Overview

Dataset statistics

Number of variables9
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory83.4 B

Variable types

Text1
Numeric7
DateTime1

Dataset

Description샘플 데이터
Author행자부 주민망
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=67

Alerts

총인구수(tot_popltn_co) has unique valuesUnique
세대수(tot_hshld_co) has unique valuesUnique
남자인구수(male_popltn_co) has unique valuesUnique
여자인구수(female_popltn_co) has unique valuesUnique

Reproduction

Analysis started2024-01-14 06:50:12.182501
Analysis finished2024-01-14 06:50:18.905477
Duration6.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구코드(atdrc_code_se)
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-01-14T15:50:18.995624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0666667
Min length3

Characters and Unicode

Total characters92
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)30.0%

Sample

1st row금천구
2nd row서대문구
3rd row양천구
4th row은평구
5th row마포구
ValueCountFrequency (%)
노원구 3
 
10.0%
관악구 2
 
6.7%
구로구 2
 
6.7%
은평구 2
 
6.7%
도봉구 2
 
6.7%
양천구 2
 
6.7%
강남구 2
 
6.7%
광진구 2
 
6.7%
서초구 2
 
6.7%
금천구 2
 
6.7%
Other values (9) 9
30.0%
2024-01-14T15:50:19.264111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 32
34.8%
천 4
 
4.3%
서 4
 
4.3%
강 4
 
4.3%
노 3
 
3.3%
로 3
 
3.3%
원 3
 
3.3%
도 2
 
2.2%
문 2
 
2.2%
대 2
 
2.2%
Other values (21) 33
35.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 32
34.8%
천 4
 
4.3%
서 4
 
4.3%
강 4
 
4.3%
노 3
 
3.3%
로 3
 
3.3%
원 3
 
3.3%
도 2
 
2.2%
문 2
 
2.2%
대 2
 
2.2%
Other values (21) 33
35.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 32
34.8%
천 4
 
4.3%
서 4
 
4.3%
강 4
 
4.3%
노 3
 
3.3%
로 3
 
3.3%
원 3
 
3.3%
도 2
 
2.2%
문 2
 
2.2%
대 2
 
2.2%
Other values (21) 33
35.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 32
34.8%
천 4
 
4.3%
서 4
 
4.3%
강 4
 
4.3%
노 3
 
3.3%
로 3
 
3.3%
원 3
 
3.3%
도 2
 
2.2%
문 2
 
2.2%
대 2
 
2.2%
Other values (21) 33
35.9%

자치구명(atdrc_nm)
Real number (ℝ)

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11388
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T15:50:19.365547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11153.5
Q111267.5
median11395
Q311470
95-th percentile11696.5
Maximum11740
Range630
Interquartile range (IQR)202.5

Descriptive statistics

Standard deviation165.57424
Coefficient of variation (CV)0.01453936
Kurtosis-0.25192258
Mean11388
Median Absolute Deviation (MAD)97.5
Skewness0.36535005
Sum341640
Variance27414.828
MonotonicityNot monotonic
2024-01-14T15:50:19.563095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
11440 3
 
10.0%
11410 3
 
10.0%
11470 3
 
10.0%
11200 2
 
6.7%
11260 2
 
6.7%
11350 2
 
6.7%
11170 2
 
6.7%
11380 2
 
6.7%
11305 1
 
3.3%
11680 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
11110 1
3.3%
11140 1
3.3%
11170 2
6.7%
11200 2
6.7%
11260 2
6.7%
11290 1
3.3%
11305 1
3.3%
11320 1
3.3%
11350 2
6.7%
11380 2
6.7%
ValueCountFrequency (%)
11740 1
 
3.3%
11710 1
 
3.3%
11680 1
 
3.3%
11620 1
 
3.3%
11545 1
 
3.3%
11500 1
 
3.3%
11470 3
10.0%
11440 3
10.0%
11410 3
10.0%
11380 2
6.7%

년월(년월)
Real number (ℝ)

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201706.4
Minimum201404
Maximum202008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T15:50:19.766309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201404
5-th percentile201407.45
Q1201505
median201711
Q3201878.25
95-th percentile202005.65
Maximum202008
Range604
Interquartile range (IQR)373.25

Descriptive statistics

Standard deviation201.00276
Coefficient of variation (CV)0.00099651157
Kurtosis-1.1335574
Mean201706.4
Median Absolute Deviation (MAD)194
Skewness-0.11439678
Sum6051192
Variance40402.11
MonotonicityNot monotonic
2024-01-14T15:50:19.914613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
201712 3
 
