Overview

Dataset statistics

Number of variables3
Number of observations10000
Missing cells3437
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory332.0 KiB
Average record size in memory34.0 B

Variable types

Text1
Numeric2

Dataset

Description격자인구(격자, 격자별인구수, 행정동) 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=113

Alerts

격자별인구수 has 3437 (34.4%) missing valuesMissing
격자별인구수 has 1561 (15.6%) zerosZeros

Reproduction

Analysis started2024-01-09 20:56:31.239023
Analysis finished2024-01-09 20:56:31.894359
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

격자
Text

Distinct9672
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-10T05:56:32.126999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9159
Min length6

Characters and Unicode

Total characters69159
Distinct characters14
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9350 ?
Unique (%)93.5%

Sample

1st rowㅻ08b68b
2nd rowㅻ81a71b
3rd rowㅻ51a77a
4th rowㅻ21a02a
5th rowㅻ34b45a
ValueCountFrequency (%)
ㅻ62a81b 3
 
< 0.1%
ㅻ63b66a 3
 
< 0.1%
ㅻ64b73b 3
 
< 0.1%
ㅻ63b50a 3
 
< 0.1%
ㅻ77b74b 3
 
< 0.1%
ㅻ64a69b 3
 
< 0.1%
ㅻ64b69b 2
 
< 0.1%
ㅻ74b75a 2
 
< 0.1%
ㅻ33b47b 2
 
< 0.1%
ㅻ28a81b 2
 
< 0.1%
Other values (9662) 9974
99.7%
2024-01-10T05:56:32.538025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10009
14.5%
b 9991
14.4%
ㅻ 8898
12.9%
6 4778
6.9%
7 4262
 
6.2%
5 4129
 
6.0%
2 3928
 
5.7%
3 3887
 
5.6%
9 3881
 
5.6%
1 3823
 
5.5%
Other values (4) 11573
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40000
57.8%
Lowercase Letter 20000
28.9%
Other Letter 9159
 
13.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 4778
11.9%
7 4262
10.7%
5 4129
10.3%
2 3928
9.8%
3 3887
9.7%
9 3881
9.7%
1 3823
9.6%
4 3797
9.5%
0 3769
9.4%
8 3746
9.4%
Lowercase Letter
ValueCountFrequency (%)
a 10009
50.0%
b 9991
50.0%
Other Letter
ValueCountFrequency (%)
ㅻ 8898
97.2%
쇰 261
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 40000
57.8%
Latin 20000
28.9%
Hangul 9159
 
13.2%

Most frequent character per script

Common
ValueCountFrequency (%)
6 4778
11.9%
7 4262
10.7%
5 4129
10.3%
2 3928
9.8%
3 3887
9.7%
9 3881
9.7%
1 3823
9.6%
4 3797
9.5%
0 3769
9.4%
8 3746
9.4%
Latin
ValueCountFrequency (%)
a 10009
50.0%
b 9991
50.0%
Hangul
ValueCountFrequency (%)
ㅻ 8898
97.2%
쇰 261
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
86.8%
Compat Jamo 8898
 
12.9%
Hangul 261
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10009
16.7%
b 9991
16.7%
6 4778
8.0%
7 4262
7.1%
5 4129
6.9%
2 3928
 
6.5%
3 3887
 
6.5%
9 3881
 
6.5%
1 3823
 
6.4%
4 3797
 
6.3%
Other values (2) 7515
12.5%
Compat Jamo
ValueCountFrequency (%)
ㅻ 8898
100.0%
Hangul
ValueCountFrequency (%)
쇰 261
100.0%

격자별인구수
Real number (ℝ)

MISSING  ZEROS 

Distinct473
Distinct (%)7.2%
Missing3437
Missing (%)34.4%
Infinite0
Infinite (%)0.0%
Mean105.79232
Minimum0
Maximum8872
Zeros1561
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:56:32.671512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median16
Q338
95-th percentile213.9
Maximum8872
Range8872
Interquartile range (IQR)32

Descriptive statistics

Standard deviation527.5653
Coefficient of variation (CV)4.9868015
Kurtosis100.26981
Mean105.79232
Median Absolute Deviation (MAD)16
Skewness9.1901255
Sum694315
Variance278325.15
MonotonicityNot monotonic
2024-01-10T05:56:32.794935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1561
15.6%
7 222
 
