Overview

Dataset statistics

Number of variables8
Number of observations276
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.9 KiB
Average record size in memory66.5 B

Variable types

Numeric2
Categorical6

Alerts

unit_nm has constant value ""Constant
age_area_detl_type_nm is highly overall correlated with age_byrg_type_nmHigh correlation
age_byrg_type_nm is highly overall correlated with age_area_detl_type_nmHigh correlation
esntl_no is highly overall correlated with pntm_hftm_nm and 1 other fieldsHigh correlation
rspns_rate is highly overall correlated with plan_yn_nmHigh correlation
pntm_hftm_nm is highly overall correlated with esntl_noHigh correlation
pchs_kwa_sell_plan_cont is highly overall correlated with esntl_noHigh correlation
plan_yn_nm is highly overall correlated with rspns_rateHigh correlation
esntl_no has unique valuesUnique

Reproduction

Analysis started2023-12-11 22:31:42.098011
Analysis finished2023-12-11 22:31:44.337649
Duration2.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

esntl_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.5
Minimum1
Maximum276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T07:31:44.404571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.75
Q169.75
median138.5
Q3207.25
95-th percentile262.25
Maximum276
Range275
Interquartile range (IQR)137.5

Descriptive statistics

Standard deviation79.818544
Coefficient of variation (CV)0.57630718
Kurtosis-1.2
Mean138.5
Median Absolute Deviation (MAD)69
Skewness0
Sum38226
Variance6371
MonotonicityStrictly increasing
2023-12-12T07:31:44.518754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
184 1
 
0.4%
190 1
 
0.4%
189 1
 
0.4%
188 1
 
0.4%
187 1
 
0.4%
186 1
 
0.4%
185 1
 
0.4%
183 1
 
0.4%
175 1
 
0.4%
Other values (266) 266
96.4%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
276 1
0.4%
275 1
0.4%
274 1
0.4%
273 1
0.4%
272 1
0.4%
271 1
0.4%
270 1
0.4%
269 1
0.4%
268 1
0.4%
267 1
0.4%

pntm_hftm_nm
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
20년하반기
44 
21년상반기
44 
21년하반기
44 
22년상반기
44 
22년하반기
44 
Other values (2)
56 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20년상반기
2nd row20년상반기
3rd row20년상반기
4th row20년상반기
5th row20년상반기

Common Values

ValueCountFrequency (%)
20년하반기 44
15.9%
21년상반기 44
15.9%
21년하반기 44
15.9%
22년상반기 44
15.9%
22년하반기 44
15.9%
23년상반기 44
15.9%
20년상반기 12
 
4.3%

Length

2023-12-12T07:31:44.751951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:31:44.997042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20년하반기 44
15.9%
21년상반기 44
15.9%
21년하반기 44
15.9%
22년상반기 44
15.9%
22년하반기 44
15.9%
23년상반기 44
15.9%
20년상반기 12
 
4.3%

pchs_kwa_sell_plan_cont
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
매입
144 
매도
132 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
매입 144
52.2%
매도 132
47.8%

Length

2023-12-12T07:31:45.271475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:31:45.480071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
매입 144
52.2%
매도 132
47.8%

age_byrg_type_nm
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
연령별
130 
거주지역별
120 
전체
26 

Length

Max length5
Median length3
Mean length3.7753623
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전체
2nd row전체
3rd row연령별
4th row연령별
5th row연령별

Common Values

ValueCountFrequency (%)
연령별 130
47.1%
거주지역별 120
43.5%
전체 26
 
9.4%

Length

2023-12-12T07:31:45.745690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:31:45.886484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연령별 130
47.1%
거주지역별 120
43.5%
전체 26
 
9.4%

age_area_detl_type_nm
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
전체
26 
20대 이하
26 
30대
26 
40대
26 
50대
26 
Other values (6)
146 

Length

Max length6
Median length3
Mean length3.1231884
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 26
9.4%
20대 이하 26
9.4%
30대 26
9.4%
40대 26
9.4%
50대 26
9.4%
60대 이상 26
9.4%
서울 24
8.7%
경기 24
8.7%
인천 24
8.7%
광역시 24
8.7%

Length

2023-12-12T07:31:46.016564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 26
 
7.9%
20대 26
 
7.9%
이하 26
 
7.9%
30대 26
 
7.9%
40대 26
 
7.9%
50대 26
 
7.9%
60대 26
 
7.9%
이상 26
 
7.9%
서울 24
 
7.3%
경기 24
 
7.3%
Other values (3) 72
22.0%

plan_yn_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
있다
138 
없다
138 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row있다
2nd row없다
3rd row있다
4th row있다
5th row있다

Common Values

ValueCountFrequency (%)
있다 138
50.0%
없다 138
50.0%

Length

2023-12-12T07:31:46.140072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:31:46.215701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
있다 138
50.0%
없다 138
50.0%

rspns_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.01087
Minimum14
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2023-12-12T07:31:46.306973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile28
Q136
median50
Q364
95-th percentile72
Maximum86
Range72
Interquartile range (IQR)28

