Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.3 KiB
Average record size in memory115.3 B

Variable types

Numeric10
Categorical1
Text2

Alerts

ctprvn_cd is highly overall correlated with signgu_cd and 2 other fieldsHigh correlation
signgu_cd is highly overall correlated with ctprvn_cd and 2 other fieldsHigh correlation
adstrd_cd is highly overall correlated with ctprvn_cd and 2 other fieldsHigh correlation
pblprfr_cas_co is highly overall correlated with dynmc_popltn_coHigh correlation
dynmc_popltn_co is highly overall correlated with pblprfr_cas_co and 2 other fieldsHigh correlation
pblprfr_cas_co_per_dynmc_popltn_co is highly overall correlated with dynmc_popltn_co and 1 other fieldsHigh correlation
dynmc_popltn_per_spnd_sm_price is highly overall correlated with dynmc_popltn_co and 1 other fieldsHigh correlation
ctprvn_nm is highly overall correlated with ctprvn_cd and 2 other fieldsHigh correlation
seq_no has unique valuesUnique
spnd_sm_price has unique valuesUnique
dynmc_popltn_co has unique valuesUnique
pblprfr_cas_co_per_dynmc_popltn_co has unique valuesUnique
dynmc_popltn_per_spnd_sm_price has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:11:30.310302
Analysis finished2023-12-10 10:11:49.518399
Duration19.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.04
Minimum1
Maximum1433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:49.632315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum1433
Range1432
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation238.33361
Coefficient of variation (CV)2.5616252
Kurtosis28.983988
Mean93.04
Median Absolute Deviation (MAD)25.5
Skewness5.4701221
Sum9304
Variance56802.907
MonotonicityNot monotonic
2023-12-10T19:11:49.854529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
1433 1
1.0%
1432 1
1.0%
1431 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%

base_ym
Real number (ℝ)

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201923.4
Minimum201805
Maximum202012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:50.090107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201805
5-th percentile201806.95
Q1201905
median201910
Q3202006.25
95-th percentile202011
Maximum202012
Range207
Interquartile range (IQR)101.25

Descriptive statistics

Standard deviation70.277373
Coefficient of variation (CV)0.00034803976
Kurtosis-0.94400772
Mean201923.4
Median Absolute Deviation (MAD)95
Skewness-0.20697608
Sum20192340
Variance4938.9091
MonotonicityNot monotonic
2023-12-10T19:11:50.328618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
201911 9
 
9.0%
201905 7
 
7.0%
201910 7
 
7.0%
202011 6
 
6.0%
202010 6
 
6.0%
202006 5
 
5.0%
201909 5
 
5.0%
201912 5
 
5.0%
201906 5
 
5.0%
201811 4
 
4.0%
Other values (16) 41
41.0%
ValueCountFrequency (%)
201805 2
2.0%
201806 3
3.0%
201807 1
 
1.0%
201808 2
2.0%
201809 2
2.0%
201810 3
3.0%
201811 4
4.0%
201812 1
 
1.0%
201903 2
2.0%
201904 4
4.0%
ValueCountFrequency (%)
202012 3
 
3.0%
202011 6
6.0%
202010 6
6.0%
202009 2
 
2.0%
202008 4
4.0%
202007 4
4.0%
202006 5
5.0%
202005 3
 
3.0%
201912 5
5.0%
201911 9
9.0%

ctprvn_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.48
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:50.537259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q122
median28.5
Q335
95-th percentile38
Maximum39
Range28
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.3490518
Coefficient of variation (CV)0.30382285
Kurtosis-0.72044807
Mean27.48
Median Absolute Deviation (MAD)6.5
Skewness-0.52366759
Sum2748
Variance69.706667
MonotonicityNot monotonic
2023-12-10T19:11:50.795282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
22 12
12.0%
36 12
12.0%
21 12
12.0%
11 11
11.0%
32 9
9.0%
35 8
8.0%
24 7
7.0%
31 4
 
