Overview

Dataset statistics

Number of variables12
Number of observations1496
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory153.5 KiB
Average record size in memory105.1 B

Variable types

Numeric8
Categorical2
Text1
DateTime1

Alerts

instt_code has constant value ""Constant
last_load_dttm has constant value ""Constant
dong_cnt is highly overall correlated with house_cnt and 5 other fieldsHigh correlation
house_cnt is highly overall correlated with dong_cnt and 5 other fieldsHigh correlation
tot_pop_cnt is highly overall correlated with dong_cnt and 5 other fieldsHigh correlation
m_pop_cnt is highly overall correlated with dong_cnt and 5 other fieldsHigh correlation
f_pop_cnt is highly overall correlated with dong_cnt and 5 other fieldsHigh correlation
pop_ratio is highly overall correlated with dong_cnt and 5 other fieldsHigh correlation
pop_density is highly overall correlated with gugunHigh correlation
gugun is highly overall correlated with dong_cnt and 6 other fieldsHigh correlation
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 22:45:20.817000
Analysis finished2024-04-16 22:45:27.274774
Duration6.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct1496
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23426.5
Minimum22679
Maximum24174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-17T07:45:27.333829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22679
5-th percentile22753.75
Q123052.75
median23426.5
Q323800.25
95-th percentile24099.25
Maximum24174
Range1495
Interquartile range (IQR)747.5

Descriptive statistics

Standard deviation432.00231
Coefficient of variation (CV)0.018440754
Kurtosis-1.2
Mean23426.5
Median Absolute Deviation (MAD)374
Skewness0
Sum35046044
Variance186626
MonotonicityNot monotonic
2024-04-17T07:45:27.473939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24039 1
 
0.1%
23201 1
 
0.1%
23210 1
 
0.1%
23209 1
 
0.1%
23208 1
 
0.1%
23207 1
 
0.1%
23206 1
 
0.1%
23205 1
 
0.1%
23204 1
 
0.1%
23203 1
 
0.1%
Other values (1486) 1486
99.3%
ValueCountFrequency (%)
22679 1
0.1%
22680 1
0.1%
22681 1
0.1%
22682 1
0.1%
22683 1
0.1%
22684 1
0.1%
22685 1
0.1%
22686 1
0.1%
22687 1
0.1%
22688 1
0.1%
ValueCountFrequency (%)
24174 1
0.1%
24173 1
0.1%
24172 1
0.1%
24171 1
0.1%
24170 1
0.1%
24169 1
0.1%
24168 1
0.1%
24167 1
0.1%
24166 1
0.1%
24165 1
0.1%

gugun
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
부산광역시
 
88
부산광역시 중구
 
88
부산광역시 서구
 
88
부산광역시 동구
 
88
부산광역시 영도구
 
88
Other values (12)
1056 

Length

Max length10
Median length9
Mean length8.5882353
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시
2nd row부산광역시 중구
3rd row부산광역시 서구
4th row부산광역시 동구
5th row부산광역시 영도구

Common Values

ValueCountFrequency (%)
부산광역시 88
 
5.9%
부산광역시 중구 88
 
5.9%
부산광역시 서구 88
 
5.9%
부산광역시 동구 88
 
5.9%
부산광역시 영도구 88
 
5.9%
부산광역시 부산진구 88
 
5.9%
부산광역시 동래구 88
 
5.9%
부산광역시 남구 88
 
5.9%
부산광역시 북구 88
 
5.9%
부산광역시 해운대구 88
 
5.9%
Other values (7) 616
41.2%

Length

2024-04-17T07:45:27.583119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 1496
51.5%
해운대구 88
 
3.0%
사상구 88
 
3.0%
수영구 88
 
3.0%
연제구 88
 
3.0%
강서구 88
 
3.0%
금정구 88
 
3.0%
사하구 88
 
3.0%
북구 88
 
3.0%
중구 88
 
3.0%
Other values (7) 616
21.2%
Distinct65
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-04-17T07:45:27.766677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.2045455
Min length4

