Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.4 KiB
Average record size in memory106.3 B

Variable types

Numeric4
Text1
Categorical7

Alerts

SD_CD has constant value ""Constant
SD_NM has constant value ""Constant
SGG_CD is highly overall correlated with 분뇨, 쓰레기처리시설 and 1 other fieldsHigh correlation
SGG_KOR_NM is highly overall correlated with 분뇨, 쓰레기처리시설 and 1 other fieldsHigh correlation
분뇨, 쓰레기처리시설 is highly overall correlated with 공용 and 2 other fieldsHigh correlation
공용 is highly overall correlated with 분뇨, 쓰레기처리시설High correlation
공공용시설 is highly imbalanced (91.9%)Imbalance
묘지관리시설 is highly imbalanced (82.7%)Imbalance
발전시설 is highly imbalanced (60.2%)Imbalance
id has unique valuesUnique
gid has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:36:48.063035
Analysis finished2023-12-10 13:36:51.500801
Duration3.44 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
2023-12-10T22:36:51.605163image/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
2023-12-10T22:36:51.808596image/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
2023-12-10T22:36:52.272913image/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 row나나7578
2nd row나나7582
3rd row나나7678
4th row나나7679
5th row나나7682
ValueCountFrequency (%)
나나7578 1
 
1.0%
나나8490 1
 
1.0%
나나8583 1
 
1.0%
나나8581 1
 
1.0%
나나8577 1
 
1.0%
나나8572 1
 
1.0%
나나8571 1
 
1.0%
나나8570 1
 
1.0%
나나8565 1
 
1.0%
나나8564 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T22:36:52.840661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 200
33.3%
8 127
21.2%
7 85
14.2%
5 35
 
5.8%
6 31
 
5.2%
9 30
 
5.0%
4 28
 
4.7%
1 19
 
3.2%
2 18
 
3.0%
3 16
 
2.7%

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 85
21.2%
5 35
 
8.8%
6 31
 
7.8%
9 30
 
7.5%
4 28
 
7.0%
1 19
 
4.8%
2 18
 
4.5%
3 16
 
4.0%
0 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 85
21.2%
5 35
 
8.8%
6 31
 
7.8%
9 30
 
7.5%
4 28
 
7.0%
1 19
 
4.8%
2 18
 
4.5%
3 16
 
4.0%
0 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 85
21.2%
5 35
 
8.8%
6 31
 
7.8%
9 30
 
7.5%
4 28
 
7.0%
1 19
 
4.8%
2 18
 
4.5%
3 16
 
4.0%
0 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

2023-12-10T22:36:52.991702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:53.088906image/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

2023-12-10T22:36:53.225024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:53.405995image/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
64 
50130
36 

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 64
64.0%
50130 36
36.0%

Length

2023-12-10T22:36:53.536216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:53.667289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50110 64
64.0%
50130 36
36.0%

SGG_KOR_NM
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제주시
64 
서귀포시
36 

Length

Max length4
Median length3
Mean length3.36
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 64
64.0%
서귀포시 36
36.0%

Length

2023-12-10T22:36:53.815864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:53.966697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 64
64.0%
서귀포시 36
36.0%

공공용시설
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-99
99 
1
 
1

Length

Max length3
Median length3
Mean length2.98
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row-99
2nd row-99
3rd row-99
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-99 99
99.0%
1 1
 
1.0%

Length

2023-12-10T22:36:54.129042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:54.265493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99 99
99.0%
1 1
 
1.0%

묘지관리시설
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-99
96 
1
 
3
2
 
1

Length

Max length3
Median length3
Mean length2.92
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row-99
2nd row-99
3rd row-99
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-99 96
96.0%
1 3
 
3.0%
2 1
 
1.0%

Length

2023-12-10T22:36:54.400155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:54.525132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99 96
96.0%
1 3
 
3.0%
2 1
 
1.0%

발전시설
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
-99
84 
1
12 
2
 
3
3
 
1

Length

Max length3
Median length3
Mean length2.68
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row-99
2nd row-99
3rd row1
4th row-99
5th row-99

Common Values

ValueCountFrequency (%)
-99 84
84.0%
1 12
 
12.0%
2 3
 
3.0%
3 1
 
1.0%

Length

2023-12-10T22:36:54.638242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:36:54.754699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99 84
84.0%
1 12
 