4.0%
34 4
 
4.0%
38 4
 
4.0%
Other values (5) 17
17.0%
ValueCountFrequency (%)
11 11
11.0%
21 12
12.0%
22 12
12.0%
24 7
7.0%
25 4
 
4.0%
26 4
 
4.0%
31 4
 
4.0%
32 9
9.0%
33 3
 
3.0%
34 4
 
4.0%
ValueCountFrequency (%)
39 3
 
3.0%
38 4
 
4.0%
37 3
 
3.0%
36 12
12.0%
35 8
8.0%
34 4
 
4.0%
33 3
 
3.0%
32 9
9.0%
31 4
 
4.0%
26 4
 
4.0%

ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
대구광역시
12 
전라남도
12 
부산광역시
12 
서울특별시
11 
강원도
Other values (10)
44 

Length

Max length7
Median length6
Mean length4.46
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
대구광역시 12
12.0%
전라남도 12
12.0%
부산광역시 12
12.0%
서울특별시 11
11.0%
강원도 9
9.0%
전라북도 8
8.0%
광주광역시 7
7.0%
경기도 4
 
4.0%
충청남도 4
 
4.0%
경상남도 4
 
4.0%
Other values (5) 17
17.0%

Length

2023-12-10T19:11:51.072275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대구광역시 12
12.0%
전라남도 12
12.0%
부산광역시 12
12.0%
서울특별시 11
11.0%
강원도 9
9.0%
전라북도 8
8.0%
광주광역시 7
7.0%
경기도 4
 
4.0%
충청남도 4
 
4.0%
경상남도 4
 
4.0%
Other values (5) 17
17.0%

signgu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27590.29
Minimum11010
Maximum39020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:51.315767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11010
5-th percentile11118
Q122017.5
median28525.5
Q335040
95-th percentile38127.75
Maximum39020
Range28010
Interquartile range (IQR)13022.5

Descriptive statistics

Standard deviation8387.1279
Coefficient of variation (CV)0.30398839
Kurtosis-0.74529276
Mean27590.29
Median Absolute Deviation (MAD)6514.5
Skewness-0.51095187
Sum2759029
Variance70343915
MonotonicityNot monotonic
2023-12-10T19:11:51.618786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22050 6
 
6.0%
11140 3
 
3.0%
36030 3
 
3.0%
24040 3
 
3.0%
35040 3
 
3.0%
21080 3
 
3.0%
35011 3
 
3.0%
37050 2
 
2.0%
21090 2
 
2.0%
22030 2
 
2.0%
Other values (53) 70
70.0%
ValueCountFrequency (%)
11010 1
 
1.0%
11030 1
 
1.0%
11040 2
2.0%
11080 1
 
1.0%
11120 1
 
1.0%
11140 3
3.0%
11170 1
 
1.0%
11230 1
 
1.0%
21060 2
2.0%
21070 2
2.0%
ValueCountFrequency (%)
39020 2
2.0%
39010 1
1.0%
38370 2
2.0%
38115 1
1.0%
38080 1
1.0%
37050 2
2.0%
37020 1
1.0%
36420 1
1.0%
36400 1
1.0%
36390 1
1.0%
Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:11:52.037518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.94
Min length2

Characters and Unicode

Total characters294
Distinct characters66
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)29.0%

Sample

1st row수원시 장안구
2nd row북구
3rd row보은군
4th row용산구
5th row마포구
ValueCountFrequency (%)
북구 14
 
13.3%
중구 5
 
4.8%
서구 4
 
3.8%
동구 4
 
3.8%
남구 4
 
3.8%
마포구 3
 
2.9%
순천시 3
 
2.9%
정읍시 3
 
2.9%
전주시 3
 
2.9%
완산구 3
 
2.9%
Other values (45) 59
56.2%
2023-12-10T19:11:52.672787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
60
20.4%
32
 
