Overview

Dataset statistics

Number of variables7
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory61.3 B

Variable types

Numeric2
Text1
Categorical4

Alerts

SD_CD has constant value ""Constant
SD_NM has constant value ""Constant
SGG_CD is highly overall correlated with SGG_KOR_NMHigh correlation
SGG_KOR_NM is highly overall correlated with SGG_CDHigh correlation
id has unique valuesUnique
gid has unique valuesUnique

Reproduction

Analysis started2024-04-21 10:42:43.177262
Analysis finished2024-04-21 10:42:44.413933
Duration1.24 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-21T19:42:44.647587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2024-04-21T19:42:45.084589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

gid
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
2024-04-21T19:42:46.255136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters11
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

Unique100 ?
Unique (%)100.0%

Sample

1st row나나7582
2nd row나나7679
3rd row나나7680
4th row나나7779
5th row나나7780
ValueCountFrequency (%)
나나7582 1
 
1.0%
나나8785 1
 
1.0%
나나8972 1
 
1.0%
나나8895 1
 
1.0%
나나8894 1
 
1.0%
나나8892 1
 
1.0%
나나8885 1
 
1.0%
나나8878 1
 
1.0%
나나8874 1
 
1.0%
나나8872 1
 
1.0%
Other values (90) 90
90.0%
2024-04-21T19:42:47.777920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 200
33.3%
8 114
19.0%
7 75
 
12.5%
9 70
 
11.7%
1 28
 
4.7%
2 20
 
3.3%
6 20
 
3.3%
0 20
 
3.3%
4 19
 
3.2%
3 18
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 400
66.7%
Other Letter 200
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 114
28.5%
7 75
18.8%
9 70
17.5%
1 28
 
7.0%
2 20
 
5.0%
6 20
 
5.0%
0 20
 
5.0%
4 19
 
4.8%
3 18
 
4.5%
5 16
 
4.0%
Other Letter
ValueCountFrequency (%)
나 200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 400
66.7%
Hangul 200
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
8 114
28.5%
7 75
18.8%
9 70
17.5%
1 28
 
7.0%
2 20
 
5.0%
6 20
 
5.0%
0 20
 
5.0%
4 19
 
4.8%
3 18
 
4.5%
5 16
 
4.0%
Hangul
ValueCountFrequency (%)
나 200
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 400
66.7%
Hangul 200
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 200
100.0%
ASCII
ValueCountFrequency (%)
8 114
28.5%
7 75
18.8%
9 70
17.5%
1 28
 
7.0%
2 20
 
5.0%
6 20
 
5.0%
0 20
 
5.0%
4 19
 
4.8%
3 18
 
4.5%
5 16
 
4.0%

SD_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
50
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50
2nd row50
3rd row50
4th row50
5th row50

Common Values

ValueCountFrequency (%)
50 100
100.0%

Length

2024-04-21T19:42:48.180061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T19:42:48.469662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50 100
100.0%

SD_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
제주
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제주
2nd row제주
3rd row제주
4th row제주
5th row제주

Common Values

ValueCountFrequency (%)
제주 100
100.0%

Length

2024-04-21T19:42:48.769556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T19:42:49.058012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주 100
100.0%

SGG_CD
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
50110
62 
50130
38 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50110
2nd row50110
3rd row50110
4th row50110
5th row50110

Common Values

ValueCountFrequency (%)
50110 62
62.0%
50130 38
38.0%

Length

2024-04-21T19:42:49.357377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T19:42:49.652790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50110 62
62.0%
50130 38
38.0%

SGG_KOR_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
제주시
62 
서귀포시
38 

Length

Max length4
Median length3
Mean length3.38
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제주시
2nd row제주시
3rd row제주시
4th row제주시
5th row제주시

Common Values

ValueCountFrequency (%)
제주시 62
62.0%
서귀포시 38
38.0%

Length

2024-04-21T19:42:49.994534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T19:42:50.303188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 62
62.0%
서귀포시 38
38.0%

교육연구
Real number (ℝ)

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-21T19:42:50.808905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile15
Maximum39
Range38
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.8809743
Coefficient of variation (CV)1.225203
Kurtosis12.82424
Mean4.8
Median Absolute Deviation (MAD)2
Skewness3.0791548
Sum480
Variance34.585859
MonotonicityNot monotonic
2024-04-21T19:42:51.197400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 37
37.0%
3 12
 
12.0%
2 9
 
9.0%
4 7
 
7.0%
6 6
 
6.0%
5 6
 
6.0%
9 5
 
5.0%
7 4
 
4.0%
8 3
 
3.0%
13 3
 
3.0%
Other values (7) 8
 
8.0%
ValueCountFrequency (%)
1 37
37.0%
2 9
 
9.0%
3 12
 
12.0%
4 7
 
7.0%
5 6
 
6.0%
6 6
 
6.0%
7 4
 
4.0%
8 3
 
3.0%
9 5
 
5.0%
10 1
 
1.0%
ValueCountFrequency (%)
39 1
 
1.0%
27 1
 
1.0%
22 1
 
1.0%
17 1
 
1.0%
15 2
 
2.0%
14 1
 
1.0%
13 3
3.0%
10 1
 
1.0%
9 5
5.0%
8 3
3.0%

Interactions

2024-04-21T19:42:43.763704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:42:43.469144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:42:43.910763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T19:42:43.605459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T19:42:51.459204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idgidSGG_CDSGG_KOR_NM교육연구
id1.0001.0000.0000.0000.113
gid1.0001.0001.0001.0001.000
SGG_CD0.0001.0001.0000.9990.000
SGG_KOR_NM0.0001.0000.9991.0000.000
교육연구0.1131.0000.0000.0001.000
2024-04-21T19:42:51.719114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SGG_CDSGG_KOR_NM
SGG_CD1.0000.979
SGG_KOR_NM0.9791.000
2024-04-21T19:42:51.956390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
id교육연구SGG_CDSGG_KOR_NM
id1.0000.0430.0000.000
교육연구0.0431.0000.0000.000
SGG_CD0.0000.0001.0000.979
SGG_KOR_NM0.0000.0000.9791.000

Missing values

2024-04-21T19:42:44.131420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T19:42:44.329051image/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

idgidSD_CDSD_NMSGG_CDSGG_KOR_NM교육연구
01나나758250제주50110제주시1
12나나767950제주50110제주시3
23나나768050제주50110제주시8
34나나777950제주50110제주시14
45나나778050제주50110제주시1
56나나778450제주50110제주시9
67나나778550제주50110제주시2
78나나787650제주50130서귀포시1
89나나788350제주50110제주시3
910나나797450제주50130서귀포시1
idgidSD_CDSD_NMSGG_CDSGG_KOR_NM교육연구
9091나나917350제주50130서귀포시27
9192나나917450제주50130서귀포시1
9293나나917750제주50130서귀포시1
9394나나918050제주50130서귀포시6
9495나나919050제주50110제주시3
9596나나919450제주50110제주시6
9697나나919650제주50110제주시3
9798나나919750제주50110제주시5
9899나나929050제주50110제주시1
99100나나929350제주50110제주시4