Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.9 KiB
Average record size in memory142.3 B

Variable types

Numeric11
Categorical4
Text1

Alerts

base_year has constant value ""Constant
examin_year has constant value ""Constant
seq_no is highly overall correlated with base_mtHigh correlation
base_mt is highly overall correlated with seq_noHigh correlation
ctprvn_cd is highly overall correlated with signgu_cd and 3 other fieldsHigh correlation
signgu_cd is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
adstrd_cd is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
base_year_spnd_price is highly overall correlated with examin_year_spnd_priceHigh correlation
examin_year_spnd_price is highly overall correlated with base_year_spnd_priceHigh correlation
base_year_dynmc_popltn_co is highly overall correlated with examin_year_dynmc_popltn_coHigh correlation
examin_year_dynmc_popltn_co is highly overall correlated with base_year_dynmc_popltn_coHigh correlation
ctprvn_nm is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
signgu_nm is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
seq_no has unique valuesUnique
base_year_spnd_price has unique valuesUnique
examin_year_spnd_price has unique valuesUnique
examin_year_dynmc_popltn_co has unique valuesUnique
dynmc_popltn_decr_rt has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:57:11.963950
Analysis finished2023-12-10 09:57:43.333205
Duration31.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.44
Minimum1
Maximum213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:43.437835image/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
Maximum213
Range212
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation39.382178
Coefficient of variation (CV)0.6977707
Kurtosis5.317571
Mean56.44
Median Absolute Deviation (MAD)25.5
Skewness1.700625
Sum5644
Variance1550.956
MonotonicityNot monotonic
2023-12-10T18:57:43.670922image/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 (%)
213 1
1.0%
212 1
1.0%
211 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_year
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 100
100.0%

Length

2023-12-10T18:57:43.857277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:57:44.011120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 100
100.0%

examin_year
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2020
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 100
100.0%

Length

2023-12-10T18:57:44.290288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:57:44.451137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 100
100.0%

base_mt
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2
Minimum5
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:44.605076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q16
median7
Q38
95-th percentile9
Maximum12
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4284271
Coefficient of variation (CV)0.19839266
Kurtosis2.1073329
Mean7.2
Median Absolute Deviation (MAD)1
Skewness0.84874203
Sum720
Variance2.040404
MonotonicityNot monotonic
2023-12-10T18:57:44.780700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
7 29
29.0%
8 29
29.0%
6 18
18.0%
5 12
12.0%
9 9
 
9.0%
12 3
 
3.0%
ValueCountFrequency (%)
5 12
12.0%
6 18
18.0%
7 29
29.0%
8 29
29.0%
9 9
 
9.0%
12 3
 
3.0%
ValueCountFrequency (%)
12 3
 
3.0%
9 9
 
9.0%
8 29
29.0%
7 29
29.0%
6 18
18.0%
5 12
12.0%

ctprvn_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.77
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:44.975304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q124
median33
Q335
95-th percentile38
Maximum39
Range28
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.6908118
Coefficient of variation (CV)0.25834101
Kurtosis-0.18082473
Mean29.77
Median Absolute Deviation (MAD)4.5
Skewness-0.85052232
Sum2977
Variance59.148586
MonotonicityNot monotonic
2023-12-10T18:57:45.186727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
35 19
19.0%
37 11
11.0%
24 10
10.0%
38 10
10.0%
21 10
10.0%
32 8
8.0%
33 7
 
7.0%
22 7
 
7.0%
11 6
 
6.0%
25 4
 
4.0%
Other values (4) 8
8.0%
ValueCountFrequency (%)
11 6
6.0%
21 10
10.0%
22 7
7.0%
24 10
10.0%
25 4
 
4.0%
26 1
 
1.0%
31 2
 
2.0%
32 8
8.0%
33 7
7.0%
34 3
 
3.0%
ValueCountFrequency (%)
39 2
 
2.0%
38 10
10.0%
37 11
11.0%
35 19
19.0%
34 3
 
3.0%
33 7
 
7.0%
32 8
8.0%
31 2
 
2.0%
26 1
 
1.0%
25 4
 
4.0%

ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전라북도
19 
경상북도
11 
광주광역시
10 
경상남도
10 
부산광역시
10 
Other values (9)
40 