10.9%
19
 
6.5%
15
 
5.1%
10
 
3.4%
8
 
2.7%
8
 
2.7%
7
 
2.4%
6
 
2.0%
6
 
2.0%
Other values (56) 123
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 289
98.3%
Space Separator 5
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
60
20.8%
32
 
11.1%
19
 
6.6%
15
 
5.2%
10
 
3.5%
8
 
2.8%
8
 
2.8%
7
 
2.4%
6
 
2.1%
6
 
2.1%
Other values (55) 118
40.8%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 289
98.3%
Common 5
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
60
20.8%
32
 
11.1%
19
 
6.6%
15
 
5.2%
10
 
3.5%
8
 
2.8%
8
 
2.8%
7
 
2.4%
6
 
2.1%
6
 
2.1%
Other values (55) 118
40.8%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 289
98.3%
ASCII 5
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
60
20.8%
32
 
11.1%
19
 
6.6%
15
 
5.2%
10
 
3.5%
8
 
2.8%
8
 
2.8%
7
 
2.4%
6
 
2.1%
6
 
2.1%
Other values (55) 118
40.8%
ASCII
ValueCountFrequency (%)
5
100.0%

adstrd_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2759080.4
Minimum1101064
Maximum3902060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:52.931669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101064
5-th percentile1111851.4
Q12201811.2
median2852606
Q33504060
95-th percentile3812828
Maximum3902060
Range2800996
Interquartile range (IQR)1302248.8

Descriptive statistics

Standard deviation838703.16
Coefficient of variation (CV)0.30397924
Kurtosis-0.74526116
Mean2759080.4
Median Absolute Deviation (MAD)651454
Skewness-0.51096373
Sum2.7590804 × 108
Variance7.0342299 × 1011
MonotonicityNot monotonic
2023-12-10T19:11:53.227257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3504060 3
 
3.0%
3501175 3
 
3.0%
2404064 3
 
3.0%
2205052 3
 
3.0%
2205077 3
 
3.0%
2203066 2
 
2.0%
2106065 2
 
2.0%
2403054 2
 
2.0%
2107070 2
 
2.0%
3206051 2
 
2.0%
Other values (62) 75
75.0%
ValueCountFrequency (%)
1101064 1
1.0%
1103074 1
1.0%
1104055 1
1.0%
1104056 1
1.0%
1108058 1
1.0%
1112051 1
1.0%
1114068 1
1.0%
1114073 1
1.0%
1114078 1
1.0%
1117052 1
1.0%
ValueCountFrequency (%)
3902060 2
2.0%
3901064 1
1.0%
3837034 2
2.0%
3811554 1
1.0%
3808031 1
1.0%
3705070 1
1.0%
3705051 1
1.0%
3702063 1
1.0%
3642011 1
1.0%
3640011 1
1.0%
Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:11:53.679563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.3
Min length2

Characters and Unicode

Total characters330
Distinct characters96
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)49.0%

Sample

1st row정자1동
2nd row관음동
3rd row보은읍
4th row한남동
5th row성산2동
ValueCountFrequency (%)
풍남동 3
 
3.0%
문화동 3
 
3.0%
초산동 3
 
3.0%
칠성동 3
 
3.0%
관음동 3
 
3.0%
구포1동 2
 
2.0%
대흥동 2
 
2.0%
장흥읍 2
 
2.0%
사직동 2
 
2.0%
대연1동 2
 
2.0%
Other values (62) 75
75.0%
2023-12-10T19:11:54.436803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81
24.5%
1 19
 
5.8%
16
 
4.8%
14
 
4.2%
6
 
1.8%
5
 
1.5%
5
 
1.5%
5
 
1.5%
5
 
1.5%
5
 
1.5%
Other values (86) 169
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 303
91.8%
Decimal Number 25
 