10.0%
201704 3
 
10.0%
201505 2
 
6.7%
201504 2
 
6.7%
201708 1
 
3.3%
201404 1
 
3.3%
201409 1
 
3.3%
202007 1
 
3.3%
201805 1
 
3.3%
201408 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
201404 1
 
3.3%
201407 1
 
3.3%
201408 1
 
3.3%
201409 1
 
3.3%
201412 1
 
3.3%
201504 2
6.7%
201505 2
6.7%
201607 1
 
3.3%
201704 3
10.0%
201708 1
 
3.3%
ValueCountFrequency (%)
202008 1
3.3%
202007 1
3.3%
202004 1
3.3%
202001 1
3.3%
201907 1
3.3%
201906 1
3.3%
201904 1
3.3%
201902 1
3.3%
201807 1
3.3%
201806 1
3.3%

총인구수(tot_popltn_co)
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean353412.07
Minimum126270
Maximum594315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T15:50:20.024319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126270
5-th percentile150067.6
Q1233419.5
median378769
Q3440003.75
95-th percentile551992.55
Maximum594315
Range468045
Interquartile range (IQR)206584.25

Descriptive statistics

Standard deviation128750.16
Coefficient of variation (CV)0.36430608
Kurtosis-0.82720317
Mean353412.07
Median Absolute Deviation (MAD)75671.5
Skewness-0.15322732
Sum10602362
Variance1.6576605 × 1010
MonotonicityNot monotonic
2024-01-14T15:50:20.127319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
228755 1
 
3.3%
442119 1
 
3.3%
478019 1
 
3.3%
149524 1
 
3.3%
373252 1
 
3.3%
218929 1
 
3.3%
378199 1
 
3.3%
405271 1
 
3.3%
305785 1
 
3.3%
300410 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
126270 1
3.3%
149524 1
3.3%
150732 1
3.3%
153780 1
3.3%
218929 1
3.3%
226161 1
3.3%
228755 1
3.3%
233017 1
3.3%
234627 1
3.3%
300410 1
3.3%
ValueCountFrequency (%)
594315 1
3.3%
559697 1
3.3%
542576 1
3.3%
483473 1
3.3%
478019 1
3.3%
451253 1
3.3%
447667 1
3.3%
442119 1
3.3%
433658 1
3.3%
431423 1
3.3%

세대수(tot_hshld_co)
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162817.1
Minimum61069
Maximum269220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T15:50:20.238384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61069
5-th percentile62333
Q1135458.5
median168673.5
Q3179872
95-th percentile254808.6
Maximum269220
Range208151
Interquartile range (IQR)44413.5

Descriptive statistics

Standard deviation53496.907
Coefficient of variation (CV)0.32857057
Kurtosis0.13493831
Mean162817.1
Median Absolute Deviation (MAD)26779
Skewness0.0018816287
Sum4884513
Variance2.8619191 × 109
MonotonicityNot monotonic
2024-01-14T15:50:20.334736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
171426 1
 
3.3%
248840 1
 
3.3%
100534 1
 
3.3%
229712 1
 
3.3%
138984 1
 
3.3%
259692 1
 
3.3%
185610 1
 
3.3%
135147 1
 
3.3%
136393 1
 
3.3%
269220 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
61069 1
3.3%
61091 1
3.3%
63851 1
3.3%
100534 1
3.3%
109708 1
3.3%
126579 1
3.3%
133458 1
3.3%
135147 1
3.3%
136393 1
3.3%
138984 1
3.3%
ValueCountFrequency (%)
269220 1
3.3%
259692 1
3.3%
248840 1
3.3%
243049 1
3.3%
229712 1
3.3%
186744 1
3.3%
185610 1
3.3%
180747 1
3.3%
177247 1
3.3%
174263 1
3.3%

세대당인구(hshld_popltn_avrg_co)
Real number (ℝ)

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3163333
Minimum1.85
Maximum2.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T15:50:20.438255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile1.999
Q12.1425
median2.34
Q32.4825
95-th percentile2.631
Maximum2.65
Range0.8
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.21884467
Coefficient of variation (CV)0.094478918
Kurtosis-0.87075406
Mean2.3163333
Median Absolute Deviation (MAD)0.17
Skewness-0.18961481
Sum69.49
Variance0.047892989
MonotonicityNot monotonic
2024-01-14T15:50:20.565404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2.62 2
 