Characters and Unicode

Total characters7786
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOct-22
2nd rowOct-22
3rd rowOct-22
4th rowOct-22
5th rowOct-22
ValueCountFrequency (%)
2017 221
 
14.8%
2018 204
 
13.6%
2009 17
 
1.1%
jun-20 17
 
1.1%
mar-20 17
 
1.1%
apr-20 17
 
1.1%
oct-22 17
 
1.1%
dec-19 17
 
1.1%
may-19 17
 
1.1%
apr-19 17
 
1.1%
Other values (55) 935
62.5%
2024-04-17T07:45:28.076296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 1513
19.4%
1 969
12.4%
- 901
11.6%
0 867
11.1%
a 255
 
3.3%
7 238
 
3.1%
J 221
 
2.8%
8 221
 
2.8%
9 221
 
2.8%
e 221
 
2.8%
Other values (23) 2159
27.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4182
53.7%
Lowercase Letter 1802
23.1%
Dash Punctuation 901
 
11.6%
Uppercase Letter 901
 
11.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 255
14.2%
e 221
12.3%
u 204
11.3%
r 170
9.4%
n 153
8.5%
p 153
8.5%
c 136
7.5%
b 85
 
4.7%
y 85
 
4.7%
v 68
 
3.8%
Other values (4) 272
15.1%
Decimal Number
ValueCountFrequency (%)
2 1513
36.2%
1 969
23.2%
0 867
20.7%
7 238
 
5.7%
8 221
 
5.3%
9 221
 
5.3%
3 102
 
2.4%
4 17
 
0.4%
5 17
 
0.4%
6 17
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
J 221
24.5%
M 170
18.9%
A 153
17.0%
F 85
 
9.4%
N 68
 
7.5%
D 68
 
7.5%
O 68
 
7.5%
S 68
 
7.5%
Dash Punctuation
ValueCountFrequency (%)
- 901
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5083
65.3%
Latin 2703
34.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 255
 
9.4%
J 221
 
8.2%
e 221
 
8.2%
u 204
 
7.5%
M 170
 
6.3%
r 170
 
6.3%
A 153
 
5.7%
n 153
 
5.7%
p 153
 
5.7%
c 136
 
5.0%
Other values (12) 867
32.1%
Common
ValueCountFrequency (%)
2 1513
29.8%
1 969
19.1%
- 901
17.7%
0 867
17.1%
7 238
 
4.7%
8 221
 
4.3%
9 221
 
4.3%
3 102
 
2.0%
4 17
 
0.3%
5 17
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1513
19.4%
1 969
12.4%
- 901
11.6%
0 867
11.1%
a 255
 
3.3%
7 238
 
3.1%
J 221
 
2.8%
8 221
 
2.8%
9 221
 
2.8%
e 221
 
2.8%
Other values (23) 2159
27.7%

dong_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.255348
Minimum5
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-17T07:45:28.190551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q111
median13
Q317
95-th percentile205
Maximum223
Range218
Interquartile range (IQR)6

Descriptive statistics

Standard deviation45.657885
Coefficient of variation (CV)1.8823843
Kurtosis11.918818
Mean24.255348
Median Absolute Deviation (MAD)3
Skewness3.7114147
Sum36286
Variance2084.6424
MonotonicityNot monotonic
2024-04-17T07:45:28.280388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
13 260
17.4%
12 253
16.9%
17 129
8.6%
16 129
8.6%
18 89
 