Length

Max length7
Median length6
Mean length4.34
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row경상북도
2nd row광주광역시
3rd row충청북도
4th row강원도
5th row경상남도

Common Values

ValueCountFrequency (%)
전라북도 19
19.0%
경상북도 11
11.0%
광주광역시 10
10.0%
경상남도 10
10.0%
부산광역시 10
10.0%
강원도 8
8.0%
충청북도 7
 
7.0%
대구광역시 7
 
7.0%
서울특별시 6
 
6.0%
대전광역시 4
 
4.0%
Other values (4) 8
8.0%

Length

2023-12-10T18:57:45.449700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라북도 19
19.0%
경상북도 11
11.0%
광주광역시 10
10.0%
경상남도 10
10.0%
부산광역시 10
10.0%
강원도 8
8.0%
충청북도 7
 
7.0%
대구광역시 7
 
7.0%
서울특별시 6
 
6.0%
대전광역시 4
 
4.0%
Other values (4) 8
8.0%

signgu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29882.07
Minimum11010
Maximum39020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:45.747872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11010
5-th percentile11164
Q124030
median33175
Q335380
95-th percentile38127.75
Maximum39020
Range28010
Interquartile range (IQR)11350

Descriptive statistics

Standard deviation7731.5166
Coefficient of variation (CV)0.2587343
Kurtosis-0.20047531
Mean29882.07
Median Absolute Deviation (MAD)4385
Skewness-0.85183738
Sum2988207
Variance59776349
MonotonicityNot monotonic
2023-12-10T18:57:46.072931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24030 5
 
5.0%
37020 5
 
5.0%
37060 4
 
4.0%
32310 4
 
4.0%
38090 4
 
4.0%
35380 4
 
4.0%
21010 4
 
4.0%
35012 4
 
4.0%
35310 3
 
3.0%
35011 3
 
3.0%
Other values (42) 60
60.0%
ValueCountFrequency (%)
11010 3
3.0%
11050 2
2.0%
11170 1
 
1.0%
21010 4
4.0%
21060 2
2.0%
21070 1
 
1.0%
21080 1
 
1.0%
21090 1
 
1.0%
21100 1
 
1.0%
22010 1
 
1.0%
ValueCountFrequency (%)
39020 1
 
1.0%
39010 1
 
1.0%
38370 3
3.0%
38115 1
 
1.0%
38090 4
4.0%
38070 1
 
1.0%
38060 1
 
1.0%
37060 4
4.0%
37040 1
 
1.0%
37020 5
5.0%

signgu_nm
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
중구
 
7
북구
 
6
남구
 
6
경주시
 
5
부안군
 
4
Other values (39)
72 

Length

Max length7
Median length3
Mean length3.13
Min length2

Unique

Unique21 ?
Unique (%)21.0%

Sample

1st row영주시
2nd row서구
3rd row충주시
4th row홍천군
5th row거제시

Common Values

ValueCountFrequency (%)
중구 7
 
7.0%
북구 6
 
6.0%
남구 6
 
6.0%
경주시 5
 
5.0%
부안군 4
 
4.0%
전주시 덕진구 4
 
4.0%
서구 4
 
4.0%
거제시 4
 
4.0%
홍천군 4
 
4.0%
영주시 4
 
4.0%
Other values (34) 52
52.0%

Length

2023-12-10T18:57:46.561049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
중구 7
 
6.4%
남구 7
 
6.4%
전주시 7
 
6.4%
북구 6
 
5.5%
경주시 5
 
4.6%
거제시 4
 
3.7%
영주시 4
 
3.7%
홍천군 4
 
3.7%
서구 4
 
3.7%
덕진구 4
 
3.7%
Other values (36) 57
52.3%

adstrd_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2988256.8
Minimum1101064
Maximum3902060
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:46.852757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101064
5-th percentile1116452.6
Q12403054
median3317531
Q33538011
95-th percentile3812828
Maximum3902060
Range2800996
Interquartile range (IQR)1134957

Descriptive statistics

Standard deviation773144.61
Coefficient of variation (CV)0.25872764
Kurtosis-0.20045583
Mean2988256.8
Median Absolute Deviation (MAD)438526
Skewness-0.85182768
Sum2.9882568 × 108
Variance5.9775259 × 1011
MonotonicityNot monotonic
2023-12-10T18:57:47.283839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3702067 5
 
