Overview

Dataset statistics

Number of variables12
Number of observations986
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory101.2 KiB
Average record size in memory105.1 B

Variable types

Numeric8
Categorical4

Alerts

instt_code has constant value ""Constant
last_load_dttm has constant value ""Constant
skey is highly overall correlated with rate_yearHigh correlation
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
rate_year is highly overall correlated with skeyHigh correlation
skey has unique valuesUnique

Reproduction

Analysis started2024-04-16 22:48:48.838284
Analysis finished2024-04-16 22:48:54.812388
Duration5.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct986
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19193.5
Minimum18701
Maximum19686
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2024-04-17T07:48:54.871857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18701
5-th percentile18750.25
Q118947.25
median19193.5
Q319439.75
95-th percentile19636.75
Maximum19686
Range985
Interquartile range (IQR)492.5

Descriptive statistics

Standard deviation284.77798
Coefficient of variation (CV)0.01483721
Kurtosis-1.2
Mean19193.5
Median Absolute Deviation (MAD)246.5
Skewness0
Sum18924791
Variance81098.5
MonotonicityNot monotonic
2024-04-17T07:48:54.975988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19597 1
 
0.1%
19044 1
 
0.1%
19046 1
 
0.1%
19047 1
 
0.1%
19048 1
 
0.1%
19049 1
 
0.1%
19050 1
 
0.1%
19051 1
 
0.1%
19052 1
 
0.1%
19053 1
 
0.1%
Other values (976) 976
99.0%
ValueCountFrequency (%)
18701 1
0.1%
18702 1
0.1%
18703 1
0.1%
18704 1
0.1%
18705 1
0.1%
18706 1
0.1%
18707 1
0.1%
18708 1
0.1%
18709 1
0.1%
18710 1
0.1%
ValueCountFrequency (%)
19686 1
0.1%
19685 1
0.1%
19684 1
0.1%
19683 1
0.1%
19682 1
0.1%
19681 1
0.1%
19680 1
0.1%
19679 1
0.1%
19678 1
0.1%
19677 1
0.1%

gugun
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
부산광역시 강서구
 
58
부산광역시 연제구
 
58
부산광역시 수영구
 
58
부산광역시 사상구
 
58
부산광역시 기장군
 
58
Other values (12)
696 

Length

Max length10
Median length9
Mean length8.5882353
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 강서구
2nd row부산광역시 연제구
3rd row부산광역시 수영구
4th row부산광역시 사상구
5th row부산광역시 기장군

Common Values

ValueCountFrequency (%)
부산광역시 강서구 58
 
5.9%
부산광역시 연제구 58
 
5.9%
부산광역시 수영구 58
 
5.9%
부산광역시 사상구 58
 
5.9%
부산광역시 기장군 58
 
5.9%
부산광역시 58
 
5.9%
부산광역시 중구 58
 
5.9%
부산광역시 서구 58
 
5.9%
부산광역시 동구 58
 
5.9%
부산광역시 영도구 58
 
5.9%
Other values (7) 406
41.2%

Length

2024-04-17T07:48:55.074855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 986
51.5%
영도구 58
 
3.0%
사하구 58
 
3.0%
해운대구 58
 
3.0%
북구 58
 
3.0%
남구 58
 
3.0%
동래구 58
 
3.0%
부산진구 58
 
3.0%
동구 58
 
3.0%
강서구 58
 
3.0%
Other values (7) 406
21.2%

rate_year
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2017
221 
2018
204 
2020-08
 
17
2020-09
 
17
2020-10
 
17
Other values (30)
510 

Length

Max length7
Median length4
Mean length5.1896552
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-06
2nd row2020-06
3rd row2020-06
4th row2020-06
5th row2020-06

Common Values

ValueCountFrequency (%)
2017 221
22.4%
2018 204
20.7%
2020-08 17
 
1.7%
2020-09 17
 
1.7%
2020-10 17
 
1.7%
2020-11 17
 
1.7%
2015 17
 
1.7%
2016 17
 
1.7%
2007 17
 
1.7%
2020-03 17
 
1.7%
Other values (25) 425
43.1%

Length

2024-04-17T07:48:55.169717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017 221
22.4%
2018 204
20.7%
2020-06 17
 