Descriptive statistics

Standard deviation15.617818
Coefficient of variation (CV)0.31228848
Kurtosis-1.2427528
Mean50.01087
Median Absolute Deviation (MAD)14
Skewness-0.0011483533
Sum13803
Variance243.91625
MonotonicityNot monotonic
2023-12-12T07:31:46.421124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 12
 
4.3%
31 12
 
4.3%
66 11
 
4.0%
36 10
 
3.6%
64 10
 
3.6%
34 10
 
3.6%
62 9
 
3.3%
38 9
 
3.3%
35 8
 
2.9%
60 7
 
2.5%
Other values (49) 178
64.5%
ValueCountFrequency (%)
14 1
 
0.4%
18 1
 
0.4%
19 1
 
0.4%
23 1
 
0.4%
24 2
 
0.7%
25 1
 
0.4%
27 4
1.4%
28 4
1.4%
29 7
2.5%
30 6
2.2%
ValueCountFrequency (%)
86 1
 
0.4%
82 1
 
0.4%
81 1
 
0.4%
77 1
 
0.4%
76 2
 
0.7%
75 1
 
0.4%
73 4
1.4%
72 4
1.4%
71 7
2.5%
70 6
2.2%

unit_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
%
276 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row%
2nd row%
3rd row%
4th row%
5th row%

Common Values

ValueCountFrequency (%)
% 276
100.0%

Length

2023-12-12T07:31:46.530241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:31:46.599494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
276
100.0%

Interactions

2023-12-12T07:31:43.983204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:43.625650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:44.060118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:31:43.745033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:31:46.650258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
esntl_nopntm_hftm_nmpchs_kwa_sell_plan_contage_byrg_type_nmage_area_detl_type_nmplan_yn_nmrspns_rate
esntl_no1.0000.9360.6570.1200.0000.0000.506
pntm_hftm_nm0.9361.0000.1330.0000.0000.0000.416
pchs_kwa_sell_plan_cont0.6570.1331.0000.0000.0000.0000.273
age_byrg_type_nm0.1200.0000.0001.0001.0000.0000.033
age_area_detl_type_nm0.0000.0000.0001.0001.0000.0000.000
plan_yn_nm0.0000.0000.0000.0000.0001.0000.718
rspns_rate0.5060.4160.2730.0330.0000.7181.000
2023-12-12T07:31:46.758857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
plan_yn_nmage_area_detl_type_nmpchs_kwa_sell_plan_contpntm_hftm_nmage_byrg_type_nm
plan_yn_nm1.0000.0000.0000.0000.000
age_area_detl_type_nm0.0001.0000.0000.0000.985
pchs_kwa_sell_plan_cont0.0000.0001.0000.1410.000
pntm_hftm_nm0.0000.0000.1411.0000.000
age_byrg_type_nm0.0000.9850.0000.0001.000
2023-12-12T07:31:46.858416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
esntl_norspns_ratepntm_hftm_nmpchs_kwa_sell_plan_contage_byrg_type_nmage_area_detl_type_nmplan_yn_nm
esntl_no1.000-0.0140.8330.5110.0860.0000.000
rspns_rate-0.0141.0000.2240.2060.0160.0000.553
pntm_hftm_nm0.8330.2241.0000.1410.0000.0000.000
pchs_kwa_sell_plan_cont0.5110.2060.1411.0000.0000.0000.000
age_byrg_type_nm0.0860.0160.0000.0001.0000.9850.000
age_area_detl_type_nm0.0000.0000.0000.0000.9851.0000.000
plan_yn_nm0.0000.5530.0000.0000.0000.0001.000

Missing values

2023-12-12T07:31:44.176614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:31:44.288728image/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

esntl_nopntm_hftm_nmpchs_kwa_sell_plan_contage_byrg_type_nmage_area_detl_type_nmplan_yn_nmrspns_rateunit_nm
0120년상반기매입전체전체있다71%
1220년상반기매입전체전체없다29%
2320년상반기매입연령별20대 이하있다45%
3420년상반기매입연령별30대있다69%
4520년상반기매입연령별40대있다72%
5620년상반기매입연령별50대있다75%
6720년상반기매입연령별60대 이상있다81%
7820년상반기매입연령별20대 이하없다55%
8920년상반기매입연령별30대없다31%
91020년상반기매입연령별40대없다28%
esntl_nopntm_hftm_nmpchs_kwa_sell_plan_contage_byrg_type_nmage_area_detl_type_nmplan_yn_nmrspns_rateunit_nm
26626723년상반기매도거주지역별서울있다35%
26726823년상반기매도거주지역별경기있다42%
26826923년상반기매도거주지역별인천있다50%
26927023년상반기매도거주지역별광역시있다38%
27027123년상반기매도거주지역별지방있다41%
27127223년상반기매도거주지역별서울없다66%
27227323년상반기매도거주지역별경기없다58%
27327423년상반기매도거주지역별인천없다51%
27427523년상반기매도거주지역별광역시없다62%
27527623년상반기매도거주지역별지방없다59%