Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.6 KiB
Average record size in memory67.3 B

Variable types

Numeric1
Categorical7

Alerts

examin_begin_de has constant value ""Constant
respond_id has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:44:43.027686
Analysis finished2023-12-10 09:44:45.072669
Duration2.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

respond_id
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53329164
Minimum53322261
Maximum53377409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:44:45.295233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53322261
5-th percentile53322604
Q153324721
median53327922
Q353330449
95-th percentile53332455
Maximum53377409
Range55148
Interquartile range (IQR)5728

Descriptive statistics

Standard deviation9061.2614
Coefficient of variation (CV)0.00016991194
Kurtosis22.740755
Mean53329164
Median Absolute Deviation (MAD)3116
Skewness4.590753
Sum5.3329164 × 109
Variance82106459
MonotonicityNot monotonic
2023-12-10T18:44:45.661869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53322261 1
 
1.0%
53329111 1
 
1.0%
53330031 1
 
1.0%
53329910 1
 
1.0%
53329778 1
 
1.0%
53329648 1
 
1.0%
53329595 1
 
1.0%
53329574 1
 
1.0%
53329531 1
 
1.0%
53329305 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
53322261 1
1.0%
53322372 1
1.0%
53322435 1
1.0%
53322536 1
1.0%
53322603 1
1.0%
53322604 1
1.0%
53323135 1
1.0%
53323175 1
1.0%
53323279 1
1.0%
53323334 1
1.0%
ValueCountFrequency (%)
53377409 1
1.0%
53377360 1
1.0%
53377333 1
1.0%
53332779 1
1.0%
53332652 1
1.0%
53332445 1
1.0%
53332434 1
1.0%
53332409 1
1.0%
53332370 1
1.0%
53332295 1
1.0%

examin_begin_de
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20220404
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20220404
2nd row20220404
3rd row20220404
4th row20220404
5th row20220404

Common Values

ValueCountFrequency (%)
20220404 100
100.0%

Length

2023-12-10T18:44:45.961690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:46.149801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20220404 100
100.0%

sexdstn_flag_cd
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
M
56 
F
44 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
M 56
56.0%
F 44
44.0%

Length

2023-12-10T18:44:46.438040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:46.645842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 56
56.0%
f 44
44.0%

agrde_flag_nm
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
50대
27 
30대
21 
60대
18 
40대
17 
20대
17 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60대
2nd row30대
3rd row30대
4th row60대
5th row40대

Common Values

ValueCountFrequency (%)
50대 27
27.0%
30대 21
21.0%
60대 18
18.0%
40대 17
17.0%
20대 17
17.0%

Length

2023-12-10T18:44:47.020157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:47.320328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50대 27
27.0%
30대 21
21.0%
60대 18
18.0%
40대 17
17.0%
20대 17
17.0%
Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
36 
경기도
21 
부산광역시
충청남도
경상북도
Other values (11)
29 

Length

Max length7
Median length5
Mean length4.42
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row서울특별시
2nd row경기도
3rd row경기도
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 36
36.0%
경기도 21
21.0%
부산광역시 6
 
6.0%
충청남도 4
 
4.0%
경상북도 4
 
4.0%
울산광역시 4
 
4.0%
대전광역시 4
 
4.0%
광주광역시 4
 
4.0%
대구광역시 3
 
3.0%
강원도 3
 
3.0%
Other values (6) 11
 
11.0%

Length

2023-12-10T18:44:47.646546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 36
36.0%
경기도 21
21.0%
부산광역시 6
 
6.0%
충청남도 4
 
4.0%
경상북도 4
 
4.0%
울산광역시 4
 
4.0%
대전광역시 4
 
4.0%
광주광역시 4
 
4.0%
대구광역시 3
 
3.0%
강원도 3
 
3.0%
Other values (6) 11
 
11.0%
Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
300이상500만원 미만
33 
300만원 미만
26 
500이상700만원 미만
19 
700만원 이상
17 
무응답

Length

Max length13
Median length13
Mean length10.35
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row300이상500만원 미만
2nd row500이상700만원 미만
3rd row300만원 미만
4th row무응답
5th row700만원 이상

Common Values

ValueCountFrequency (%)
300이상500만원 미만 33
33.0%
300만원 미만 26
26.0%
500이상700만원 미만 19
19.0%
700만원 이상 17
17.0%
무응답 5
 
5.0%

Length

2023-12-10T18:44:47.918700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:48.167264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미만 78
40.0%
300이상500만원 33
16.9%
300만원 26
 
13.3%
500이상700만원 19
 
9.7%
700만원 17
 
8.7%
이상 17
 
8.7%
무응답 5
 
2.6%
Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
비슷하다
42 
증가한 편이다
30 
감소한 편이다
18 
크게 감소했다
크게 증가했다
 
4

Length

Max length7
Median length7
Mean length5.74
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비슷하다
2nd row비슷하다
3rd row크게 증가했다
4th row증가한 편이다
5th row비슷하다

Common Values

ValueCountFrequency (%)
비슷하다 42
42.0%
증가한 편이다 30
30.0%
감소한 편이다 18
18.0%
크게 감소했다 6
 
6.0%
크게 증가했다 4
 
4.0%

Length

2023-12-10T18:44:48.450204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:48.743673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
편이다 48
30.4%
비슷하다 42
26.6%
증가한 30
19.0%
감소한 18
 
11.4%
크게 10
 
6.3%
감소했다 6
 
3.8%
증가했다 4
 
2.5%
Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
증가할 것이다
46 
비슷할 것이다
37 
감소할 것이다
13 
크게 증가할 것이다
 
