Overview

Dataset statistics

Number of variables3
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory938.0 B
Average record size in memory30.3 B

Variable types

Categorical1
Numeric2

Dataset

Description경기도 시험 회차별 공인중개사 자격현황
Author국토교통부
URLhttps://www.data.go.kr/data/15063455/fileData.do

Alerts

시도명 has constant value ""Constant
시험회차 is highly overall correlated with 관리대상자수High correlation
관리대상자수 is highly overall correlated with 시험회차High correlation
시험회차 has unique valuesUnique
관리대상자수 has unique valuesUnique
시험회차 has 1 (3.2%) zerosZeros

Reproduction

Analysis started2023-12-12 10:20:52.011731
Analysis finished2023-12-12 10:20:52.700975
Duration0.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size380.0 B
경기도
31 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 31
100.0%

Length

2023-12-12T19:20:52.784119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:20:52.878534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 31
100.0%

시험회차
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15
Minimum0
Maximum30
Zeros1
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T19:20:52.975301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5
Q17.5
median15
Q322.5
95-th percentile28.5
Maximum30
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0921211
Coefficient of variation (CV)0.60614141
Kurtosis-1.2
Mean15
Median Absolute Deviation (MAD)8
Skewness0
Sum465
Variance82.666667
MonotonicityStrictly increasing
2023-12-12T19:20:53.126370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 1
 
3.2%
1 1
 
3.2%
30 1
 
3.2%
29 1
 
3.2%
28 1
 
3.2%
27 1
 
3.2%
26 1
 
3.2%
25 1
 
3.2%
24 1
 
3.2%
23 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
0 1
3.2%
1 1
3.2%
2 1
3.2%
3 1
3.2%
4 1
3.2%
5 1
3.2%
6 1
3.2%
7 1
3.2%
8 1
3.2%
9 1
3.2%
ValueCountFrequency (%)
30 1
3.2%
29 1
3.2%
28 1
3.2%
27 1
3.2%
26 1
3.2%
25 1
3.2%
24 1
3.2%
23 1
3.2%
22 1
3.2%
21 1
3.2%

관리대상자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3597.129
Minimum1
Maximum10232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-12T19:20:53.278332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile87.5
Q1581.5
median3938
Q35031.5
95-th percentile8861
Maximum10232
Range10231
Interquartile range (IQR)4450

Descriptive statistics

Standard deviation2810.5159
Coefficient of variation (CV)0.78132195
Kurtosis-0.059281321
Mean3597.129
Median Absolute Deviation (MAD)1674
Skewness0.50247652
Sum111511
Variance7898999.4
MonotonicityNot monotonic
2023-12-12T19:20:53.404241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1
 
3.2%
3938 1
 
3.2%
8007 1
 
3.2%
4724 1
 
3.2%
5878 1
 
3.2%
5152 1
 
3.2%
3226 1
 
3.2%
1928 1
 
3.2%
2264 1
 
3.2%
2791 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1 1
3.2%
43 1
3.2%
132 1
3.2%
183 1
3.2%
199 1
3.2%
232 1
3.2%
306 1
3.2%
340 1
3.2%
823 1
3.2%
1928 1
3.2%
ValueCountFrequency (%)
10232 1
3.2%
9715 1
3.2%
8007 1
3.2%
6337 1
3.2%
5878 1
3.2%
5333 1
3.2%
5319 1
3.2%
5152 1
3.2%
4911 1
3.2%
4825 1
3.2%

Interactions

2023-12-12T19:20:52.318528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:52.093364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:52.441772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:20:52.197499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:20:53.489157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시험회차관리대상자수
시험회차1.0000.545
관리대상자수0.5451.000
2023-12-12T19:20:53.587557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시험회차관리대상자수
시험회차1.0000.590
관리대상자수0.5901.000

Missing values

2023-12-12T19:20:52.571889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:20:52.660255image/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

시도명시험회차관리대상자수
0경기도01
1경기도13938
2경기도2232
3경기도343
4경기도4340
5경기도5306
6경기도6183
7경기도7199
8경기도8132
9경기도9823
시도명시험회차관리대상자수
21경기도214911
22경기도223729
23경기도232791
24경기도242264
25경기도251928
26경기도263226
27경기도275152
28경기도285878
29경기도294724
30경기도308007