Overview

Dataset statistics

Number of variables4
Number of observations154
Missing cells312
Missing cells (%)50.6%
Duplicate rows7
Duplicate rows (%)4.5%
Total size in memory5.2 KiB
Average record size in memory34.9 B

Variable types

Unsupported1
Numeric1
Categorical1
Text1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2743/F/1/datasetView.do

Alerts

Dataset has 7 (4.5%) duplicate rowsDuplicates
Unnamed: 0 has 154 (100.0%) missing valuesMissing
2017년 월간 ‘서울사랑’ 독자 만족도 조사 has 134 (87.0%) missing valuesMissing
Unnamed: 3 has 24 (15.6%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 05:25:37.504092
Analysis finished2023-12-11 05:25:38.262147
Duration0.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing154
Missing (%)100.0%
Memory size1.5 KiB

2017년 월간 ‘서울사랑’ 독자 만족도 조사
Real number (ℝ)

MISSING 

Distinct20
Distinct (%)100.0%
Missing134
Missing (%)87.0%
Infinite0
Infinite (%)0.0%
Mean10.5
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2023-12-11T14:25:38.339317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q15.75
median10.5
Q315.25
95-th percentile19.05
Maximum20
Range19
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation5.9160798
Coefficient of variation (CV)0.56343617
Kurtosis-1.2
Mean10.5
Median Absolute Deviation (MAD)5
Skewness0
Sum210
Variance35
MonotonicityStrictly increasing
2023-12-11T14:25:38.468610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
12 1
 
0.6%
20 1
 
0.6%
19 1
 
0.6%
18 1
 
0.6%
17 1
 
0.6%
16 1
 
0.6%
15 1
 
0.6%
14 1
 
0.6%
13 1
 
0.6%
1 1
 
0.6%
Other values (10) 10
 
6.5%
(Missing) 134
87.0%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
20 1
0.6%
19 1
0.6%
18 1
0.6%
17 1
0.6%
16 1
0.6%
15 1
0.6%
14 1
0.6%
13 1
0.6%
12 1
0.6%
11 1
0.6%

Unnamed: 2
Categorical

Distinct33
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
20 
2
19 
3
17 
4
15 
5
11 
Other values (28)
72 

Length

Max length105
Median length1
Mean length7.2077922
Min length1

Unique

Unique18 ?
Unique (%)11.7%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row선생님께서는 최근 1년 동안 「서울사랑」을 읽어보신 적이 있습니까?(책자, 홈페이지 모두 포함)

Common Values

ValueCountFrequency (%)
1 20
13.0%
2 19
12.3%
3 17
11.0%
4 15
9.7%
5 11
 
7.1%
99 11
 
7.1%
6 10
 
6.5%
7 8
 
5.2%
8 7
 
4.5%
9 5
 
3.2%
Other values (23) 31
20.1%

Length

2023-12-11T14:25:38.650781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 20
 
5.8%
2 19
 
5.5%
3 17
 
4.9%
4 15
 
4.3%
5 11
 
3.2%
99 11
 
3.2%
6 10
 
2.9%
7 8
 
2.3%
8 7
 
2.0%
것을 6
 
1.7%
Other values (154) 223
64.3%

Unnamed: 3
Text

MISSING 

Distinct109
Distinct (%)83.8%
Missing24
Missing (%)15.6%
Memory size1.3 KiB
2023-12-11T14:25:39.007476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length25
Mean length11.123077
Min length2

Characters and Unicode

Total characters1446
Distinct characters259
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

Unique97 ?
Unique (%)74.6%

Sample

1st row그렇다
2nd row아니다
3rd row아주 좋다
4th row대체로 좋은 편이다
5th row약간의 개선이 필요하다
ValueCountFrequency (%)
등 13
 
3.5%
기타 11
 
3.0%
및 8
 
2.2%
필요하다 7
 
1.9%
정보 6
 
1.6%
개선이 5
 
1.4%
5
 
1.4%
편이다 4
 
1.1%
원 4
 
1.1%
도움이 4
 
1.1%
Other values (249) 302
81.8%
2023-12-11T14:25:39.612569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
239
 
16.5%
기 40
 
2.8%
, 39
 
2.7%
다 39
 
2.7%
구 33
 
2.3%
이 30
 
2.1%
서 28
 
1.9%
관 22
 
1.5%
하 21
 
1.5%
· 20
 
1.4%
Other values (249) 935
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1093
75.6%
Space Separator 239
 