3
크게 감소할 것이다
 
1

Length

Max length10
Median length7
Mean length7.12
Min length7

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row증가할 것이다
2nd row감소할 것이다
3rd row크게 증가할 것이다
4th row증가할 것이다
5th row비슷할 것이다

Common Values

ValueCountFrequency (%)
증가할 것이다 46
46.0%
비슷할 것이다 37
37.0%
감소할 것이다 13
 
13.0%
크게 증가할 것이다 3
 
3.0%
크게 감소할 것이다 1
 
1.0%

Length

2023-12-10T18:44:49.059451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:49.311174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
것이다 100
49.0%
증가할 49
24.0%
비슷할 37
 
18.1%
감소할 14
 
6.9%
크게 4
 
2.0%

Interactions

2023-12-10T18:44:44.020450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:44:49.518318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
respond_idsexdstn_flag_cdagrde_flag_nmanswrr_oc_area_nmhshld_income_dgree_nmlsr_ct_expndtr_tndcy_valuelsr_ct_expndtr_inten_value
respond_id1.0000.0000.1130.4590.0000.1610.155
sexdstn_flag_cd0.0001.0000.2900.4000.0000.0000.000
agrde_flag_nm0.1130.2901.0000.2490.1350.0000.000
answrr_oc_area_nm0.4590.4000.2491.0000.1670.4670.000
hshld_income_dgree_nm0.0000.0000.1350.1671.0000.3840.000
lsr_ct_expndtr_tndcy_value0.1610.0000.0000.4670.3841.0000.836
lsr_ct_expndtr_inten_value0.1550.0000.0000.0000.0000.8361.000
2023-12-10T18:44:49.780666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
hshld_income_dgree_nmlsr_ct_expndtr_inten_valuelsr_ct_expndtr_tndcy_valueagrde_flag_nmsexdstn_flag_cdanswrr_oc_area_nm
hshld_income_dgree_nm1.0000.0000.1500.0460.0000.070
lsr_ct_expndtr_inten_value0.0001.0000.4670.0000.0000.000
lsr_ct_expndtr_tndcy_value0.1500.4671.0000.0000.0000.241
agrde_flag_nm0.0460.0000.0001.0000.3480.115
sexdstn_flag_cd0.0000.0000.0000.3481.0000.289
answrr_oc_area_nm0.0700.0000.2410.1150.2891.000
2023-12-10T18:44:50.036292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
respond_idsexdstn_flag_cdagrde_flag_nmanswrr_oc_area_nmhshld_income_dgree_nmlsr_ct_expndtr_tndcy_valuelsr_ct_expndtr_inten_value
respond_id1.0000.0000.1000.2690.0000.1250.112
sexdstn_flag_cd0.0001.0000.3480.2890.0000.0000.000
agrde_flag_nm0.1000.3481.0000.1150.0460.0000.000
answrr_oc_area_nm0.2690.2890.1151.0000.0700.2410.000
hshld_income_dgree_nm0.0000.0000.0460.0701.0000.1500.000
lsr_ct_expndtr_tndcy_value0.1250.0000.0000.2410.1501.0000.467
lsr_ct_expndtr_inten_value0.1120.0000.0000.0000.0000.4671.000

Missing values

2023-12-10T18:44:44.561540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:44:44.935822image/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

respond_idexamin_begin_desexdstn_flag_cdagrde_flag_nmanswrr_oc_area_nmhshld_income_dgree_nmlsr_ct_expndtr_tndcy_valuelsr_ct_expndtr_inten_value
05332226120220404M60대서울특별시300이상500만원 미만비슷하다증가할 것이다
15337733320220404F30대경기도500이상700만원 미만비슷하다감소할 것이다
25332237220220404M30대경기도300만원 미만크게 증가했다크게 증가할 것이다
35332243520220404M60대서울특별시무응답증가한 편이다증가할 것이다
45332253620220404F40대서울특별시700만원 이상비슷하다비슷할 것이다
55332260320220404M60대서울특별시300이상500만원 미만증가한 편이다크게 증가할 것이다
65332260420220404F40대서울특별시300만원 미만비슷하다비슷할 것이다
75337736020220404M60대대구광역시300만원 미만감소한 편이다감소할 것이다
85332313520220404M40대서울특별시500이상700만원 미만감소한 편이다증가할 것이다
95332317520220404F50대서울특별시300만원 미만비슷하다비슷할 것이다
respond_idexamin_begin_desexdstn_flag_cdagrde_flag_nmanswrr_oc_area_nmhshld_income_dgree_nmlsr_ct_expndtr_tndcy_valuelsr_ct_expndtr_inten_value
905333214820220404F20대인천광역시500이상700만원 미만크게 증가했다감소할 것이다
915333224120220404F50대경기도300만원 미만증가한 편이다증가할 것이다
925333226220220404M40대서울특별시300이상500만원 미만증가한 편이다증가할 것이다
935333229520220404M50대경기도500이상700만원 미만비슷하다증가할 것이다
945333237020220404F50대서울특별시300만원 미만감소한 편이다증가할 것이다
955333240920220404M60대광주광역시500이상700만원 미만비슷하다비슷할 것이다
965333243420220404F30대대전광역시300이상500만원 미만증가한 편이다증가할 것이다
975333244520220404F50대전라북도300이상500만원 미만크게 감소했다감소할 것이다
985333265220220404M50대경기도300이상500만원 미만비슷하다비슷할 것이다
995333277920220404M40대광주광역시300만원 미만감소한 편이다감소할 것이다