Overview

Dataset statistics

Number of variables12
Number of observations969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory98.5 KiB
Average record size in memory104.1 B

Variable types

Numeric8
Categorical2
DateTime2

Alerts

last_load_dttm has constant value ""Constant
instt_code is highly overall correlated with dong_cnt and 7 other fieldsHigh correlation
gugun is highly overall correlated with dong_cnt and 7 other fieldsHigh correlation
dong_cnt is highly overall correlated with house_cnt and 6 other fieldsHigh correlation
house_cnt is highly overall correlated with dong_cnt and 6 other fieldsHigh correlation
tot_pop_cnt is highly overall correlated with dong_cnt and 6 other fieldsHigh correlation
m_pop_cnt is highly overall correlated with dong_cnt and 6 other fieldsHigh correlation
f_pop_cnt is highly overall correlated with dong_cnt and 6 other fieldsHigh correlation
pop_ratio is highly overall correlated with dong_cnt and 6 other fieldsHigh correlation
pop_density is highly overall correlated with gugun and 1 other fieldsHigh correlation
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 22:51:45.684011
Analysis finished2024-04-16 22:51:51.789449
Duration6.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14306
Minimum13822
Maximum14790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-04-17T07:51:51.849432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13822
5-th percentile13870.4
Q114064
median14306
Q314548
95-th percentile14741.6
Maximum14790
Range968
Interquartile range (IQR)484

Descriptive statistics

Standard deviation279.87051
Coefficient of variation (CV)0.019563156
Kurtosis-1.2
Mean14306
Median Absolute Deviation (MAD)242
Skewness0
Sum13862514
Variance78327.5
MonotonicityNot monotonic
2024-04-17T07:51:51.960093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14675 1
 
0.1%
14101 1
 
0.1%
14073 1
 
0.1%
14074 1
 
0.1%
14075 1
 
0.1%
14076 1
 
0.1%
14077 1
 
0.1%
14078 1
 
0.1%
14079 1
 
0.1%
14080 1
 
0.1%
Other values (959) 959
99.0%
ValueCountFrequency (%)
13822 1
0.1%
13823 1
0.1%
13824 1
0.1%
13825 1
0.1%
13826 1
0.1%
13827 1
0.1%
13828 1
0.1%
13829 1
0.1%
13830 1
0.1%
13831 1
0.1%
ValueCountFrequency (%)
14790 1
0.1%
14789 1
0.1%
14788 1
0.1%
14787 1
0.1%
14786 1
0.1%
14785 1
0.1%
14784 1
0.1%
14783 1
0.1%
14782 1
0.1%
14781 1
0.1%

gugun
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
부산광역시 동구
 
57
부산광역시 영도구
 
57
부산광역시 부산진구
 
57
부산광역시 동래구
 
57
부산광역시 남구
 
57
Other values (12)
684 

Length

Max length10
Median length9
Mean length8.5882353
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 동구
2nd row부산광역시 영도구
3rd row부산광역시 부산진구
4th row부산광역시 동래구
5th row부산광역시 남구

Common Values

ValueCountFrequency (%)
부산광역시 동구 57
 
5.9%
부산광역시 영도구 57
 
5.9%
부산광역시 부산진구 57
 
5.9%
부산광역시 동래구 57
 
5.9%
부산광역시 남구 57
 
5.9%
부산광역시 북구 57
 
5.9%
부산광역시 해운대구 57
 
5.9%
부산광역시 사하구 57
 
5.9%
부산광역시 금정구 57
 
5.9%
부산광역시 강서구 57
 
5.9%
Other values (7) 399
41.2%

Length

2024-04-17T07:51:52.067446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 969
51.5%
금정구 57
 