5.0%
3706062 4
 
4.0%
2403054 4
 
4.0%
3231011 4
 
4.0%
3809061 4
 
4.0%
3538011 4
 
4.0%
2101060 4
 
4.0%
3501257 4
 
4.0%
3531011 3
 
3.0%
3501175 3
 
3.0%
Other values (44) 61
61.0%
ValueCountFrequency (%)
1101064 2
2.0%
1101073 1
 
1.0%
1105065 2
2.0%
1117052 1
 
1.0%
2101060 4
4.0%
2106065 2
2.0%
2107070 1
 
1.0%
2108057 1
 
1.0%
2109066 1
 
1.0%
2110056 1
 
1.0%
ValueCountFrequency (%)
3902060 1
 
1.0%
3901065 1
 
1.0%
3837034 3
3.0%
3811554 1
 
1.0%
3809061 4
4.0%
3807011 1
 
1.0%
3806052 1
 
1.0%
3706062 4
4.0%
3704062 1
 
1.0%
3702067 5
5.0%
Distinct54
Distinct (%)54.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:57:47.744049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.33
Min length2

Characters and Unicode

Total characters333
Distinct characters78
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

Unique30 ?
Unique (%)30.0%

Sample

1st row가흥1동
2nd row금호1동
3rd row성내·충인동
4th row홍천읍
5th row장승포동
ValueCountFrequency (%)
보덕동 5
 
5.0%
장승포동 4
 
4.0%
부안읍 4
 
4.0%
영주2동 4
 
4.0%
덕진동 4
 
4.0%
사직동 4
 
4.0%
가흥1동 4
 
4.0%
홍천읍 4
 
4.0%
문화동 3
 
3.0%
금서면 3
 
3.0%
Other values (44) 61
61.0%
2023-12-10T18:57:48.470614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
76
22.8%
21
 
6.3%
1 13
 
3.9%
10
 
3.0%
9
 
2.7%
2 9
 
2.7%
7
 
2.1%
7
 
2.1%
6
 
1.8%
6
 
1.8%
Other values (68) 169
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 307
92.2%
Decimal Number 24
 
7.2%
Other Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
76
24.8%
21
 
6.8%
10
 
3.3%
9
 
2.9%
7
 
2.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
5
 
1.6%
5
 
1.6%
Other values (64) 155
50.5%
Decimal Number
ValueCountFrequency (%)
1 13
54.2%
2 9
37.5%
5 2
 
8.3%
Other Punctuation
ValueCountFrequency (%)
· 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 307
92.2%
Common 26
 
7.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
76
24.8%
21
 
6.8%
10
 
3.3%
9
 
2.9%
7
 
2.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
5
 
1.6%
5
 
1.6%
Other values (64) 155
50.5%
Common
ValueCountFrequency (%)
1 13
50.0%
2 9
34.6%
5 2
 
7.7%
· 2
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 307
92.2%
ASCII 24
 
7.2%
None 2
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
76
24.8%
21
 
6.8%
10
 
3.3%
9
 
2.9%
7
 
2.3%
7
 
2.3%
6
 
2.0%
6
 
2.0%
5
 
1.6%
5
 
1.6%
Other values (64) 155
50.5%
ASCII
ValueCountFrequency (%)
1 13
54.2%
2 9
37.5%
5 2
 
8.3%
None
ValueCountFrequency (%)
· 2
100.0%

base_year_spnd_price
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4640958 × 109
Minimum27602755
Maximum9.4326997 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:48.806918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27602755
5-th percentile1.7311832 × 108
Q18.9882337 × 108
median2.7490811 × 109
Q34.9071946 × 109
95-th percentile2.3788901 × 1010
Maximum9.4326997 × 1010
Range9.4299395 × 1010
Interquartile range (IQR)4.0083712 × 109

Descriptive statistics

Standard deviation1.6399262 × 1010
Coefficient of variation (CV)2.1970862
Kurtosis14.946809
Mean7.4640958 × 109
Median Absolute Deviation (MAD)2.0233368 × 109
Skewness3.8790597
Sum7.4640958 × 1011
Variance2.6893579 × 1020
MonotonicityNot monotonic
2023-12-10T18:57:49.189794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1938669212 1
 
