Overview

Dataset statistics

Number of variables9
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory75.3 B

Variable types

Numeric1
Categorical8

Alerts

examin_begin_de has constant value ""Constant
respond_id has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:43:55.969284
Analysis finished2023-12-10 09:43:57.861476
Duration1.89 second
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%
Mean318657.95
Minimum4065
Maximum1146802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:57.992348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4065
5-th percentile30540.2
Q1219295.75
median340890
Q3409642.5
95-th percentile444638.25
Maximum1146802
Range1142737
Interquartile range (IQR)190346.75

Descriptive statistics

Standard deviation188980.78
Coefficient of variation (CV)0.59305216
Kurtosis6.722029
Mean318657.95
Median Absolute Deviation (MAD)80568
Skewness1.6487939
Sum31865795
Variance3.5713737 × 1010
MonotonicityNot monotonic
2023-12-10T18:43:58.309855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4065 1
 
1.0%
382667 1
 
1.0%
409280 1
 
1.0%
409165 1
 
1.0%
405917 1
 
1.0%
403174 1
 
1.0%
398652 1
 
1.0%
397295 1
 
1.0%
396550 1
 
1.0%
394487 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
4065 1
1.0%
9357 1
1.0%
12856 1
1.0%
18328 1
1.0%
27618 1
1.0%
30694 1
1.0%
37619 1
1.0%
47792 1
1.0%
50946 1
1.0%
69355 1
1.0%
ValueCountFrequency (%)
1146802 1
1.0%
1081578 1
1.0%
1034791 1
1.0%
444789 1
1.0%
444643 1
1.0%
444638 1
1.0%
444145 1
1.0%
443562 1
1.0%
443086 1
1.0%
442690 1
1.0%

examin_begin_de
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20211103
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20211103
2nd row20211103
3rd row20211103
4th row20211103
5th row20211103

Common Values

ValueCountFrequency (%)
20211103 100
100.0%

Length

2023-12-10T18:43:58.590487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:58.768515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20211103 100
100.0%

sexdstn_flag_cd
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
F
53 
M
47 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
F 53
53.0%
M 47
47.0%

Length

2023-12-10T18:43:59.004076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:59.184297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
f 53
53.0%
m 47
47.0%

agrde_flag_nm
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20대
34 
30대
28 
50대
18 
40대
12 
60대
8 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30대
2nd row20대
3rd row60대
4th row40대
5th row30대

Common Values

ValueCountFrequency (%)
20대 34
34.0%
30대 28
28.0%
50대 18
18.0%
40대 12
 
12.0%
60대 8
 
8.0%

Length

2023-12-10T18:43:59.403884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:59.619131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20대 34
34.0%
30대 28
28.0%
50대 18
18.0%
40대 12
 
12.0%
60대 8
 
8.0%

answrr_oc_area_nm
Categorical

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
25 
경기도
21 
인천광역시
10 
대구광역시
7 
부산광역시
5 
Other values (12)
32 

Length

Max length7
Median length5
Mean length4.38
Min length3

Unique

Unique4 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 25
25.0%
경기도 21
21.0%
인천광역시 10
 
10.0%
대구광역시 7
 
7.0%
부산광역시 5
 
5.0%
전라북도 5
 
5.0%
경상북도 5
 
5.0%
충청북도 5
 
5.0%
대전광역시 4
 
4.0%
충청남도 3
 
3.0%
Other values (7) 10
 
10.0%

Length

2023-12-10T18:44:00.383193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 25
25.0%
경기도 21
21.0%
인천광역시 10
 
10.0%
대구광역시 7
 
7.0%
부산광역시 5
 
5.0%
전라북도 5
 
5.0%
경상북도 5
 
5.0%
충청북도 5
 
5.0%
대전광역시 4
 
4.0%
충청남도 3
 
3.0%
Other values (7) 10
 
10.0%

hshld_income_dgree_nm
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
200만원이하
43 
모름/무응답
27 
200만원초과400만원이하
22 
400만원초과700만원이하
8 

Length

Max length14
Median length7
Mean length8.83
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row400만원초과700만원이하
2nd row모름/무응답
3rd row200만원이하
4th row200만원초과400만원이하
5th row200만원초과400만원이하

Common Values

ValueCountFrequency (%)
200만원이하 43
43.0%
모름/무응답 27
27.0%
200만원초과400만원이하 22
22.0%
400만원초과700만원이하 8
 
