Overview

Dataset statistics

Number of variables14
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.7%
Total size in memory18.1 KiB
Average record size in memory123.9 B

Variable types

Categorical6
Numeric8

Dataset

DescriptionSample
Author고려대학교 세종산학협력단
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KRUHSHLD000000000001

Alerts

STDYY has constant value ""Constant
FOUR_GEN_HSHLD_CO has constant value ""Constant
ATPT_NM has constant value ""Constant
SIGNGU_NM has constant value ""Constant
Dataset has 1 (0.7%) duplicate rowsDuplicates
ADSTRD_NM is highly overall correlated with ADSTRD_CODE and 1 other fieldsHigh correlation
CSOPAR_CODE is highly overall correlated with ADSTRD_CODE and 1 other fieldsHigh correlation
ONE_PERSON_HSHLD_CO is highly overall correlated with TOT_HSHLD_CO and 1 other fieldsHigh correlation
ADSTRD_CODE is highly overall correlated with CSOPAR_CODE and 1 other fieldsHigh correlation
TOT_HSHLD_CO is highly overall correlated with ONE_PERSON_HSHLD_CO and 2 other fieldsHigh correlation
AVRG_MBHS_CO is highly overall correlated with TWO_GEN_HSHLD_COHigh correlation
ONE_GEN_HSHLD_CO is highly overall correlated with ONE_PERSON_HSHLD_CO and 1 other fieldsHigh correlation
TWO_GEN_HSHLD_CO is highly overall correlated with TOT_HSHLD_CO and 1 other fieldsHigh correlation
THREE_GEN_HSHLD_CO has 20 (13.3%) zerosZeros
ONE_PERSON_HSHLD_CO has 10 (6.7%) zerosZeros
NON_BLD_HSHLD_CO has 118 (78.7%) zerosZeros
TOT_HSHLD_CO has 8 (5.3%) zerosZeros
AVRG_MBHS_CO has 8 (5.3%) zerosZeros
ONE_GEN_HSHLD_CO has 11 (7.3%) zerosZeros
TWO_GEN_HSHLD_CO has 11 (7.3%) zerosZeros

Reproduction

Analysis started2023-12-10 06:13:40.059709
Analysis finished2023-12-10 06:13:51.339887
Duration11.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDYY
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2015
150 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 150
100.0%

Length

2023-12-10T15:13:51.429013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:13:51.583772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 150
100.0%

CSOPAR_CODE
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1101060000000
99 
1101070000000
27 
1101050000000
24 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1101070000000
2nd row1101070000000
3rd row1101070000000
4th row1101070000000
5th row1101070000000

Common Values

ValueCountFrequency (%)
1101060000000 99
66.0%
1101070000000 27
 
18.0%
1101050000000 24
 
16.0%

Length

2023-12-10T15:13:51.728265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:13:51.886909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1101060000000 99
66.0%
1101070000000 27
 
18.0%
1101050000000 24
 
16.0%

THREE_GEN_HSHLD_CO
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.733333
Minimum0
Maximum23
Zeros20
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:13:52.044902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median11
Q315
95-th percentile19
Maximum23
Range23
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.6123864
Coefficient of variation (CV)0.52289315
Kurtosis-0.15731296
Mean10.733333
Median Absolute Deviation (MAD)4
Skewness-0.40064076
Sum1610
Variance31.498881
MonotonicityNot monotonic
2023-12-10T15:13:52.211156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 20
13.3%
15 18
12.0%
11 15
10.0%
9 12
8.0%
14 11
 
7.3%
8 10
 
6.7%
10 10
 
6.7%
7 9
 
6.0%
13 8
 
5.3%
12 8
 
5.3%
Other values (9) 29
19.3%
ValueCountFrequency (%)
0 20
13.3%
5 1
 
0.7%
6 4
 
2.7%
7 9
6.0%
8 10
6.7%
9 12
8.0%
10 10
6.7%
11 15
10.0%
12 8
 
5.3%
13 8
 
5.3%
ValueCountFrequency (%)
23 3
 
2.0%
21 1
 
0.7%
20 3
 
2.0%
19 2
 
1.3%
18 4
 
2.7%
17 5
 
3.3%
16 6
 
4.0%
15 18
12.0%
14 11
7.3%
13 8
5.3%

FOUR_GEN_HSHLD_CO
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0
150 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 150
100.0%

Length

2023-12-10T15:13:52.398829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:13:52.546426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 150
100.0%