1.0%
4445289963 1
 
1.0%
969652878 1
 
1.0%
2597778362 1
 
1.0%
1472482856 1
 
1.0%
232911418 1
 
1.0%
3395816124 1
 
1.0%
582623479 1
 
1.0%
3222144133 1
 
1.0%
4970922549 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
27602755 1
1.0%
29539767 1
1.0%
38121259 1
1.0%
42041698 1
1.0%
156968747 1
1.0%
173968294 1
1.0%
180015216 1
1.0%
195114201 1
1.0%
199916615 1
1.0%
200651993 1
1.0%
ValueCountFrequency (%)
94326997487 1
1.0%
76176309363 1
1.0%
70113328936 1
1.0%
68168285653 1
1.0%
67741839020 1
1.0%
21475588938 1
1.0%
19068060255 1
1.0%
15663992293 1
1.0%
14752868443 1
1.0%
14208843961 1
1.0%

examin_year_spnd_price
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9503579 × 109
Minimum15921333
Maximum8.8771938 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:49.536282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15921333
5-th percentile1.3089278 × 108
Q18.0875631 × 108
median2.466984 × 109
Q34.1964256 × 109
95-th percentile1.8777654 × 1010
Maximum8.8771938 × 1010
Range8.8756017 × 1010
Interquartile range (IQR)3.3876693 × 109

Descriptive statistics

Standard deviation1.3626337 × 1010
Coefficient of variation (CV)2.2900029
Kurtosis21.12356
Mean5.9503579 × 109
Median Absolute Deviation (MAD)1.6905 × 109
Skewness4.4829478
Sum5.9503579 × 1011
Variance1.8567705 × 1020
MonotonicityNot monotonic
2023-12-10T18:57:49.837826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1937209777 1
 
1.0%
4053049134 1
 
1.0%
818693980 1
 
1.0%
2491023933 1
 
1.0%
1517352501 1
 
1.0%
211976337 1
 
1.0%
3204162465 1
 
1.0%
520497693 1
 
1.0%
3216531950 1
 
1.0%
3648512967 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
15921333 1
1.0%
30496448 1
1.0%
34073628 1
1.0%
39972432 1
1.0%
127521596 1
1.0%
131070216 1
1.0%
154386974 1
1.0%
161905948 1
1.0%
172601359 1
1.0%
194714368 1
1.0%
ValueCountFrequency (%)
88771938012 1
1.0%
69969650317 1
1.0%
57872970505 1
1.0%
56509525812 1
1.0%
22161432435 1
1.0%
18599560084 1
1.0%
13512704690 1
1.0%
13192505304 1
1.0%
12944760633 1
1.0%
11217764388 1
1.0%

spnd_decr_rt
Real number (ℝ)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.4329
Minimum9.45
Maximum160.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:50.118695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.45
5-th percentile58.6375
Q178.5025
median88.46
Q395.8
95-th percentile104.0335
Maximum160.97
Range151.52
Interquartile range (IQR)17.2975

Descriptive statistics

Standard deviation16.721978
Coefficient of variation (CV)0.19346774
Kurtosis7.0581983
Mean86.4329
Median Absolute Deviation (MAD)7.94
Skewness-0.36881142
Sum8643.29
Variance279.62454
MonotonicityNot monotonic
2023-12-10T18:57:50.397944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.85 2
 
2.0%
99.92 1
 
1.0%
97.54 1
 
1.0%
95.89 1
 
1.0%
103.05 1
 
1.0%
91.01 1
 
1.0%
94.36 1
 
1.0%
89.34 1
 
1.0%
99.83 1
 
1.0%
73.4 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
9.45 1
1.0%
53.9 1
1.0%
53.95 1
1.0%
55.86 1
1.0%
56.69 1
1.0%
58.74 1
1.0%
61.43 1
1.0%
64.23 1
1.0%
66.61 1
1.0%
66.8 1
1.0%
ValueCountFrequency (%)
160.97 1
1.0%
114.66 1
1.0%
110.48 1
1.0%
105.28 1
1.0%
104.86 1
1.0%
103.99 1
1.0%
103.34 1
1.0%
103.05 1
1.0%
101.43 1
1.0%
100.75 1
1.0%

base_year_dynmc_popltn_co
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean784736.37
Minimum5861
Maximum9922740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:50.720901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5861
5-th percentile6725.85
Q154296
median133038.5
Q3392803.75
95-th percentile4056501.1
Maximum9922740
Range9916879
Interquartile range (IQR)338507.75