2.2%
8 194
 
1.9%
6 189
 
1.9%
9 183
 
1.8%
10 161
 
1.6%
12 155
 
1.6%
11 146
 
1.5%
13 141
 
1.4%
15 138
 
1.4%
Other values (463) 3473
34.7%
(Missing) 3437
34.4%
ValueCountFrequency (%)
0 1561
15.6%
6 189
 
1.9%
7 222
 
2.2%
8 194
 
1.9%
9 183
 
1.8%
10 161
 
1.6%
11 146
 
1.5%
12 155
 
1.6%
13 141
 
1.4%
14 112
 
1.1%
ValueCountFrequency (%)
8872 1
< 0.1%
8226 1
< 0.1%
7988 1
< 0.1%
7864 1
< 0.1%
7844 1
< 0.1%
7587 1
< 0.1%
7035 1
< 0.1%
6842 1
< 0.1%
6756 1
< 0.1%
6461 1
< 0.1%

행정동코드
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4427312 × 109
Minimum4.413 × 109
Maximum4.4825 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T05:56:32.900081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.413 × 109
5-th percentile4.413 × 109
Q14.418 × 109
median4.423 × 109
Q34.477 × 109
95-th percentile4.4825 × 109
Maximum4.4825 × 109
Range69500000
Interquartile range (IQR)59000000

Descriptive statistics

Standard deviation29429712
Coefficient of variation (CV)0.0066242388
Kurtosis-1.7848851
Mean4.4427312 × 109
Median Absolute Deviation (MAD)9900000
Skewness0.36474869
Sum4.4427312 × 1013
Variance8.6610796 × 1014
MonotonicityNot monotonic
2024-01-10T05:56:33.006117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4415000000 907
 
9.1%
4421000000 810
 
8.1%
4427000000 776
 
7.8%
4418000000 724
 
7.2%
4413000000 706
 
7.1%
4482500000 690
 
6.9%
4476000000 681
 
6.8%
4471000000 631
 
6.3%
4420000000 615
 
6.2%
4423000000 612
 
6.1%
Other values (7) 2848
28.5%
ValueCountFrequency (%)
4413000000 706
7.1%
4413100000 494
4.9%
4413300000 231
 
2.3%
4415000000 907
9.1%
4418000000 724
7.2%
4420000000 615
6.2%
4421000000 810
8.1%
4423000000 612
6.1%
4425000000 84
 
0.8%
4427000000 776
7.8%
ValueCountFrequency (%)
4482500000 690
6.9%
4481000000 607
6.1%
4480000000 524
5.2%
4479000000 515
5.1%
4477000000 393
3.9%
4476000000 681
6.8%
4471000000 631
6.3%
4427000000 776
7.8%
4425000000 84
 
0.8%
4423000000 612
6.1%

Interactions

2024-01-10T05:56:31.612705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:56:31.429916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:56:31.694521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T05:56:31.517011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T05:56:33.079385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자별인구수행정동코드
격자별인구수1.0000.109
행정동코드0.1091.000
2024-01-10T05:56:33.155350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
격자별인구수행정동코드
격자별인구수1.000-0.050
행정동코드-0.0501.000

Missing values

2024-01-10T05:56:31.799767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T05:56:31.860146image/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

격자격자별인구수행정동코드
23390ㅻ08b68b<NA>4421000000
13723ㅻ81a71b<NA>4413100000
25660ㅻ51a77a104420000000
15473ㅻ21a02a<NA>4418000000
39055ㅻ34b45a<NA>4480000000
20030ㅻ71b80a<NA>4413300000
30016ㅻ67b70a46014413000000
38380ㅻ27a41b274480000000
30164ㅻ66a76b94413000000
27070ㅻ57b70b<NA>4420000000
격자격자별인구수행정동코드
20813ㅻ16a60b504421000000
38059ㅻ14a48b74480000000
31362ㅻ70b60a94413000000
5864쇰05b82b<NA>4471000000
18706ㅻ51a04b<NA>4476000000
3543879a66b174482500000
17169ㅻ38a09b74476000000
737ㅻ64b20a204415000000
25541ㅻ54b52a224420000000
9063ㅻ14b91b94427000000