ONE_PERSON_HSHLD_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.626667
Minimum0
Maximum178
Zeros10
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:13:52.705245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124.5
median39.5
Q357.75
95-th percentile96.75
Maximum178
Range178
Interquartile range (IQR)33.25

Descriptive statistics

Standard deviation29.821065
Coefficient of variation (CV)0.68355131
Kurtosis3.0942466
Mean43.626667
Median Absolute Deviation (MAD)16.5
Skewness1.2163927
Sum6544
Variance889.29593
MonotonicityNot monotonic
2023-12-10T15:13:52.968457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
6.7%
32 6
 
4.0%
8 5
 
3.3%
30 4
 
2.7%
48 4
 
2.7%
31 4
 
2.7%
24 4
 
2.7%
47 4
 
2.7%
42 4
 
2.7%
37 3
 
2.0%
Other values (62) 102
68.0%
ValueCountFrequency (%)
0 10
6.7%
5 1
 
0.7%
8 5
3.3%
9 1
 
0.7%
10 2
 
1.3%
11 1
 
0.7%
12 3
 
2.0%
13 3
 
2.0%
14 1
 
0.7%
16 1
 
0.7%
ValueCountFrequency (%)
178 1
0.7%
161 1
0.7%
109 1
0.7%
107 1
0.7%
103 1
0.7%
101 1
0.7%
99 2
1.3%
94 1
0.7%
92 2
1.3%
90 2
1.3%

NON_BLD_HSHLD_CO
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3266667
Minimum0
Maximum13
Zeros118
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:13:53.167011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6785167
Coefficient of variation (CV)2.0189825
Kurtosis2.6118191
Mean1.3266667
Median Absolute Deviation (MAD)0
Skewness1.8512922
Sum199
Variance7.1744519
MonotonicityNot monotonic
2023-12-10T15:13:53.353764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 118
78.7%
5 15
 
10.0%
6 7
 
4.7%
7 5
 
3.3%
8 3
 
2.0%
13 1
 
0.7%
10 1
 
0.7%
ValueCountFrequency (%)
0 118
78.7%
5 15
 
10.0%
6 7
 
4.7%
7 5
 
3.3%
8 3
 
2.0%
10 1
 
0.7%
13 1
 
0.7%
ValueCountFrequency (%)
13 1
 
0.7%
10 1
 
0.7%
8 3
 
2.0%
7 5
 
3.3%
6 7
 
4.7%
5 15
 
10.0%
0 118
78.7%

ADSTRD_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1110552 × 109
Minimum1.1110515 × 109
Maximum1.11106 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:13:53.529815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110515 × 109
5-th percentile1.1110515 × 109
Q11.111053 × 109
median1.111056 × 109
Q31.111057 × 109
95-th percentile1.111058 × 109
Maximum1.11106 × 109
Range8500
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation2374.8896
Coefficient of variation (CV)2.1375083 × 10-6
Kurtosis-0.84573224
Mean1.1110552 × 109
Median Absolute Deviation (MAD)2000
Skewness-0.1083188
Sum1.6665827 × 1011
Variance5640100.7
MonotonicityIncreasing
2023-12-10T15:13:53.779134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1111056000 36
24.0%
1111051500 27
18.0%
1111055000 21
14.0%
1111058000 21
14.0%
1111053000 18
12.0%
1111057000 15
10.0%
1111054000 6
 
4.0%
1111060000 6
 
4.0%
ValueCountFrequency (%)
1111051500 27
18.0%
1111053000 18
12.0%
1111054000 6
 
4.0%
1111055000 21
14.0%
1111056000 36
24.0%
1111057000 15
10.0%
1111058000 21
14.0%
1111060000 6
 
4.0%
ValueCountFrequency (%)
1111060000 6
 
4.0%
1111058000 21
14.0%
1111057000 15
10.0%
1111056000 36
24.0%
1111055000 21
14.0%
1111054000 6
 
4.0%
1111053000 18
12.0%
1111051500 27
18.0%

ATPT_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
서울
150 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 150
100.0%

Length

2023-12-10T15:13:53.986331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:13:54.147278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 150
100.0%

SIGNGU_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
종로구
150 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
종로구 150
100.0%

Length

2023-12-10T15:13:54.305712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:13:54.443612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종로구 150
100.0%

ADSTRD_NM
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
평창동
36 
청운효자동
27 
부암동
21 
교남동
21 
사직동
18 
Other values (3)
27 

