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

시도코드 has constant value ""Constant
시도명 has constant value ""Constant
시군구코드 has constant value ""Constant
시군구명 has constant value ""Constant
y절편 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
기울기 is highly overall correlated with y절편 and 3 other fieldsHigh correlation
10cm 침수심 유발 강우량 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
20cm 침수심 유발 강우량 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
50cm 침수심 유발 강우량 is highly overall correlated with 기울기 and 3 other fieldsHigh correlation
아이디 has unique valuesUnique
격자번호 has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:13:06.291312
Analysis finished2023-12-10 11:13:07.146934
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아이디
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
2023-12-10T20:13:07.586590image/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
2023-12-10T20:13:07.845247image/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%

격자번호
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:13:08.276330image/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%
2023-12-10T20:13:08.776879image/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%

시도코드
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

2023-12-10T20:13:08.974269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:09.106772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26 100
100.0%

시도명
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

2023-12-10T20:13:09.243654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:09.363866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산 100
100.0%

시군구코드
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

2023-12-10T20:13:09.495842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:09.621693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26440 100
100.0%

시군구명
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

2023-12-10T20:13:09.760610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:09.928212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강서구 100
100.0%

기울기
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

2023-12-10T20:13:10.068316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:10.218113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2.4 61
61.0%
2.98 27
27.0%
3.65 12
 
12.0%

y절편
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

2023-12-10T20:13:10.384392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:10.552987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9.59 61
61.0%
11.21 27
27.0%
13.66 12
 
12.0%

10cm 침수심 유발 강우량
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

2023-12-10T20:13:10.741386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:10.905431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
33.59 61
61.0%
18.6 27
27.0%
22.84 12
 
12.0%

20cm 침수심 유발 강우량
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

2023-12-10T20:13:11.055518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:11.196191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
57.59 61
61.0%
48.41 27
27.0%
59.34 12
 
12.0%

50cm 침수심 유발 강우량
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

2023-12-10T20:13:11.370890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:13:11.524935image/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

2023-12-10T20:13:06.722970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:13:11.631650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디격자번호기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
아이디1.0001.0000.1380.1380.1380.1380.138
격자번호1.0001.0001.0001.0001.0001.0001.000
기울기0.1381.0001.0001.0001.0001.0001.000
y절편0.1381.0001.0001.0001.0001.0001.000
10cm 침수심 유발 강우량0.1381.0001.0001.0001.0001.0001.000
20cm 침수심 유발 강우량0.1381.0001.0001.0001.0001.0001.000
50cm 침수심 유발 강우량0.1381.0001.0001.0001.0001.0001.000
2023-12-10T20:13:11.804003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
y절편기울기10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
y절편1.0001.0001.0001.0001.000
기울기1.0001.0001.0001.0001.000
10cm 침수심 유발 강우량1.0001.0001.0001.0001.000
20cm 침수심 유발 강우량1.0001.0001.0001.0001.000
50cm 침수심 유발 강우량1.0001.0001.0001.0001.000
2023-12-10T20:13:11.942416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
아이디1.0000.0000.0000.0000.0000.000
기울기0.0001.0001.0001.0001.0001.000
y절편0.0001.0001.0001.0001.0001.000
10cm 침수심 유발 강우량0.0001.0001.0001.0001.0001.000
20cm 침수심 유발 강우량0.0001.0001.0001.0001.0001.000
50cm 침수심 유발 강우량0.0001.0001.0001.0001.0001.000

Missing values

2023-12-10T20:13:06.849538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:13:07.055957image/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

아이디격자번호시도코드시도명시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
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
아이디격자번호시도코드시도명시군구코드시군구명기울기y절편10cm 침수심 유발 강우량20cm 침수심 유발 강우량50cm 침수심 유발 강우량
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