Overview

Dataset statistics

Number of variables24
Number of observations192
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory39.7 KiB
Average record size in memory211.7 B

Variable types

Categorical11
Text3
Numeric10

Alerts

3급여(중) has constant value ""Constant
1급남(중) is highly imbalanced (84.4%)Imbalance
1급여(중) is highly imbalanced (93.0%)Imbalance
1급계(중) is highly imbalanced (87.7%)Imbalance
2급남(중) is highly imbalanced (84.4%)Imbalance
2급여(중) is highly imbalanced (88.0%)Imbalance
2급계(중) is highly imbalanced (84.9%)Imbalance
3급남(중) is highly imbalanced (91.6%)Imbalance
3급계(중) is highly imbalanced (91.6%)Imbalance
3급남(경) has 40 (20.8%) zerosZeros
3급여(경) has 49 (25.5%) zerosZeros
4급남(경) has 81 (42.2%) zerosZeros
4급여(경) has 78 (40.6%) zerosZeros
4급계(경) has 73 (38.0%) zerosZeros
5급남(경) has 86 (44.8%) zerosZeros
5급여(경) has 97 (50.5%) zerosZeros
6급남(경) has 122 (63.5%) zerosZeros
6급여(경) has 122 (63.5%) zerosZeros
6급계(경) has 122 (63.5%) zerosZeros

Reproduction

Analysis started2024-03-14 00:36:17.627670
Analysis finished2024-03-14 00:36:17.813998
Duration0.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

Distinct14
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
전주시
15 
군산시
15 
익산시
15 
정읍시
15 
김제시
15 
Other values (9)
117 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주시 15
 
7.8%
군산시 15
 
7.8%
익산시 15
 
7.8%
정읍시 15
 
7.8%
김제시 15
 
7.8%
완주군 15
 
7.8%
고창군 15
 
7.8%
부안군 15
 
7.8%
남원시 14
 
7.3%
장수군 13
 
6.8%
Other values (4) 45
23.4%

Length

2024-03-14T09:36:17.864250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주시 15
 
7.8%
군산시 15
 
7.8%
익산시 15
 
7.8%
정읍시 15
 
7.8%
김제시 15
 
7.8%
완주군 15
 
7.8%
고창군 15
 
7.8%
부안군 15
 
7.8%
남원시 14
 
7.3%
장수군 13
 
6.8%
Other values (4) 45
23.4%

장애유형
Categorical

Distinct16
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
소계
14 
지체
14 
시각
14 
청각
14 
언어
14 
Other values (11)
122 

Length

Max length5
Median length2
Mean length2.2916667
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row소계
2nd row지체
3rd row시각
4th row청각
5th row언어

Common Values

ValueCountFrequency (%)
소계 14
 
7.3%
지체 14
 
7.3%
시각 14
 
7.3%
청각 14
 
7.3%
언어 14
 
7.3%
지적 14
 
7.3%
뇌병변 14
 
7.3%
정신 14
 
7.3%
간질 14
 
7.3%
신장 13
 
6.8%
Other values (6) 53
27.6%

Length

2024-03-14T09:36:17.975025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소계 14
 
7.3%
지체 14
 
7.3%
시각 14
 
7.3%
청각 14
 
7.3%
언어 14
 
7.3%
지적 14
 
7.3%
뇌병변 14
 
7.3%
정신 14
 
7.3%
간질 14
 
7.3%
신장 13
 
6.8%
Other values (6) 53
27.6%

합계
Text

Distinct116
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-03-14T09:36:18.233398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.1770833
Min length2

Characters and Unicode

Total characters610
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)47.4%

Sample

1st row4,865
2nd row2,423
3rd row375
4th row485
5th row58
ValueCountFrequency (%)
1 13
 
6.8%
2 13
 
6.8%
5 9
 
4.7%
3 8
 
4.2%
6 7
 
3.6%
4 5
 
2.6%
11 4
 
2.1%
10 4
 
2.1%
20 3
 
1.6%
7 3
 
1.6%
Other values (106) 123
64.1%
2024-03-14T09:36:18.592570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
192
31.5%
1 94
15.4%
2 65
 
