Overview

Dataset statistics

Number of variables8
Number of observations151
Missing cells25
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory66.9 B

Variable types

Categorical3
DateTime1
Text2
Numeric2

Dataset

Description해당 데이터는 인천광역시 남동구의 단체장 업무추진비 사용내역에 관련된 자료로서, 인천광역시 남동구 단체장 업무추진비 사용내역의 사용자, 일자, 장소, 집행목적, 대상인원수, 금액, 결제방법, 비목의 정보를 확인할 수 있다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15104633&srcSe=7661IVAWM27C61E190

Alerts

사용자 has constant value ""Constant
대상인원수 is highly overall correlated with 금액High correlation
금액 is highly overall correlated with 대상인원수High correlation
대상인원수 has 25 (16.6%) missing valuesMissing

Reproduction

Analysis started2024-01-28 05:01:00.481575
Analysis finished2024-01-28 05:01:01.342833
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사용자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
구청장
151 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
구청장 151
100.0%

Length

2024-01-28T14:01:01.399976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:01:01.474963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구청장 151
100.0%

일자
Date

Distinct99
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Minimum2022-08-02 00:00:00
Maximum2023-02-28 00:00:00
2024-01-28T14:01:01.566628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:01:01.689075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

장소
Text

Distinct80
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-01-28T14:01:01.905022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length5.3443709
Min length2

Characters and Unicode

Total characters807
Distinct characters190
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)37.7%

Sample

1st row이천쌀밥집 화정
2nd row사무실
3rd row전복촌
4th row수라
5th row사무실
ValueCountFrequency (%)
사무실 30
 
17.1%
남동구청직원복지회 9
 
5.1%
수라 9
 
5.1%
1개소 5
 
2.9%
외 5
 
2.9%
모심갈비 5
 
2.9%
총무과 5
 
2.9%
포청천부 4
 
2.3%
이화찹쌀순대 3
 
1.7%
하나로마트 3
 
1.7%
Other values (74) 97
55.4%
2024-01-28T14:01:02.244227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
무 35
 
4.3%
사 34
 
4.2%
실 30
 
3.7%
25
 
3.1%
구 20
 
2.5%
수 19
 
2.4%
원 18
 
2.2%
남 16
 
2.0%
동 16
 
2.0%
정 15
 
1.9%
Other values (180) 579
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 752
93.2%
Space Separator 25
 
3.1%
Close Punctuation 7
 
0.9%
Open Punctuation 7
 
0.9%
Decimal Number 6
 
0.7%
Other Symbol 5
 
0.6%
Dash Punctuation 2
 
0.2%
Uppercase Letter 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
무 35
 
4.7%
사 34
 
4.5%
실 30
 
4.0%
구 20
 
2.7%
수 19
 
2.5%
원 18
 
2.4%
남 16
 
2.1%
동 16
 
2.1%
정 15
 
2.0%
리 13
 
1.7%
Other values (171) 536
71.3%
Uppercase Letter
ValueCountFrequency (%)
U 1
50.0%
C 1
50.0%
Space Separator
ValueCountFrequency (%)
25
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Decimal Number
ValueCountFrequency (%)
1 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 757
93.8%
Common 48
 
5.9%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
무 35
 
4.6%
사 34
 
4.5%
실 30
 
4.0%
구 20
 
2.6%
수 19
 
2.5%
원 18
 
2.4%
남 16
 
2.1%
동 16
 
2.1%
정 15
 
2.0%
리 13
 
1.7%
Other values (172) 541
71.5%
Common
ValueCountFrequency (%)
25
52.1%
) 7
 
14.6%
( 7
 
14.6%
1 6
 
12.5%
- 2
 
4.2%
& 1
 
2.1%
Latin
ValueCountFrequency (%)
U 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 752
93.2%
ASCII 50
 
6.2%
None 5
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
무 35
 
4.7%
사 34
 
4.5%
실 30
 
4.0%
구 20
 
2.7%
수 19
 
2.5%
원 18
 
2.4%
남 16
 
2.1%
동 16
 
2.1%
정 15
 
2.0%
리 13
 
1.7%
Other values (171) 536
71.3%
ASCII
ValueCountFrequency (%)
25
50.0%
) 7
 
14.0%
( 7
 
14.0%
1 6
 
12.0%
- 2
 
4.0%
U 1
 
2.0%
& 1
 
2.0%
C 1
 
2.0%
None
ValueCountFrequency (%)
㈜ 5
100.0%

집행목적
Text

Distinct122
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-01-28T14:01:02.423209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length32
Mean length22.788079
Min length13