16.5%
Other Punctuation 76
 
5.3%
Open Punctuation 9
 
0.6%
Close Punctuation 9
 
0.6%
Decimal Number 8
 
0.6%
Uppercase Letter 6
 
0.4%
Lowercase Letter 5
 
0.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 40
 
3.7%
다 39
 
3.6%
구 33
 
3.0%
이 30
 
2.7%
서 28
 
2.6%
관 22
 
2.0%
하 21
 
1.9%
지 19
 
1.7%
정 19
 
1.7%
자 18
 
1.6%
Other values (226) 824
75.4%
Decimal Number
ValueCountFrequency (%)
0 2
25.0%
6 2
25.0%
2 1
12.5%
5 1
12.5%
3 1
12.5%
1 1
12.5%
Uppercase Letter
ValueCountFrequency (%)
S 2
33.3%
N 1
16.7%
F 1
16.7%
D 1
16.7%
P 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 39
51.3%
· 20
26.3%
/ 12
 
15.8%
: 5
 
6.6%
Lowercase Letter
ValueCountFrequency (%)
o 2
40.0%
k 1
20.0%
b 1
20.0%
e 1
20.0%
Space Separator
ValueCountFrequency (%)
239
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1093
75.6%
Common 342
 
23.7%
Latin 11
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 40
 
3.7%
다 39
 
3.6%
구 33
 
3.0%
이 30
 
2.7%
서 28
 
2.6%
관 22
 
2.0%
하 21
 
1.9%
지 19
 
1.7%
정 19
 
1.7%
자 18
 
1.6%
Other values (226) 824
75.4%
Common
ValueCountFrequency (%)
239
69.9%
, 39
 
11.4%
· 20
 
5.8%
/ 12
 
3.5%
( 9
 
2.6%
) 9
 
2.6%
: 5
 
1.5%
0 2
 
0.6%
6 2
 
0.6%
2 1
 
0.3%
Other values (4) 4
 
1.2%
Latin
ValueCountFrequency (%)
S 2
18.2%
o 2
18.2%
N 1
9.1%
k 1
9.1%
b 1
9.1%
e 1
9.1%
F 1
9.1%
D 1
9.1%
P 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1093
75.6%
ASCII 333
 
23.0%
None 20
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
239
71.8%
, 39
 
11.7%
/ 12
 
3.6%
( 9
 
2.7%
) 9
 
2.7%
: 5
 
1.5%
S 2
 
0.6%
o 2
 
0.6%
0 2
 
0.6%
6 2
 
0.6%
Other values (12) 12
 
3.6%
Hangul
ValueCountFrequency (%)
기 40
 
3.7%
다 39
 
3.6%
구 33
 
3.0%
이 30
 
2.7%
서 28
 
2.6%
관 22
 
2.0%
하 21
 
1.9%
지 19
 
1.7%
정 19
 
1.7%
자 18
 
1.6%
Other values (226) 824
75.4%
None
ValueCountFrequency (%)
· 20
100.0%

Interactions

2023-12-11T14:25:37.750782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T14:25:39.751525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2017년 월간 ‘서울사랑’ 독자 만족도 조사Unnamed: 2
2017년 월간 ‘서울사랑’ 독자 만족도 조사1.0000.832
Unnamed: 20.8321.000
2023-12-11T14:25:39.874762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2017년 월간 ‘서울사랑’ 독자 만족도 조사Unnamed: 2
2017년 월간 ‘서울사랑’ 독자 만족도 조사1.0000.000
Unnamed: 20.0001.000

Missing values

2023-12-11T14:25:37.918900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T14:25:38.051866image/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.
2023-12-11T14:25:38.182060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 02017년 월간 ‘서울사랑’ 독자 만족도 조사Unnamed: 2Unnamed: 3
0<NA><NA><NA><NA>
1<NA><NA><NA><NA>
2<NA><NA><NA><NA>
3<NA><NA><NA><NA>
4<NA>1선생님께서는 최근 1년 동안 「서울사랑」을 읽어보신 적이 있습니까?(책자, 홈페이지 모두 포함)<NA>
5<NA><NA>1그렇다
6<NA><NA>2아니다
7<NA>2「서울사랑」을 읽으신 후 전반적으로 어떤 느낌이 드셨습니까?<NA>
8<NA><NA>1아주 좋다
9<NA><NA>2대체로 좋은 편이다
Unnamed: 02017년 월간 ‘서울사랑’ 독자 만족도 조사Unnamed: 2Unnamed: 3
144<NA>20선생님께서는 주로 무슨 일을 하고 계십니까?<NA>
145<NA><NA>1자영업
146<NA><NA>2사무/기술직
147<NA><NA>3생산/기능직
148<NA><NA>4판매/서비스직
149<NA><NA>5자유/전문직
150<NA><NA>6전업주부
151<NA><NA>7학생
152<NA><NA>8무직/취업준비/기타
153<NA><NA>9응답하고 싶지 않음

Duplicate rows

Most frequently occurring

2017년 월간 ‘서울사랑’ 독자 만족도 조사Unnamed: 2Unnamed: 3# duplicates
5<NA>99기타11
6<NA><NA><NA>4
0<NA>1기사내용2
1<NA>1아주 좋다2
2<NA>2대체로 좋은 편이다2
3<NA>3약간의 개선이 필요하다2
4<NA>4대폭적인 개선이 필요하다2