Overview

Dataset statistics

Number of variables9
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory84.3 B

Variable types

Categorical5
Text1
Numeric3

Dataset

Description장애인택시운전원 정도별 취업 현황
Author경기복지재단(경기도장애인복지종합지원센터)
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=U49ZDOB4WRYHYH03B72426198590&infSeq=1

Alerts

기준년도 has constant value ""Constant
심한장애 남성(명) is highly overall correlated with 총계(명) and 1 other fieldsHigh correlation
심한장애 총계(명) is highly overall correlated with 총계(명) and 1 other fieldsHigh correlation
총계(명) is highly overall correlated with 심하지않은장애 총계(명) and 3 other fieldsHigh correlation
심하지않은장애 총계(명) is highly overall correlated with 총계(명) and 1 other fieldsHigh correlation
심하지않은장애 남성(명) is highly overall correlated with 총계(명) and 1 other fieldsHigh correlation
심하지않은장애 여성(명) is highly imbalanced (79.4%)Imbalance
심한장애 여성(명) is highly imbalanced (79.4%)Imbalance
시군명 has unique valuesUnique
총계(명) has 16 (51.6%) zerosZeros
심하지않은장애 총계(명) has 18 (58.1%) zerosZeros
심하지않은장애 남성(명) has 19 (61.3%) zerosZeros

Reproduction

Analysis started2024-03-13 00:07:40.760869
Analysis finished2024-03-13 00:07:41.864712
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023
31 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 31
100.0%

Length

2024-03-13T09:07:41.912567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:07:41.985735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 31
100.0%

시군명
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2024-03-13T09:07:42.134412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0967742
Min length3

Characters and Unicode

Total characters96
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row가평군
2nd row고양시
3rd row과천시
4th row광명시
5th row광주시
ValueCountFrequency (%)
가평군 1
 
3.2%
안양시 1
 
3.2%
하남시 1
 
3.2%
포천시 1
 
3.2%
평택시 1
 
3.2%
파주시 1
 
3.2%
이천시 1
 
3.2%
의정부시 1
 
3.2%
의왕시 1
 
3.2%
용인시 1
 
3.2%
Other values (21) 21
67.7%
2024-03-13T09:07:42.390182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 96
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 96
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
30.2%
천 6
 
6.2%
양 5
 
5.2%
주 5
 
5.2%
군 4
 
4.2%
성 3
 
3.1%
포 3
 
3.1%
안 3
 
3.1%
평 3
 
3.1%
남 3
 
3.1%
Other values (28) 32
33.3%

총계(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4516129
Minimum0
Maximum6
Zeros16
Zeros (%)51.6%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-03-13T09:07:42.478530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.5
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation1.8409909
Coefficient of variation (CV)1.2682381
Kurtosis-0.069913256
Mean1.4516129
Median Absolute Deviation (MAD)0
Skewness1.0244276
Sum45
Variance3.3892473
MonotonicityNot monotonic
2024-03-13T09:07:42.557108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 16
51.6%
2 5
 
16.1%
3 3
 
9.7%
5 2
 
6.5%
1 2
 
6.5%
4 2
 
6.5%
6 1
 
3.2%
ValueCountFrequency (%)
0 16
51.6%
1 2
 
6.5%
2 5
 
16.1%
3 3
 
9.7%
4 2
 
6.5%
5 2
 
6.5%
6 1
 
3.2%
ValueCountFrequency (%)
6 1
 
3.2%
5 2
 
6.5%
4 2
 
6.5%
3 3
 
9.7%
2 5
 
16.1%
1 2
 
6.5%
0 16
51.6%

심하지않은장애 총계(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1
Minimum0
Maximum5
Zeros18
Zeros (%)58.1%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-03-13T09:07:42.638235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4605935
Coefficient of variation (CV)1.4605935
Kurtosis0.89824507
Mean1
Median Absolute Deviation (MAD)0
Skewness1.3722575
Sum31
Variance2.1333333
MonotonicityNot monotonic
2024-03-13T09:07:42.719580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 18
58.1%
2 4
 
12.9%
1 4
 
12.9%
4 2
 
6.5%
3 2
 
6.5%
5 1
 
3.2%
ValueCountFrequency (%)
0 18
58.1%
1 4
 
12.9%
2 4
 
12.9%
3 2
 
6.5%
4 2
 
6.5%
5 1
 
3.2%
ValueCountFrequency (%)
5 1
 
3.2%
4 2
 
6.5%
3 2
 
6.5%
2 4
 
12.9%
1 4
 
12.9%
0 18
58.1%

심하지않은장애 여성(명)
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
0
30 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 30
96.8%
1 1
 
3.2%

Length

2024-03-13T09:07:42.806359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:07:42.880035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
96.8%
1 1
 
3.2%

심하지않은장애 남성(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.96774194
Minimum0
Maximum5
Zeros19
Zeros (%)61.3%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-03-13T09:07:42.945770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4715948
Coefficient of variation (CV)1.520648
Kurtosis0.91889772
Mean0.96774194
Median Absolute Deviation (MAD)0
Skewness1.4008382
Sum30
Variance2.1655914
MonotonicityNot monotonic
2024-03-13T09:07:43.024604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19
61.3%
2 4
 
