Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory97.3 B

Variable types

Numeric1
Text1
Categorical9

Alerts

SD_CD has constant value ""Constant
SD_NM has constant value ""Constant
SGG_CD has constant value ""Constant
SGG_KOR_NM has constant value ""Constant
Depth_10 is highly overall correlated with inclination and 3 other fieldsHigh correlation
inclination is highly overall correlated with intercept and 3 other fieldsHigh correlation
Depth_20 is highly overall correlated with inclination and 3 other fieldsHigh correlation
Depth_50 is highly overall correlated with inclination and 3 other fieldsHigh correlation
intercept is highly overall correlated with inclination and 3 other fieldsHigh correlation
id has unique valuesUnique
gid has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:49:56.219216
Analysis finished2023-12-10 10:49:57.383007
Duration1.16 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%
Mean84035.54
Minimum83913
Maximum84131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:49:57.496152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83913
5-th percentile83938.95
Q183990.75
median84031.5
Q384083.25
95-th percentile84117.05
Maximum84131
Range218
Interquartile range (IQR)92.5

Descriptive statistics

Standard deviation57.045157
Coefficient of variation (CV)0.0006788218
Kurtosis-0.88320699
Mean84035.54
Median Absolute Deviation (MAD)46
Skewness-0.19079231
Sum8403554
Variance3254.1499
MonotonicityStrictly increasing
2023-12-10T19:49:57.724243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83913 1
 
1.0%
84060 1
 
1.0%
84083 1
 
1.0%
84082 1
 
1.0%
84081 1
 
1.0%
84080 1
 
1.0%
84079 1
 
1.0%
84065 1
 
1.0%
84064 1
 
1.0%
84063 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
83913 1
1.0%
83914 1
1.0%
83929 1
1.0%
83930 1
1.0%
83938 1
1.0%
83939 1
1.0%
83940 1
1.0%
83955 1
1.0%
83956 1
1.0%
83957 1
1.0%
ValueCountFrequency (%)
84131 1
1.0%
84130 1
1.0%
84129 1
1.0%
84128 1
1.0%
84118 1
1.0%
84117 1
1.0%
84115 1
1.0%
84114 1
1.0%
84113 1
1.0%
84112 1
1.0%

gid
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:49:58.253966image/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 row마라1784
2nd row마라1785
3rd row마라1873
4th row마라1874
5th row마라1883
ValueCountFrequency (%)
마라1784 1
 
1.0%
마라2279 1
 
1.0%
마라2375 1
 
1.0%
마라2374 1
 
1.0%
마라2373 1
 
1.0%
마라2372 1
 
1.0%
마라2286 1
 
1.0%
마라2285 1
 
1.0%
마라2284 1
 
1.0%
마라2283 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:49:59.029413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 113
18.8%
마 100
16.7%
라 100
16.7%
7 56
9.3%
8 55
9.2%
1 44
 
7.3%
4 28
 
4.7%
3 25
 
4.2%
0 24
 
4.0%
9 20
 
3.3%
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 (%)
2 113
28.2%
7 56
14.0%
8 55
13.8%
1 44
 
11.0%
4 28
 
7.0%
3 25
 
6.2%
0 24
 
6.0%
9 20
 
5.0%
5 18
 
4.5%
6 17
 
4.2%
Other Letter
ValueCountFrequency (%)
마 100
50.0%
라 100
50.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
2 113
28.2%
7 56
14.0%
8 55
13.8%
1 44
 
11.0%
4 28
 
7.0%
3 25
 
6.2%
0 24
 
6.0%
9 20
 
5.0%
5 18
 
4.5%
6 17
 
4.2%
Hangul
ValueCountFrequency (%)
마 100
50.0%
라 100
50.0%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 113
28.2%
7 56
14.0%
8 55
13.8%
1 44
 
11.0%
4 28
 
7.0%
3 25
 
6.2%
0 24
 
6.0%
9 20
 
5.0%
5 18
 
4.5%
6 17
 
4.2%
Hangul
ValueCountFrequency (%)
마 100
50.0%
라 100
50.0%

SD_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
26
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26
2nd row26
3rd row26
4th row26
5th row26

Common Values

ValueCountFrequency (%)
26 100
100.0%

Length

2023-12-10T19:49:59.269057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:59.478302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26 100
100.0%

SD_NM
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산 100
100.0%

Length

2023-12-10T19:49:59.737298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:49:59.939131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산 100
100.0%

SGG_CD
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
26440
100 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26440
2nd row26440
3rd row26440
4th row26440
5th row26440

Common Values

ValueCountFrequency (%)
26440 100
100.0%

Length

2023-12-10T19:50:00.165181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:00.344833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26440 100
100.0%

SGG_KOR_NM
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강서구 100
100.0%

Length

2023-12-10T19:50:00.497339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:00.664102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강서구 100
100.0%

inclination
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2.4
61 
2.98
27 
3.65
12 

Length

Max length4
Median length3
Mean length3.39
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.98
2nd row3.65
3rd row2.4
4th row2.4
5th row2.98