5.9%
9 88
 
5.9%
11 88
 
5.9%
10 88
 
5.9%
5 88
 
5.9%
20 79
 
5.3%
Other values (14) 205
13.7%
ValueCountFrequency (%)
5 88
 
5.9%
7 23
 
1.5%
8 65
 
4.3%
9 88
 
5.9%
10 88
 
5.9%
11 88
 
5.9%
12 253
16.9%
13 260
17.4%
14 14
 
0.9%
16 129
8.6%
ValueCountFrequency (%)
223 1
 
0.1%
217 1
 
0.1%
215 2
 
0.1%
214 2
 
0.1%
210 2
 
0.1%
206 25
1.7%
205 55
3.7%
25 6
 
0.4%
23 2
 
0.1%
21 1
 
0.1%

house_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct1455
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175734.8
Minimum21595
Maximum1562595
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-17T07:45:28.380972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21595
5-th percentile24011.5
Q155215.75
median97024
Q3122421
95-th percentile1456607
Maximum1562595
Range1541000
Interquartile range (IQR)67205.25

Descriptive statistics

Standard deviation332392.56
Coefficient of variation (CV)1.8914442
Kurtosis11.762821
Mean175734.8
Median Absolute Deviation (MAD)39566
Skewness3.668783
Sum2.6289926 × 108
Variance1.1048482 × 1011
MonotonicityNot monotonic
2024-04-17T07:45:28.498971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23284 3
 
0.2%
54896 2
 
0.1%
171558 2
 
0.1%
96667 2
 
0.1%
166981 2
 
0.1%
53342 2
 
0.1%
107184 2
 
0.1%
165959 2
 
0.1%
43427 2
 
0.1%
165314 2
 
0.1%
Other values (1445) 1475
98.6%
ValueCountFrequency (%)
21595 1
0.1%
21605 1
0.1%
21920 1
0.1%
22064 1
0.1%
22132 1
0.1%
22953 1
0.1%
22975 1
0.1%
23001 1
0.1%
23006 1
0.1%
23017 2
0.1%
ValueCountFrequency (%)
1562595 1
0.1%
1561666 1
0.1%
1561008 1
0.1%
1559593 1
0.1%
1559462 1
0.1%
1558052 1
0.1%
1556938 1
0.1%
1556293 1
0.1%
1556086 1
0.1%
1555867 1
0.1%

tot_pop_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct1472
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408129.55
Minimum41732
Maximum3615101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-17T07:45:28.615335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41732
5-th percentile46940.75
Q1126865.75
median224210.5
Q3305045
95-th percentile3384927.8
Maximum3615101
Range3573369
Interquartile range (IQR)178179.25

Descriptive statistics

Standard deviation772017.94
Coefficient of variation (CV)1.8916002
Kurtosis11.650617
Mean408129.55
Median Absolute Deviation (MAD)86721.5
Skewness3.6528652
Sum6.1056181 × 108
Variance5.960117 × 1011
MonotonicityNot monotonic
2024-04-17T07:45:28.729823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177230 2
 
0.1%
268074 2
 
0.1%
234624 2
 
0.1%
3520306 2
 
0.1%
181725 2
 
0.1%
46066 2
 
0.1%
111945 2
 
0.1%
90856 2
 
0.1%
42734 2
 
0.1%
125347 2
 
0.1%
Other values (1462) 1476
98.7%
ValueCountFrequency (%)
41732 1
0.1%
41862 1
0.1%
41890 1
0.1%
41909 1
0.1%
41913 1
0.1%
41957 1
0.1%
42001 1
0.1%
42136 1
0.1%
42267 1
0.1%
42455 1
0.1%
ValueCountFrequency (%)
3615101 1
0.1%
3600381 1
0.1%
3596063 1
0.1%
3586079 1
0.1%
3574340 1
0.1%
3573533 1
0.1%
3563578 1
0.1%
3559780 1
0.1%
3557716 1
0.1%
3546887 1
0.1%

m_pop_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct1471
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200802.72
Minimum20594
Maximum1801832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-17T07:45:28.840701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20594
5-th percentile23319.75
Q165904.5
median111007.5
Q3151255
95-th percentile1656935
Maximum1801832
Range1781238
Interquartile range (IQR)85350.5