Length

Max length5
Median length3
Mean length3.36
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
평창동 36
24.0%
청운효자동 27
18.0%
부암동 21
14.0%
교남동 21
14.0%
사직동 18
12.0%
무악동 15
10.0%
삼청동 6
 
4.0%
가회동 6
 
4.0%

Length

2023-12-10T15:13:54.600990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:13:54.783558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
평창동 36
24.0%
청운효자동 27
18.0%
부암동 21
14.0%
교남동 21
14.0%
사직동 18
12.0%
무악동 15
10.0%
삼청동 6
 
4.0%
가회동 6
 
4.0%

TOT_HSHLD_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.36
Minimum0
Maximum377
Zeros8
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:13:55.018117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q1153
median177
Q3206
95-th percentile262.1
Maximum377
Range377
Interquartile range (IQR)53

Descriptive statistics

Standard deviation66.01162
Coefficient of variation (CV)0.38522187
Kurtosis2.0583814
Mean171.36
Median Absolute Deviation (MAD)28.5
Skewness-0.82561935
Sum25704
Variance4357.534
MonotonicityNot monotonic
2023-12-10T15:13:55.241863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
5.3%
171 6
 
4.0%
206 6
 
4.0%
160 4
 
2.7%
184 4
 
2.7%
177 4
 
2.7%
179 3
 
2.0%
214 3
 
2.0%
174 3
 
2.0%
153 3
 
2.0%
Other values (86) 106
70.7%
ValueCountFrequency (%)
0 8
5.3%
1 1
 
0.7%
3 1
 
0.7%
8 1
 
0.7%
28 1
 
0.7%
49 1
 
0.7%
70 1
 
0.7%
89 1
 
0.7%
110 1
 
0.7%
119 1
 
0.7%
ValueCountFrequency (%)
377 1
0.7%
338 1
0.7%
305 1
0.7%
287 1
0.7%
276 1
0.7%
275 1
0.7%
266 1
0.7%
263 1
0.7%
261 1
0.7%
248 1
0.7%

AVRG_MBHS_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4273333
Minimum0
Maximum3.4
Zeros8
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:13:55.453679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q12.3
median2.5
Q32.8
95-th percentile3.2
Maximum3.4
Range3.4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.67643922
Coefficient of variation (CV)0.27867587
Kurtosis6.6210822
Mean2.4273333
Median Absolute Deviation (MAD)0.3
Skewness-2.4328479
Sum364.1
Variance0.45757002
MonotonicityNot monotonic
2023-12-10T15:13:55.637531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2.5 20
13.3%
2.9 16
10.7%
2.8 15
10.0%
2.7 15
10.0%
2.4 15
10.0%
2.2 13
8.7%
2.3 12
8.0%
2.6 10
6.7%
0.0 8
 
5.3%
3.2 7
 
4.7%
Other values (8) 19
12.7%
ValueCountFrequency (%)
0.0 8
 
5.3%
1.0 1
 
0.7%
1.3 1
 
0.7%
1.7 1
 
0.7%
2.0 3
 
2.0%
2.1 6
 
4.0%
2.2 13
8.7%
2.3 12
8.0%
2.4 15
10.0%
2.5 20
13.3%
ValueCountFrequency (%)
3.4 1
 
0.7%
3.3 2
 
1.3%
3.2 7
 
4.7%
3.0 4
 
2.7%
2.9 16
10.7%
2.8 15
10.0%
2.7 15
10.0%
2.6 10
6.7%
2.5 20
13.3%
2.4 15
10.0%

ONE_GEN_HSHLD_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.673333
Minimum0
Maximum64
Zeros11
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:13:55.825843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q123
median30
Q339
95-th percentile46.55
Maximum64
Range64
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.107453
Coefficient of variation (CV)0.44172499
Kurtosis0.5582838
Mean29.673333
Median Absolute Deviation (MAD)8
Skewness-0.39853578
Sum4451
Variance171.80532
MonotonicityNot monotonic
2023-12-10T15:13:56.056714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 11
 
