Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory70.3 B

Variable types

Numeric2
Categorical5
Text1

Alerts

월 has constant value ""Constant
시도코드 has constant value ""Constant
시도명 has constant value ""Constant
시군구코드 is highly overall correlated with 아이디 and 1 other fieldsHigh correlation
시군구명 is highly overall correlated with 아이디 and 1 other fieldsHigh correlation
아이디 is highly overall correlated with 시군구코드 and 1 other fieldsHigh correlation
아이디 has unique valuesUnique
격자번호 has unique valuesUnique
생활 has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:37:44.285826
Analysis finished2023-12-10 11:37:45.621584
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아이디
Real number (ℝ)

HIGH CORRELATION  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
2023-12-10T20:37:45.736859image/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
2023-12-10T20:37:45.990640image/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%

월
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-10T20:37:46.201211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:46.340706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:37:46.773739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters600
Distinct characters12
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 row다사5955
2nd row다사4350
3rd row다사4245
4th row다사4246
5th row다사4450
ValueCountFrequency (%)
다사5955 1
 
1.0%
다사6058 1
 
1.0%
다사5060 1
 
1.0%
다사4954 1
 
1.0%
다사4860 1
 
1.0%
다사5258 1
 
1.0%
다사4758 1
 
1.0%
다사4654 1
 
1.0%
다사4856 1
 
1.0%
다사5159 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T20:37:47.727121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
다 100
16.7%
사 100
16.7%
4 92
15.3%
5 79
13.2%
6 63
10.5%
0 33
 
5.5%
9 28
 
4.7%
1 26
 
4.3%
2 22
 
3.7%
3 22
 
3.7%
Other values (2) 35
 
5.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 92
23.0%
5 79
19.8%
6 63
15.8%
0 33
 
8.2%
9 28
 
7.0%
1 26
 
6.5%
2 22
 
5.5%
3 22
 
5.5%
8 18
 
4.5%
7 17
 
4.2%
Other Letter
ValueCountFrequency (%)
다 100
50.0%
사 100
50.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
4 92
23.0%
5 79
19.8%
6 63
15.8%
0 33
 
8.2%
9 28
 
7.0%
1 26
 
6.5%
2 22
 
5.5%
3 22
 
5.5%
8 18
 
4.5%
7 17
 
4.2%
Hangul
ValueCountFrequency (%)
다 100
50.0%
사 100
50.0%

Most occurring blocks

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

Most frequent character per block

Hangul
ValueCountFrequency (%)
다 100
50.0%
사 100
50.0%
ASCII
ValueCountFrequency (%)
4 92
23.0%
5 79
19.8%
6 63
15.8%
0 33
 
8.2%
9 28
 
7.0%
1 26
 
6.5%
2 22
 
5.5%
3 22
 
5.5%
8 18
 
4.5%
7 17
 
4.2%

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11
2nd row11
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 100
100.0%

Length

2023-12-10T20:37:47.933958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:48.082622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 100
100.0%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 100
100.0%

Length

2023-12-10T20:37:48.208989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:48.353997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 100
100.0%

시군구코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11380
31 
11350
29 
11470
23 
11500
16 
11230
 
1

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row11230
2nd row11470
3rd row11470
4th row11470
5th row11470

Common Values

ValueCountFrequency (%)
11380 31
31.0%
11350 29
29.0%
11470 23
23.0%
11500 16
16.0%
11230 1
 
1.0%

Length

2023-12-10T20:37:48.501429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:48.654150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11380 31
31.0%
11350 29
29.0%
11470 23
23.0%
11500 16
16.0%
11230 1
 
1.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
은평구
31 
노원구
29 
양천구
23 
강서구
16 
동대문구
 
1

Length

Max length4
Median length3
Mean length3.01
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row동대문구
2nd row양천구
3rd row양천구
4th row양천구
5th row양천구

Common Values

ValueCountFrequency (%)
은평구 31
31.0%
노원구 29
29.0%
양천구 23
23.0%
강서구 16
16.0%
동대문구 1
 