7.6%
Other Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
81
26.7%
16
 
5.3%
14
 
4.6%
6
 
2.0%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
Other values (81) 156
51.5%
Decimal Number
ValueCountFrequency (%)
1 19
76.0%
2 3
 
12.0%
5 2
 
8.0%
3 1
 
4.0%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 303
91.8%
Common 27
 
8.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
81
26.7%
16
 
5.3%
14
 
4.6%
6
 
2.0%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
Other values (81) 156
51.5%
Common
ValueCountFrequency (%)
1 19
70.4%
2 3
 
11.1%
· 2
 
7.4%
5 2
 
7.4%
3 1
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 303
91.8%
ASCII 25
 
7.6%
None 2
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
81
26.7%
16
 
5.3%
14
 
4.6%
6
 
2.0%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
5
 
1.7%
Other values (81) 156
51.5%
ASCII
ValueCountFrequency (%)
1 19
76.0%
2 3
 
12.0%
5 2
 
8.0%
3 1
 
4.0%
None
ValueCountFrequency (%)
· 2
100.0%

spnd_sm_price
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7045359 × 109
Minimum1.7260136 × 108
Maximum4.6976547 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:54.714818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.7260136 × 108
5-th percentile2.3209641 × 108
Q11.0274736 × 109
median1.921796 × 109
Q34.3307486 × 109
95-th percentile1.1643095 × 1010
Maximum4.6976547 × 1010
Range4.6803945 × 1010
Interquartile range (IQR)3.3032749 × 109

Descriptive statistics

Standard deviation6.0178193 × 109
Coefficient of variation (CV)1.6244462
Kurtosis29.444272
Mean3.7045359 × 109
Median Absolute Deviation (MAD)1.2989466 × 109
Skewness4.8407158
Sum3.7045359 × 1011
Variance3.621415 × 1019
MonotonicityNot monotonic
2023-12-10T19:11:54.988000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1023958628 1
 
1.0%
1028645320 1
 
1.0%
2633771468 1
 
1.0%
314902195 1
 
1.0%
1511105515 1
 
1.0%
3665337581 1
 
1.0%
5022931196 1
 
1.0%
3078117857 1
 
1.0%
324462911 1
 
1.0%
13512704690 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
172601359 1
1.0%
202250499 1
1.0%
207348175 1
1.0%
215307336 1
1.0%
216611325 1
1.0%
232911418 1
1.0%
233946751 1
1.0%
260025851 1
1.0%
274243217 1
1.0%
314902195 1
1.0%
ValueCountFrequency (%)
46976546609 1
1.0%
28097876050 1
1.0%
20943582078 1
1.0%
13512704690 1
1.0%
13192505304 1
1.0%
11561546832 1
1.0%
9766483716 1
1.0%
8101877509 1
1.0%
7988468176 1
1.0%
7570947218 1
1.0%

pblprfr_cas_co
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.85
Minimum1
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:55.209544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile22.15
Maximum59
Range58
Interquartile range (IQR)5

Descriptive statistics

Standard deviation9.1091862
Coefficient of variation (CV)1.5571258
Kurtosis12.765079
Mean5.85
Median Absolute Deviation (MAD)1
Skewness3.1962101
Sum585
Variance82.977273
MonotonicityNot monotonic
2023-12-10T19:11:55.430446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 34
34.0%
2 24
24.0%
3 6
 
6.0%
4 5
 
5.0%
6 5
 
5.0%
5 4
 
4.0%
8 3
 
3.0%
7 3
 
3.0%
22 2
 
2.0%
20 2
 
2.0%
Other values (11) 12
 
12.0%
ValueCountFrequency (%)
1 34
34.0%
2 24
24.0%
3 6
 
6.0%
4 5
 
5.0%
5 4
 
4.0%
6 5
 
5.0%
7 3
 
3.0%
8 3
 
3.0%
9 1
 
1.0%
11 1
 
1.0%
ValueCountFrequency (%)
59 1
1.0%
38 1
1.0%
31 1
1.0%
28 1
1.0%
25 1
1.0%
22 2
2.0%
21 1
1.0%
20 2
2.0%
18 2
2.0%
16 1
1.0%

dynmc_popltn_co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean666706.09
Minimum3371
Maximum12146625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:55.691801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3371
5-th percentile16661
Q159999.5
median128059.5
Q3343209
95-th percentile4009715.7
Maximum12146625
Range12143254
Interquartile range (IQR)283209.5