3.0%
중구 57
 
3.0%
기장군 57
 
3.0%
사상구 57
 
3.0%
수영구 57
 
3.0%
연제구 57
 
3.0%
강서구 57
 
3.0%
사하구 57
 
3.0%
동구 57
 
3.0%
Other values (7) 399
21.2%
Distinct34
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
Minimum2007-01-01 00:00:00
Maximum2020-10-01 00:00:00
2024-04-17T07:51:52.162074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:52.255479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)

dong_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.330237
Minimum5
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-04-17T07:51:52.346139image/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 deviation45.814262
Coefficient of variation (CV)1.8830175
Kurtosis11.943629
Mean24.330237
Median Absolute Deviation (MAD)3
Skewness3.7128322
Sum23576
Variance2098.9466
MonotonicityNot monotonic
2024-04-17T07:51:52.434354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
13 167
17.2%
12 160
16.5%
17 98
10.1%
16 67
6.9%
18 58
 
6.0%
11 57
 
5.9%
10 57
 
5.9%
5 57
 
5.9%
9 57
 
5.9%
20 48
 
5.0%
Other values (14) 143
14.8%
ValueCountFrequency (%)
5 57
 
5.9%
7 23
 
2.4%
8 34
 
3.5%
9 57
 
5.9%
10 57
 
5.9%
11 57
 
5.9%
12 160
16.5%
13 167
17.2%
14 14
 
1.4%
16 67
6.9%
ValueCountFrequency (%)
223 1
 
0.1%
217 1
 
0.1%
215 2
 
0.2%
214 2
 
0.2%
210 2
 
0.2%
206 25
2.6%
205 24
2.5%
25 6
 
0.6%
23 2
 
0.2%
21 1
 
0.1%

house_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct937
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172344.45
Minimum21595
Maximum1524888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-04-17T07:51:52.542411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21595
5-th percentile23310.6
Q154994
median96785
Q3120647
95-th percentile1431350
Maximum1524888
Range1503293
Interquartile range (IQR)65653

Descriptive statistics

Standard deviation326047.88
Coefficient of variation (CV)1.8918386
Kurtosis11.751181
Mean172344.45
Median Absolute Deviation (MAD)40894
Skewness3.66596
Sum1.6700177 × 108
Variance1.0630722 × 1011
MonotonicityNot monotonic
2024-04-17T07:51:52.668241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23284 3
 
0.3%
85892 2
 
0.2%
51953 2
 
0.2%
55651 2
 
0.2%
165959 2
 
0.2%
96667 2
 
0.2%
97021 2
 
0.2%
79788 2
 
0.2%
23282 2
 
0.2%
113898 2
 
0.2%
Other values (927) 948
97.8%
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
0.2%
ValueCountFrequency (%)
1524888 1
0.1%
1523122 1
0.1%
1520321 1
0.1%
1517200 1
0.1%
1513956 1
0.1%
1511199 1
0.1%
1507504 1
0.1%
1505897 1
0.1%
1502333 1
0.1%
1499152 1
0.1%

tot_pop_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct951
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412941.32
Minimum43644
Maximum3615101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-04-17T07:51:52.782880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43644
5-th percentile47741.2
Q1125905
median231239
Q3309239
95-th percentile3462747
Maximum3615101
Range3571457
Interquartile range (IQR)183334

Descriptive statistics

Standard deviation781343.04
Coefficient of variation (CV)1.8921406
Kurtosis11.642933
Mean412941.32
Median Absolute Deviation (MAD)97414
Skewness3.6502259
Sum4.0014014 × 108
Variance6.1049695 × 1011
MonotonicityNot monotonic
2024-04-17T07:51:52.894535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
271967 2
 
0.2%
181725 2
 
0.2%
207729 2
 
0.2%
111945 2
 
0.2%
338112 2
 
0.2%
374504 2
 
0.2%
305045 2
 
0.2%
46066 2
 
0.2%
248917 2
 
0.2%
3520306 2
 
0.2%
Other values (941) 949
97.9%
ValueCountFrequency (%)
43644 1
0.1%
43648 1
0.1%
43691 1
0.1%
43721 1
0.1%
43722 1
0.1%
43835 1
0.1%
43931 1
0.1%
44031 1
0.1%
44072 1
0.1%
44107 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 