Characters and Unicode

Total characters3441
Distinct characters263
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)74.8%

Sample

1st row남동구 구정 홍보를 위한 기자 간담회
2nd row직원경조사비 지출(유**)
3rd row호우경보에 따른 비상근무 담당부서 격려 간담회
4th row주요 현안 사업 추진을 위한 정책 간담회
5th row직원경조사비 지출(송**)
ValueCountFrequency (%)
간담회 81
 
9.7%
위한 47
 
5.6%
격려 30
 
3.6%
2023년 28
 
3.4%
직원경조사비 25
 
3.0%
구입 24
 
2.9%
추진 15
 
1.8%
따른 14
 
1.7%
내방민원 12
 
1.4%
응대를 12
 
1.4%
Other values (290) 546
65.5%
2024-01-28T14:01:02.701070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
691
 
20.1%
회 96
 
2.8%
* 88
 
2.6%
담 87
 
2.5%
간 83
 
2.4%
2 73
 
2.1%
원 73
 
2.1%
사 61
 
1.8%
구 55
 
1.6%
한 53
 
1.5%
Other values (253) 2081
60.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2363
68.7%
Space Separator 691
 
20.1%
Decimal Number 145
 
4.2%
Other Punctuation 110
 
3.2%
Uppercase Letter 70
 
2.0%
Open Punctuation 31
 
0.9%
Close Punctuation 31
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 96
 
4.1%
담 87
 
3.7%
간 83
 
3.5%
원 73
 
3.1%
사 61
 
2.6%
구 55
 
2.3%
한 53
 
2.2%
위 53
 
2.2%
직 50
 
2.1%
비 42
 
1.8%
Other values (235) 1710
72.4%
Decimal Number
ValueCountFrequency (%)
2 73
50.3%
0 36
24.8%
3 31
21.4%
1 2
 
1.4%
8 2
 
1.4%
5 1
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
A 20
28.6%
Y 10
14.3%
T 10
14.3%
L 10
14.3%
K 10
14.3%
D 10
14.3%
Other Punctuation
ValueCountFrequency (%)
* 88
80.0%
, 21
 
19.1%
. 1
 
0.9%
Space Separator
ValueCountFrequency (%)
691
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2363
68.7%
Common 1008
29.3%
Latin 70
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 96
 
4.1%
담 87
 
3.7%
간 83
 
3.5%
원 73
 
3.1%
사 61
 
2.6%
구 55
 
2.3%
한 53
 
2.2%
위 53
 
2.2%
직 50
 
2.1%
비 42
 
1.8%
Other values (235) 1710
72.4%
Common
ValueCountFrequency (%)
691
68.6%
* 88
 
8.7%
2 73
 
7.2%
0 36
 
3.6%
( 31
 
3.1%
) 31
 
3.1%
3 31
 
3.1%
, 21
 
2.1%
1 2
 
0.2%
8 2
 
0.2%
Other values (2) 2
 
0.2%
Latin
ValueCountFrequency (%)
A 20
28.6%
Y 10
14.3%
T 10
14.3%
L 10
14.3%
K 10
14.3%
D 10
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2363
68.7%
ASCII 1078
31.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
691
64.1%
* 88
 
8.2%
2 73
 
6.8%
0 36
 
3.3%
( 31
 
2.9%
) 31
 
2.9%
3 31
 
2.9%
, 21
 
1.9%
A 20
 
1.9%
Y 10
 
0.9%
Other values (8) 46
 
4.3%
Hangul
ValueCountFrequency (%)
회 96
 
4.1%
담 87
 
3.7%
간 83
 
3.5%
원 73
 
3.1%
사 61
 
2.6%
구 55
 
2.3%
한 53
 
2.2%
위 53
 
2.2%
직 50
 
2.1%
비 42
 
1.8%
Other values (235) 1710
72.4%

대상인원수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)22.2%
Missing25
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean9.0396825
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-01-28T14:01:02.801937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q314
95-th percentile22.75
Maximum70
Range69
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.2211937
Coefficient of variation (CV)1.0200794
Kurtosis14.367611
Mean9.0396825
Median Absolute Deviation (MAD)4
Skewness2.8089473
Sum1139
Variance85.030413
MonotonicityNot monotonic
2024-01-28T14:01:02.899909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 17
11.3%
2 16
10.6%
6 12
 
7.9%
4 10
 
6.6%
5 9
 
6.0%
8 6
 
4.0%
3 6
 
4.0%
18 5
 
3.3%
20 5
 
3.3%
15 4
 
2.6%
Other values (18) 36
23.8%
(Missing) 25
16.6%
ValueCountFrequency (%)
1 17
11.3%
2 16
10.6%
3 6
 