6.7%
2.34 2
 
6.7%
2.46 2
 
6.7%
2.54 1
 
3.3%
2.18 1
 
3.3%
2.57 1
 
3.3%
2.23 1
 
3.3%
2.11 1
 
3.3%
2.64 1
 
3.3%
2.03 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
1.85 1
3.3%
1.99 1
3.3%
2.01 1
3.3%
2.03 1
3.3%
2.08 1
3.3%
2.11 1
3.3%
2.12 1
3.3%
2.13 1
3.3%
2.18 1
3.3%
2.19 1
3.3%
ValueCountFrequency (%)
2.65 1
3.3%
2.64 1
3.3%
2.62 2
6.7%
2.57 1
3.3%
2.54 1
3.3%
2.52 1
3.3%
2.49 1
3.3%
2.46 2
6.7%
2.44 1
3.3%
2.4 1
3.3%

남자인구수(male_popltn_co)
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182104.37
Minimum60139
Maximum320480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T15:50:20.688882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60139
5-th percentile61175.55
Q1125857
median190388.5
Q3233120
95-th percentile300661.05
Maximum320480
Range260341
Interquartile range (IQR)107263

Descriptive statistics

Standard deviation76145.948
Coefficient of variation (CV)0.41814455
Kurtosis-0.79275457
Mean182104.37
Median Absolute Deviation (MAD)45012.5
Skewness-0.1490833
Sum5463131
Variance5.7982054 × 109
MonotonicityNot monotonic
2024-01-14T15:50:20.821897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
319062 1
 
3.3%
157312 1
 
3.3%
278171 1
 
3.3%
167155 1
 
3.3%
258407 1
 
3.3%
255912 1
 
3.3%
157743 1
 
3.3%
150937 1
 
3.3%
187960 1
 
3.3%
74662 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
60139 1
3.3%
60735 1
3.3%
61714 1
3.3%
74025 1
3.3%
74662 1
3.3%
75614 1
3.3%
110605 1
3.3%
117497 1
3.3%
150937 1
3.3%
157312 1
3.3%
ValueCountFrequency (%)
320480 1
3.3%
319062 1
3.3%
278171 1
3.3%
258407 1
3.3%
255912 1
3.3%
252383 1
3.3%
237588 1
3.3%
233214 1
3.3%
232838 1
3.3%
229690 1
3.3%

여자인구수(female_popltn_co)
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean182477.07
Minimum61187
Maximum332802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-01-14T15:50:20.989272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61187
5-th percentile62629.3
Q1147287.75
median180128
Q3204407.75
95-th percentile303139.3
Maximum332802
Range271615
Interquartile range (IQR)57120

Descriptive statistics

Standard deviation74324.664
Coefficient of variation (CV)0.40730962
Kurtosis-0.42262881
Mean182477.07
Median Absolute Deviation (MAD)32503.5
Skewness0.11741912
Sum5474312
Variance5.5241558 × 109
MonotonicityNot monotonic
2024-01-14T15:50:21.127760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
118352 1
 
3.3%
61327 1
 
3.3%
61187 1
 
3.3%
162829 1
 
3.3%
204359 1
 
3.3%
285095 1
 
3.3%
78354 1
 
3.3%
148298 1
 
3.3%
177515 1
 
3.3%
197070 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
61187 1
3.3%
61327 1
3.3%
64221 1
3.3%
75262 1
3.3%
78354 1
3.3%
116393 1
3.3%
118352 1
3.3%
146951 1
3.3%
148298 1
3.3%
158869 1
3.3%
ValueCountFrequency (%)
332802 1
3.3%
307237 1
3.3%
298131 1
3.3%
285095 1
3.3%
284150 1
3.3%
245902 1
3.3%
243251 1
3.3%
204424 1
3.3%
204359 1
3.3%
204108 1
3.3%

적재일시(ldadng_dt)
Date

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2018-01-05 16:52:25
Maximum2021-06-21 11:02:17
2024-01-14T15:50:21.225510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:21.318694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)