10.7%
3 48
 
7.9%
5 41
 
6.7%
4 33
 
5.4%
6 32
 
5.2%
0 25
 
4.1%
8 22
 
3.6%
7 22
 
3.6%
Other values (2) 36
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 404
66.2%
Space Separator 192
31.5%
Other Punctuation 14
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 94
23.3%
2 65
16.1%
3 48
11.9%
5 41
10.1%
4 33
 
8.2%
6 32
 
7.9%
0 25
 
6.2%
8 22
 
5.4%
7 22
 
5.4%
9 22
 
5.4%
Space Separator
ValueCountFrequency (%)
192
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 610
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
192
31.5%
1 94
15.4%
2 65
 
10.7%
3 48
 
7.9%
5 41
 
6.7%
4 33
 
5.4%
6 32
 
5.2%
0 25
 
4.1%
8 22
 
3.6%
7 22
 
3.6%
Other values (2) 36
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 610
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192
31.5%
1 94
15.4%
2 65
 
10.7%
3 48
 
7.9%
5 41
 
6.7%
4 33
 
5.4%
6 32
 
5.2%
0 25
 
4.1%
8 22
 
3.6%
7 22
 
3.6%
Other values (2) 36
 
5.9%

1급남(중)
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
185 
1
 
6
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row2
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 185
96.4%
1 6
 
3.1%
2 1
 
0.5%

Length

2024-03-14T09:36:18.699595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:18.787492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 185
96.4%
1 6
 
3.1%
2 1
 
0.5%

1급여(중)
Categorical

IMBALANCE 

Distinct4
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
189 
5
 
1
4
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)1.6%

Sample

1st row5
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 189
98.4%
5 1
 
0.5%
4 1
 
0.5%
1 1
 
0.5%

Length

2024-03-14T09:36:18.874237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:18.968677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 189
98.4%
5 1
 
0.5%
4 1
 
0.5%
1 1
 
0.5%

1급계(중)
Categorical

IMBALANCE 

Distinct5
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
185 
1
 
4
7
 
1
5
 
1
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)1.6%

Sample

1st row7
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 185
96.4%
1 4
 
2.1%
7 1
 
0.5%
5 1
 
0.5%
2 1
 
0.5%

Length

2024-03-14T09:36:19.062329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:19.150757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 185
96.4%
1 4
 
2.1%
7 1
 
0.5%
5 1
 
0.5%
2 1
 
0.5%

2급남(중)
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
185 
1
 
6
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row2
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 185
96.4%
1 6
 
3.1%
2 1
 
0.5%

Length

2024-03-14T09:36:19.240489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:19.313498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 185
96.4%
1 6
 
3.1%
2 1
 
0.5%

2급여(중)
Categorical

IMBALANCE 

Distinct4
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
186 
1
 
4
8
 
1
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row8
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 186
96.9%
1 4
 
2.1%
8 1
 
0.5%
6 1
 
0.5%

Length

2024-03-14T09:36:19.396400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:19.472069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 186
96.9%
1 4
 
2.1%
8 1
 
0.5%
6 1
 
0.5%

2급계(중)
Categorical

IMBALANCE 

Distinct5
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
183 
1
 
5
2
 
2
10
 
1
7
 
1

Length

Max length2
Median length1
Mean length1.0052083
Min length1

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row10
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 183
95.3%
1 5
 
2.6%
2 2
 
1.0%
10 1
 
0.5%
7 1
 
0.5%

Length

2024-03-14T09:36:19.553529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:19.635449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 183
95.3%
1 5
 
2.6%
2 2
 
1.0%
10 1
 
0.5%
7 1
 
0.5%

3급남(중)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
190 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 190
99.0%
1 2
 
1.0%

Length

2024-03-14T09:36:19.718356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:19.789989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 190
99.0%
1 2
 
1.0%

3급여(중)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
192 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 192
100.0%