Descriptive statistics

Standard deviation1822184.1
Coefficient of variation (CV)2.7331146
Kurtosis20.224429
Mean666706.09
Median Absolute Deviation (MAD)97662
Skewness4.333883
Sum66670609
Variance3.3203551 × 1012
MonotonicityNot monotonic
2023-12-10T19:11:55.996825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54474 1
 
1.0%
789018 1
 
1.0%
510455 1
 
1.0%
454344 1
 
1.0%
13623 1
 
1.0%
38925 1
 
1.0%
53152 1
 
1.0%
291006 1
 
1.0%
171815 1
 
1.0%
210693 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
3371 1
1.0%
11822 1
1.0%
13536 1
1.0%
13623 1
1.0%
16300 1
1.0%
16680 1
1.0%
19868 1
1.0%
20124 1
1.0%
21600 1
1.0%
22175 1
1.0%
ValueCountFrequency (%)
12146625 1
1.0%
7684338 1
1.0%
7228481 1
1.0%
6677776 1
1.0%
6109362 1
1.0%
3899208 1
1.0%
1944816 1
1.0%
1906520 1
1.0%
1357662 1
1.0%
1229232 1
1.0%

pblprfr_cas_co_per_dynmc_popltn_co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96026.94
Minimum914
Maximum834722
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:56.320066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum914
5-th percentile7958.75
Q123270.75
median61766
Q3107787.25
95-th percentile302976.9
Maximum834722
Range833808
Interquartile range (IQR)84516.5

Descriptive statistics

Standard deviation119108.33
Coefficient of variation (CV)1.2403637
Kurtosis15.039632
Mean96026.94
Median Absolute Deviation (MAD)39583.5
Skewness3.2230988
Sum9602694
Variance1.4186794 × 1010
MonotonicityNot monotonic
2023-12-10T19:11:56.572541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27237 1
 
1.0%
131503 1
 
1.0%
85075 1
 
1.0%
25241 1
 
1.0%
2724 1
 
1.0%
38925 1
 
1.0%
7593 1
 
1.0%
97002 1
 
1.0%
24545 1
 
1.0%
70231 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
914 1
1.0%
2724 1
1.0%
3371 1
1.0%
7156 1
1.0%
7593 1
1.0%
7978 1
1.0%
8340 1
1.0%
9033 1
1.0%
9343 1
1.0%
9934 1
1.0%
ValueCountFrequency (%)
834722 1
1.0%
476913 1
1.0%
379685 1
1.0%
349608 1
1.0%
325813 1
1.0%
301775 1
1.0%
285993 1
1.0%
284844 1
1.0%
247881 1
1.0%
244374 1
1.0%

dynmc_popltn_per_spnd_sm_price
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46418.99
Minimum190
Maximum969610
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:11:57.166010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum190
5-th percentile738.6
Q13251
median14503.5
Q344874.75
95-th percentile137725.7
Maximum969610
Range969420
Interquartile range (IQR)41623.75

Descriptive statistics

Standard deviation108574.24
Coefficient of variation (CV)2.3390048
Kurtosis53.934563
Mean46418.99
Median Absolute Deviation (MAD)13226
Skewness6.6688575
Sum4641899
Variance1.1788365 × 1010
MonotonicityNot monotonic
2023-12-10T19:11:57.439227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18797 1
 