Distinct947
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203673.13
Minimum21652
Maximum1801832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-04-17T07:51:53.007186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21652
5-th percentile23738.4
Q165359
median116727
Q3153591
95-th percentile1701267
Maximum1801832
Range1780180
Interquartile range (IQR)88232

Descriptive statistics

Standard deviation385325.22
Coefficient of variation (CV)1.8918805
Kurtosis11.662989
Mean203673.13
Median Absolute Deviation (MAD)46848
Skewness3.6534727
Sum1.9735926 × 108
Variance1.4847553 × 1011
MonotonicityNot monotonic
2024-04-17T07:51:53.130592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44889 2
 
0.2%
137817 2
 
0.2%
1736878 2
 
0.2%
55081 2
 
0.2%
100988 2
 
0.2%
86570 2
 
0.2%
169379 2
 
0.2%
182588 2
 
0.2%
65532 2
 
0.2%
151255 2
 
0.2%
Other values (937) 949
97.9%
ValueCountFrequency (%)
21652 1
0.1%
21660 1
0.1%
21663 1
0.1%
21688 1
0.1%
21702 1
0.1%
21755 1
0.1%
21791 1
0.1%
21839 1
0.1%
21899 1
0.1%
21902 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 

Distinct947
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209268.19
Minimum21984
Maximum1813269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-04-17T07:51:53.250778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21984
5-th percentile24002.8
Q162321
median113806
Q3155617
95-th percentile1761480
Maximum1813269
Range1791285
Interquartile range (IQR)93296

Descriptive statistics

Standard deviation396036.67
Coefficient of variation (CV)1.8924838
Kurtosis11.624076
Mean209268.19
Median Absolute Deviation (MAD)49745
Skewness3.6469324
Sum2.0278088 × 108
Variance1.5684504 × 1011
MonotonicityNot monotonic
2024-04-17T07:51:53.362446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57547 3
 
0.3%
95155 2
 
0.2%
83817 2
 
0.2%
56864 2
 
0.2%
168733 2
 
0.2%
106741 2
 
0.2%
191916 2
 
0.2%
1783428 2
 
0.2%
142073 2
 
0.2%
153790 2
 
0.2%
Other values (937) 948
97.8%
ValueCountFrequency (%)
21984 1
0.1%
21996 1
0.1%
22020 1
0.1%
22028 1
0.1%
22033 1
0.1%
22080 1
0.1%
22140 1
0.1%
22168 1
0.1%
22192 1
0.1%
22205 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 

Distinct177
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.766956
Minimum1.2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-04-17T07:51:53.492705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.32
Q13.6
median6.6
Q38.7
95-th percentile100
Maximum100
Range98.8
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation22.25764
Coefficient of variation (CV)1.8915376
Kurtosis11.632601
Mean11.766956
Median Absolute Deviation (MAD)2.8
Skewness3.6493127
Sum11402.18
Variance495.40252
MonotonicityNot monotonic
2024-04-17T07:51:53.631961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 57
 
5.9%
1.3 48
 
5.0%
3.2 48
 
5.0%
5.1 38
 
3.9%
2.6 37
 
3.8%
11.9 36
 
3.7%
3.5 28
 
2.9%
7.7 27
 
2.8%
5.9 27
 
2.8%
7.9 24
 
2.5%
Other values (167) 599
61.8%
ValueCountFrequency (%)
1.2 1
 
0.1%
1.3 48
5.0%
1.35 1
 
0.1%
1.36 1
 
0.1%
1.38 1
 
0.1%
1.39 1
 
0.1%
1.4 2
 
0.2%
1.41 2
 
0.2%
1.52 1
 
0.1%
1.55 1
 
0.1%
ValueCountFrequency (%)
100.0 57
5.9%
12.08 1
 
0.1%
12.04 1
 
0.1%
12.02 1
 
0.1%
12.0 4
 
0.4%
11.96 1
 
0.1%
11.95 1
 
0.1%
11.93 1
 
0.1%
11.9 36
3.7%
11.87 1
 
0.1%

pop_density
Real number (ℝ)

