복지법인아동수_법인단체등아동수_민간아동수_가정아동수_부모협동아동수_직장
692201411200성동1아동정원수821035500202251316400305
693201411170용산3정원충족률88.85%91.21%84.27%85.21%89.17%91.22%72.73%82.01%
694201411170용산2아동현원수5038140075386178689324474
695201411170용산1아동정원수5670153589453200397933578
696201411140중구3정원충족률84.43%84.73%0.%82.2%93.01%99.05%0.%76.5%
697201411140중구2아동현원수3410153103514263140788
698201411140중구1아동정원수40391807042745831701030
699201411110종로3정원충족률85.45%89.37%84.87%93.29%88.84%87.84%0.%76.48%
700201411110종로2아동현원수4040176610113984413001060
701201411110종로1아동정원수4728197611914995014801386