8.0%

Length

2023-12-10T18:44:00.667456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:00.989609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
200만원이하 43
43.0%
모름/무응답 27
27.0%
200만원초과400만원이하 22
22.0%
400만원초과700만원이하 8
 
8.0%

labor_empmn_stle_nm
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
정규직근로자
59 
비정규직/일용직근로자
15 
무직/퇴직
14 
사업자
 
5
학생
 
4

Length

Max length11
Median length6
Mean length6.24
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사업자
2nd row전업주부
3rd row전업주부
4th row정규직근로자
5th row정규직근로자

Common Values

ValueCountFrequency (%)
정규직근로자 59
59.0%
비정규직/일용직근로자 15
 
15.0%
무직/퇴직 14
 
14.0%
사업자 5
 
5.0%
학생 4
 
4.0%
전업주부 3
 
3.0%

Length

2023-12-10T18:44:01.215150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:01.427713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정규직근로자 59
59.0%
비정규직/일용직근로자 15
 
15.0%
무직/퇴직 14
 
14.0%
사업자 5
 
5.0%
학생 4
 
4.0%
전업주부 3
 
3.0%

indvdl_ecnm_crstat_recog_value
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
보통/그저 그렇다
36 
약간 좋지 않다
33 
매우 좋지 않다
27 
약간 좋다
4 

Length

Max length9
Median length8
Mean length8.24
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row매우 좋지 않다
2nd row매우 좋지 않다
3rd row약간 좋지 않다
4th row보통/그저 그렇다
5th row약간 좋지 않다

Common Values

ValueCountFrequency (%)
보통/그저 그렇다 36
36.0%
약간 좋지 않다 33
33.0%
매우 좋지 않다 27
27.0%
약간 좋다 4
 
4.0%

Length

2023-12-10T18:44:01.824265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:02.061341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
좋지 60
23.1%
않다 60
23.1%
약간 37
14.2%
보통/그저 36
13.8%
그렇다 36
13.8%
매우 27
10.4%
좋다 4
 
1.5%

nation_ecnm_crstat_recog_value
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
약간 좋지 않다
43 
매우 좋지 않다
22 
보통/그저 그렇다
22 
약간 좋다
13 

Length

Max length9
Median length8
Mean length7.83
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row매우 좋지 않다
2nd row매우 좋지 않다
3rd row매우 좋지 않다
4th row약간 좋다
5th row약간 좋지 않다

Common Values

ValueCountFrequency (%)
약간 좋지 않다 43
43.0%
매우 좋지 않다 22
22.0%
보통/그저 그렇다 22
22.0%
약간 좋다 13
 
13.0%

Length

2023-12-10T18:44:02.321544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:44:02.555442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
좋지 65
24.5%
않다 65
24.5%
약간 56
21.1%
매우 22
 