7.3%
25 8
 
5.3%
39 7
 
4.7%
29 7
 
4.7%
43 6
 
4.0%
33 6
 
4.0%
30 6
 
4.0%
36 6
 
4.0%
35 6
 
4.0%
40 6
 
4.0%
Other values (31) 81
54.0%
ValueCountFrequency (%)
0 11
7.3%
6 1
 
0.7%
7 1
 
0.7%
9 1
 
0.7%
11 1
 
0.7%
15 2
 
1.3%
17 2
 
1.3%
18 2
 
1.3%
19 4
 
2.7%
20 4
 
2.7%
ValueCountFrequency (%)
64 2
 
1.3%
60 1
 
0.7%
57 1
 
0.7%
49 2
 
1.3%
48 1
 
0.7%
47 1
 
0.7%
46 4
2.7%
45 1
 
0.7%
44 2
 
1.3%
43 6
4.0%

TWO_GEN_HSHLD_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.253333
Minimum0
Maximum160
Zeros11
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:13:56.269239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q174
median87.5
Q3105
95-th percentile129.2
Maximum160
Range160
Interquartile range (IQR)31

Descriptive statistics

Standard deviation32.910329
Coefficient of variation (CV)0.3906116
Kurtosis1.5082442
Mean84.253333
Median Absolute Deviation (MAD)14.5
Skewness-1.0406408
Sum12638
Variance1083.0898
MonotonicityNot monotonic
2023-12-10T15:13:56.480944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
7.3%
90 6
 
4.0%
84 6
 
4.0%
74 5
 
3.3%
80 5
 
3.3%
100 4
 
2.7%
78 4
 
2.7%
79 4
 
2.7%
89 4
 
2.7%
77 3
 
2.0%
Other values (61) 98
65.3%
ValueCountFrequency (%)
0 11
7.3%
7 1
 
0.7%
10 1
 
0.7%
25 1
 
0.7%
37 1
 
0.7%
48 1
 
0.7%
55 1
 
0.7%
61 1
 
0.7%
64 2
 
1.3%
65 1
 
0.7%
ValueCountFrequency (%)
160 1
0.7%
152 1
0.7%
143 1
0.7%
136 1
0.7%
134 1
0.7%
133 2
1.3%
131 1
0.7%
127 2
1.3%
125 1
0.7%
121 1
0.7%

Interactions

2023-12-10T15:13:49.398239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:40.560748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:41.731512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:42.871821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:44.434414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:45.826423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:47.113169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:48.278772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:49.502696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:40.657458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:41.819551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:43.133628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:44.591278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:46.016048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:47.253507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:48.401409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:49.628336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:40.775457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:41.938576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:43.306331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:44.736340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:46.190651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:47.400408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:48.562195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:49.784526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:40.898549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:42.094965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:43.456094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:44.898179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:46.322665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:47.552322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:48.719727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:49.908520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:41.276099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:42.236441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:43.631349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:45.025109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:46.461833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:47.709252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:48.858482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:50.430903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:41.388173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:42.417418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:43.843697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:45.207754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:46.597198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:47.838450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:48.998452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:50.578455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:41.492088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:42.590356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:44.064544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:45.387335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:46.788557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:47.981369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:49.129603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:50.707603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:41.594337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:42.733410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:44.212860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:45.579798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:46.950232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:48.116133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:13:49.255984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:13:56.652330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
CSOPAR_CODETHREE_GEN_HSHLD_COONE_PERSON_HSHLD_CONON_BLD_HSHLD_COADSTRD_CODEADSTRD_NMTOT_HSHLD_COAVRG_MBHS_COONE_GEN_HSHLD_COTWO_GEN_HSHLD_CO
CSOPAR_CODE1.0000.2610.3130.0951.0001.0000.0000.3310.3820.303
THREE_GEN_HSHLD_CO0.2611.0000.4290.3420.4340.4120.5970.4280.5490.586
ONE_PERSON_HSHLD_CO0.3130.4291.0000.6080.5030.6390.7520.8610.5520.376
NON_BLD_HSHLD_CO0.0950.3420.6081.0000.2870.1470.6470.2880.2960.000
ADSTRD_CODE1.0000.4340.5030.2871.0001.0000.5010.5630.4840.528
ADSTRD_NM1.0000.4120.6390.1471.0001.0000.4980.7230.5210.533
TOT_HSHLD_CO0.0000.5970.7520.6470.5010.4981.0000.7180.9260.907
AVRG_MBHS_CO0.3310.4280.8610.2880.5630.7230.7181.0000.6760.568
ONE_GEN_HSHLD_CO0.3820.5490.5520.2960.4840.5210.9260.6761.0000.864
TWO_GEN_HSHLD_CO0.3030.5860.3760.0000.5280.5330.9070.5680.8641.000
2023-12-10T15:13:56.844599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADSTRD_NMCSOPAR_CODE
ADSTRD_NM1.0000.983
CSOPAR_CODE0.9831.000
2023-12-10T15:13:56.986068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
THREE_GEN_HSHLD_COONE_PERSON_HSHLD_CONON_BLD_HSHLD_COADSTRD_CODETOT_HSHLD_COAVRG_MBHS_COONE_GEN_HSHLD_COTWO_GEN_HSHLD_COCSOPAR_CODEADSTRD_NM
THREE_GEN_HSHLD_CO1.0000.206-0.026-0.1460.4190.4110.2930.4630.1140.215
ONE_PERSON_HSHLD_CO0.2061.0000.389-0.2400.772-0.4730.5520.1400.2050.260
NON_BLD_HSHLD_CO-0.0260.3891.000-0.1400.343-0.2600.3560.0370.0610.076
ADSTRD_CODE-0.146-0.240-0.1401.000-0.178-0.145-0.127-0.0880.9831.000
TOT_HSHLD_CO0.4190.7720.343-0.1781.000-0.1340.7540.6120.0000.262
AVRG_MBHS_CO0.411-0.473-0.260-0.145-0.1341.000-0.0860.5320.2180.317
ONE_GEN_HSHLD_CO0.2930.5520.356-0.1270.754-0.0861.0000.3570.2410.282
TWO_GEN_HSHLD_CO0.4630.1400.037-0.0880.6120.5320.3571.0000.1780.297
CSOPAR_CODE0.1140.2050.0610.9830.0000.2180.2410.1781.0000.983
ADSTRD_NM0.2150.2600.0761.0000.2620.3170.2820.2970.9831.000

