Overview

Dataset statistics

Number of variables9
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory83.9 B

Variable types

Text1
Numeric7
Categorical1

Dataset

Description송파구 행정동별 남자수, 여자수, 통/반수 등
Author서울특별시 송파구
URLhttps://www.data.go.kr/data/15052393/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
남자수 is highly overall correlated with 여자수 and 5 other fieldsHigh correlation
여자수 is highly overall correlated with 남자수 and 5 other fieldsHigh correlation
세대수 is highly overall correlated with 남자수 and 4 other fieldsHigh correlation
통수 is highly overall correlated with 남자수 and 4 other fieldsHigh correlation
반수 is highly overall correlated with 남자수 and 4 other fieldsHigh correlation
면적(㎢) is highly overall correlated with 남자수 and 2 other fieldsHigh correlation
전월인구수 is highly overall correlated with 남자수 and 5 other fieldsHigh correlation
동명 has unique valuesUnique
남자수 has unique valuesUnique
여자수 has unique valuesUnique
반수 has unique valuesUnique
전월인구수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:33:09.154679
Analysis finished2023-12-12 14:33:15.219461
Duration6.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-12T23:33:15.357785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.925926
Min length12

Characters and Unicode

Total characters349
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row풍납1동
2nd row풍납2동
3rd row거여1동
4th row거여2동
5th row마천1동
ValueCountFrequency (%)
풍납1동 1
 
3.7%
가락본동 1
 
3.7%
잠실6동 1
 
3.7%
잠실4동 1
 
3.7%
잠실3동 1
 
3.7%
잠실2동 1
 
3.7%
잠실본동 1
 
3.7%
위례동 1
 
3.7%
장지동 1
 
3.7%
문정2동 1
 
3.7%
Other values (17) 17
63.0%
2023-12-12T23:33:15.644906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
247
70.8%
동 27
 
7.7%
2 8
 
2.3%
1 7
 
2.0%
잠 6
 
1.7%
실 6
 
1.7%
가 3
 
0.9%
락 3
 
0.9%
문 2
 
0.6%
납 2
 
0.6%
Other values (26) 38
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Space Separator 247
70.8%
Other Letter 83
 
23.8%
Decimal Number 19
 
5.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
32.5%
잠 6
 
7.2%
실 6
 
7.2%
가 3
 
3.6%
락 3
 
3.6%
문 2
 
2.4%
납 2
 
2.4%
본 2
 
2.4%
파 2
 
2.4%
송 2
 
2.4%
Other values (19) 28
33.7%
Decimal Number
ValueCountFrequency (%)
2 8
42.1%
1 7
36.8%
6 1
 
5.3%
3 1
 
5.3%
4 1
 
5.3%
7 1
 
5.3%
Space Separator
ValueCountFrequency (%)
247
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 266
76.2%
Hangul 83
 
23.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
32.5%
잠 6
 
7.2%
실 6
 
7.2%
가 3
 
3.6%
락 3
 
3.6%
문 2
 
2.4%
납 2
 
2.4%
본 2
 
2.4%
파 2
 
2.4%
송 2
 
2.4%
Other values (19) 28
33.7%
Common
ValueCountFrequency (%)
247
92.9%
2 8
 
3.0%
1 7
 
2.6%
6 1
 
0.4%
3 1
 
0.4%
4 1
 
0.4%
7 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 266
76.2%
Hangul 83
 
23.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
247
92.9%
2 8
 
3.0%
1 7
 
2.6%
6 1
 
0.4%
3 1
 
0.4%
4 1
 
0.4%
7 1
 
0.4%
Hangul
ValueCountFrequency (%)
동 27
32.5%
잠 6
 
7.2%
실 6
 
7.2%
가 3
 
3.6%
락 3
 
3.6%
문 2
 
2.4%
납 2
 
2.4%
본 2
 
2.4%
파 2
 
2.4%
송 2
 
2.4%
Other values (19) 28
33.7%

남자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12209.444
Minimum4799
Maximum19932
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:33:15.759216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4799
5-th percentile6974.7
Q19681.5
median12206
Q314718
95-th percentile17440.1
Maximum19932
Range15133
Interquartile range (IQR)5036.5