1.7%
2019-07 17
 
1.7%
2019-02 17
 
1.7%
2019-03 17
 
1.7%
2019-04 17
 
1.7%
2019-05 17
 
1.7%
2019-06 17
 
1.7%
2020-05 17
 
1.7%
Other values (25) 425
43.1%

dong_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.326572
Minimum5
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2024-04-17T07:48:55.257818image/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 deviation45.806621
Coefficient of variation (CV)1.8829871
Kurtosis11.942451
Mean24.326572
Median Absolute Deviation (MAD)3
Skewness3.712766
Sum23986
Variance2098.2465
MonotonicityNot monotonic
2024-04-17T07:48:55.347507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
13 170
17.2%
12 163
16.5%
17 99
10.0%
16 69
7.0%
18 59
 
6.0%
10 58
 
5.9%
5 58
 
5.9%
9 58
 
5.9%
11 58
 
5.9%
20 49
 
5.0%
Other values (14) 145
14.7%
ValueCountFrequency (%)
5 58
 
5.9%
7 23
 
2.3%
8 35
 
3.5%
9 58
 
5.9%
10 58
 
5.9%
11 58
 
5.9%
12 163
16.5%
13 170
17.2%
14 14
 
1.4%
16 69
7.0%
ValueCountFrequency (%)
223 1
 
0.1%
217 1
 
0.1%
215 2
 
0.2%
214 2
 
0.2%
210 2
 
0.2%
206 25
2.5%
205 25
2.5%
25 6
 
0.6%
23 2
 
0.2%
21 1
 
0.1%

house_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct953
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172471.82
Minimum21595
Maximum1527721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2024-04-17T07:48:55.446094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21595
5-th percentile23318
Q155000
median96810
Q3120650.75
95-th percentile1433775.5
Maximum1527721
Range1506126
Interquartile range (IQR)65650.75

Descriptive statistics

Standard deviation326285.26
Coefficient of variation (CV)1.8918178
Kurtosis11.751401
Mean172471.82
Median Absolute Deviation (MAD)40893
Skewness3.6660542
Sum1.7005721 × 108
Variance1.0646207 × 1011
MonotonicityNot monotonic
2024-04-17T07:48:55.558402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23284 3
 
0.3%
79788 2
 
0.2%
120249 2
 
0.2%
137679 2
 
0.2%
43413 2
 
0.2%
1467555 2
 
0.2%
165314 2
 
0.2%
113755 2
 
0.2%
96667 2
 
0.2%
23017 2
 
0.2%
Other values (943) 965
97.9%
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 (%)
1527721 1
0.1%
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%

tot_pop_cnt
Real number (ℝ)

HIGH CORRELATION 

Distinct968
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412804.67
Minimum43571
Maximum3615101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2024-04-17T07:48:55.920820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43571
5-th percentile47662
Q1125929.25
median230958
Q3309136.25
95-th percentile3460919.5
Maximum3615101
Range3571530
Interquartile range (IQR)183207

Descriptive statistics

Standard deviation781071.28
Coefficient of variation (CV)1.8921086
Kurtosis11.642685
Mean412804.67
Median Absolute Deviation (MAD)97065.5
Skewness3.6502851
Sum4.0702541 × 108
Variance6.1007235 × 1011
MonotonicityNot monotonic
2024-04-17T07:48:56.028297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123079 2
 
0.2%
338112 2
 
0.2%
125347 2
 
0.2%
374504 2
 
0.2%
90856 2
 
0.2%
271967 2
 
0.2%
111945 2
 
0.2%
46066 2
 
0.2%
163920 2
 
0.2%
234624 2
 
0.2%
Other values (958) 966
98.0%
ValueCountFrequency (%)
43571 1
0.1%
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%
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 

Distinct963
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203586.74
Minimum21609
Maximum1801832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2024-04-17T07:48:56.145998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21609
5-th percentile23685.75
Q165370.5
median116509.5
Q3153521.75
95-th percentile1699861.5
Maximum1801832
Range1780223
Interquartile range (IQR)88151.25

Descriptive statistics

Standard deviation385155.8
Coefficient of variation (CV)1.8918511
Kurtosis11.662922
Mean203586.74
Median Absolute Deviation (MAD)46618
Skewness3.6535496
Sum2.0073653 × 108
Variance1.4834499 × 1011
MonotonicityNot monotonic
2024-04-17T07:48:56.255214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84444 2
 
0.2%
137236 2
 
0.2%
55081 2
 
0.2%
202206 2
 
0.2%
1736878 2
 
0.2%
139826 2
 
0.2%
151255 2
 
0.2%
22843 2
 
0.2%
81662 2
 
0.2%
133173 2
 
0.2%
Other values (953) 966
98.0%
ValueCountFrequency (%)
21609 1
0.1%
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%
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 