Descriptive statistics

Standard deviation1719449.6
Coefficient of variation (CV)2.1911175
Kurtosis13.145577
Mean784736.37
Median Absolute Deviation (MAD)98894.5
Skewness3.44274
Sum78473637
Variance2.9565069 × 1012
MonotonicityNot monotonic
2023-12-10T18:57:50.995300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58272 2
 
2.0%
104328 1
 
1.0%
9016437 1
 
1.0%
51708 1
 
1.0%
24905 1
 
1.0%
62071 1
 
1.0%
86919 1
 
1.0%
10499 1
 
1.0%
297416 1
 
1.0%
348260 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
5861 1
1.0%
5905 1
1.0%
6097 1
1.0%
6192 1
1.0%
6628 1
1.0%
6731 1
1.0%
10499 1
1.0%
20856 1
1.0%
21815 1
1.0%
22190 1
1.0%
ValueCountFrequency (%)
9922740 1
1.0%
9016437 1
1.0%
5970320 1
1.0%
5178811 1
1.0%
4377262 1
1.0%
4039619 1
1.0%
4002644 1
1.0%
3829621 1
1.0%
3538370 1
1.0%
2568930 1
1.0%

examin_year_dynmc_popltn_co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean484510.6
Minimum4073
Maximum7996620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:51.609565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4073
5-th percentile14341.6
Q152530.75
median122230
Q3304348
95-th percentile2587976
Maximum7996620
Range7992547
Interquartile range (IQR)251817.25

Descriptive statistics

Standard deviation1077369.8
Coefficient of variation (CV)2.2236248
Kurtosis24.649122
Mean484510.6
Median Absolute Deviation (MAD)99794.5
Skewness4.4014264
Sum48451060
Variance1.1607256 × 1012
MonotonicityNot monotonic
2023-12-10T18:57:51.849960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
92476 1
 
1.0%
2108004 1
 
1.0%
167964 1
 
1.0%
15760 1
 
1.0%
112680 1
 
1.0%
56695 1
 
1.0%
163374 1
 
1.0%
26668 1
 
1.0%
173850 1
 
1.0%
166893 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
4073 1
1.0%
4635 1
1.0%
5054 1
1.0%
5738 1
1.0%
13536 1
1.0%
14384 1
1.0%
14951 1
1.0%
15079 1
1.0%
15760 1
1.0%
15999 1
1.0%
ValueCountFrequency (%)
7996620 1
1.0%
3415217 1
1.0%
3404289 1
1.0%
3364097 1
1.0%
2817782 1
1.0%
2575881 1
1.0%
2504466 1
1.0%
2108004 1
1.0%
1944816 1
1.0%
1829932 1
1.0%

dynmc_popltn_decr_rt
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.3436
Minimum1.64
Maximum760.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:57:52.112398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.64
5-th percentile8.6445
Q143.785
median85.225
Q3188.9575
95-th percentile544.814
Maximum760.97
Range759.33
Interquartile range (IQR)145.1725

Descriptive statistics

Standard deviation171.64544
Coefficient of variation (CV)1.1493324
Kurtosis3.2434938
Mean149.3436
Median Absolute Deviation (MAD)58.585
Skewness1.9312323
Sum14934.36
Variance29462.156
MonotonicityNot monotonic
2023-12-10T18:57:52.395619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.64 1
 
1.0%
52.67 1
 
1.0%
86.18 1
 
1.0%
30.48 1
 
1.0%
452.44 1
 
1.0%
91.34 1
 
1.0%
187.96 1
 
1.0%
254.01 1
 
1.0%
58.45 1
 
1.0%
286.4 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.64 1
1.0%
1.81 1
1.0%
3.8 1
1.0%
4.84 1
1.0%
7.02 1
1.0%
8.73 1
1.0%
10.4 1
1.0%
14.26 1
1.0%
16.31 1
1.0%
17.14 1
1.0%
ValueCountFrequency (%)
760.97 1
1.0%
709.29 1
1.0%
691.61 1
1.0%
643.81 1
1.0%
586.31 1
1.0%
542.63 1
1.0%
524.73 1
1.0%
470.59 1
1.0%
452.44 1
1.0%
444.46 1
1.0%