Length

2024-03-14T09:36:19.875385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:19.957973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 192
100.0%

3급계(중)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
0
190 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 190
99.0%
1 2
 
1.0%

Length

2024-03-14T09:36:20.059930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:36:20.146533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 190
99.0%
1 2
 
1.0%

3급남(경)
Real number (ℝ)

ZEROS 

Distinct70
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.541667
Minimum0
Maximum986
Zeros40
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T09:36:20.237850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q331.25
95-th percentile224.75
Maximum986
Range986
Interquartile range (IQR)30.25

Descriptive statistics

Standard deviation116.37868
Coefficient of variation (CV)2.5005268
Kurtosis29.844821
Mean46.541667
Median Absolute Deviation (MAD)6
Skewness4.8903242
Sum8936
Variance13543.998
MonotonicityNot monotonic
2024-03-14T09:36:20.408819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40
20.8%
1 19
 
9.9%
2 16
 
8.3%
5 9
 
4.7%
3 7
 
3.6%
9 7
 
3.6%
12 4
 
2.1%
4 4
 
2.1%
7 4
 
2.1%
14 4
 
2.1%
Other values (60) 78
40.6%
ValueCountFrequency (%)
0 40
20.8%
1 19
9.9%
2 16
 
8.3%
3 7
 
3.6%
4 4
 
2.1%
5 9
 
4.7%
6 3
 
1.6%
7 4
 
2.1%
8 3
 
1.6%
9 7
 
3.6%
ValueCountFrequency (%)
986 1
0.5%
710 1
0.5%
600 1
0.5%
395 1
0.5%
387 1
0.5%
345 1
0.5%
302 1
0.5%
277 1
0.5%
264 1
0.5%
233 1
0.5%

3급여(경)
Real number (ℝ)

ZEROS 

Distinct68
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.71875
Minimum0
Maximum814
Zeros49
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T09:36:20.548863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q333.75
95-th percentile181.7
Maximum814
Range814
Interquartile range (IQR)33.75

Descriptive statistics

Standard deviation94.179255
Coefficient of variation (CV)2.432394
Kurtosis30.51587
Mean38.71875
Median Absolute Deviation (MAD)4
Skewness4.9038167
Sum7434
Variance8869.732
MonotonicityNot monotonic
2024-03-14T09:36:20.694891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
25.5%
1 17
 
8.9%
2 15
 
7.8%
4 9
 
4.7%
3 8
 
4.2%
6 5
 
2.6%
14 5
 
2.6%
5 4
 
2.1%
7 4
 
2.1%
45 3
 
1.6%
Other values (58) 73
38.0%
ValueCountFrequency (%)
0 49
25.5%
1 17
 
8.9%
2 15
 
7.8%
3 8
 
4.2%
4 9
 
4.7%
5 4
 
2.1%
6 5
 
2.6%
7 4
 
2.1%
8 1
 
0.5%
9 1
 
0.5%
ValueCountFrequency (%)
814 1
0.5%
557 1
0.5%
453 1
0.5%
344 1
0.5%
330 1
0.5%
288 1
0.5%
268 1
0.5%
208 1
0.5%
187 1
0.5%
185 1
0.5%

3급계(경)
Text

Distinct88
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-03-14T09:36:20.871336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length2.7760417
Min length2

Characters and Unicode

Total characters533
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)34.4%

Sample

1st row1,800
2nd row487
3rd row54
4th row116
5th row32
ValueCountFrequency (%)
0 30
 
15.6%
1 17
 
8.9%
2 10
 
5.2%
10 9
 
4.7%
4 9
 
4.7%
3 8
 
4.2%
6 5
 
2.6%
32 4
 
2.1%
15 4
 
2.1%
5 4
 
2.1%
Other values (78) 92
47.9%
2024-03-14T09:36:21.190896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
192
36.0%
1 76
 