Distinct964
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209217.93
Minimum21962
Maximum1813269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2024-04-17T07:48:56.372146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21962
5-th percentile23976.25
Q162342.25
median113670.5
Q3155514.75
95-th percentile1761058
Maximum1813269
Range1791307
Interquartile range (IQR)93172.5

Descriptive statistics

Standard deviation395934.49
Coefficient of variation (CV)1.8924501
Kurtosis11.623685
Mean209217.93
Median Absolute Deviation (MAD)49636
Skewness3.6469772
Sum2.0628888 × 108
Variance1.5676412 × 1011
MonotonicityNot monotonic
2024-04-17T07:48:56.499538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57547 3
 
0.3%
95155 2
 
0.2%
46070 2
 
0.2%
168980 2
 
0.2%
115316 2
 
0.2%
44957 2
 
0.2%
142073 2
 
0.2%
82258 2
 
0.2%
153790 2
 
0.2%
23223 2
 
0.2%
Other values (954) 965
97.9%
ValueCountFrequency (%)
21962 1
0.1%
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%
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 

Distinct178
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.766815
Minimum1.2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2024-04-17T07:48:56.616085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.3125
Q13.6
median6.6
Q38.7
95-th percentile100
Maximum100
Range98.8
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation22.25722
Coefficient of variation (CV)1.8915246
Kurtosis11.632123
Mean11.766815
Median Absolute Deviation (MAD)2.8
Skewness3.6493573
Sum11602.08
Variance495.38386
MonotonicityNot monotonic
2024-04-17T07:48:56.729824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 58
 
5.9%
3.2 49
 
5.0%
1.3 49
 
5.0%
5.1 39
 
4.0%
2.6 38
 
3.9%
11.9 36
 
3.7%
3.5 28
 
2.8%
5.9 27
 
2.7%
7.7 27
 
2.7%
7.9 25
 
2.5%
Other values (168) 610
61.9%
ValueCountFrequency (%)
1.2 1
 
0.1%
1.3 49
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 58
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 

Distinct877
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9241.0811
Minimum305
Maximum18190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.8 KiB
2024-04-17T07:48:56.833580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum305
5-th percentile730
Q16255.5
median8211.5
Q312734
95-th percentile17482.75
Maximum18190
Range17885
Interquartile range (IQR)6478.5

Descriptive statistics

Standard deviation5211.7577
Coefficient of variation (CV)0.56397705
Kurtosis-0.87865269
Mean9241.0811
Median Absolute Deviation (MAD)3660
Skewness0.13308583
Sum9111706
Variance27162419
MonotonicityNot monotonic
2024-04-17T07:48:56.944092image/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%
754 3
 
0.3%
3814 3
 
0.3%
738 3
 
0.3%
765 3
 
0.3%
8008 3
 
0.3%
8105 3
 
0.3%
737 3
 
0.3%
Other values (867) 944
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

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
6260000
986 

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 986
100.0%

Length

2024-04-17T07:48:57.050422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T07:48:57.117425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6260000 986
100.0%

last_load_dttm
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.8 KiB
2022-04-01 05:57:03
986 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-04-01 05:57:03
2nd row2022-04-01 05:57:03
3rd row2022-04-01 05:57:03
4th row2022-04-01 05:57:03
5th row2022-04-01 05:57:03

Common Values

ValueCountFrequency (%)
2022-04-01 05:57:03 986
100.0%

Length

2024-04-17T07:48:57.187958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T07:48:57.253682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-04-01 986
50.0%
05:57:03 986
50.0%