Interactions

2024-01-14T15:50:17.728691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:14.106021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:14.775190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.295587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.956186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.562838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:17.159380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:17.825865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:14.265975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:14.851343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.363248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.053927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.647719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:17.243680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:17.941520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:14.366565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:14.932155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.446823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.160058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.735859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:17.325052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:18.047409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:14.458236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.001236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.540239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.256344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.820306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:17.409270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:18.135464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:14.532912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.067317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.640909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.334077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.901414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:17.484453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:18.240927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:14.621347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.148765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.746361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.421351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.991502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:17.575205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:18.340143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:14.697987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.224746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:15.875290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:16.492853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:17.070613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T15:50:17.648708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T15:50:21.397432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구코드(atdrc_code_se)자치구명(atdrc_nm)년월(년월)총인구수(tot_popltn_co)세대수(tot_hshld_co)세대당인구(hshld_popltn_avrg_co)남자인구수(male_popltn_co)여자인구수(female_popltn_co)적재일시(ldadng_dt)
자치구코드(atdrc_code_se)1.0000.5660.7680.0000.0000.8100.0000.0000.000
자치구명(atdrc_nm)0.5661.0000.0000.4010.6870.6320.2970.0000.000
년월(년월)0.7680.0001.0000.0000.5980.0000.3250.2970.454
총인구수(tot_popltn_co)0.0000.4010.0001.0000.5500.0000.0000.4580.000
세대수(tot_hshld_co)0.0000.6870.5980.5501.0000.0000.4680.4430.447
세대당인구(hshld_popltn_avrg_co)0.8100.6320.0000.0000.0001.0000.0000.0000.000
남자인구수(male_popltn_co)0.0000.2970.3250.0000.4680.0001.0000.1660.000
여자인구수(female_popltn_co)0.0000.0000.2970.4580.4430.0000.1661.0000.632
적재일시(ldadng_dt)0.0000.0000.4540.0000.4470.0000.0000.6321.000
2024-01-14T15:50:21.545162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구명(atdrc_nm)년월(년월)총인구수(tot_popltn_co)세대수(tot_hshld_co)세대당인구(hshld_popltn_avrg_co)남자인구수(male_popltn_co)여자인구수(female_popltn_co)
자치구명(atdrc_nm)1.0000.0060.344-0.156-0.1440.1840.086
년월(년월)0.0061.0000.0300.0520.1540.122-0.019
총인구수(tot_popltn_co)0.3440.0301.000-0.368-0.0950.275-0.195
세대수(tot_hshld_co)-0.1560.052-0.3681.0000.119-0.2180.060
세대당인구(hshld_popltn_avrg_co)-0.1440.154-0.0950.1191.000-0.191-0.042
남자인구수(male_popltn_co)0.1840.1220.275-0.218-0.1911.000-0.107
여자인구수(female_popltn_co)0.086-0.019-0.1950.060-0.042-0.1071.000

Missing values

2024-01-14T15:50:18.492997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T15:50:18.852277image/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

자치구코드(atdrc_code_se)자치구명(atdrc_nm)년월(년월)총인구수(tot_popltn_co)세대수(tot_hshld_co)세대당인구(hshld_popltn_avrg_co)남자인구수(male_popltn_co)여자인구수(female_popltn_co)적재일시(ldadng_dt)
0금천구111102019072287551714262.443190621183522018-01-05 16:52:25
1서대문구112902016071537801867442.12756141163932018-12-05 10:20:57
2양천구11740201804594315610912.221928173072372020-11-03 10:50:11
3은평구112002020043451561688652.132296902041082018-01-05 16:52:25
4마포구112602019023947881684822.52617141469512018-01-05 16:52:25
5도봉구11350201704150732610692.39607351998312018-01-05 16:52:25
6강북구114102020084045011742632.251840531729952018-01-05 16:52:25
7용산구116202014074336581739431.852523831987542018-01-05 16:52:25
8관악구111402017103080871807472.011929232459022021-01-21 15:43:29
9동대문구114702018064476671626062.49215440642212020-07-22 20:11:55
자치구코드(atdrc_code_se)자치구명(atdrc_nm)년월(년월)총인구수(tot_popltn_co)세대수(tot_hshld_co)세대당인구(hshld_popltn_avrg_co)남자인구수(male_popltn_co)여자인구수(female_popltn_co)적재일시(ldadng_dt)
20중랑구114702018074215791334581.992064231827412018-01-05 16:52:25
21도봉구113802014084834731673012.032251862044242020-07-22 20:11:55
22성동구115452018053004102692202.64746621970702018-12-05 10:20:57
23구로구113202015053057851363932.621879601775152019-03-11 09:30:24
24서초구114402017044052711351472.111509371482982018-11-05 09:53:36
25강서구114102017123781991856102.46157743783542018-01-05 16:52:25
26금천구114402020072189292596922.342559122850952018-01-05 16:52:25
27노원구117102014093732521389842.232584072043592018-01-05 16:52:25
28은평구111702014041495242297122.571671551628292018-01-05 16:52:25
29노원구116802017084780191005342.18278171611872018-01-05 16:52:25