Descriptive statistics

Standard deviation379805.82
Coefficient of variation (CV)1.8914376
Kurtosis11.677922
Mean200802.72
Median Absolute Deviation (MAD)42224
Skewness3.6568329
Sum3.0040087 × 108
Variance1.4425246 × 1011
MonotonicityNot monotonic
2024-04-17T07:45:28.951788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151255 2
 
0.1%
81662 2
 
0.1%
119308 2
 
0.1%
86570 2
 
0.1%
1736878 2
 
0.1%
22843 2
 
0.1%
55081 2
 
0.1%
44786 2
 
0.1%
141260 2
 
0.1%
83201 2
 
0.1%
Other values (1461) 1476
98.7%
ValueCountFrequency (%)
20594 1
0.1%
20662 1
0.1%
20698 1
0.1%
20703 1
0.1%
20725 1
0.1%
20738 1
0.1%
20778 1
0.1%
20846 1
0.1%
20951 1
0.1%
21055 1
0.1%
ValueCountFrequency (%)
1801832 1
0.1%
1791455 1
0.1%
1791273 1
0.1%
1783378 1
0.1%
1778834 1
0.1%
1774993 1
0.1%
1767963 1
0.1%
1762869 1
0.1%
1761594 1
0.1%
1752465 1
0.1%

f_pop_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct1468
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean207326.83
Minimum21138
Maximum1813269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-17T07:45:29.068268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21138
5-th percentile23615
Q162914.75
median111301
Q3153742.75
95-th percentile1727992.8
Maximum1813269
Range1792131
Interquartile range (IQR)90828

Descriptive statistics

Standard deviation392235.22
Coefficient of variation (CV)1.8918691
Kurtosis11.625686
Mean207326.83
Median Absolute Deviation (MAD)44618.5
Skewness3.6489483
Sum3.1016093 × 108
Variance1.5384847 × 1011
MonotonicityNot monotonic
2024-04-17T07:45:29.184044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57547 3
 
0.2%
106741 2
 
0.1%
115316 2
 
0.1%
107259 2
 
0.1%
21996 2
 
0.1%
82258 2
 
0.1%
44957 2
 
0.1%
1783428 2
 
0.1%
23223 2
 
0.1%
56864 2
 
0.1%
Other values (1458) 1475
98.6%
ValueCountFrequency (%)
21138 1
0.1%
21164 1
0.1%
21184 1
0.1%
21210 1
0.1%
21219 1
0.1%
21223 1
0.1%
21228 1
0.1%
21290 1
0.1%
21316 1
0.1%
21400 1
0.1%
ValueCountFrequency (%)
1813269 1
0.1%
1808926 1
0.1%
1804790 1
0.1%
1802701 1
0.1%
1798540 1
0.1%
1798186 1
0.1%
1795615 1
0.1%
1795506 1
0.1%
1794847 1
0.1%
1794422 1
0.1%

pop_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct186
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.766297
Minimum1.2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-17T07:45:29.299900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.3
Q13.6
median6.5
Q38.67
95-th percentile100
Maximum100
Range98.8
Interquartile range (IQR)5.07

Descriptive statistics

Standard deviation22.24796
Coefficient of variation (CV)1.8908209
Kurtosis11.624311
Mean11.766297
Median Absolute Deviation (MAD)2.6
Skewness3.6505218
Sum17602.38
Variance494.97173
MonotonicityNot monotonic
2024-04-17T07:45:29.419326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 88
 
5.9%
3.2 73
 
4.9%
1.3 71
 
4.7%
2.6 61
 
4.1%
7.9 53
 
3.5%
6.1 48
 
3.2%
5.2 47
 
3.1%
5.1 40
 
2.7%
11.9 36
 
2.4%
3.3 35
 
2.3%
Other values (176) 944
63.1%
ValueCountFrequency (%)
1.2 9
 
0.6%
1.3 71
4.7%
1.35 1
 
0.1%
1.36 1
 
0.1%
1.38 1
 
0.1%
1.39 1
 
0.1%
1.4 2
 
0.1%
1.41 2
 
0.1%
1.52 1
 
0.1%
1.55 1
 
0.1%
ValueCountFrequency (%)
100.0 88
5.9%
12.08 1
 
0.1%
12.04 1
 
0.1%
12.02 1
 
0.1%
12.0 4
 
0.3%
11.96 1
 
0.1%
11.95 1
 
0.1%
11.93 1
 
0.1%
11.9 36
2.4%
11.87 1
 
0.1%

pop_density
Real number (ℝ)