12.0%
2 3
 
3.0%
3 1
 
1.0%

분뇨, 쓰레기처리시설
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-36.11
Minimum-99
Maximum41
Zeros0
Zeros (%)0.0%
Negative40
Negative (%)40.0%
Memory size1.0 KiB
2023-12-10T22:36:54.853876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median1
Q32.25
95-th percentile23
Maximum41
Range140
Interquartile range (IQR)101.25

Descriptive statistics

Standard deviation52.021731
Coefficient of variation (CV)-1.4406461
Kurtosis-1.8167723
Mean-36.11
Median Absolute Deviation (MAD)11.5
Skewness-0.35996896
Sum-3611
Variance2706.2605
MonotonicityNot monotonic
2023-12-10T22:36:54.994811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
-99 40
40.0%
1 22
22.0%
2 13
 
13.0%
3 7
 
7.0%
4 4
 
4.0%
23 3
 
3.0%
24 1
 
1.0%
11 1
 
1.0%
18 1
 
1.0%
6 1
 
1.0%
Other values (7) 7
 
7.0%
ValueCountFrequency (%)
-99 40
40.0%
1 22
22.0%
2 13
 
13.0%
3 7
 
7.0%
4 4
 
4.0%
6 1
 
1.0%
8 1
 
1.0%
9 1
 
1.0%
11 1
 
1.0%
14 1
 
1.0%
ValueCountFrequency (%)
41 1
 
1.0%
26 1
 
1.0%
24 1
 
1.0%
23 3
3.0%
21 1
 
1.0%
18 1
 
1.0%
17 1
 
1.0%
14 1
 
1.0%
11 1
 
1.0%
9 1
 
1.0%

업무시설
Real number (ℝ)

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-60.17
Minimum-99
Maximum15
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)62.0%
Memory size1.0 KiB
2023-12-10T22:36:55.136624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-99
5-th percentile-99
Q1-99
median-99
Q31
95-th percentile5.1
Maximum15
Range114
Interquartile range (IQR)100

Descriptive statistics

Standard deviation49.893635
Coefficient of variation (CV)-0.82921116
Kurtosis-1.7688744
Mean-60.17
Median Absolute Deviation (MAD)0
Skewness0.50791102
Sum-6017
Variance2489.3748
MonotonicityNot monotonic
2023-12-10T22:36:55.257967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
-99 62
62.0%
1 18
 
18.0%
2 6
 
6.0%
5 4
 
4.0%
3 3
 
3.0%
4 2
 
2.0%
15 1
 
1.0%
7 1
 
1.0%
11 1
 
1.0%
13 1
 
1.0%
ValueCountFrequency (%)
-99 62
62.0%
1 18
 
18.0%
2 6
 
6.0%
3 3
 
3.0%
4 2
 
2.0%
5 4
 
4.0%
7 1
 
1.0%
8 1
 
1.0%
11 1
 
1.0%
13 1
 
1.0%
ValueCountFrequency (%)
15 1
 
1.0%
13 1
 
1.0%
11 1
 
1.0%
8 1
 
1.0%
7 1
 
1.0%
5 4
 
4.0%
4 2
 
2.0%
3 3
 
3.0%
2 6
 
6.0%
1 18
18.0%

공용
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.97
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:36:55.409918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile23.05
Maximum41
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation7.2424883
Coefficient of variation (CV)1.4572411
Kurtosis7.3201082
Mean4.97
Median Absolute Deviation (MAD)1
Skewness2.6123484
Sum497
Variance52.453636
MonotonicityNot monotonic
2023-12-10T22:36:55.563750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 40
40.0%
2 19
19.0%
3 11
 
11.0%
4 6
 
6.0%
7 2
 
2.0%
26 2
 
2.0%
5 2
 
2.0%
23 2
 
2.0%
9 2
 
2.0%
6 2
 
2.0%
Other values (10) 12
 
12.0%
ValueCountFrequency (%)
1 40
40.0%
2 19
19.0%
3 11
 
11.0%
4 6
 
6.0%
5 2
 
2.0%
6 2
 
2.0%
7 2
 
2.0%
8 1
 
1.0%
9 2
 
2.0%
11 2
 
2.0%
ValueCountFrequency (%)
41 1
1.0%
26 2
2.0%
25 1
1.0%
24 1
1.0%
23 2
2.0%
18 1
1.0%
17 1
1.0%
15 1
1.0%
14 1
1.0%
13 2
2.0%

