Overview

Dataset statistics

Number of variables3
Number of observations151
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory25.9 B

Variable types

Text2
Numeric1

Dataset

Description충청남도 홈페이지의 회원가입현황으로써 월별 가입자 수 데이터 입니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=377&beforeMenuCd=DOM_000000201001001000&publicdatapk=15063379

Alerts

년월 has unique valuesUnique
누적가입자수 has unique valuesUnique

Reproduction

Analysis started2024-01-09 20:05:29.374406
Analysis finished2024-01-09 20:05:29.751246
Duration0.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월
Text

UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-01-10T05:05:30.105749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters906
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)100.0%

Sample

1st rowJan-21
2nd rowDec-20
3rd rowNov-20
4th rowOct-20
5th rowSep-20
ValueCountFrequency (%)
jan-21 1
 
0.7%
jul-11 1
 
0.7%
dec-12 1
 
0.7%
nov-12 1
 
0.7%
oct-12 1
 
0.7%
sep-12 1
 
0.7%
aug-12 1
 
0.7%
jul-12 1
 
0.7%
jun-12 1
 
0.7%
may-12 1
 
0.7%
Other values (141) 141
93.4%
2024-01-10T05:05:30.675019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 151
16.7%
1 133
 
14.7%
0 42
 
4.6%
J 40
 
4.4%
a 39
 
4.3%
u 38
 
4.2%
e 37
 
4.1%
n 27
 
3.0%
r 25
 
2.8%
M 25
 
2.8%
Other values (23) 349
38.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 302
33.3%
Lowercase Letter 302
33.3%
Dash Punctuation 151
16.7%
Uppercase Letter 151
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 39
12.9%
u 38
12.6%
e 37
12.3%
n 27
8.9%
r 25
8.3%
p 25
8.3%
c 24
7.9%
l 13
 
4.3%
v 13
 
4.3%
o 13
 
4.3%
Other values (4) 48
15.9%
Decimal Number
ValueCountFrequency (%)
1 133
44.0%
0 42
 
13.9%
2 25
 
8.3%
9 24
 
7.9%
8 16
 
5.3%
7 14
 
4.6%
4 12
 
4.0%
6 12
 
4.0%
5 12
 
4.0%
3 12
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
J 40
26.5%
M 25
16.6%
A 24
15.9%
S 13
 
8.6%
N 13
 
8.6%
O 12
 
7.9%
F 12
 
7.9%
D 12
 
7.9%
Dash Punctuation
ValueCountFrequency (%)
- 151
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 453
50.0%
Latin 453
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
J 40
 
8.8%
a 39
 
8.6%
u 38
 
8.4%
e 37
 
8.2%
n 27
 
6.0%
r 25
 
5.5%
M 25
 
5.5%
p 25
 
5.5%
A 24
 
5.3%
c 24
 
5.3%
Other values (12) 149
32.9%
Common
ValueCountFrequency (%)
- 151
33.3%
1 133
29.4%
0 42
 
9.3%
2 25
 
5.5%
9 24
 
5.3%
8 16
 
3.5%
7 14
 
3.1%
4 12
 
2.6%
6 12
 
2.6%
5 12
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 906
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 151
16.7%
1 133
 
14.7%
0 42
 
4.6%
J 40
 
4.4%
a 39
 
4.3%
u 38
 
4.2%
e 37
 
4.1%
n 27
 
3.0%
r 25
 
2.8%
M 25
 
2.8%
Other values (23) 349
38.5%

가입건수
Real number (ℝ)

Distinct100
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.695364
Minimum1
Maximum764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-01-10T05:05:30.874962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.5
Q121
median64
Q3138.5
95-th percentile255.5
Maximum764
Range763
Interquartile range (IQR)117.5

Descriptive statistics

Standard deviation96.146069
Coefficient of variation (CV)1.0719179
Kurtosis15.515273
Mean89.695364
Median Absolute Deviation (MAD)50
Skewness2.871385
Sum13544
Variance9244.0666
MonotonicityNot monotonic
2024-01-10T05:05:31.520620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 6
 
4.0%
21 5
 
3.3%
1 5
 
3.3%
9 5
 
3.3%
25 4
 
2.6%
33 4
 
2.6%
12 3
 
2.0%
143 3
 
2.0%
140 3
 
2.0%
19 3
 
2.0%
Other values (90) 110
72.8%
ValueCountFrequency (%)
1 5
3.3%
4 1
 
0.7%
5 1
 
0.7%
6 1
 
0.7%
7 1
 
0.7%
8 1
 
0.7%
9 5
3.3%
10 1
 
0.7%
11 1
 
0.7%
12 3
2.0%
ValueCountFrequency (%)
764 1
0.7%
395 1
0.7%
324 1
0.7%
284 1
0.7%
266 1
0.7%
261 2
1.3%
258 1
0.7%
253 1
0.7%
248 1
0.7%
237 1
0.7%

누적가입자수
Text

UNIQUE 

Distinct151
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2024-01-10T05:05:32.004915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9470199
Min length1

Characters and Unicode

Total characters747
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)100.0%

Sample

1st row13,544
2nd row13,460
3rd row13,324
4th row13,195
5th row13,042
ValueCountFrequency (%)
13,544 1
 
0.7%
1,468 1
 
0.7%
1,920 1
 
0.7%
1,901 1
 
0.7%
1,884 1
 
0.7%
1,875 1
 
0.7%
1,866 1
 
0.7%
1,826 1
 
0.7%
1,807 1
 
0.7%
1,776 1
 
0.7%
Other values (141) 141
93.4%
2024-01-10T05:05:32.654486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 139
18.6%
1 104
13.9%
2 84
11.2%
7 58
7.8%
6 58
7.8%
3 57
7.6%
4 55
 
7.4%
8 55
 
7.4%
9 53
 
7.1%
0 48
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 608
81.4%
Other Punctuation 139
 
18.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 104
17.1%
2 84
13.8%
7 58
9.5%
6 58
9.5%
3 57
9.4%
4 55
9.0%
8 55
9.0%
9 53
8.7%
0 48
7.9%
5 36
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 747
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 139
18.6%
1 104
13.9%
2 84
11.2%
7 58
7.8%
6 58
7.8%
3 57
7.6%
4 55
 
7.4%
8 55
 
7.4%
9 53
 
7.1%
0 48
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 747
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 139
18.6%
1 104
13.9%
2 84
11.2%
7 58
7.8%
6 58
7.8%
3 57
7.6%
4 55
 
7.4%
8 55
 
7.4%
9 53
 
7.1%
0 48
 
6.4%

Interactions

2024-01-10T05:05:29.470160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2024-01-10T05:05:29.614083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:05:29.712984image/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

년월가입건수누적가입자수
0Jan-218413,544
1Dec-2013613,460
2Nov-2012913,324
3Oct-2015313,195
4Sep-2026113,042
5Aug-2032412,781
6Jul-2016012,457
7Jun-2020012,297
8May-2017412,097
9Apr-2025311,923
년월가입건수누적가입자수
141Apr-0989931
142Mar-0959842
143Feb-099783
144Jan-095774
145Sep-081769
146Jul-081768
147Mar-081767
148Jan-081766
149Nov-07764765
150Jun-0711