Descriptive statistics

Standard deviation3749.9076
Coefficient of variation (CV)0.30713171
Kurtosis-0.58352827
Mean12209.444
Median Absolute Deviation (MAD)2874
Skewness0.079280073
Sum329655
Variance14061807
MonotonicityNot monotonic
2023-12-12T23:33:15.883287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
7485 1
 
3.7%
11821 1
 
3.7%
4799 1
 
3.7%
8262 1
 
3.7%
12585 1
 
3.7%
17158 1
 
3.7%
17561 1
 
3.7%
13731 1
 
3.7%
13835 1
 
3.7%
16662 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
4799 1
3.7%
6756 1
3.7%
7485 1
3.7%
7917 1
3.7%
8262 1
3.7%
8454 1
3.7%
9332 1
3.7%
10031 1
3.7%
10334 1
3.7%
10478 1
3.7%
ValueCountFrequency (%)
19932 1
3.7%
17561 1
3.7%
17158 1
3.7%
16662 1
3.7%
16597 1
3.7%
16100 1
3.7%
15504 1
3.7%
13932 1
3.7%
13835 1
3.7%
13731 1
3.7%

여자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12969.222
Minimum5467
Maximum20848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:33:16.024705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5467
5-th percentile6807.7
Q19860.5
median13315
Q316453.5
95-th percentile19062.5
Maximum20848
Range15381
Interquartile range (IQR)6593

Descriptive statistics

Standard deviation4159.6327
Coefficient of variation (CV)0.32073108
Kurtosis-0.923986
Mean12969.222
Median Absolute Deviation (MAD)3590
Skewness0.06686047
Sum350169
Variance17302544
MonotonicityNot monotonic
2023-12-12T23:33:16.143285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
7612 1
 
3.7%
12965 1
 
3.7%
5467 1
 
3.7%
9063 1
 
3.7%
13520 1
 
3.7%
18800 1
 
3.7%
19175 1
 
3.7%
15310 1
 
3.7%
14571 1
 
3.7%
17680 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
5467 1
3.7%
6463 1
3.7%
7612 1
3.7%
8433 1
3.7%
8685 1
3.7%
9063 1
3.7%
9725 1
3.7%
9996 1
3.7%
10203 1
3.7%
10639 1
3.7%
ValueCountFrequency (%)
20848 1
3.7%
19175 1
3.7%
18800 1
3.7%
17680 1
3.7%
17372 1
3.7%
17279 1
3.7%
17046 1
3.7%
15861 1
3.7%
15310 1
3.7%
14571 1
3.7%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10289.148
Minimum3476
Maximum16677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:33:16.274566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3476
5-th percentile5586.9
Q17251.5
median9148
Q312826
95-th percentile16084.9
Maximum16677
Range13201
Interquartile range (IQR)5574.5

Descriptive statistics

Standard deviation3711.4453
Coefficient of variation (CV)0.36071454
Kurtosis-1.0279753
Mean10289.148
Median Absolute Deviation (MAD)3122
Skewness0.13432147
Sum277807
Variance13774826
MonotonicityNot monotonic
2023-12-12T23:33:16.451737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
12270 2
 
7.4%
6694 1
 
3.7%
11812 1
 
3.7%
3476 1
 
3.7%
5932 1
 
3.7%
9027 1
 
3.7%
11996 1
 
3.7%
14901 1
 
3.7%
9010 1
 
3.7%
13404 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
3476 1
3.7%
5439 1
3.7%
5932 1
3.7%
5993 1
3.7%
6195 1
3.7%
6694 1
3.7%
7224 1
3.7%
7279 1
3.7%
8424 1
3.7%
8513 1
3.7%
ValueCountFrequency (%)
16677 1
3.7%
16270 1
3.7%
15653 1
3.7%
15403 1
3.7%
14901 1
3.7%
13404 1
3.7%
13274 1
3.7%
12378 1
3.7%
12270 2
7.4%
11996 1
3.7%

통수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.740741
Minimum10
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:33:16.624009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q122
median26
Q334
95-th percentile42.1
Maximum44
Range34
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.8125461
Coefficient of variation (CV)0.31767523
Kurtosis-0.60871362
Mean27.740741
Median Absolute Deviation (MAD)6
Skewness0.040661858
Sum749
Variance77.660969
MonotonicityNot monotonic
2023-12-12T23:33:16.744785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
22 3
 