Common Values

ValueCountFrequency (%)
2.4 61
61.0%
2.98 27
27.0%
3.65 12
 
12.0%

Length

2023-12-10T19:50:00.857435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:01.067436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2.4 61
61.0%
2.98 27
27.0%
3.65 12
 
12.0%

intercept
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
9.59
61 
-11.21
27 
-13.66
12 

Length

Max length6
Median length4
Mean length4.78
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-11.21
2nd row-13.66
3rd row9.59
4th row9.59
5th row-11.21

Common Values

ValueCountFrequency (%)
9.59 61
61.0%
-11.21 27
27.0%
-13.66 12
 
12.0%

Length

2023-12-10T19:50:01.268524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:01.522988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9.59 61
61.0%
11.21 27
27.0%
13.66 12
 
12.0%

Depth_10
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
33.59
61 
18.6
27 
22.84
12 

Length

Max length5
Median length5
Mean length4.73
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18.6
2nd row22.84
3rd row33.59
4th row33.59
5th row18.6

Common Values

ValueCountFrequency (%)
33.59 61
61.0%
18.6 27
27.0%
22.84 12
 
12.0%

Length

2023-12-10T19:50:01.814598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:02.041123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
33.59 61
61.0%
18.6 27
27.0%
22.84 12
 
12.0%

Depth_20
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
57.59
61 
48.41
27 
59.34
12 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48.41
2nd row59.34
3rd row57.59
4th row57.59
5th row48.41

Common Values

ValueCountFrequency (%)
57.59 61
61.0%
48.41 27
27.0%
59.34 12
 
12.0%

Length

2023-12-10T19:50:02.255370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:02.448300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
57.59 61
61.0%
48.41 27
27.0%
59.34 12
 
12.0%

Depth_50
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
129.59
61 
137.85
27 
168.85
12 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row137.85
2nd row168.85
3rd row129.59
4th row129.59
5th row137.85

Common Values

ValueCountFrequency (%)
129.59 61
61.0%
137.85 27
27.0%
168.85 12
 
12.0%

Length

2023-12-10T19:50:02.651387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:50:02.812342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
129.59 61
61.0%
137.85 27
27.0%
168.85 12
 
12.0%

Interactions

2023-12-10T19:49:56.806990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:50:03.300814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idgidinclinationinterceptDepth_10Depth_20Depth_50
id1.0001.0000.1380.1380.1380.1380.138
gid1.0001.0001.0001.0001.0001.0001.000
inclination0.1381.0001.0001.0001.0001.0001.000
intercept0.1381.0001.0001.0001.0001.0001.000
Depth_100.1381.0001.0001.0001.0001.0001.000
Depth_200.1381.0001.0001.0001.0001.0001.000
Depth_500.1381.0001.0001.0001.0001.0001.000
2023-12-10T19:50:03.559370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Depth_10inclinationDepth_20Depth_50intercept
Depth_101.0001.0001.0001.0001.000
inclination1.0001.0001.0001.0001.000
Depth_201.0001.0001.0001.0001.000
Depth_501.0001.0001.0001.0001.000
intercept1.0001.0001.0001.0001.000
2023-12-10T19:50:03.759511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idinclinationinterceptDepth_10Depth_20Depth_50
id1.0000.0000.0000.0000.0000.000
inclination0.0001.0001.0001.0001.0001.000
intercept0.0001.0001.0001.0001.0001.000
Depth_100.0001.0001.0001.0001.0001.000
Depth_200.0001.0001.0001.0001.0001.000
Depth_500.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T19:49:57.007669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:49:57.300097image/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_NMinclinationinterceptDepth_10Depth_20Depth_50
083913마라178426부산26440강서구2.98-11.2118.648.41137.85
183914마라178526부산26440강서구3.65-13.6622.8459.34168.85
283929마라187326부산26440강서구2.49.5933.5957.59129.59
383930마라187426부산26440강서구2.49.5933.5957.59129.59
483938마라188326부산26440강서구2.98-11.2118.648.41137.85
583939마라188426부산26440강서구2.98-11.2118.648.41137.85
683940마라188526부산26440강서구3.65-13.6622.8459.34168.85
783955마라197026부산26440강서구2.49.5933.5957.59129.59
883956마라197126부산26440강서구2.49.5933.5957.59129.59
983957마라197226부산26440강서구2.49.5933.5957.59129.59
idgidSD_CDSD_NMSGG_CDSGG_KOR_NMinclinationinterceptDepth_10Depth_20Depth_50
9084112마라248426부산26440강서구2.98-11.2118.648.41137.85
9184113마라248526부산26440강서구2.98-11.2118.648.41137.85
9284114마라248626부산26440강서구3.65-13.6622.8459.34168.85
9384115마라248726부산26440강서구3.65-13.6622.8459.34168.85
9484117마라248926부산26440강서구3.65-13.6622.8459.34168.85
9584118마라249026부산26440강서구3.65-13.6622.8459.34168.85
9684128마라257526부산26440강서구2.49.5933.5957.59129.59
9784129마라257626부산26440강서구2.49.5933.5957.59129.59
9884130마라257726부산26440강서구2.49.5933.5957.59129.59
9984131마라257826부산26440강서구2.49.5933.5957.59129.59