Overview

Dataset statistics

Number of variables3
Number of observations1243
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory30.5 KiB
Average record size in memory25.1 B

Variable types

Text2
Numeric1

Dataset

Description경기도 경기통계시스템 통계조사조직관계
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=UMDVJAE8G5SGAC3RF7AY33553217&infSeq=1

Alerts

조사별관리ID has unique valuesUnique

Reproduction

Analysis started2023-12-10 22:17:30.284270
Analysis finished2023-12-10 22:17:30.566938
Duration0.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조사별관리ID
Text

UNIQUE 

Distinct1243
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
2023-12-11T07:17:30.782279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters8701
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1243 ?
Unique (%)100.0%

Sample

1st rowA000930
2nd rowA000931
3rd rowA000932
4th rowA000933
5th rowA000934
ValueCountFrequency (%)
a000930 1
 
0.1%
a000608 1
 
0.1%
a000615 1
 
0.1%
a000614 1
 
0.1%
a000613 1
 
0.1%
a000612 1
 
0.1%
a000611 1
 
0.1%
a000610 1
 
0.1%
a000600 1
 
0.1%
a000607 1
 
0.1%
Other values (1233) 1233
99.2%
2023-12-11T07:17:31.156024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3937
45.2%
A 1243
 
14.3%
1 699
 
8.0%
2 399
 
4.6%
3 355
 
4.1%
4 348
 
4.0%
9 344
 
4.0%
5 344
 
4.0%
6 344
 
4.0%
7 344
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7458
85.7%
Uppercase Letter 1243
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3937
52.8%
1 699
 
9.4%
2 399
 
5.3%
3 355
 
4.8%
4 348
 
4.7%
9 344
 
4.6%
5 344
 
4.6%
6 344
 
4.6%
7 344
 
4.6%
8 344
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
A 1243
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7458
85.7%
Latin 1243
 
14.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3937
52.8%
1 699
 
9.4%
2 399
 
5.3%
3 355
 
4.8%
4 348
 
4.7%
9 344
 
4.6%
5 344
 
4.6%
6 344
 
4.6%
7 344
 
4.6%
8 344
 
4.6%
Latin
ValueCountFrequency (%)
A 1243
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3937
45.2%
A 1243
 
14.3%
1 699
 
8.0%
2 399
 
4.6%
3 355
 
4.1%
4 348
 
4.0%
9 344
 
4.0%
5 344
 
4.0%
6 344
 
4.0%
7 344
 
4.0%

통계조사ID
Text

Distinct1239
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size9.8 KiB
2023-12-11T07:17:31.438487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters8701
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1235 ?
Unique (%)99.4%

Sample

1st row2006086
2nd row2004030
3rd row2004029
4th row2006070
5th row2004028
ValueCountFrequency (%)
9b02001 2
 
0.2%
9b01003 2
 
0.2%
9b00035 2
 
0.2%
2006028 2
 
0.2%
1990009 1
 
0.1%
1998043 1
 
0.1%
1975032 1
 
0.1%
1997037 1
 
0.1%
1997038 1
 
0.1%
1998034 1
 
0.1%
Other values (1229) 1229
98.9%
2023-12-11T07:17:31.799266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2729
31.4%
1 1314
15.1%
9 1279
14.7%
2 932
 
10.7%
6 562
 
6.5%
7 410
 
4.7%
3 379
 
4.4%
5 351
 
4.0%
8 342
 
3.9%
4 335
 
3.9%
Other values (2) 68
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8633
99.2%
Uppercase Letter 68
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2729
31.6%
1 1314
15.2%
9 1279
14.8%
2 932
 
10.8%
6 562
 
6.5%
7 410
 
4.7%
3 379
 
4.4%
5 351
 
4.1%
8 342
 
4.0%
4 335
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
B 65
95.6%
A 3
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 8633
99.2%
Latin 68
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2729
31.6%
1 1314
15.2%
9 1279
14.8%
2 932
 
10.8%
6 562
 
6.5%
7 410
 
4.7%
3 379
 
4.4%
5 351
 
4.1%
8 342
 
4.0%
4 335
 
3.9%
Latin
ValueCountFrequency (%)
B 65
95.6%
A 3
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2729
31.4%
1 1314
15.1%
9 1279
14.7%
2 932
 
10.7%
6 562
 
6.5%
7 410
 
4.7%
3 379
 
4.4%
5 351
 
4.0%
8 342
 
3.9%
4 335
 
3.9%
Other values (2) 68
 
0.8%

조직번호
Real number (ℝ)

Distinct183
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234.07643
Minimum101
Maximum994
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.1 KiB
2023-12-11T07:17:31.918297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile101
Q1117
median202
Q3340
95-th percentile387.9
Maximum994
Range893
Interquartile range (IQR)223

Descriptive statistics

Standard deviation148.83929
Coefficient of variation (CV)0.63585765
Kurtosis8.7733652
Mean234.07643
Median Absolute Deviation (MAD)96
Skewness2.3119349
Sum290957
Variance22153.133
MonotonicityNot monotonic
2023-12-11T07:17:32.018966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
345 80
 
6.4%
101 73
 
5.9%
117 65
 
5.2%
361 47
 
3.8%
116 43
 
3.5%
118 41
 
3.3%
114 38
 
3.1%
340 36
 
2.9%
331 34
 
2.7%
136 33
 
2.7%
Other values (173) 753
60.6%
ValueCountFrequency (%)
101 73
5.9%
102 10
 
0.8%
103 4
 
0.3%
105 6
 
0.5%
106 28
 
2.3%
109 5
 
0.4%
110 26
 
2.1%
111 14
 
1.1%
112 6
 
0.5%
113 11
 
0.9%
ValueCountFrequency (%)
994 1
0.1%
993 1
0.1%
989 1
0.1%
987 1
0.1%
986 1
0.1%
985 1
0.1%
979 1
0.1%
971 1
0.1%
969 1
0.1%
967 1
0.1%

Interactions

2023-12-11T07:17:30.361721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-11T07:17:30.474463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:17:30.542559image/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

조사별관리ID통계조사ID조직번호
0A0009302006086156
1A0009312004030156
2A0009322004029156
3A0009332006070156
4A0009342004028156
5A0009352004027156
6A0009362006128156
7A0009372004036163
8A0009382006115163
9A0009392004024201
조사별관리ID통계조사ID조직번호
1233A0009201976067154
1234A0009212006156155
1235A0009222002010155
1236A0009232006154155
1237A0009242006145155
1238A0009252006144155
1239A0009262006118155
1240A0009272006117155
1241A0009282006092156
1242A0009292006088156