Interactions

2023-12-10T18:57:39.732627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:13.210439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:15.643055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:18.058445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:20.309783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:23.300374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:25.653572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:29.031044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:31.764179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:34.075117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:36.799521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:39.971336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:13.365779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:15.818226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:18.167885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:20.621059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:23.511257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:25.835537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:29.487336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:31.951174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:34.248114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:37.012436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:40.243433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:13.978018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:16.068852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:18.305833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:20.976227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:23.714332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:26.010152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:29.962247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:32.167442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:34.453234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:37.236330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:40.478467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:14.179238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:16.365667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:18.425311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:21.152604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:23.969623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:26.181266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:30.168250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:32.363368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:34.658976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:37.474740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:40.672833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:14.417835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:16.571952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:18.618258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:21.374609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:24.156673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:26.809563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:30.374074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:32.590272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:34.910475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:37.688396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:40.862632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:14.608493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:16.790679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:18.907400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:21.692648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:24.346764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:27.055815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:30.568680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:32.863421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:35.086619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:37.982483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:41.041802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:14.772976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:16.986280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:19.183835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:22.066887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:24.520517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:27.226960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:30.737841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:33.055804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:35.259022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:38.263240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:41.221560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:14.939857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:17.166100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:19.406006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:22.339509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:24.765309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:27.407633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:30.959802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:33.242148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:35.449394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:38.510309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:41.866264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:15.105197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:17.428835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:19.617105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:22.564436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:25.010673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:27.606926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:31.155733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:33.423446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:35.780039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:38.772616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:42.136546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:15.277962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:17.713878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:19.794946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:22.839608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:25.224511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:27.847476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:31.330607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:33.632606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:36.098180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:39.158743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:42.364260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:15.462399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:17.912244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:20.037604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:23.060792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:25.438677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:28.448514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:31.551622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:33.861268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:36.558510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:57:39.455367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:57:52.606952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nobase_mtctprvn_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nmbase_year_spnd_priceexamin_year_spnd_pricespnd_decr_rtbase_year_dynmc_popltn_coexamin_year_dynmc_popltn_codynmc_popltn_decr_rt
seq_no1.0000.9810.0000.0000.0000.0000.0000.0000.0000.0000.5330.2470.0000.000
base_mt0.9811.0000.0000.0000.0000.0000.0000.0000.0000.0000.5120.1540.0000.000
ctprvn_cd0.0000.0001.0001.0001.0000.9711.0001.0000.4230.3090.2930.2290.0000.000
ctprvn_nm0.0000.0001.0001.0001.0000.9831.0001.0000.4900.2570.0000.2240.0000.000
signgu_cd0.0000.0001.0001.0001.0000.9591.0001.0000.4610.3180.1550.0000.0000.000
signgu_nm0.0000.0000.9710.9830.9591.0000.9571.0000.7350.6020.8410.0000.0000.606
adstrd_cd0.0000.0001.0001.0001.0000.9571.0001.0000.4420.3120.2960.2800.0000.000
adstrd_nm0.0000.0001.0001.0001.0001.0001.0001.0000.0520.0000.9050.4270.1270.525
base_year_spnd_price0.0000.0000.4230.4900.4610.7350.4420.0521.0000.9900.3830.2750.0000.000
examin_year_spnd_price0.0000.0000.3090.2570.3180.6020.3120.0000.9901.0000.4140.2510.0000.000
spnd_decr_rt0.5330.5120.2930.0000.1550.8410.2960.9050.3830.4141.0000.0000.1950.242
base_year_dynmc_popltn_co0.2470.1540.2290.2240.0000.0000.2800.4270.2750.2510.0001.0000.8150.000
examin_year_dynmc_popltn_co0.0000.0000.0000.0000.0000.0000.0000.1270.0000.0000.1950.8151.0000.000
dynmc_popltn_decr_rt0.0000.0000.0000.0000.0000.6060.0000.5250.0000.0000.2420.0000.0001.000
2023-12-10T18:57:52.873957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
signgu_nmctprvn_nm
signgu_nm1.0000.671
ctprvn_nm0.6711.000
2023-12-10T18:57:53.065540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_nobase_mtctprvn_cdsigngu_cdadstrd_cdbase_year_spnd_priceexamin_year_spnd_pricespnd_decr_rtbase_year_dynmc_popltn_coexamin_year_dynmc_popltn_codynmc_popltn_decr_rtctprvn_nmsigngu_nm
seq_no1.0000.971-0.097-0.106-0.106-0.182-0.203-0.158-0.231-0.2000.0030.0000.000
base_mt0.9711.000-0.105-0.116-0.116-0.156-0.181-0.165-0.229-0.1780.0200.0000.000
ctprvn_cd-0.097-0.1051.0000.9940.9940.3210.3330.124-0.1480.1080.2710.9620.630
signgu_cd-0.106-0.1160.9941.0001.0000.3010.3190.163-0.1830.0840.2930.9380.613
adstrd_cd-0.106-0.1160.9941.0001.0000.3020.3200.162-0.1820.0840.2920.9380.613
base_year_spnd_price-0.182-0.1560.3210.3010.3021.0000.984-0.2300.2660.3340.0660.2550.326
examin_year_spnd_price-0.203-0.1810.3330.3190.3200.9841.000-0.1050.2510.2970.0450.1240.238
spnd_decr_rt-0.158-0.1650.1240.1630.162-0.230-0.1051.000-0.199-0.283-0.0950.0000.412
base_year_dynmc_popltn_co-0.231-0.229-0.148-0.183-0.1820.2660.251-0.1991.0000.611-0.4690.0940.000
examin_year_dynmc_popltn_co-0.200-0.1780.1080.0840.0840.3340.297-0.2830.6111.0000.3390.0000.000
dynmc_popltn_decr_rt0.0030.0200.2710.2930.2920.0660.045-0.095-0.4690.3391.0000.0000.190
ctprvn_nm0.0000.0000.9620.9380.9380.2550.1240.0000.0940.0000.0001.0000.671
signgu_nm0.0000.0000.6300.6130.6130.3260.2380.4120.0000.0000.1900.6711.000