1.0%
1304 1
 
1.0%
5160 1
 
1.0%
693 1
 
1.0%
110923 1
 
1.0%
94164 1
 
1.0%
94501 1
 
1.0%
10578 1
 
1.0%
1888 1
 
1.0%
64135 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
190 1
1.0%
364 1
1.0%
402 1
1.0%
616 1
1.0%
693 1
1.0%
741 1
1.0%
760 1
1.0%
866 1
1.0%
985 1
1.0%
1103 1
1.0%
ValueCountFrequency (%)
969610 1
1.0%
360483 1
1.0%
244564 1
1.0%
169025 1
1.0%
148189 1
1.0%
137175 1
1.0%
132962 1
1.0%
126164 1
1.0%
123725 1
1.0%
114080 1
1.0%

Interactions

2023-12-10T19:11:47.459212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:31.230481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:32.961372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:34.767402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:36.709871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:38.266986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:40.056106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:41.917208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:43.779140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:45.400100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:47.639124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:31.403603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:33.141390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:34.935733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:36.877153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:38.421647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:40.244229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:42.098322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:43.942952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:45.563347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:47.819990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:31.552202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:33.302207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:35.105507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:37.011222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:38.602331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:40.439760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:42.306633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:44.079055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:45.711130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:48.010120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:31.700559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:33.476139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:35.251163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:37.165197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:38.760546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:40.623927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:42.522220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:44.226267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:45.865639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:48.174331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:31.901595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:33.633244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:35.404849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:37.326061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:38.934961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:40.802419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:42.695462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:44.392420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:46.037725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:48.357014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:32.133946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:33.803404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:35.568807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:37.515440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:39.111113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:40.987993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:42.927062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:44.609426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:46.190388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:48.516250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:32.285853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:33.961514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:35.701534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:37.653616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:39.272324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:41.133976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:43.089109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:44.780884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:46.335685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:48.678588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:32.444167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:34.164803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:36.198919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:37.800920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:39.440689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:41.312558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:43.265069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:44.946528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:46.574743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:48.827827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:32.621227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:34.421215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:36.376959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:37.958604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:39.626940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:41.485158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:43.427049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:45.103256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:47.146948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:48.958274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:32.787997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:34.602442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:36.534154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:38.085483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:39.819608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:41.736788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:43.624801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:45.242617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:11:47.286379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:11:57.636940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nobase_ymctprvn_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nmspnd_sm_pricepblprfr_cas_codynmc_popltn_copblprfr_cas_co_per_dynmc_popltn_codynmc_popltn_per_spnd_sm_price
seq_no1.0000.3000.0000.0000.0000.7230.0000.5700.0000.6940.8040.2180.000
base_ym0.3001.0000.0940.0000.0730.2890.0730.3590.1840.4070.0830.0000.067
ctprvn_cd0.0000.0941.0001.0000.9970.9210.9971.0000.3200.0000.1480.0000.067
ctprvn_nm0.0000.0001.0001.0000.9880.9720.9881.0000.5170.0000.0000.3430.428
signgu_cd0.0000.0730.9970.9881.0000.9231.0001.0000.2250.0000.1730.0000.000
signgu_nm0.7230.2890.9210.9720.9231.0000.9231.0000.9530.3140.0000.0000.970
adstrd_cd0.0000.0730.9970.9881.0000.9231.0001.0000.2250.0000.1730.0000.000
adstrd_nm0.5700.3591.0001.0001.0001.0001.0001.0001.0000.7800.0000.0000.988
spnd_sm_price0.0000.1840.3200.5170.2250.9530.2251.0001.0000.6450.4780.4410.653
pblprfr_cas_co0.6940.4070.0000.0000.0000.3140.0000.7800.6451.0000.8860.2130.000
dynmc_popltn_co0.8040.0830.1480.0000.1730.0000.1730.0000.4780.8861.0000.4810.000
pblprfr_cas_co_per_dynmc_popltn_co0.2180.0000.0000.3430.0000.0000.0000.0000.4410.2130.4811.0000.000
dynmc_popltn_per_spnd_sm_price0.0000.0670.0670.4280.0000.9700.0000.9880.6530.0000.0000.0001.000
2023-12-10T19:11:57.920968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nobase_ymctprvn_cdsigngu_cdadstrd_cdspnd_sm_pricepblprfr_cas_codynmc_popltn_copblprfr_cas_co_per_dynmc_popltn_codynmc_popltn_per_spnd_sm_pricectprvn_nm
seq_no1.0000.076-0.076-0.072-0.069-0.0030.089-0.041-0.1370.0200.000
base_ym0.0761.0000.1150.1270.127-0.0890.008-0.090-0.1380.0330.000
ctprvn_cd-0.0760.1151.0000.9960.996-0.201-0.034-0.048-0.072-0.0470.956
signgu_cd-0.0720.1270.9961.0001.000-0.212-0.048-0.057-0.076-0.0370.911
adstrd_cd-0.0690.1270.9961.0001.000-0.212-0.048-0.056-0.074-0.0380.911
spnd_sm_price-0.003-0.089-0.201-0.212-0.2121.0000.1010.1150.1270.4920.257
pblprfr_cas_co0.0890.008-0.034-0.048-0.0480.1011.0000.538-0.095-0.4280.000
dynmc_popltn_co-0.041-0.090-0.048-0.057-0.0560.1150.5381.0000.731-0.7560.000
pblprfr_cas_co_per_dynmc_popltn_co-0.137-0.138-0.072-0.076-0.0740.127-0.0950.7311.000-0.5450.148
dynmc_popltn_per_spnd_sm_price0.0200.033-0.047-0.037-0.0380.492-0.428-0.756-0.5451.0000.176
ctprvn_nm0.0000.0000.9560.9110.9110.2570.0000.0000.1480.1761.000

