Overview

Dataset statistics

Number of variables7
Number of observations264
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.3 KiB
Average record size in memory59.5 B

Variable types

Categorical3
Numeric3
DateTime1

Dataset

Description광주광역시 북구 1인가구에 대한 데이터로 동별, 1인가구수, 성별·연령별(10세 단위) 현황 항목으로 구성되어 있습니다.
Author광주광역시 북구
URLhttps://www.data.go.kr/data/15085684/fileData.do

Alerts

시군구 has constant value ""Constant
데이터기준일자 has constant value ""Constant
계 is highly overall correlated with 남 and 1 other fieldsHigh correlation
남 is highly overall correlated with 계 and 1 other fieldsHigh correlation
여 is highly overall correlated with 계 and 1 other fieldsHigh correlation
남 has 15 (5.7%) zerosZeros
여 has 10 (3.8%) zerosZeros

Reproduction

Analysis started2024-03-14 09:18:57.535281
Analysis finished2024-03-14 09:19:00.736354
Duration3.2 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
광주광역시 북구
264 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광주광역시 북구
2nd row광주광역시 북구
3rd row광주광역시 북구
4th row광주광역시 북구
5th row광주광역시 북구

Common Values

ValueCountFrequency (%)
광주광역시 북구 264
100.0%

Length

2024-03-14T18:19:00.940531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T18:19:01.243010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주광역시 264
50.0%
북구 264
50.0%

행정기관
Categorical

Distinct27
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
두암2동
 
11
용봉동
 
11
오치2동
 
11
문화동
 
11
양산동
 
11
Other values (22)
209 

Length

Max length4
Median length3
Mean length3.3598485
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중흥1동
2nd row중흥1동
3rd row중흥1동
4th row중흥1동
5th row중흥1동

Common Values

ValueCountFrequency (%)
두암2동 11
 
4.2%
용봉동 11
 
4.2%
오치2동 11
 
4.2%
문화동 11
 
4.2%
양산동 11
 
4.2%
신용동 10
 
3.8%
매곡동 10
 
3.8%
건국동 10
 
3.8%
오치1동 10
 
3.8%
동림동 10
 
3.8%
Other values (17) 159
60.2%

Length

2024-03-14T18:19:01.599609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
두암2동 11
 
4.2%
오치2동 11
 
4.2%
문화동 11
 
4.2%
양산동 11
 
4.2%
용봉동 11
 
4.2%
석곡동 10
 
3.8%
우산동 10
 
3.8%
삼각동 10
 
3.8%
중흥동 10
 
3.8%
풍향동 10
 
3.8%
Other values (17) 159
60.2%

연령
Categorical

Distinct11
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
10세-19세
27 
20세-29세
27 
30세-39세
27 
40세-49세
27 
50세-59세
27 
Other values (6)
129 

Length

Max length9
Median length7
Mean length7.0227273
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10세-19세
2nd row20세-29세
3rd row30세-39세
4th row40세-49세
5th row50세-59세

Common Values

ValueCountFrequency (%)
10세-19세 27
10.2%
20세-29세 27
10.2%
30세-39세 27
10.2%
40세-49세 27
10.2%
50세-59세 27
10.2%
60세-69세 27
10.2%
70세-79세 27
10.2%
80세-89세 27
10.2%
90세-99세 27
10.2%
100세-109세 12
4.5%

Length

2024-03-14T18:19:02.042814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10세-19세 27
10.2%
20세-29세 27
10.2%
30세-39세 27
10.2%
40세-49세 27
10.2%
50세-59세 27
10.2%
60세-69세 27
10.2%
70세-79세 27
10.2%
80세-89세 27
10.2%
90세-99세 27
10.2%
100세-109세 12
4.5%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean317.4053
Minimum1
Maximum2751
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-03-14T18:19:02.429677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q134.75
median251
Q3465.25
95-th percentile873.7
Maximum2751
Range2750
Interquartile range (IQR)430.5

Descriptive statistics

Standard deviation328.14366
Coefficient of variation (CV)1.0338317
Kurtosis11.166513
Mean317.4053
Median Absolute Deviation (MAD)215.5
Skewness2.2765535
Sum83795
Variance107678.26
MonotonicityNot monotonic
2024-03-14T18:19:03.063266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 15
 
5.7%
2 8
 
3.0%
6 8
 
3.0%
4 5
 
1.9%
18 4
 
1.5%
265 3
 
1.1%
245 3
 
1.1%
417 3
 
1.1%
7 3
 
1.1%
353 2
 
0.8%
Other values (193) 210
79.5%
ValueCountFrequency (%)
1 15
5.7%
2 8
3.0%
3 2
 
0.8%
4 5
 
1.9%
6 8
3.0%
7 3
 
1.1%
8 2
 
0.8%
9 2
 
0.8%
10 1
 
0.4%
11 1
 
0.4%
ValueCountFrequency (%)
2751 1
0.4%
1311 1
0.4%
1282 1
0.4%
1274 1
0.4%
1144 1
0.4%
1112 1
0.4%
1101 1
0.4%
1081 1
0.4%
1067 1
0.4%
1021 1
0.4%