4.0%
4 10
6.6%
5 9
6.0%
6 12
7.9%
7 4
 
2.6%
8 6
 
4.0%
9 3
 
2.0%
10 3
 
2.0%
ValueCountFrequency (%)
70 1
 
0.7%
35 1
 
0.7%
30 1
 
0.7%
26 1
 
0.7%
24 2
 
1.3%
23 1
 
0.7%
22 1
 
0.7%
21 2
 
1.3%
20 5
3.3%
19 2
 
1.3%

금액
Real number (ℝ)

HIGH CORRELATION 

Distinct105
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263647.28
Minimum14000
Maximum3600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-01-28T14:01:03.008688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14000
5-th percentile38750
Q171000
median124500
Q3288700
95-th percentile549000
Maximum3600000
Range3586000
Interquartile range (IQR)217700

Descriptive statistics

Standard deviation463745.65
Coefficient of variation (CV)1.7589624
Kurtosis31.414596
Mean263647.28
Median Absolute Deviation (MAD)74500
Skewness5.2796796
Sum39810740
Variance2.1506003 × 1011
MonotonicityNot monotonic
2024-01-28T14:01:03.129280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50000 16
 
10.6%
100000 15
 
9.9%
220000 3
 
2.0%
108000 3
 
2.0%
64000 3
 
2.0%
72000 2
 
1.3%
476000 2
 
1.3%
200000 2
 
1.3%
60000 2
 
1.3%
198000 2
 
1.3%
Other values (95) 101
66.9%
ValueCountFrequency (%)
14000 1
0.7%
17000 1
0.7%
24000 1
0.7%
25000 1
0.7%
30000 1
0.7%
36000 1
0.7%
38000 1
0.7%
38100 1
0.7%
39400 1
0.7%
45000 1
0.7%
ValueCountFrequency (%)
3600000 1
0.7%
3000000 1
0.7%
2879950 1
0.7%
1400000 1
0.7%
1221000 1
0.7%
1000000 1
0.7%
624000 1
0.7%
565000 1
0.7%
533000 1
0.7%
495000 1
0.7%

결제방법
Categorical

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
신용카드
120 
현금
31 

Length

Max length4
Median length4
Mean length3.589404
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신용카드
2nd row현금
3rd row신용카드
4th row신용카드
5th row현금

Common Values

ValueCountFrequency (%)
신용카드 120
79.5%
현금 31
 
20.5%

Length

2024-01-28T14:01:03.250748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:01:03.340489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신용카드 120
79.5%
현금 31
 
20.5%

비목
Categorical

Distinct6
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
기관
90 
현안
31 
구정
14 
건전
 
6
시책(현안)
 
6

Length

Max length6
Median length2
Mean length2.2649007
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row현안
2nd row기관
3rd row기관
4th row현안
5th row기관

Common Values

ValueCountFrequency (%)
기관 90
59.6%
현안 31
 
20.5%
구정 14
 
9.3%
건전 6
 
4.0%
시책(현안) 6
 
4.0%
시책(구정) 4
 
2.6%

Length

2024-01-28T14:01:03.437612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:01:03.546133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기관 90
59.6%
현안 31
 
20.5%
구정 14
 
9.3%
건전 6
 
4.0%
시책(현안 6
 
4.0%
시책(구정 4
 
2.6%

Interactions

2024-01-28T14:01:01.004562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:01:00.857360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:01:01.084030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:01:00.931818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T14:01:03.622711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자장소대상인원수금액결제방법비목
일자1.0000.0000.6100.0000.0000.903
장소0.0001.0000.9080.7271.0000.880
대상인원수0.6100.9081.0000.9220.5960.308
금액0.0000.7270.9221.0000.1850.000
결제방법0.0001.0000.5960.1851.0000.530
비목0.9030.8800.3080.0000.5301.000
2024-01-28T14:01:03.703115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
결제방법비목
결제방법1.0000.378
비목0.3781.000
2024-01-28T14:01:04.084867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대상인원수금액결제방법비목
대상인원수1.0000.8360.4250.115
금액0.8361.0000.1930.000
결제방법0.4250.1931.0000.378
비목0.1150.0000.3781.000

Missing values

2024-01-28T14:01:01.191737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T14:01:01.302578image/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

사용자일자장소집행목적대상인원수금액결제방법비목
0구청장2022-08-02이천쌀밥집 화정남동구 구정 홍보를 위한 기자 간담회11220000신용카드현안
1구청장2022-08-08사무실직원경조사비 지출(유**)150000현금기관
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