Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory79.3 B

Variable types

Numeric4
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
총인구 is highly overall correlated with 취약인구 and 1 other fieldsHigh correlation
취약인구 is highly overall correlated with 총인구 and 1 other fieldsHigh correlation
생활 is highly overall correlated with 총인구 and 1 other fieldsHigh correlation
id has unique valuesUnique
gid has unique valuesUnique
취약인구 has 26 (26.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:36:15.667262
Analysis finished2023-12-10 13:36:18.623851
Duration2.96 seconds
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
2023-12-10T22:36:18.759525image/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
2023-12-10T22:36:19.000826image/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 size932.0 B
2023-12-10T22:36:19.404956image/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 row나나7577
2nd row나나7578
3rd row나나7580
4th row나나7582
5th row나나7583
ValueCountFrequency (%)
나나7577 1
 
1.0%
나나8088 1
 
1.0%
나나8182 1
 
1.0%
나나8181 1
 
1.0%
나나8180 1
 
1.0%
나나8179 1
 
1.0%
나나8178 1
 
1.0%
나나8177 1
 
1.0%
나나8176 1
 
1.0%
나나8175 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T22:36:19.978998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 200
33.3%
8 127
21.2%
7 113
18.8%
1 29
 
4.8%
2 28
 
4.7%
9 23
 
3.8%
6 20
 
3.3%
0 20
 
3.3%
5 17
 
2.8%
3 12
 
2.0%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 127
31.8%
7 113
28.2%
1 29
 
7.2%
2 28
 
7.0%
9 23
 
5.8%
6 20
 
5.0%
0 20
 
5.0%
5 17
 
4.2%
3 12
 
3.0%
4 11
 
2.8%
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 (%)
8 127
31.8%
7 113
28.2%
1 29
 
7.2%
2 28
 
7.0%
9 23
 
5.8%
6 20
 
5.0%
0 20
 
5.0%
5 17
 
4.2%
3 12
 
3.0%
4 11
 
2.8%
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 (%)
8 127
31.8%
7 113
28.2%
1 29
 
7.2%
2 28
 
7.0%
9 23
 
5.8%
6 20
 
5.0%
0 20
 
5.0%
5 17
 
4.2%
3 12
 
3.0%
4 11
 
2.8%

SD_CD
Categorical

CONSTANT 

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

2023-12-10T22:36:20.195312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:20.331115image/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 size932.0 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

2023-12-10T22:36:20.452824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:20.565197image/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 size932.0 B
50110
75 
50130
25 

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 75
75.0%
50130 25
 
25.0%

Length

2023-12-10T22:36:20.717384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:20.829022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50110 75
75.0%
50130 25
 
25.0%

SGG_KOR_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제주시
75 
서귀포시
25 

Length

Max length4
Median length3
Mean length3.25
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 75
75.0%
서귀포시 25
 
25.0%

Length

2023-12-10T22:36:20.949093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:21.080925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 75
75.0%
서귀포시 25
 
25.0%

총인구
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.09
Minimum6
Maximum656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:21.221327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7
Q121
median80
Q3175.5
95-th percentile405.7
Maximum656
Range650
Interquartile range (IQR)154.5

Descriptive statistics

Standard deviation141.65409
Coefficient of variation (CV)1.0973281
Kurtosis2.8986329
Mean129.09
Median Absolute Deviation (MAD)62.5
Skewness1.7127287
Sum12909
Variance20065.881
MonotonicityNot monotonic
2023-12-10T22:36:21.397889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 5
 
5.0%
7 4
 
4.0%
69 3
 
3.0%
36 3
 
3.0%
10 3
 
3.0%
6 3
 
3.0%
92 2
 
2.0%
8 2
 
2.0%
118 2
 
2.0%
58 2
 
2.0%
Other values (66) 71
71.0%
ValueCountFrequency (%)
6 3
3.0%
7 4
4.0%
8 2
2.0%
9 1
 
1.0%
10 3
3.0%
11 1
 
1.0%
13 2
2.0%
14 1
 
1.0%
15 1
 
1.0%
17 1
 
1.0%
ValueCountFrequency (%)
656 1
1.0%
574 1
1.0%
569 1
1.0%
565 1
1.0%
419 1
1.0%
405 1
1.0%
387 1
1.0%
352 1
1.0%
342 1
1.0%
333 1
1.0%

취약인구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.22
Minimum0
Maximum199
Zeros26
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:21.576227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22.5
Q364.75
95-th percentile158.1
Maximum199
Range199
Interquartile range (IQR)64.75

Descriptive statistics

Standard deviation49.943115
Coefficient of variation (CV)1.1829255
Kurtosis1.6517468
Mean42.22
Median Absolute Deviation (MAD)22.5
Skewness1.4814327
Sum4222
Variance2494.3147
MonotonicityNot monotonic
2023-12-10T22:36:21.754587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
26.0%
6 5
 