14.3%
0 57
 
10.7%
2 42
 
7.9%
3 39
 
7.3%
4 30
 
5.6%
5 25
 
4.7%
6 21
 
3.9%
7 20
 
3.8%
8 15
 
2.8%
Other values (2) 16
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 338
63.4%
Space Separator 192
36.0%
Other Punctuation 3
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 76
22.5%
0 57
16.9%
2 42
12.4%
3 39
11.5%
4 30
 
8.9%
5 25
 
7.4%
6 21
 
6.2%
7 20
 
5.9%
8 15
 
4.4%
9 13
 
3.8%
Space Separator
ValueCountFrequency (%)
192
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 533
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
192
36.0%
1 76
 
14.3%
0 57
 
10.7%
2 42
 
7.9%
3 39
 
7.3%
4 30
 
5.6%
5 25
 
4.7%
6 21
 
3.9%
7 20
 
3.8%
8 15
 
2.8%
Other values (2) 16
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 533
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192
36.0%
1 76
 
14.3%
0 57
 
10.7%
2 42
 
7.9%
3 39
 
7.3%
4 30
 
5.6%
5 25
 
4.7%
6 21
 
3.9%
7 20
 
3.8%
8 15
 
2.8%
Other values (2) 16
 
3.0%

4급남(경)
Real number (ℝ)

ZEROS 

Distinct51
Distinct (%)26.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.65625
Minimum0
Maximum449
Zeros81
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T09:36:21.525665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q314
95-th percentile115.4
Maximum449
Range449
Interquartile range (IQR)14

Descriptive statistics

Standard deviation57.582416
Coefficient of variation (CV)2.6589283
Kurtosis23.839745
Mean21.65625
Median Absolute Deviation (MAD)2
Skewness4.500642
Sum4158
Variance3315.7346
MonotonicityNot monotonic
2024-03-14T09:36:21.626726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
42.2%
3 12
 
6.2%
1 12
 
6.2%
5 8
 
4.2%
2 8
 
4.2%
4 5
 
2.6%
20 4
 
2.1%
8 4
 
2.1%
7 4
 
2.1%
6 4
 
2.1%
Other values (41) 50
26.0%
ValueCountFrequency (%)
0 81
42.2%
1 12
 
6.2%
2 8
 
4.2%
3 12
 
6.2%
4 5
 
2.6%
5 8
 
4.2%
6 4
 
2.1%
7 4
 
2.1%
8 4
 
2.1%
9 2
 
1.0%
ValueCountFrequency (%)
449 1
0.5%
342 1
0.5%
282 1
0.5%
248 1
0.5%
206 1
0.5%
193 1
0.5%
184 1
0.5%
170 1
0.5%
124 1
0.5%
122 1
0.5%

4급여(경)
Real number (ℝ)

ZEROS 

Distinct60
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.052083
Minimum0
Maximum543
Zeros78
Zeros (%)40.6%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T09:36:21.729846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q314.25
95-th percentile207.15
Maximum543
Range543
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation82.962979
Coefficient of variation (CV)2.5883802
Kurtosis14.764201
Mean32.052083
Median Absolute Deviation (MAD)2
Skewness3.6950518
Sum6154
Variance6882.8559
MonotonicityNot monotonic
2024-03-14T09:36:21.845024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
40.6%
1 17
 
8.9%
3 9
 
4.7%
2 9
 
4.7%
4 7
 
3.6%
5 6
 
3.1%
8 5
 
2.6%
6 5
 
2.6%
12 3
 
1.6%
13 2
 
1.0%
Other values (50) 51
26.6%
ValueCountFrequency (%)
0 78
40.6%
1 17
 
8.9%
2 9
 
4.7%
3 9
 
4.7%
4 7
 
3.6%
5 6
 
3.1%
6 5
 
2.6%
7 1
 
0.5%
8 5
 
2.6%
10 1
 
0.5%
ValueCountFrequency (%)
543 1
0.5%
458 1
0.5%
382 1
0.5%
359 1
0.5%
340 1
0.5%
334 1
0.5%
275 1
0.5%
257 1
0.5%
232 1
0.5%
211 1
0.5%

4급계(경)
Real number (ℝ)