HIGH CORRELATION 

Distinct1293
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9105.1845
Minimum305
Maximum18190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.3 KiB
2024-04-17T07:45:29.528129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum305
5-th percentile753.75
Q15935.75
median8076.5
Q312622
95-th percentile17428.25
Maximum18190
Range17885
Interquartile range (IQR)6686.25

Descriptive statistics

Standard deviation5175.7727
Coefficient of variation (CV)0.56844237
Kurtosis-0.88349978
Mean9105.1845
Median Absolute Deviation (MAD)3565
Skewness0.17435674
Sum13621356
Variance26788623
MonotonicityNot monotonic
2024-04-17T07:45:29.652492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
766 12
 
0.8%
817 7
 
0.5%
818 7
 
0.5%
816 6
 
0.4%
819 6
 
0.4%
678 5
 
0.3%
8071 4
 
0.3%
8008 3
 
0.2%
7851 3
 
0.2%
3812 3
 
0.2%
Other values (1283) 1440
96.3%
ValueCountFrequency (%)
305 1
0.1%
312 1
0.1%
356 1
0.1%
368 1
0.1%
369 1
0.1%
377 1
0.1%
384 1
0.1%
387 1
0.1%
412 1
0.1%
414 1
0.1%
ValueCountFrequency (%)
18190 1
0.1%
17960 1
0.1%
17927 1
0.1%
17799 2
0.1%
17797 1
0.1%
17787 1
0.1%
17781 1
0.1%
17779 1
0.1%
17767 1
0.1%
17751 1
0.1%

instt_code
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
6260000
1496 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6260000
2nd row6260000
3rd row6260000
4th row6260000
5th row6260000

Common Values

ValueCountFrequency (%)
6260000 1496
100.0%

Length

2024-04-17T07:45:29.773544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T07:45:29.863474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6260000 1496
100.0%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Minimum2023-08-01 05:57:03
Maximum2023-08-01 05:57:03
2024-04-17T07:45:29.923561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:29.994668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T07:45:26.177307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:21.296097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:21.962469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:22.795743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:23.550222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.213307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.895487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.545886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:26.258097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:21.373470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:22.039118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:22.881632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:23.634892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.295255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.977990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.629281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:26.333312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:21.451947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:22.109288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:22.967356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:23.713345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.366649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.050613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.706686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:26.416967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:21.539461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:22.431699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:23.054889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:23.798693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.467478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.136967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.785390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:26.502128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:21.633720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:22.502629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:23.153768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:23.882987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.565760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.225963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.865089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:26.582103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:21.713283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:22.576672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:23.262876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:23.974464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.658497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.309000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.942470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:26.663251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:21.791882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:22.651532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:23.353101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.056979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.738605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.389644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:26.028764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:26.736502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:21.877538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:22.721914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:23.449505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.133152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:24.815198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:25.464521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:45:26.100407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T07:45:30.057175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeygugunrate_yeardong_cnthouse_cnttot_pop_cntm_pop_cntf_pop_cntpop_ratiopop_density
skey1.0000.0000.9920.0000.2530.0000.1670.0000.0000.296
gugun0.0001.0000.0001.0000.8060.9730.9310.9880.9900.969
rate_year0.9920.0001.0000.0000.0000.0000.0000.0000.0000.000
dong_cnt0.0001.0000.0001.0001.0001.0001.0001.0001.0000.998
house_cnt0.2530.8060.0001.0001.0000.6760.6760.6760.6760.767
tot_pop_cnt0.0000.9730.0001.0000.6761.0000.9920.9980.9980.822
m_pop_cnt0.1670.9310.0001.0000.6760.9921.0000.9860.9870.810
f_pop_cnt0.0000.9880.0001.0000.6760.9980.9861.0001.0000.832
pop_ratio0.0000.9900.0001.0000.6760.9980.9871.0001.0000.830
pop_density0.2960.9690.0000.9980.7670.8220.8100.8320.8301.000
2024-04-17T07:45:30.166023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeydong_cnthouse_cnttot_pop_cntm_pop_cntf_pop_cntpop_ratiopop_densitygugun
skey1.000-0.0260.088-0.046-0.050-0.0410.002-0.0960.000
dong_cnt-0.0261.0000.8150.8040.7990.8120.8040.0090.995
house_cnt0.0880.8151.0000.9850.9830.9870.991-0.0810.596
tot_pop_cnt-0.0460.8040.9851.0000.9990.9990.998-0.0910.943
m_pop_cnt-0.0500.7990.9830.9991.0000.9970.997-0.1040.846
f_pop_cnt-0.0410.8120.9870.9990.9971.0000.997-0.0700.982
pop_ratio0.0020.8040.9910.9980.9970.9971.000-0.1020.984
pop_density-0.0960.009-0.081-0.091-0.104-0.070-0.1021.0000.853
gugun0.0000.9950.5960.9430.8460.9820.9840.8531.000