5.0%
38 3
 
3.0%
17 3
 
3.0%
81 3
 
3.0%
9 3
 
3.0%
8 2
 
2.0%
37 2
 
2.0%
121 2
 
2.0%
22 2
 
2.0%
Other values (44) 49
49.0%
ValueCountFrequency (%)
0 26
26.0%
6 5
 
5.0%
8 2
 
2.0%
9 3
 
3.0%
11 1
 
1.0%
13 1
 
1.0%
14 1
 
1.0%
15 1
 
1.0%
17 3
 
3.0%
18 1
 
1.0%
ValueCountFrequency (%)
199 1
1.0%
193 1
1.0%
189 1
1.0%
179 1
1.0%
160 1
1.0%
158 1
1.0%
150 1
1.0%
124 1
1.0%
121 2
2.0%
112 1
1.0%

생활
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.31
Minimum6
Maximum849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:21.945494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile7
Q124.75
median97
Q3248.75
95-th percentile565.6
Maximum849
Range843
Interquartile range (IQR)224

Descriptive statistics

Standard deviation190.98176
Coefficient of variation (CV)1.1148314
Kurtosis2.5182273
Mean171.31
Median Absolute Deviation (MAD)82.5
Skewness1.6448624
Sum17131
Variance36474.034
MonotonicityNot monotonic
2023-12-10T22:36:22.094520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 4
 
4.0%
10 3
 
3.0%
21 3
 
3.0%
6 3
 
3.0%
24 3
 
3.0%
25 2
 
2.0%
30 2
 
2.0%
97 2
 
2.0%
8 2
 
2.0%
86 2
 
2.0%
Other values (72) 74
74.0%
ValueCountFrequency (%)
6 3
3.0%
7 4
4.0%
8 2
2.0%
9 1
 
1.0%
10 3
3.0%
11 1
 
1.0%
13 1
 
1.0%
14 1
 
1.0%
15 1
 
1.0%
17 1
 
1.0%
ValueCountFrequency (%)
849 1
1.0%
768 1
1.0%
763 1
1.0%
744 1
1.0%
577 1
1.0%
565 1
1.0%
537 1
1.0%
476 1
1.0%
463 1
1.0%
440 1
1.0%

Interactions

2023-12-10T22:36:17.803093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:16.070321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:16.678585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:17.225337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:17.907028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:16.270403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:16.816462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:17.364215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:18.019359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:16.427559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:16.959621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:17.521334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:18.146174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:16.564445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:17.100548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:17.675076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:36:22.491463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idgidSGG_CDSGG_KOR_NM총인구취약인구생활
id1.0001.0000.3680.3680.2880.5630.238
gid1.0001.0001.0001.0001.0001.0001.000
SGG_CD0.3681.0001.0000.9990.0000.1520.129
SGG_KOR_NM0.3681.0000.9991.0000.0000.1520.129
총인구0.2881.0000.0000.0001.0000.8660.988
취약인구0.5631.0000.1520.1520.8661.0000.924
생활0.2381.0000.1290.1290.9880.9241.000
2023-12-10T22:36:22.621491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SGG_CDSGG_KOR_NM
SGG_CD1.0000.973
SGG_KOR_NM0.9731.000
2023-12-10T22:36:22.766637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
id총인구취약인구생활SGG_CDSGG_KOR_NM
id1.000-0.053-0.061-0.0530.2690.269
총인구-0.0531.0000.9690.9980.0000.000
취약인구-0.0610.9691.0000.9790.1080.108
생활-0.0530.9980.9791.0000.1210.121
SGG_CD0.2690.0000.1080.1211.0000.973
SGG_KOR_NM0.2690.0000.1080.1210.9731.000

Missing values

2023-12-10T22:36:18.313789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:36:18.538845image/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_NM총인구취약인구생활
01나나757750제주50110제주시15015
12나나757850제주50110제주시11842160
23나나758050제주50110제주시7827105
34나나758250제주50110제주시14240182
45나나758350제주50110제주시21021
56나나767650제주50130서귀포시12138159
67나나767750제주50110제주시11841159
78나나767850제주50110제주시24024
89나나767950제주50110제주시15044194
910나나768050제주50110제주시19859257
idgidSD_CDSD_NMSGG_CDSGG_KOR_NM총인구취약인구생활
9091나나828050제주50110제주시13243175
9192나나828150제주50110제주시801797
9293나나828250제주50110제주시21021
9394나나828350제주50110제주시231104335
9495나나828550제주50110제주시707
9596나나828650제주50110제주시21021
9697나나828750제주50110제주시707
9798나나828850제주50110제주시32394417
9899나나828950제주50110제주시33390423
99100나나829150제주50110제주시14014