8.3%
보통/그저 22
 
8.3%
그렇다 22
 
8.3%
좋다 13
 
4.9%

Interactions

2023-12-10T18:43:57.145434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:44:02.704263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
respond_idsexdstn_flag_cdagrde_flag_nmanswrr_oc_area_nmhshld_income_dgree_nmlabor_empmn_stle_nmindvdl_ecnm_crstat_recog_valuenation_ecnm_crstat_recog_value
respond_id1.0000.1380.5370.0000.0740.0000.0070.028
sexdstn_flag_cd0.1381.0000.2260.0000.0000.1930.0000.000
agrde_flag_nm0.5370.2261.0000.2060.1860.2660.0830.276
answrr_oc_area_nm0.0000.0000.2061.0000.1960.2390.4050.000
hshld_income_dgree_nm0.0740.0000.1860.1961.0000.5060.3880.200
labor_empmn_stle_nm0.0000.1930.2660.2390.5061.0000.2600.117
indvdl_ecnm_crstat_recog_value0.0070.0000.0830.4050.3880.2601.0000.717
nation_ecnm_crstat_recog_value0.0280.0000.2760.0000.2000.1170.7171.000
2023-12-10T18:44:02.933858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
nation_ecnm_crstat_recog_valueagrde_flag_nmhshld_income_dgree_nmindvdl_ecnm_crstat_recog_valueanswrr_oc_area_nmsexdstn_flag_cdlabor_empmn_stle_nm
nation_ecnm_crstat_recog_value1.0000.2260.0780.3570.0000.0000.071
agrde_flag_nm0.2261.0000.1500.0640.0920.2720.181
hshld_income_dgree_nm0.0780.1501.0000.1580.0940.0000.346
indvdl_ecnm_crstat_recog_value0.3570.0640.1581.0000.2160.0000.167
answrr_oc_area_nm0.0000.0920.0940.2161.0000.0000.099
sexdstn_flag_cd0.0000.2720.0000.0000.0001.0000.134
labor_empmn_stle_nm0.0710.1810.3460.1670.0990.1341.000
2023-12-10T18:44:03.143054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
respond_idsexdstn_flag_cdagrde_flag_nmanswrr_oc_area_nmhshld_income_dgree_nmlabor_empmn_stle_nmindvdl_ecnm_crstat_recog_valuenation_ecnm_crstat_recog_value
respond_id1.0000.1700.2210.0000.0570.0000.0000.000
sexdstn_flag_cd0.1701.0000.2720.0000.0000.1340.0000.000
agrde_flag_nm0.2210.2721.0000.0920.1500.1810.0640.226
answrr_oc_area_nm0.0000.0000.0921.0000.0940.0990.2160.000
hshld_income_dgree_nm0.0570.0000.1500.0941.0000.3460.1580.078
labor_empmn_stle_nm0.0000.1340.1810.0990.3461.0000.1670.071
indvdl_ecnm_crstat_recog_value0.0000.0000.0640.2160.1580.1671.0000.357
nation_ecnm_crstat_recog_value0.0000.0000.2260.0000.0780.0710.3571.000

Missing values

2023-12-10T18:43:57.445547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:43:57.732799image/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_nmlabor_empmn_stle_nmindvdl_ecnm_crstat_recog_valuenation_ecnm_crstat_recog_value
0406520211103M30대부산광역시400만원초과700만원이하사업자매우 좋지 않다매우 좋지 않다
142049420211103F20대대전광역시모름/무응답전업주부매우 좋지 않다매우 좋지 않다
2935720211103F60대서울특별시200만원이하전업주부약간 좋지 않다매우 좋지 않다
31285620211103M40대서울특별시200만원초과400만원이하정규직근로자보통/그저 그렇다약간 좋다
41832820211103M30대경기도200만원초과400만원이하정규직근로자약간 좋지 않다약간 좋지 않다
52761820211103M50대대전광역시200만원이하정규직근로자보통/그저 그렇다보통/그저 그렇다
63069420211103F30대서울특별시200만원이하정규직근로자약간 좋지 않다약간 좋지 않다
736567320211103M20대서울특별시200만원이하무직/퇴직매우 좋지 않다매우 좋지 않다
83761920211103F60대인천광역시400만원초과700만원이하정규직근로자보통/그저 그렇다약간 좋지 않다
94779220211103M40대부산광역시모름/무응답정규직근로자보통/그저 그렇다보통/그저 그렇다
respond_idexamin_begin_desexdstn_flag_cdagrde_flag_nmanswrr_oc_area_nmhshld_income_dgree_nmlabor_empmn_stle_nmindvdl_ecnm_crstat_recog_valuenation_ecnm_crstat_recog_value
9044269020211103M20대대구광역시200만원초과400만원이하정규직근로자약간 좋다약간 좋다
9144308620211103M20대경기도200만원이하정규직근로자약간 좋지 않다약간 좋지 않다
9244356220211103F20대대전광역시모름/무응답무직/퇴직보통/그저 그렇다보통/그저 그렇다
9344414520211103F20대경기도200만원이하정규직근로자보통/그저 그렇다약간 좋지 않다
9444463820211103F20대경상북도모름/무응답비정규직/일용직근로자보통/그저 그렇다보통/그저 그렇다
9544464320211103M20대대구광역시모름/무응답학생약간 좋지 않다매우 좋지 않다
9644478920211103M20대서울특별시200만원이하정규직근로자약간 좋지 않다약간 좋지 않다
97103479120211103F30대서울특별시200만원이하정규직근로자매우 좋지 않다약간 좋지 않다
98108157820211103F30대서울특별시200만원초과400만원이하정규직근로자보통/그저 그렇다약간 좋지 않다
99114680220211103F30대경기도200만원초과400만원이하정규직근로자보통/그저 그렇다약간 좋지 않다