Missing values

2024-04-17T07:45:26.841825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T07:45:27.222919image/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

skeygugunrate_yeardong_cnthouse_cnttot_pop_cntm_pop_cntf_pop_cntpop_ratiopop_densityinstt_codelast_load_dttm
024039부산광역시Oct-222051556293337045016485951721855100.0437662600002023-08-01 05:57:03
124040부산광역시 중구Oct-229240564213620846212901.31488962600002023-08-01 05:57:03
224041부산광역시 서구Oct-22135365710749052192552983.2770562600002023-08-01 05:57:03
324042부산광역시 동구Oct-2212465358903043518455122.6902062600002023-08-01 05:57:03
424043부산광역시 영도구Oct-22115444011057454641559333.3778762600002023-08-01 05:57:03
524044부산광역시 부산진구Oct-222017676535625117116418508710.61200762600002023-08-01 05:57:03
624045부산광역시 동래구Oct-22131196292749921330651419278.21653662600002023-08-01 05:57:03
724046부산광역시 남구Oct-22171179642629041284581344467.8980362600002023-08-01 05:57:03
824047부산광역시 북구Oct-22131238312818711387581431138.4716062600002023-08-01 05:57:03
924048부산광역시 해운대구Oct-221817082839276318851120425211.7762162600002023-08-01 05:57:03
skeygugunrate_yeardong_cnthouse_cnttot_pop_cntm_pop_cntf_pop_cntpop_ratiopop_densityinstt_codelast_load_dttm
148622762부산광역시 기장군201154280610949554906545893.0550262600002023-08-01 05:57:03
148722763부산광역시 동래구2011131062442846491407401439097.941711762600002023-08-01 05:57:03
148822764부산광역시 서구2012135313812204060585614553.42879362600002023-08-01 05:57:03
148922765부산광역시 동구201214438599990749576503312.81026862600002023-08-01 05:57:03
149022766부산광역시 영도구2012115790014142270643707793.961000962600002023-08-01 05:57:03
149122767부산광역시 부산진구20122516069939503219477720025511.051330562600002023-08-01 05:57:03
149222768부산광역시 남구2012191133972946731457311489428.251099162600002023-08-01 05:57:03
149322769부산광역시 북구2012131153063140851566081574778.79798062600002023-08-01 05:57:03
149422770부산광역시 해운대구20121816005343152621075922076712.08838662600002023-08-01 05:57:03
149522771부산광역시 사하구2012161343273567691793251774449.98855262600002023-08-01 05:57:03