12.9%
1 3
 
9.7%
4 2
 
6.5%
3 2
 
6.5%
5 1
 
3.2%
ValueCountFrequency (%)
0 19
61.3%
1 3
 
9.7%
2 4
 
12.9%
3 2
 
6.5%
4 2
 
6.5%
5 1
 
3.2%
ValueCountFrequency (%)
5 1
 
3.2%
4 2
 
6.5%
3 2
 
6.5%
2 4
 
12.9%
1 3
 
9.7%
0 19
61.3%

심한장애 총계(명)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size380.0 B
0
21 
1
7 
2
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 21
67.7%
1 7
 
22.6%
2 2
 
6.5%
3 1
 
3.2%

Length

2024-03-13T09:07:43.113538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:07:43.210527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
67.7%
1 7
 
22.6%
2 2
 
6.5%
3 1
 
3.2%

심한장애 여성(명)
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
0
30 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 30
96.8%
1 1
 
3.2%

Length

2024-03-13T09:07:43.332815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:07:43.409077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
96.8%
1 1
 
3.2%

심한장애 남성(명)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
0
21 
1
6 
2
 
2
3
 
1
<NA>
 
1

Length

Max length4
Median length1
Mean length1.0967742
Min length1

Unique

Unique2 ?
Unique (%)6.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 21
67.7%
1 6
 
19.4%
2 2
 
6.5%
3 1
 
3.2%
<NA> 1
 
3.2%

Length

2024-03-13T09:07:43.487192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:07:43.571035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
67.7%
1 6
 
19.4%
2 2
 
6.5%
3 1
 
3.2%
na 1
 
3.2%

Interactions

2024-03-13T09:07:41.484234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:07:41.049278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:07:41.272383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:07:41.562503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:07:41.139345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:07:41.342222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:07:41.625477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:07:41.210375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:07:41.416700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T09:07:43.627888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명총계(명)심하지않은장애 총계(명)심하지않은장애 여성(명)심하지않은장애 남성(명)심한장애 총계(명)심한장애 여성(명)심한장애 남성(명)
시군명1.0001.0001.0001.0001.0001.0001.0001.000
총계(명)1.0001.0000.8020.0000.7720.7860.3570.795
심하지않은장애 총계(명)1.0000.8021.0000.3830.9990.5760.3830.595
심하지않은장애 여성(명)1.0000.0000.3831.0000.0000.2100.0000.308
심하지않은장애 남성(명)1.0000.7720.9990.0001.0000.5230.3830.538
심한장애 총계(명)1.0000.7860.5760.2100.5231.0000.2100.999
심한장애 여성(명)1.0000.3570.3830.0000.3830.2101.0000.000
심한장애 남성(명)1.0000.7950.5950.3080.5380.9990.0001.000
2024-03-13T09:07:43.716389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
심한장애 남성(명)심한장애 여성(명)심하지않은장애 여성(명)심한장애 총계(명)
심한장애 남성(명)1.0000.0000.1890.965
심한장애 여성(명)0.0001.0000.0000.122
심하지않은장애 여성(명)0.1890.0001.0000.122
심한장애 총계(명)0.9650.1220.1221.000
2024-03-13T09:07:43.797312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총계(명)심하지않은장애 총계(명)심하지않은장애 남성(명)심하지않은장애 여성(명)심한장애 총계(명)심한장애 여성(명)심한장애 남성(명)
총계(명)1.0000.9450.9150.0000.6360.3390.647
심하지않은장애 총계(명)0.9451.0000.9710.2460.3880.2460.404
심하지않은장애 남성(명)0.9150.9711.0000.0000.3420.2460.353
심하지않은장애 여성(명)0.0000.2460.0001.0000.1220.0000.189
심한장애 총계(명)0.6360.3880.3420.1221.0000.1220.965
심한장애 여성(명)0.3390.2460.2460.0000.1221.0000.000
심한장애 남성(명)0.6470.4040.3530.1890.9650.0001.000

Missing values

2024-03-13T09:07:41.713373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T09:07:41.819018image/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

기준년도시군명총계(명)심하지않은장애 총계(명)심하지않은장애 여성(명)심하지않은장애 남성(명)심한장애 총계(명)심한장애 여성(명)심한장애 남성(명)
02023가평군0000000
12023고양시5505000
22023과천시0000000
32023광명시1000101
42023광주시2202000
52023구리시0000000
62023군포시1000101
72023김포시0000000
82023남양주시2101101
92023동두천시0000000
기준년도시군명총계(명)심하지않은장애 총계(명)심하지않은장애 여성(명)심하지않은장애 남성(명)심한장애 총계(명)심한장애 여성(명)심한장애 남성(명)
212023오산시0000000
222023용인시4404000
232023의왕시4303101
242023의정부시2202000
252023이천시2110101
262023파주시000000<NA>
272023평택시0000000
282023포천시0000000
292023하남시0000000
302023화성시3101202