Overview

Dataset statistics

Number of variables5
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory48.1 B

Variable types

Text1
Categorical1
Numeric3

Dataset

Description"학교건강검사 표본조사 결과" 데이터는 전국 초·중·고등학생의 건강조사 표본조사 결과 관련 데이터를 제공하고 있습니다.
Author교육부
URLhttps://www.data.go.kr/data/15051013/fileData.do

Alerts

키(cm) is highly overall correlated with 몸무게(kg) and 1 other fieldsHigh correlation
몸무게(kg) is highly overall correlated with 키(cm) and 1 other fieldsHigh correlation
검사인원수 is highly overall correlated with 키(cm) and 1 other fieldsHigh correlation
몸무게(kg) has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:06:30.147297
Analysis finished2023-12-12 21:06:31.319103
Duration1.17 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-13T06:06:31.377397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.6153846
Min length4

Characters and Unicode

Total characters120
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row만 6세
2nd row만 6세
3rd row만 7세
4th row만 7세
5th row만 8세
ValueCountFrequency (%)
만 24
48.0%
6세 2
 
4.0%
7세 2
 
4.0%
8세 2
 
4.0%
9세 2
 
4.0%
10세 2
 
4.0%
11세 2
 
4.0%
12세 2
 
4.0%
13세 2
 
4.0%
만14세 2
 
4.0%
Other values (4) 8
 
16.0%
2023-12-13T06:06:31.589566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
만 26
21.7%
세 26
21.7%
24
20.0%
1 20
16.7%
6 4
 
3.3%
7 4
 
3.3%
8 4
 
3.3%
9 2
 
1.7%
0 2
 
1.7%
2 2
 
1.7%
Other values (3) 6
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52
43.3%
Decimal Number 44
36.7%
Space Separator 24
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20
45.5%
6 4
 
9.1%
7 4
 
9.1%
8 4
 
9.1%
9 2
 
4.5%
0 2
 
4.5%
2 2
 
4.5%
3 2
 
4.5%
4 2
 
4.5%
5 2
 
4.5%
Other Letter
ValueCountFrequency (%)
만 26
50.0%
세 26
50.0%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68
56.7%
Hangul 52
43.3%

Most frequent character per script

Common
ValueCountFrequency (%)
24
35.3%
1 20
29.4%
6 4
 
5.9%
7 4
 
5.9%
8 4
 
5.9%
9 2
 
2.9%
0 2
 
2.9%
2 2
 
2.9%
3 2
 
2.9%
4 2
 
2.9%
Hangul
ValueCountFrequency (%)
만 26
50.0%
세 26
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68
56.7%
Hangul 52
43.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
만 26
50.0%
세 26
50.0%
ASCII
ValueCountFrequency (%)
24
35.3%
1 20
29.4%
6 4
 
5.9%
7 4
 
5.9%
8 4
 
5.9%
9 2
 
2.9%
0 2
 
2.9%
2 2
 
2.9%
3 2
 
2.9%
4 2
 
2.9%

성별
Categorical

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
남
13 
여
13 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남
2nd row여
3rd row남
4th row여
5th row남

Common Values

ValueCountFrequency (%)
남 13
50.0%
여 13
50.0%

Length

2023-12-13T06:06:31.697979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:06:31.790407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 13
50.0%
여 13
50.0%

키(cm)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.37308
Minimum119.3
Maximum173.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T06:06:31.863158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum119.3
5-th percentile121.45
Q1136.65
median156.4
Q3160.7
95-th percentile173.2
Maximum173.7
Range54.4
Interquartile range (IQR)24.05

Descriptive statistics

Standard deviation17.397829
Coefficient of variation (CV)0.11569776
Kurtosis-1.0918138
Mean150.37308
Median Absolute Deviation (MAD)13.3
Skewness-0.39704886
Sum3909.7
Variance302.68445
MonotonicityNot monotonic
2023-12-13T06:06:31.957477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
160.7 2
 
7.7%
120.4 1
 
3.8%
119.3 1
 
3.8%
173.7 1
 
3.8%
173.3 1
 
3.8%
160.6 1
 
3.8%
172.9 1
 
3.8%
160.5 1
 
3.8%
171.8 1
 
3.8%
159.8 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
119.3 1
3.8%
120.4 1
3.8%
124.6 1
3.8%
125.7 1
3.8%
130.6 1
3.8%
131.6 1
3.8%
136.5 1
3.8%
137.1 1
3.8%
142.9 1
3.8%
143.3 1
3.8%
ValueCountFrequency (%)
173.7 1
3.8%
173.3 1
3.8%
172.9 1
3.8%
171.8 1
3.8%
168.7 1
3.8%
164.2 1
3.8%
160.7 2
7.7%
160.6 1
3.8%
160.5 1
3.8%
159.8 1
3.8%