남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct171
Distinct (%)64.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.11742
Minimum0
Maximum1467
Zeros15
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-03-14T18:19:03.473318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median109
Q3234
95-th percentile489.1
Maximum1467
Range1467
Interquartile range (IQR)227

Descriptive statistics

Standard deviation187.79187
Coefficient of variation (CV)1.1802093
Kurtosis9.3885043
Mean159.11742
Median Absolute Deviation (MAD)104
Skewness2.2819855
Sum42007
Variance35265.785
MonotonicityNot monotonic
2024-03-14T18:19:03.909478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
7.6%
0 15
 
5.7%
2 7
 
2.7%
4 7
 
2.7%
3 7
 
2.7%
5 5
 
1.9%
7 4
 
1.5%
6 4
 
1.5%
20 3
 
1.1%
93 3
 
1.1%
Other values (161) 189
71.6%
ValueCountFrequency (%)
0 15
5.7%
1 20
7.6%
2 7
 
2.7%
3 7
 
2.7%
4 7
 
2.7%
5 5
 
1.9%
6 4
 
1.5%
7 4
 
1.5%
9 1
 
0.4%
10 1
 
0.4%
ValueCountFrequency (%)
1467 1
0.4%
874 1
0.4%
772 1
0.4%
744 1
0.4%
656 1
0.4%
642 1
0.4%
629 1
0.4%
620 1
0.4%
598 1
0.4%
590 1
0.4%

여
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct177
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.28788
Minimum0
Maximum1284
Zeros10
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-03-14T18:19:04.331279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q123.75
median125
Q3232.25
95-th percentile445.5
Maximum1284
Range1284
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation158.35908
Coefficient of variation (CV)1.0004498
Kurtosis9.4319288
Mean158.28788
Median Absolute Deviation (MAD)104.5
Skewness2.1046962
Sum41788
Variance25077.597
MonotonicityNot monotonic
2024-03-14T18:19:04.784940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14
 
5.3%
0 10
 
3.8%
3 6
 
2.3%
4 5
 
1.9%
120 5
 
1.9%
15 4
 
1.5%
2 4
 
1.5%
5 3
 
1.1%
17 3
 
1.1%
118 3
 
1.1%
Other values (167) 207
78.4%
ValueCountFrequency (%)
0 10
3.8%
1 14
5.3%
2 4
 
1.5%
3 6
2.3%
4 5
 
1.9%
5 3
 
1.1%
6 2
 
0.8%
7 2
 
0.8%
8 1
 
0.4%
11 1
 
0.4%
ValueCountFrequency (%)
1284 1
0.4%
653 1
0.4%
618 1
0.4%
598 1
0.4%
597 1
0.4%
562 1
0.4%
558 1
0.4%
516 1
0.4%
508 1
0.4%
502 1
0.4%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Minimum2024-01-02 00:00:00
Maximum2024-01-02 00:00:00
2024-03-14T18:19:05.128540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:19:05.428651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-14T18:18:59.355912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:18:57.843781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:18:58.588672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:18:59.601970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:18:58.083908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:18:58.840231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:18:59.858456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:18:58.338038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T18:18:59.102944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T18:19:05.636238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관연령계남여
행정기관1.0000.0000.3900.4340.345
연령0.0001.0000.5140.5260.485
계0.3900.5141.0000.8740.830
남0.4340.5260.8741.0000.900
여0.3450.4850.8300.9001.000
2024-03-14T18:19:05.896068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정기관연령
행정기관1.0000.000
연령0.0001.000
2024-03-14T18:19:06.137938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계남여행정기관연령
계1.0000.9560.9510.1740.291
남0.9561.0000.8330.1880.290
여0.9510.8331.0000.1440.262
행정기관0.1740.1880.1441.0000.000
연령0.2910.2900.2620.0001.000

Missing values

2024-03-14T18:19:00.205868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T18:19:00.590461image/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

시군구행정기관연령계남여데이터기준일자
0광주광역시 북구중흥1동10세-19세1102024-01-02
1광주광역시 북구중흥1동20세-29세3881572312024-01-02
2광주광역시 북구중흥1동30세-39세4222212012024-01-02
3광주광역시 북구중흥1동40세-49세232171612024-01-02
4광주광역시 북구중흥1동50세-59세230174562024-01-02
5광주광역시 북구중흥1동60세-69세2791591202024-01-02
6광주광역시 북구중흥1동70세-79세16879892024-01-02
7광주광역시 북구중흥1동80세-89세8220622024-01-02
8광주광역시 북구중흥1동90세-99세132112024-01-02
9광주광역시 북구중흥동10세-19세2810182024-01-02
시군구행정기관연령계남여데이터기준일자
254광주광역시 북구신용동0세-9세1012024-01-02
255광주광역시 북구신용동10세-19세8532024-01-02
256광주광역시 북구신용동20세-29세3501971532024-01-02
257광주광역시 북구신용동30세-39세8265392872024-01-02
258광주광역시 북구신용동40세-49세8515343172024-01-02
259광주광역시 북구신용동50세-59세6753213542024-01-02
260광주광역시 북구신용동60세-69세5631913722024-01-02
261광주광역시 북구신용동70세-79세254851692024-01-02
262광주광역시 북구신용동80세-89세10420842024-01-02
263광주광역시 북구신용동90세-99세8172024-01-02