Missing values

2023-12-10T19:11:49.160140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:11:49.394201image/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

seq_nobase_ymctprvn_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nmspnd_sm_pricepblprfr_cas_codynmc_popltn_copblprfr_cas_co_per_dynmc_popltn_codynmc_popltn_per_spnd_sm_price
0120191231경기도31011수원시 장안구3101156정자1동10239586282544742723718797
1143120180622대구광역시22050북구2205077관음동54543712661357662226277402
2320190433충청북도33320보은군3332011보은읍181033602914769134769133796
3420181111서울특별시11030용산구1103074한남동2094358207812160021600969610
4520190611서울특별시11140마포구1114073성산2동1826980610222012491490786
5620180836전라남도36030순천시3603051향동2742432174232428581071180
6720201022대구광역시22010중구2201056성내1동578157567986677776834722866
7143220180711서울특별시11010종로구1101064이화동44207961545912146625205875364
8920191236전라남도36330구례군3633011구례읍10829203611957789577811307
91020190539제주특별자치도39020서귀포시3902060대천동1833934573219868993492306
seq_nobase_ymctprvn_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nmspnd_sm_pricepblprfr_cas_codynmc_popltn_copblprfr_cas_co_per_dynmc_popltn_codynmc_popltn_per_spnd_sm_price
909120200721부산광역시21080북구2108057덕천2동5534995736114298943908112875
919220201035전라북도35380부안군3538011부안읍133363716912225600188005912
929320201022대구광역시22030서구2203066상중이동648171548216680834038859
939420200636전라남도36380장흥군3638011장흥읍131282962121273066365310312
949520191137경상북도37050구미시3705070인동동468562609023415817079137175
959620180921부산광역시21110금정구2111059부곡3동18001425581221752217581179
969720191033충청북도33320보은군3332011보은읍23325810861864058640526996
979820180637경상북도37020경주시3702063황성동180809275471248511783514482
989920200711서울특별시11170구로구1117052구로1동1319250530465491209152024025
9910020200611서울특별시11040성동구1104056행당1동29524527671941809418031349