Overview

Dataset statistics

Number of variables6
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory55.1 B

Variable types

Numeric1
Text1
Categorical4

Dataset

Description대구광역시 남구 자체 문자를 발송하는 알리미시스템의 코드에 대한 데이터로 부서명, 발송등급, 연/월/일별 발송건수 등의 항목을 제공합니다.
Author대구광역시 남구
URLhttps://www.data.go.kr/data/15089396/fileData.do

Alerts

년발송건수 is highly overall correlated with 발송건수등급 and 2 other fieldsHigh correlation
월발송건수 is highly overall correlated with 발송건수등급 and 2 other fieldsHigh correlation
일발송건수 is highly overall correlated with 발송건수등급 and 2 other fieldsHigh correlation
발송건수등급 is highly overall correlated with 년발송건수 and 2 other fieldsHigh correlation
부서코드 has unique valuesUnique
부서명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:35:30.155044
Analysis finished2023-12-12 19:35:30.822182
Duration0.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

부서코드
Real number (ℝ)

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4400866 × 1010
Minimum3.440024 × 1010
Maximum3.440134 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-13T04:35:30.898292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.440024 × 1010
5-th percentile3.44003 × 1010
Q13.4400422 × 1010
median3.4401085 × 1010
Q33.4401238 × 1010
95-th percentile3.440132 × 1010
Maximum3.440134 × 1010
Range1100000
Interquartile range (IQR)815000

Descriptive statistics

Standard deviation410597.14
Coefficient of variation (CV)1.1935663 × 10-5
Kurtosis-1.725169
Mean3.4400866 × 1010
Median Absolute Deviation (MAD)230000
Skewness-0.3119099
Sum1.4448364 × 1012
Variance1.6859001 × 1011
MonotonicityNot monotonic
2023-12-13T04:35:31.039667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
34400440000 1
 
2.4%
34400320000 1
 
2.4%
34400240000 1
 
2.4%
34400250000 1
 
2.4%
34401340000 1
 
2.4%
34400950000 1
 
2.4%
34400940000 1
 
2.4%
34400540000 1
 
2.4%
34400300000 1
 
2.4%
34400310000 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
34400240000 1
2.4%
34400250000 1
2.4%
34400300000 1
2.4%
34400310000 1
2.4%
34400320000 1
2.4%
34400330000 1
2.4%
34400360000 1
2.4%
34400370000 1
2.4%
34400380000 1
2.4%
34400410000 1
2.4%
ValueCountFrequency (%)
34401340000 1
2.4%
34401330000 1
2.4%
34401320000 1
2.4%
34401310000 1
2.4%
34401300000 1
2.4%
34401290000 1
2.4%
34401280000 1
2.4%
34401270000 1
2.4%
34401260000 1
2.4%
34401250000 1
2.4%

부서명
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-13T04:35:31.327539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6.5
Mean length4.4285714
Min length3

Characters and Unicode

Total characters186
Distinct characters76
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

Unique42 ?
Unique (%)100.0%

Sample

1st row구청장
2nd row부구청장
3rd row기획조정실
4th row자치행정국
5th row행정지원과
ValueCountFrequency (%)
구청장 1
 
2.4%
봉덕1동 1
 
2.4%
대명10동 1
 
2.4%
토지정보과 1
 
2.4%
보건소 1
 
2.4%
보건행정과 1
 
2.4%
건강증진과 1
 
2.4%
대덕문화전당 1
 
2.4%
의회사무과 1
 
2.4%
이천동 1
 
2.4%
Other values (32) 32
76.2%
2023-12-13T04:35:31.815941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 21
 
11.3%
동 13
 
7.0%
대 10
 
5.4%
명 9
 
4.8%
정 7
 
3.8%
보 6
 
3.2%
행 5
 
2.7%
지 5
 
2.7%
건 5
 
2.7%
1 5
 
2.7%
Other values (66) 100
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 172
92.5%
Decimal Number 14
 
7.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 21
 
12.2%
동 13
 
7.6%
대 10
 
5.8%
명 9
 
5.2%
정 7
 
4.1%
보 6
 
3.5%
행 5
 
2.9%
지 5
 
2.9%
건 5
 
2.9%
원 4
 
2.3%
Other values (58) 87
50.6%
Decimal Number
ValueCountFrequency (%)
1 5
35.7%
3 2
 
14.3%
2 2
 
14.3%
0 1
 
7.1%
4 1
 
7.1%
5 1
 
7.1%
6 1
 
7.1%
9 1
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 172
92.5%
Common 14
 