몸무게(kg)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.326923
Minimum23.1
Maximum71.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T06:06:32.045017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.1
5-th percentile24.6
Q133.975
median50.15
Q357.675
95-th percentile69.75
Maximum71.1
Range48
Interquartile range (IQR)23.7

Descriptive statistics

Standard deviation15.107179
Coefficient of variation (CV)0.31920898
Kurtosis-1.2024651
Mean47.326923
Median Absolute Deviation (MAD)12
Skewness-0.11870404
Sum1230.5
Variance228.22685
MonotonicityNot monotonic
2023-12-13T06:06:32.142089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
24.2 1
 
3.8%
57.8 1
 
3.8%
57.7 1
 
3.8%
71.1 1
 
3.8%
57.6 1
 
3.8%
70.2 1
 
3.8%
57.0 1
 
3.8%
68.4 1
 
3.8%
55.7 1
 
3.8%
66.0 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
23.1 1
3.8%
24.2 1
3.8%
25.8 1
3.8%
27.2 1
3.8%
29.8 1
3.8%
31.4 1
3.8%
33.4 1
3.8%
35.7 1
3.8%
38.3 1
3.8%
40.7 1
3.8%
ValueCountFrequency (%)
71.1 1
3.8%
70.2 1
3.8%
68.4 1
3.8%
66.0 1
3.8%
62.3 1
3.8%
57.8 1
3.8%
57.7 1
3.8%
57.6 1
3.8%
57.0 1
3.8%
55.7 1
3.8%

검사인원수
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3094.0769
Minimum1242
Maximum4487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T06:06:32.230034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1242
5-th percentile1367.75
Q12720.5
median2900
Q33831.5
95-th percentile4440.5
Maximum4487
Range3245
Interquartile range (IQR)1111

Descriptive statistics

Standard deviation933.30085
Coefficient of variation (CV)0.30164113
Kurtosis-0.44335959
Mean3094.0769
Median Absolute Deviation (MAD)674.5
Skewness-0.32887024
Sum80446
Variance871050.47
MonotonicityNot monotonic
2023-12-13T06:06:32.319862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2755 2
 
7.7%
1823 1
 
3.8%
3605 1
 
3.8%
1242 1
 
3.8%
1269 1
 
3.8%
4487 1
 
3.8%
4306 1
 
3.8%
4472 1
 
3.8%
4346 1
 
3.8%
3979 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
1242 1
3.8%
1269 1
3.8%
1664 1
3.8%
1823 1
3.8%
2593 1
3.8%
2610 1
3.8%
2710 1
3.8%
2752 1
3.8%
2755 2
7.7%
2836 1
3.8%
ValueCountFrequency (%)
4487 1
3.8%
4472 1
3.8%
4346 1
3.8%
4306 1
3.8%
4080 1
3.8%
3979 1
3.8%
3845 1
3.8%
3791 1
3.8%
3605 1
3.8%
3544 1
3.8%

Interactions

2023-12-13T06:06:30.992788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:06:30.311252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:06:30.524502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:06:31.062691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:06:30.383488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:06:30.597101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:06:31.126001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:06:30.450973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:06:30.664114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:06:32.397535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분성별키(cm)몸무게(kg)검사인원수
구분1.0000.0000.9290.9100.928
성별0.0001.0000.6500.0000.000
키(cm)0.9290.6501.0000.9830.581
몸무게(kg)0.9100.0000.9831.0000.703
검사인원수0.9280.0000.5810.7031.000
2023-12-13T06:06:32.476144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
키(cm)몸무게(kg)검사인원수성별
키(cm)1.0000.9980.5540.399
몸무게(kg)0.9981.0000.5520.000
검사인원수0.5540.5521.0000.000
성별0.3990.0000.0001.000

Missing values

2023-12-13T06:06:31.215876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:06:31.287704image/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

구분성별키(cm)몸무게(kg)검사인원수
0만 6세남120.424.21823
1만 6세여119.323.11664
2만 7세남125.727.22845
3만 7세여124.625.82752
4만 8세남131.631.42871
5만 8세여130.629.82610
6만 9세남137.135.72836
7만 9세여136.533.42755
8만 10세남142.940.72929
9만 10세여143.338.32710
구분성별키(cm)몸무게(kg)검사인원수
16만14세남168.762.33845
17만14세여159.854.43544
18만 15세남171.866.04080
19만 15세여160.555.73979
20만 16세남172.968.44346
21만 16세여160.657.04472
22만 17세남173.370.24306
23만 17세여160.757.64487
24만 18세남173.771.11269
25만 18세여160.757.71242