ZEROS 

Distinct67
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.708333
Minimum0
Maximum992
Zeros73
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T09:36:21.965999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q326.25
95-th percentile318.35
Maximum992
Range992
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation139.79236
Coefficient of variation (CV)2.6028058
Kurtosis18.124418
Mean53.708333
Median Absolute Deviation (MAD)4
Skewness4.0055935
Sum10312
Variance19541.904
MonotonicityNot monotonic
2024-03-14T09:36:22.080283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73
38.0%
2 9
 
4.7%
1 7
 
3.6%
7 6
 
3.1%
11 5
 
2.6%
6 5
 
2.6%
5 5
 
2.6%
4 5
 
2.6%
3 5
 
2.6%
8 4
 
2.1%
Other values (57) 68
35.4%
ValueCountFrequency (%)
0 73
38.0%
1 7
 
3.6%
2 9
 
4.7%
3 5
 
2.6%
4 5
 
2.6%
5 5
 
2.6%
6 5
 
2.6%
7 6
 
3.1%
8 4
 
2.1%
9 2
 
1.0%
ValueCountFrequency (%)
992 1
0.5%
800 1
0.5%
641 1
0.5%
630 1
0.5%
540 1
0.5%
533 1
0.5%
459 1
0.5%
427 1
0.5%
342 1
0.5%
320 1
0.5%

5급남(경)
Real number (ℝ)

ZEROS 

Distinct52
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.541667
Minimum0
Maximum461
Zeros86
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T09:36:22.205407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q311
95-th percentile110.35
Maximum461
Range461
Interquartile range (IQR)11

Descriptive statistics

Standard deviation57.487065
Coefficient of variation (CV)2.7985589
Kurtosis25.109086
Mean20.541667
Median Absolute Deviation (MAD)1
Skewness4.6091186
Sum3944
Variance3304.7627
MonotonicityNot monotonic
2024-03-14T09:36:22.345670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 86
44.8%
1 20
 
10.4%
2 11
 
5.7%
3 10
 
5.2%
5 4
 
2.1%
4 4
 
2.1%
9 3
 
1.6%
24 2
 
1.0%
8 2
 
1.0%
13 2
 
1.0%
Other values (42) 48
25.0%
ValueCountFrequency (%)
0 86
44.8%
1 20
 
10.4%
2 11
 
5.7%
3 10
 
5.2%
4 4
 
2.1%
5 4
 
2.1%
6 1
 
0.5%
7 1
 
0.5%
8 2
 
1.0%
9 3
 
1.6%
ValueCountFrequency (%)
461 1
0.5%
306 1
0.5%
288 1
0.5%
274 1
0.5%
198 1
0.5%
185 1
0.5%
183 1
0.5%
173 1
0.5%
138 1
0.5%
112 1
0.5%

5급여(경)
Real number (ℝ)

ZEROS 

Distinct55
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.71875
Minimum0
Maximum646
Zeros97
Zeros (%)50.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T09:36:22.495153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311.5
95-th percentile207.8
Maximum646
Range646
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation89.015994
Coefficient of variation (CV)2.7206417
Kurtosis18.467837
Mean32.71875
Median Absolute Deviation (MAD)0
Skewness4.01172
Sum6282
Variance7923.8472
MonotonicityNot monotonic
2024-03-14T09:36:22.665395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97
50.5%
1 14
 
7.3%
2 6
 
3.1%
3 5
 
2.6%
4 5
 
2.6%
6 4
 
2.1%
5 3
 
1.6%
64 3
 
1.6%
7 3
 
1.6%
281 2
 
1.0%
Other values (45) 50
26.0%
ValueCountFrequency (%)
0 97
50.5%
1 14
 
7.3%
2 6
 
3.1%
3 5
 
2.6%
4 5
 
2.6%
5 3
 
1.6%
6 4
 
2.1%
7 3
 
1.6%
8 2
 
1.0%
9 2
 
1.0%
ValueCountFrequency (%)
646 1
0.5%
477 1
0.5%
444 1
0.5%
386 1
0.5%
313 1
0.5%
309 1
0.5%
281 2
1.0%
249 1
0.5%
210 1
0.5%
206 1
0.5%