Missing values

2023-12-10T18:57:42.811279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:57:43.219490image/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_yearexamin_yearbase_mtctprvn_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nmbase_year_spnd_priceexamin_year_spnd_pricespnd_decr_rtbase_year_dynmc_popltn_coexamin_year_dynmc_popltn_codynmc_popltn_decr_rt
0120192020537경상북도37060영주시3706062가흥1동1938669212193720977799.921043289247688.64
1211201920201224광주광역시24020서구2402068금호1동2310736936124661635153.95589380650109110.3
2320192020533충청북도33020충주시3302051성내·충인동100896232282276735781.555448085664310.4
3420192020532강원도32310홍천군3231011홍천읍29211079803037583655103.99333666234397.02
4520192020538경상남도38090거제시3809061장승포동761763093636996965031791.85129668135676104.63
5620192020535전라북도35380부안군3538011부안읍14545624081667847690114.66106067203598191.95
6720192020534충청남도34040아산시3404055온양5동32883069733292315854100.12221901818281.94
7212201920201221부산광역시21010중구2101060영주2동295397671592133353.9514532296444.63
8920192020537경상북도37020경주시3702067보덕동14752868443993124420367.323829621182993247.78
91020192020538경상남도38070김해시3807011진영읍38738455536481866894.1743100427564863.95
seq_nobase_yearexamin_yearbase_mtctprvn_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nmbase_year_spnd_priceexamin_year_spnd_pricespnd_decr_rtbase_year_dynmc_popltn_coexamin_year_dynmc_popltn_codynmc_popltn_decr_rt
909120192020832강원도32330영월군3233011영월읍2678113131205299986276.662327820139870560.09
919220192020935전라북도35012전주시 덕진구3501257덕진동3303113347237369774771.8646878301806643.81
929320192020934충청남도34040아산시3404055온양5동3005487745262954664787.49218151495168.54
939420192020935전라북도35040정읍시3504060초산동37174019124762005866.61114560227568198.65
949520192020921부산광역시21010중구2101060영주2동2760275530496448110.48492601438429.2
959620192020924광주광역시24030남구2403054사직동18001521613107021672.814550271900158.02
969720192020935전라북도35310완주군3531011삼례읍91524701976270185483.336731407360.51
979820192020937경상북도37020경주시3702067보덕동10873555778811164455074.62568930976253.8
989920192020924광주광역시24040북구2404064문화동22380163417260135977.12133924280185209.21
9910020192020926울산광역시26030동구2603051방어동2302562806211494140691.856081262242102.35