11.1%
26 3
 
11.1%
15 2
 
7.4%
34 2
 
7.4%
23 1
 
3.7%
10 1
 
3.7%
20 1
 
3.7%
24 1
 
3.7%
33 1
 
3.7%
39 1
 
3.7%
Other values (11) 11
40.7%
ValueCountFrequency (%)
10 1
 
3.7%
15 2
7.4%
18 1
 
3.7%
20 1
 
3.7%
21 1
 
3.7%
22 3
11.1%
23 1
 
3.7%
24 1
 
3.7%
26 3
11.1%
28 1
 
3.7%
ValueCountFrequency (%)
44 1
3.7%
43 1
3.7%
40 1
3.7%
39 1
3.7%
36 1
3.7%
35 1
3.7%
34 2
7.4%
33 1
3.7%
32 1
3.7%
31 1
3.7%

반수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.66667
Minimum52
Maximum258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:33:16.909033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile83
Q1115
median155
Q3189.5
95-th percentile238.2
Maximum258
Range206
Interquartile range (IQR)74.5

Descriptive statistics

Standard deviation52.389811
Coefficient of variation (CV)0.33440305
Kurtosis-0.59482733
Mean156.66667
Median Absolute Deviation (MAD)39
Skewness0.053355207
Sum4230
Variance2744.6923
MonotonicityNot monotonic
2023-12-12T23:33:17.038222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
114 1
 
3.7%
168 1
 
3.7%
52 1
 
3.7%
103 1
 
3.7%
138 1
 
3.7%
186 1
 
3.7%
156 1
 
3.7%
234 1
 
3.7%
97 1
 
3.7%
204 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
52 1
3.7%
77 1
3.7%
97 1
3.7%
100 1
3.7%
102 1
3.7%
103 1
3.7%
114 1
3.7%
116 1
3.7%
132 1
3.7%
133 1
3.7%
ValueCountFrequency (%)
258 1
3.7%
240 1
3.7%
234 1
3.7%
227 1
3.7%
204 1
3.7%
198 1
3.7%
193 1
3.7%
186 1
3.7%
183 1
3.7%
182 1
3.7%

면적(㎢)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2548148
Minimum0.5
Maximum3.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:33:17.189762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.516
Q10.685
median0.95
Q31.575
95-th percentile2.718
Maximum3.17
Range2.67
Interquartile range (IQR)0.89

Descriptive statistics

Standard deviation0.74960692
Coefficient of variation (CV)0.5973845
Kurtosis0.51971399
Mean1.2548148
Median Absolute Deviation (MAD)0.42
Skewness1.1433716
Sum33.88
Variance0.56191054
MonotonicityNot monotonic
2023-12-12T23:33:17.341835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.53 2
 
7.4%
0.95 2
 
7.4%
0.77 1
 
3.7%
0.96 1
 
3.7%
0.6 1
 
3.7%
2.79 1
 
3.7%
1.56 1
 
3.7%
1.49 1
 
3.7%
2.18 1
 
3.7%
0.94 1
 
3.7%
Other values (15) 15
55.6%
ValueCountFrequency (%)
0.5 1
3.7%
0.51 1
3.7%
0.53 2
7.4%
0.56 1
3.7%
0.58 1
3.7%
0.6 1
3.7%
0.77 1
3.7%
0.79 1
3.7%
0.8 1
3.7%
0.89 1
3.7%
ValueCountFrequency (%)
3.17 1
3.7%
2.79 1
3.7%
2.55 1
3.7%
2.2 1
3.7%
2.18 1
3.7%
1.65 1
3.7%
1.59 1
3.7%
1.56 1
3.7%
1.49 1
3.7%
1.37 1
3.7%

전월인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25130.407
Minimum10296
Maximum40676
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T23:33:17.562214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10296
5-th percentile13808
Q119607
median25426
Q331207
95-th percentile36512.8
Maximum40676
Range30380
Interquartile range (IQR)11600

Descriptive statistics

Standard deviation7957.9674
Coefficient of variation (CV)0.31666687
Kurtosis-0.85916985
Mean25130.407
Median Absolute Deviation (MAD)6364
Skewness0.087563935
Sum678521
Variance63329245
MonotonicityNot monotonic
2023-12-12T23:33:17.718112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
15166 1
 