Interactions

2023-12-10T22:36:50.469360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:48.627416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:49.085058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:49.825028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:50.699245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:48.716882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:49.479637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:49.927927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:50.837169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:48.839229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:49.592314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:50.029519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:50.959134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:48.983801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:49.703958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:36:50.197234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:36:55.669493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idgidSGG_CDSGG_KOR_NM공공용시설묘지관리시설발전시설분뇨, 쓰레기처리시설업무시설공용
id1.0001.0000.2490.2490.0410.2800.1670.0540.2580.274
gid1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
SGG_CD0.2491.0001.0000.9990.0000.0640.5680.7330.1120.000
SGG_KOR_NM0.2491.0000.9991.0000.0000.0640.5680.7330.1120.000
공공용시설0.0411.0000.0000.0001.0000.0000.0000.0000.0000.000
묘지관리시설0.2801.0000.0640.0640.0001.0000.0000.0380.0000.000
발전시설0.1671.0000.5680.5680.0000.0001.0000.1800.1140.000
분뇨, 쓰레기처리시설0.0541.0000.7330.7330.0000.0380.1801.0000.2170.974
업무시설0.2581.0000.1120.1120.0000.0000.1140.2171.0000.588
공용0.2741.0000.0000.0000.0000.0000.0000.9740.5881.000
2023-12-10T22:36:55.813672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공공용시설SGG_CDSGG_KOR_NM발전시설묘지관리시설
공공용시설1.0000.0000.0000.0000.000
SGG_CD0.0001.0000.9780.3860.105
SGG_KOR_NM0.0000.9781.0000.3860.105
발전시설0.0000.3860.3861.0000.000
묘지관리시설0.0000.1050.1050.0001.000
2023-12-10T22:36:55.943438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
id분뇨, 쓰레기처리시설업무시설공용SGG_CDSGG_KOR_NM공공용시설묘지관리시설발전시설
id1.000-0.0670.115-0.0790.1810.1810.0000.1650.093
분뇨, 쓰레기처리시설-0.0671.000-0.3030.6090.5210.5210.0000.0310.069
업무시설0.115-0.3031.0000.3010.1830.1830.0000.0000.106
공용-0.0790.6090.3011.0000.0000.0000.0000.0000.000
SGG_CD0.1810.5210.1830.0001.0000.9780.0000.1050.386
SGG_KOR_NM0.1810.5210.1830.0000.9781.0000.0000.1050.386
공공용시설0.0000.0000.0000.0000.0000.0001.0000.0000.000
묘지관리시설0.1650.0310.0000.0000.1050.1050.0001.0000.000
발전시설0.0930.0690.1060.0000.3860.3860.0000.0001.000

Missing values

2023-12-10T22:36:51.165477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:36:51.418061image/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_NM공공용시설묘지관리시설발전시설분뇨, 쓰레기처리시설업무시설공용
01나나757850제주50110제주시-99-99-9924-9924
12나나758250제주50110제주시-99-99-991-991
23나나767850제주50110제주시-99-991-99-991
34나나767950제주50110제주시-99-99-991-991
45나나768250제주50110제주시-99-99-991-991
56나나768350제주50110제주시-99-99-9917-9917
67나나777550제주50130서귀포시-99-99-99-9911
78나나777650제주50130서귀포시-99-991-99-991
89나나777750제주50130서귀포시-99-99-99-9922
910나나777950제주50110제주시-99-99-9923326
idgidSD_CDSD_NMSGG_CDSGG_KOR_NM공공용시설묘지관리시설발전시설분뇨, 쓰레기처리시설업무시설공용
9091나나867850제주50130서귀포시-99-99-99-9911
9192나나868450제주50110제주시-99-99-991-991
9293나나868550제주50110제주시-99-99-992-992
9394나나868650제주50110제주시-99-99-99-9944
9495나나868750제주50110제주시-99-991315
9596나나868850제주50110제주시-99-99-993-993
9697나나868950제주50110제주시-99-99-992-992
9798나나869050제주50110제주시-99-99-99-9911
9899나나869150제주50110제주시-99-99-9918-9918
99100나나869350제주50110제주시-99-99-991-991