1.0%

Length

2023-12-10T20:37:48.829001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:49.003690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
은평구 31
31.0%
노원구 29
29.0%
양천구 23
23.0%
강서구 16
16.0%
동대문구 1
 
1.0%

생활
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19202.991
Minimum181.69273
Maximum56013.644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:49.221670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum181.69273
5-th percentile789.57422
Q15103.7947
median16896.295
Q330428.953
95-th percentile44516.339
Maximum56013.644
Range55831.951
Interquartile range (IQR)25325.158

Descriptive statistics

Standard deviation14908.527
Coefficient of variation (CV)0.77636484
Kurtosis-0.75368576
Mean19202.991
Median Absolute Deviation (MAD)12290.078
Skewness0.51059981
Sum1920299.1
Variance2.2226418 × 108
MonotonicityNot monotonic
2023-12-10T20:37:49.431652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25721.31535754092 1
 
1.0%
19609.399717242875 1
 
1.0%
7490.16451255872 1
 
1.0%
4442.20153736908 1
 
1.0%
14866.96624496108 1
 
1.0%
17252.29416215763 1
 
1.0%
707.762576316128 1
 
1.0%
1648.2540008263811 1
 
1.0%
5915.142302889724 1
 
1.0%
46106.79784633226 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
181.69273278350448 1
1.0%
580.2682424093317 1
1.0%
631.5210653552972 1
1.0%
707.762576316128 1
1.0%
720.9234435742649 1
1.0%
793.187422757501 1
1.0%
854.0882543697588 1
1.0%
915.7423252128028 1
1.0%
1025.207814435979 1
1.0%
1084.0191628314158 1
1.0%
ValueCountFrequency (%)
56013.64406394056 1
1.0%
54994.25719577911 1
1.0%
47956.50516746902 1
1.0%
46106.79784633226 1
1.0%
44527.55405723026 1
1.0%
44515.74919376682 1
1.0%
44189.25420017929 1
1.0%
43589.11984188452 1
1.0%
42605.47832265973 1
1.0%
42329.76223715043 1
1.0%

Interactions

2023-12-10T20:37:45.000728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:44.738981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:45.132891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:44.865697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:37:49.559193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디격자번호시군구코드시군구명생활
아이디1.0001.0000.9370.9370.000
격자번호1.0001.0001.0001.0001.000
시군구코드0.9371.0001.0001.0000.330
시군구명0.9371.0001.0001.0000.330
생활0.0001.0000.3300.3301.000
2023-12-10T20:37:49.718993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드시군구명
시군구코드1.0001.000
시군구명1.0001.000
2023-12-10T20:37:49.857264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디생활시군구코드시군구명
아이디1.000-0.2400.6440.644
생활-0.2401.0000.1320.132
시군구코드0.6440.1321.0001.000
시군구명0.6440.1321.0001.000

Missing values

2023-12-10T20:37:45.323250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:37:45.548842image/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

아이디월격자번호시도코드시도명시군구코드시군구명생활
011다사595511서울11230동대문구25721.315358
121다사435011서울11470양천구30229.420884
231다사424511서울11470양천구23239.812655
341다사424611서울11470양천구23091.562188
451다사445011서울11470양천구33078.495522
561다사404811서울11470양천구22940.474798
671다사434511서울11470양천구32490.488504
781다사626411서울11350노원구1733.857395
891다사605911서울11350노원구9855.377281
9101다사605711서울11350노원구16488.024304
아이디월격자번호시도코드시도명시군구코드시군구명생활
90911다사455411서울11380은평구10363.019004
91921다사506211서울11380은평구580.268242
92931다사495511서울11380은평구25810.493667
93941다사515611서울11380은평구5158.80745
94951다사495911서울11380은평구15563.65756
95961다사616411서울11350노원구3771.479464
96971다사656011서울11350노원구6816.149376
97981다사616011서울11350노원구42329.762237
98991다사636411서울11350노원구720.923444
991001다사606111서울11350노원구33912.143883