Interactions

2024-04-17T07:48:53.946736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:49.275341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:49.858629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.465146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:51.150384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.015638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.702565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.347165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:54.023122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:49.340568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:49.926663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.546315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:51.222330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.087466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.779819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.415895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:54.093799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:49.409662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:49.990253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.620655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:51.295407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.161225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.853631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.482478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:54.180234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:49.490350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.073116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.707407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:51.380152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.249060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.938974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.566701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:54.263263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:49.567171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.148549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.791328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:51.700538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.348639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.024019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.647725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:54.352118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:49.640924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.238914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.874807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:51.779903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.450130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.104548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.724546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:54.441135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:49.717065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.317597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.965897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:51.859847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.538634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.188419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.801351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:54.520208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:49.783794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:50.385933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:51.062034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:51.933388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:52.621341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.263713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:48:53.871721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T07:48:57.309831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeygugunrate_yeardong_cnthouse_cnttot_pop_cntm_pop_cntf_pop_cntpop_ratiopop_density
skey1.0000.1970.9440.0000.1370.0000.1450.0000.0000.372
gugun0.1971.0000.0001.0000.7860.9910.9550.9860.9870.968
rate_year0.9440.0001.0000.0000.1350.0000.0000.0000.0000.000
dong_cnt0.0001.0000.0001.0001.0001.0001.0001.0001.0000.996
house_cnt0.1370.7860.1351.0001.0000.6750.6750.6750.6750.736
tot_pop_cnt0.0000.9910.0001.0000.6751.0000.9951.0001.0000.816
m_pop_cnt0.1450.9550.0001.0000.6750.9951.0000.9930.9940.804
f_pop_cnt0.0000.9860.0001.0000.6751.0000.9931.0001.0000.822
pop_ratio0.0000.9870.0001.0000.6751.0000.9941.0001.0000.819
pop_density0.3720.9680.0000.9960.7360.8160.8040.8220.8191.000
2024-04-17T07:48:57.404049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gugunrate_year
gugun1.0000.000
rate_year0.0001.000
2024-04-17T07:48:57.473737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
skeydong_cnthouse_cnttot_pop_cntm_pop_cntf_pop_cntpop_ratiopop_densitygugunrate_year
skey1.0000.011-0.0180.0010.004-0.001-0.0070.0440.0770.691
dong_cnt0.0111.0000.8210.8070.8020.8140.807-0.0040.9920.000
house_cnt-0.0180.8211.0000.9890.9860.9910.991-0.0770.5690.066
tot_pop_cnt0.0010.8070.9891.0000.9990.9990.999-0.0920.9840.000
m_pop_cnt0.0040.8020.9860.9991.0000.9980.998-0.1010.8980.000
f_pop_cnt-0.0010.8140.9910.9990.9981.0000.999-0.0810.9730.000
pop_ratio-0.0070.8070.9910.9990.9980.9991.000-0.0950.9760.000
pop_density0.044-0.004-0.077-0.092-0.101-0.081-0.0951.0000.8510.000
gugun0.0770.9920.5690.9840.8980.9730.9760.8511.0000.000
rate_year0.6910.0000.0660.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-17T07:48:54.629935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T07:48:54.756743image/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
019597부산광역시 강서구2020-0685515813700572436645694.075562600002022-04-01 05:57:03
119598부산광역시 연제구2020-0612914122105151014371090786.11739862600002022-04-01 05:57:03
219599부산광역시 수영구2020-06108298917870284448942545.21750362600002022-04-01 05:57:03
319600부산광역시 사상구2020-0612971132204681117221087466.4610962600002022-04-01 05:57:03
419601부산광역시 기장군2020-0657216617228085639866415.078962600002022-04-01 05:57:03
519602부산광역시2020-072051517200345078316939181756865100.0448462600002022-04-01 05:57:03
619603부산광역시 중구2020-079235444372121688220331.21544962600002022-04-01 05:57:03
719604부산광역시 서구2020-07135334010987053519563513.1785962600002022-04-01 05:57:03
819605부산광역시 동구2020-0712459549197945092468872.6931962600002022-04-01 05:57:03
919606부산광역시 영도구2020-07115491411675257748590043.3822262600002022-04-01 05:57:03
skeygugunrate_yeardong_cnthouse_cnttot_pop_cntm_pop_cntf_pop_cntpop_ratiopop_densityinstt_codelast_load_dttm
97618787부산광역시 기장군2019-0556856916722383188840354.876662600002022-04-01 05:57:03
97718788부산광역시2019-062061490226347968417117701767914100.0451962600002022-04-01 05:57:03
97818789부산광역시 중구2019-069230824447422101223731.31571562600002022-04-01 05:57:03
97918790부산광역시 서구2019-06135299411161754603570143.2798462600002022-04-01 05:57:03
98018791부산광역시 동구2019-0612433378841143492449192.5907762600002022-04-01 05:57:03
98118792부산광역시 영도구2019-06115518512045159711607403.5848262600002022-04-01 05:57:03
98218793부산광역시 부산진구2019-062016557636190917550018640910.41219862600002022-04-01 05:57:03
98318794부산광역시 동래구2019-06131099832690241311681378567.71617762600002022-04-01 05:57:03
98418795부산광역시 남구2019-06171182822846261396491449778.21061662600002022-04-01 05:57:03
98518796부산광역시 북구2019-06131196112954681464591490098.5750562600002022-04-01 05:57:03