3.7%
24821 1
 
3.7%
10296 1
 
3.7%
17315 1
 
3.7%
27705 1
 
3.7%
35985 1
 
3.7%
36739 1
 
3.7%
29024 1
 
3.7%
28414 1
 
3.7%
34444 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
10296 1
3.7%
13226 1
3.7%
15166 1
3.7%
16384 1
3.7%
17100 1
3.7%
17315 1
3.7%
19062 1
3.7%
20152 1
3.7%
20361 1
3.7%
20713 1
3.7%
ValueCountFrequency (%)
40676 1
3.7%
36739 1
3.7%
35985 1
3.7%
34444 1
3.7%
33939 1
3.7%
33576 1
3.7%
32608 1
3.7%
29806 1
3.7%
29024 1
3.7%
28414 1
3.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
2020-04-13
27 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-04-13
2nd row2020-04-13
3rd row2020-04-13
4th row2020-04-13
5th row2020-04-13

Common Values

ValueCountFrequency (%)
2020-04-13 27
100.0%

Length

2023-12-12T23:33:17.897464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:33:17.995573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-04-13 27
100.0%

Interactions

2023-12-12T23:33:14.002619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:09.420045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:10.162382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:10.948440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:11.684390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:12.486087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:13.182528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:14.090548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:09.534719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:10.283294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:11.056159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:11.801340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:12.602768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:13.302299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:14.190026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:09.634074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:10.409027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:11.171057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:11.926616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:12.701826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:13.423234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:14.287858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:09.720138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:10.533157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:11.277884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:12.033624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:12.791120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:13.539135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:14.392000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:09.825864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:10.642845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:11.382449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:12.157356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:12.872977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:13.659417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:14.494334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:09.927065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:10.748793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:11.461783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:12.273303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:12.957772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:13.757512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:14.592121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:10.043725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:10.851528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:11.574857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:12.367225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:13.075364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:33:13.869482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:33:18.071529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명남자수여자수세대수통수반수면적(㎢)전월인구수
동명1.0001.0001.0001.0001.0001.0001.0001.000
남자수1.0001.0000.9370.6440.4610.8310.6570.960
여자수1.0000.9371.0000.6620.1510.7670.2530.990
세대수1.0000.6440.6621.0000.9000.8970.0000.743
통수1.0000.4610.1510.9001.0000.9220.2630.551
반수1.0000.8310.7670.8970.9221.0000.0000.802
면적(㎢)1.0000.6570.2530.0000.2630.0001.0000.039
전월인구수1.0000.9600.9900.7430.5510.8020.0391.000
2023-12-12T23:33:18.226661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남자수여자수세대수통수반수면적(㎢)전월인구수
남자수1.0000.9920.8710.7390.7550.5320.990
여자수0.9921.0000.8750.7320.7600.5220.990
세대수0.8710.8751.0000.9050.9050.2890.873
통수0.7390.7320.9051.0000.9630.1770.727
반수0.7550.7600.9050.9631.0000.1680.745
면적(㎢)0.5320.5220.2890.1770.1681.0000.508
전월인구수0.9900.9900.8730.7270.7450.5081.000

Missing values

2023-12-12T23:33:14.729957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:33:14.859502image/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

동명남자수여자수세대수통수반수면적(㎢)전월인구수데이터기준일자
0풍납1동748576126694221140.77151662020-04-13
1풍납2동118211296510468301681.59248212020-04-13
2거여1동67566463543915770.51132262020-04-13
3거여2동845486857224351820.53171002020-04-13
4마천1동10705102039148261330.58209762020-04-13
5마천2동1033499968677261470.89203612020-04-13
6방이1동791784336195181020.5163842020-04-13
7방이2동122951331513274341980.8254262020-04-13
8오륜동933297255993151003.17190622020-04-13
9오금동199322084815653442271.65406762020-04-13
동명남자수여자수세대수통수반수면적(㎢)전월인구수데이터기준일자
17문정1동10478106398513221320.56212132020-04-13
18문정2동139321586115403361832.2298062020-04-13
19장지동166621768013404342041.37344442020-04-13
20위례동1383514571901022972.55284142020-04-13
21잠실본동137311531014901392340.94290242020-04-13
22잠실2동175611917511996261562.18367392020-04-13
23잠실3동171581880012270331861.49359852020-04-13
24잠실4동12585135209027241381.56277052020-04-13
25잠실6동826290635932201032.79173152020-04-13
26잠실7동47995467347610520.6102962020-04-13