7.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 21
 
12.2%
동 13
 
7.6%
대 10
 
5.8%
명 9
 
5.2%
정 7
 
4.1%
보 6
 
3.5%
행 5
 
2.9%
지 5
 
2.9%
건 5
 
2.9%
원 4
 
2.3%
Other values (58) 87
50.6%
Common
ValueCountFrequency (%)
1 5
35.7%
3 2
 
14.3%
2 2
 
14.3%
0 1
 
7.1%
4 1
 
7.1%
5 1
 
7.1%
6 1
 
7.1%
9 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 172
92.5%
ASCII 14
 
7.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 21
 
12.2%
동 13
 
7.6%
대 10
 
5.8%
명 9
 
5.2%
정 7
 
4.1%
보 6
 
3.5%
행 5
 
2.9%
지 5
 
2.9%
건 5
 
2.9%
원 4
 
2.3%
Other values (58) 87
50.6%
ASCII
ValueCountFrequency (%)
1 5
35.7%
3 2
 
14.3%
2 2
 
14.3%
0 1
 
7.1%
4 1
 
7.1%
5 1
 
7.1%
6 1
 
7.1%
9 1
 
7.1%

발송건수등급
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size468.0 B
S
34 
SS
6 
F
 
2

Length

Max length2
Median length1
Mean length1.1428571
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowS
3rd rowS
4th rowS
5th rowSS

Common Values

ValueCountFrequency (%)
S 34
81.0%
SS 6
 
14.3%
F 2
 
4.8%

Length

2023-12-13T04:35:31.990927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:32.110936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 34
81.0%
ss 6
 
14.3%
f 2
 
4.8%

년발송건수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size468.0 B
120000
34 
500000
8 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row120000
2nd row120000
3rd row120000
4th row120000
5th row500000

Common Values

ValueCountFrequency (%)
120000 34
81.0%
500000 8
 
19.0%

Length

2023-12-13T04:35:32.246660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:32.349279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
120000 34
81.0%
500000 8
 
19.0%

월발송건수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size468.0 B
10000
34 
100000
8 

Length

Max length6
Median length5
Mean length5.1904762
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10000
2nd row10000
3rd row10000
4th row10000
5th row100000

Common Values

ValueCountFrequency (%)
10000 34
81.0%
100000 8
 
19.0%

Length

2023-12-13T04:35:32.507640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:32.630796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10000 34
81.0%
100000 8
 
19.0%

일발송건수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size468.0 B
10000
36 
20000
6 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10000
2nd row10000
3rd row10000
4th row10000
5th row20000

Common Values

ValueCountFrequency (%)
10000 36
85.7%
20000 6
 
14.3%

Length

2023-12-13T04:35:32.757342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:35:32.883263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10000 36
85.7%
20000 6
 
14.3%

Interactions

2023-12-13T04:35:30.502992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:35:32.982994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부서코드부서명발송건수등급년발송건수월발송건수일발송건수
부서코드1.0001.0000.0000.0000.0000.000
부서명1.0001.0001.0001.0001.0001.000
발송건수등급0.0001.0001.0001.0001.0001.000
년발송건수0.0001.0001.0001.0000.9920.923
월발송건수0.0001.0001.0000.9921.0000.923
일발송건수0.0001.0001.0000.9230.9231.000
2023-12-13T04:35:33.406876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년발송건수월발송건수일발송건수발송건수등급
년발송건수1.0000.9210.7480.987
월발송건수0.9211.0000.7480.987
일발송건수0.7480.7481.0000.987
발송건수등급0.9870.9870.9871.000
2023-12-13T04:35:33.522687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부서코드발송건수등급년발송건수월발송건수일발송건수
부서코드1.0000.2170.0000.0000.000
발송건수등급0.2171.0000.9870.9870.987
년발송건수0.0000.9871.0000.9210.748
월발송건수0.0000.9870.9211.0000.748
일발송건수0.0000.9870.7480.7481.000

Missing values

2023-12-13T04:35:30.655468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:35:30.775844image/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

부서코드부서명발송건수등급년발송건수월발송건수일발송건수
034400440000구청장S1200001000010000
134400450000부구청장S1200001000010000
234400760000기획조정실S1200001000010000
334401080000자치행정국S1200001000010000
434401090000행정지원과SS50000010000020000
534401200000미래안전과S1200001000010000
634401330000평생교육홍보과SS50000010000020000
734401110000문화관광과S1200001000010000
834401120000민원정보과S1200001000010000
934401140000세무과S1200001000010000
부서코드부서명발송건수등급년발송건수월발송건수일발송건수
3234400320000봉덕3동S1200001000010000
3334400330000대명1동S1200001000010000
3434400620000대명2동S1200001000010000
3534400600000대명3동S1200001000010000
3634400360000대명4동S1200001000010000
3734400370000대명5동S1200001000010000
3834400380000대명6동S1200001000010000
3934400410000대명9동S1200001000010000
4034400420000대명10동S1200001000010000
4134400430000대명11동S1200001000010000