5급계(경)
Text

Distinct60
Distinct (%)31.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-03-14T09:36:22.809607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.46875
Min length2

Characters and Unicode

Total characters474
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)23.4%

Sample

1st row1,107
2nd row751
3rd row48
4th row154
5th row0
ValueCountFrequency (%)
0 78
40.6%
1 15
 
7.8%
2 10
 
5.2%
3 7
 
3.6%
5 6
 
3.1%
6 5
 
2.6%
7 4
 
2.1%
4 4
 
2.1%
14 3
 
1.6%
110 3
 
1.6%
Other values (50) 57
29.7%
2024-03-14T09:36:23.062370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
192
40.5%
0 90
19.0%
1 50
 
10.5%
2 29
 
6.1%
4 23
 
4.9%
5 21
 
4.4%
3 18
 
3.8%
6 16
 
3.4%
7 14
 
3.0%
8 11
 
2.3%
Other values (2) 10
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 281
59.3%
Space Separator 192
40.5%
Other Punctuation 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 90
32.0%
1 50
17.8%
2 29
 
10.3%
4 23
 
8.2%
5 21
 
7.5%
3 18
 
6.4%
6 16
 
5.7%
7 14
 
5.0%
8 11
 
3.9%
9 9
 
3.2%
Space Separator
ValueCountFrequency (%)
192
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 474
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
192
40.5%
0 90
19.0%
1 50
 
10.5%
2 29
 
6.1%
4 23
 
4.9%
5 21
 
4.4%
3 18
 
3.8%
6 16
 
3.4%
7 14
 
3.0%
8 11
 
2.3%
Other values (2) 10
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192
40.5%
0 90
19.0%
1 50
 
10.5%
2 29
 
6.1%
4 23
 
4.9%
5 21
 
4.4%
3 18
 
3.8%
6 16
 
3.4%
7 14
 
3.0%
8 11
 
2.3%
Other values (2) 10
 
2.1%

6급남(경)
Real number (ℝ)

ZEROS 

Distinct53
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.614583
Minimum0
Maximum477
Zeros122
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T09:36:23.175121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile123.5
Maximum477
Range477
Interquartile range (IQR)13

Descriptive statistics

Standard deviation61.656347
Coefficient of variation (CV)2.7263977
Kurtosis22.929639
Mean22.614583
Median Absolute Deviation (MAD)0
Skewness4.3918211
Sum4342
Variance3801.5051
MonotonicityNot monotonic
2024-03-14T09:36:23.290365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 122
63.5%
9 4
 
2.1%
13 3
 
1.6%
1 3
 
1.6%
2 3
 
1.6%
38 3
 
1.6%
4 2
 
1.0%
3 2
 
1.0%
16 2
 
1.0%
191 2
 
1.0%
Other values (43) 46
 
24.0%
ValueCountFrequency (%)
0 122
63.5%
1 3
 
1.6%
2 3
 
1.6%
3 2
 
1.0%
4 2
 
1.0%
5 1
 
0.5%
6 1
 
0.5%
7 2
 
1.0%
8 1
 
0.5%
9 4
 
2.1%
ValueCountFrequency (%)
477 1
0.5%
337 1
0.5%
336 1
0.5%
265 1
0.5%
210 1
0.5%
191 2
1.0%
174 1
0.5%
130 1
0.5%
129 1
0.5%
119 1
0.5%

6급여(경)
Real number (ℝ)

ZEROS 

Distinct54
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.916667
Minimum0
Maximum471
Zeros122
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T09:36:23.399240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.25
95-th percentile120.75
Maximum471
Range471
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation60.848652
Coefficient of variation (CV)2.6552139
Kurtosis21.785038
Mean22.916667
Median Absolute Deviation (MAD)0
Skewness4.2469376
Sum4400
Variance3702.5585
MonotonicityNot monotonic
2024-03-14T09:36:23.518147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 122
63.5%
11 5
 