HIGH CORRELATION 

Distinct861
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9245.4489
Minimum305
Maximum18190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.6 KiB
2024-04-17T07:51:53.761224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum305
5-th percentile728.8
Q16257
median8220
Q312735
95-th percentile17482.6
Maximum18190
Range17885
Interquartile range (IQR)6478

Descriptive statistics

Standard deviation5212.7339
Coefficient of variation (CV)0.5638162
Kurtosis-0.8784557
Mean9245.4489
Median Absolute Deviation (MAD)3667
Skewness0.13145005
Sum8958840
Variance27172594
MonotonicityNot monotonic
2024-04-17T07:51:54.113316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
766 12
 
1.2%
678 5
 
0.5%
8071 4
 
0.4%
3812 3
 
0.3%
17449 3
 
0.3%
8008 3
 
0.3%
8105 3
 
0.3%
754 3
 
0.3%
738 3
 
0.3%
3799 3
 
0.3%
Other values (851) 927
95.7%
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
0.2%
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

HIGH CORRELATION 

Distinct17
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
3270000
 
57
3280000
 
57
3290000
 
57
3300000
 
57
3310000
 
57
Other values (12)
684 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3270000
2nd row3280000
3rd row3290000
4th row3300000
5th row3310000

Common Values

ValueCountFrequency (%)
3270000 57
 
5.9%
3280000 57
 
5.9%
3290000 57
 
5.9%
3300000 57
 
5.9%
3310000 57
 
5.9%
3320000 57
 
5.9%
3330000 57
 
5.9%
3340000 57
 
5.9%
3350000 57
 
5.9%
3360000 57
 
5.9%
Other values (7) 399
41.2%

Length

2024-04-17T07:51:54.216008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3270000 57
 
5.9%
3360000 57
 
5.9%
3250000 57
 
5.9%
e-부산광역시 57
 
5.9%
3400000 57
 
5.9%
3390000 57
 
5.9%
3380000 57
 
5.9%
3370000 57
 
5.9%
3350000 57
 
5.9%
3280000 57
 
5.9%
Other values (7) 399
41.2%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.7 KiB
Minimum2020-12-21 16:17:17
Maximum2020-12-21 16:17:17
2024-04-17T07:51:54.291291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:54.360046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-17T07:51:50.921587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.127555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.742714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.361205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:48.031342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:48.727842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.408463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:50.063942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:51.002117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.196798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.814078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.443572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:48.107250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:48.824219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.491195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:50.136163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:51.079588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.264891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.891000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.528756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:48.185495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:48.913956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.573075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:50.449187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:51.165545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.350169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.979553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.615316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:48.283442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.006086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.660480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:50.529245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:51.252554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.426763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.058789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.700292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:48.376990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.088779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.742474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:50.609525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:51.334285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.505129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.135825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.784442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:48.483877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.170470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.825536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:50.688802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:51.418604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.591577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.213555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.870977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:48.570461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.250560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.905715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:50.769348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:51.495344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:46.666750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.284048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:47.946783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:48.647396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.325974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:49.982336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:51:50.842523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T07:51:54.420752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeygugunrate_yeardong_cnthouse_cnttot_pop_cntm_pop_cntf_pop_cntpop_ratiopop_densityinstt_code
skey1.0000.2100.9370.0000.1790.0000.1650.0000.0000.3630.210
gugun0.2101.0000.0001.0000.8480.9910.9570.9860.9870.9681.000
rate_year0.9370.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
dong_cnt0.0001.0000.0001.0001.0001.0001.0001.0001.0000.9951.000
house_cnt0.1790.8480.0001.0001.0000.9430.9430.9430.9430.7790.848
tot_pop_cnt0.0000.9910.0001.0000.9431.0000.9951.0001.0000.8160.991
m_pop_cnt0.1650.9570.0001.0000.9430.9951.0000.9940.9940.8040.957
f_pop_cnt0.0000.9860.0001.0000.9431.0000.9941.0001.0000.8220.986
pop_ratio0.0000.9870.0001.0000.9431.0000.9941.0001.0000.8190.987
pop_density0.3630.9680.0000.9950.7790.8160.8040.8220.8191.0000.968
instt_code0.2101.0000.0001.0000.8480.9910.9570.9860.9870.9681.000
2024-04-17T07:51:54.521492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
instt_codegugun
instt_code1.0001.000
gugun1.0001.000
2024-04-17T07:51:54.604219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeydong_cnthouse_cnttot_pop_cntm_pop_cntf_pop_cntpop_ratiopop_densityguguninstt_code
skey1.0000.013-0.0230.0030.0060.000-0.0070.0480.0820.082
dong_cnt0.0131.0000.8210.8070.8020.8140.807-0.0040.9920.992
house_cnt-0.0230.8211.0000.9890.9860.9910.991-0.0770.6960.696
tot_pop_cnt0.0030.8070.9891.0000.9990.9990.999-0.0910.9840.984
m_pop_cnt0.0060.8020.9860.9991.0000.9980.998-0.1010.9010.901
f_pop_cnt0.0000.8140.9910.9990.9981.0000.999-0.0810.9730.973
pop_ratio-0.0070.8070.9910.9990.9980.9991.000-0.0950.9760.976
pop_density0.048-0.004-0.077-0.091-0.101-0.081-0.0951.0000.8510.851
gugun0.0820.9920.6960.9840.9010.9730.9760.8511.0001.000
instt_code0.0820.9920.6960.9840.9010.9730.9760.8511.0001.000