Missing values

2023-12-10T15:13:50.926517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:13:51.237153image/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

STDYYCSOPAR_CODETHREE_GEN_HSHLD_COFOUR_GEN_HSHLD_COONE_PERSON_HSHLD_CONON_BLD_HSHLD_COADSTRD_CODEATPT_NMSIGNGU_NMADSTRD_NMTOT_HSHLD_COAVRG_MBHS_COONE_GEN_HSHLD_COTWO_GEN_HSHLD_CO
0201511010700000002009401111051500서울종로구청운효자동2252.23474
1201511010700000001106901111051500서울종로구청운효자동2272.529116
2201511010700000001409261111051500서울종로구청운효자동2452.233100
320151101070000000904751111051500서울종로구청운효자동1712.53080
420151101070000000802801111051500서울종로구청운효자동1342.81581
5201511010700000001105001111051500서울종로구청운효자동1842.72594
6201511010700000001503701111051500서울종로구청운효자동1712.72594
7201511010700000002103051111051500서울종로구청운효자동2103.036118
8201511010700000001502901111051500서울종로구청운효자동1552.81990
9201511010700000001405561111051500서울종로구청운효자동2022.522105
STDYYCSOPAR_CODETHREE_GEN_HSHLD_COFOUR_GEN_HSHLD_COONE_PERSON_HSHLD_CONON_BLD_HSHLD_COADSTRD_CODEATPT_NMSIGNGU_NMADSTRD_NMTOT_HSHLD_COAVRG_MBHS_COONE_GEN_HSHLD_COTWO_GEN_HSHLD_CO
14020151101060000000005501111058000서울종로구교남동701.377
1412015110106000000000001111058000서울종로구교남동32.000
1422015110106000000000001111058000서울종로구교남동11.000
1432015110106000000000001111058000서울종로구교남동00.000
14420151101060000000709251111060000서울종로구가회동2372.24390
145201511010600000001005301111060000서울종로구가회동1702.42977
146201511010600000001405801111060000서울종로구가회동1892.43974
147201511010600000001509901111060000서울종로구가회동2762.343117
148201511010600000001405251111060000서울종로구가회동1852.53381
149201511010600000001606201111060000서울종로구가회동2062.52896

Duplicate rows

Most frequently occurring

STDYYCSOPAR_CODETHREE_GEN_HSHLD_COFOUR_GEN_HSHLD_COONE_PERSON_HSHLD_CONON_BLD_HSHLD_COADSTRD_CODEATPT_NMSIGNGU_NMADSTRD_NMTOT_HSHLD_COAVRG_MBHS_COONE_GEN_HSHLD_COTWO_GEN_HSHLD_CO# duplicates
02015110106000000000001111058000서울종로구교남동00.0008