2.6%
1 4
 
2.1%
8 3
 
1.6%
22 3
 
1.6%
13 2
 
1.0%
2 2
 
1.0%
48 2
 
1.0%
3 2
 
1.0%
6 2
 
1.0%
Other values (44) 45
 
23.4%
ValueCountFrequency (%)
0 122
63.5%
1 4
 
2.1%
2 2
 
1.0%
3 2
 
1.0%
4 1
 
0.5%
5 1
 
0.5%
6 2
 
1.0%
7 1
 
0.5%
8 3
 
1.6%
11 5
 
2.6%
ValueCountFrequency (%)
471 1
0.5%
339 1
0.5%
289 1
0.5%
264 1
0.5%
208 1
0.5%
205 1
0.5%
191 1
0.5%
165 1
0.5%
161 1
0.5%
129 1
0.5%

6급계(경)
Real number (ℝ)

ZEROS 

Distinct59
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.53125
Minimum0
Maximum948
Zeros122
Zeros (%)63.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-14T09:36:23.638018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q325
95-th percentile233.8
Maximum948
Range948
Interquartile range (IQR)25

Descriptive statistics

Standard deviation122.13039
Coefficient of variation (CV)2.6823421
Kurtosis22.349166
Mean45.53125
Median Absolute Deviation (MAD)0
Skewness4.3157224
Sum8742
Variance14915.831
MonotonicityNot monotonic
2024-03-14T09:36:23.752854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 122
63.5%
24 3
 
1.6%
13 2
 
1.0%
76 2
 
1.0%
19 2
 
1.0%
28 2
 
1.0%
12 2
 
1.0%
382 2
 
1.0%
8 2
 
1.0%
2 2
 
1.0%
Other values (49) 51
26.6%
ValueCountFrequency (%)
0 122
63.5%
2 2
 
1.0%
3 1
 
0.5%
4 2
 
1.0%
5 1
 
0.5%
7 1
 
0.5%
8 2
 
1.0%
11 1
 
0.5%
12 2
 
1.0%
13 2
 
1.0%
ValueCountFrequency (%)
948 1
0.5%
675 1
0.5%
601 1
0.5%
554 1
0.5%
396 1
0.5%
382 2
1.0%
375 1
0.5%
291 1
0.5%
236 1
0.5%
232 1
0.5%

Sample

시군구장애유형합계1급남(중)1급여(중)1급계(중)2급남(중)2급여(중)2급계(중)3급남(중)3급여(중)3급계(중)3급남(경)3급여(경)3급계(경)4급남(경)4급여(경)4급계(경)5급남(경)5급여(경)5급계(경)6급남(경)6급여(경)6급계(경)
0전주시소계4,86525728101019868141,8004495439924616461,107477471948
1전주시지체2,423000011000302185487248382630274477751265289554
2전주시시각375000000000173754191837252348129107236
3전주시청각485000000000575911669661355797154275380
4전주시언어5800000000020123220626000000
5전주시지적329145167101181135316000000000
6전주시뇌병변44311201100010094194583593472875562278
7전주시정신546000101000277268545000000000
8전주시신장60000000000000224411556000
9전주시심장120000000003710000202000
시군구장애유형합계1급남(중)1급여(중)1급계(중)2급남(중)2급여(중)2급계(중)3급남(중)3급여(중)3급계(중)3급남(경)3급여(경)3급계(경)4급남(경)4급여(경)4급계(경)5급남(경)5급여(경)5급계(경)6급남(경)6급여(경)6급계(경)
182부안군지적80000000000394180000000000
183부안군뇌병변101000000000172138161430681412719
184부안군정신65000000000323365000000000
185부안군신장1000000000000000101000
186부안군심장3000000000213000000000
187부안군호흡기4000000000404000000000
188부안군간2000000000000000202000
189부안군안면1000000000011000000000
190부안군장루.요루5000000000000224011000
191부안군간질12000000000112538112000