Missing values

2024-04-17T07:51:51.613232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T07:51:51.739859image/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
014675부산광역시 동구2020-0412455789199445109468852.7932132700002020-12-21 16:17:17
114676부산광역시 영도구2020-04115481211746958138593313.4827232800002020-12-21 16:17:17
214677부산광역시 부산진구2020-042016818935996817392618604210.41213232900002020-12-21 16:17:17
314678부산광역시 동래구2020-04131135472738191333241404957.91646533000002020-12-21 16:17:17
414679부산광역시 남구2020-04171171672775991360181415818.01035033100002020-12-21 16:17:17
514680부산광역시 북구2020-04131204492915271442651472628.4740533200002020-12-21 16:17:17
614681부산광역시 해운대구2020-041816935741024219783821240411.9797133300002020-12-21 16:17:17
714682부산광역시 사하구2020-04161388133229601615951613659.3773233400002020-12-21 16:17:17
814683부산광역시 금정구2020-04161071012411001175351235657.0369433500002020-12-21 16:17:17
914684부산광역시 강서구2020-0485472513651672206643103.975233600002020-12-21 16:17:17
skeygugunrate_yeardong_cnthouse_cnttot_pop_cntm_pop_cntf_pop_cntpop_ratiopop_densityinstt_codelast_load_dttm
95914781부산광역시 남구2020-10171176852742321343351398978.01022533100002020-12-21 16:17:17
96014782부산광역시 북구2020-10131206042878671423781454898.4731233200002020-12-21 16:17:17
96114783부산광역시 해운대구2020-101817081540749219618121131111.8791733300002020-12-21 16:17:17
96214784부산광역시 사하구2020-10161388793182061589441592629.2761833400002020-12-21 16:17:17
96314785부산광역시 금정구2020-10161073452375361156371218996.9363933500002020-12-21 16:17:17
96414786부산광역시 강서구2020-1085734114141874572668464.177933600002020-12-21 16:17:17
96514787부산광역시 연제구2020-1012924752111751016751095006.11745233700002020-12-21 16:17:17
96614788부산광역시 수영구2020-10108394117878284459943235.21751033800002020-12-21 16:17:17
96714789부산광역시 사상구2020-1012973432183621105551078076.3605033900002020-12-21 16:17:17
96814790부산광역시 기장군2020-1057391517526187136881255.180334000002020-12-21 16:17:17