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

Description충청북도 시험 회차별 공인중개사 자격현황
Author국토교통부
URLhttps://www.data.go.kr/data/15063471/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 19:16:10.845370
Analysis finished2023-12-12 19:16:11.442310
Duration0.6 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
충청북도
31 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 31
100.0%

Length

2023-12-13T04:16:11.513314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:16:11.618689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 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
2023-12-13T04:16:11.723115image/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
2023-12-13T04:16:11.855292image/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%
Mean244.83871
Minimum5
Maximum1258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T04:16:12.004505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9
Q141
median245
Q3324.5
95-th percentile470
Maximum1258
Range1253
Interquartile range (IQR)283.5

Descriptive statistics

Standard deviation239.90986
Coefficient of variation (CV)0.97986898
Kurtosis10.015802
Mean244.83871
Median Absolute Deviation (MAD)113
Skewness2.5191536
Sum7590
Variance57556.74
MonotonicityNot monotonic
2023-12-13T04:16:12.160381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 1
 
3.2%
1258 1
 
3.2%
421 1
 
3.2%
297 1
 
3.2%
430 1
 
3.2%
483 1
 
3.2%
391 1
 
3.2%
245 1
 
3.2%
263 1
 
3.2%
253 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
5 1
3.2%
8 1
3.2%
10 1
3.2%
13 1
3.2%
18 1
3.2%
24 1
3.2%
25 1
3.2%
30 1
3.2%
52 1
3.2%
141 1
3.2%
ValueCountFrequency (%)
1258 1
3.2%
483 1
3.2%
457 1
3.2%
430 1
3.2%
421 1
3.2%
391 1
3.2%
358 1
3.2%
352 1
3.2%
297 1
3.2%
296 1
3.2%

Interactions

2023-12-13T04:16:11.113946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:16:10.915058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:16:11.214875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:16:11.018862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:16:12.244491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시험회차관리대상자수
시험회차1.0000.795
관리대상자수0.7951.000
2023-12-13T04:16:12.332674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시험회차관리대상자수
시험회차1.0000.675
관리대상자수0.6751.000

Missing values

2023-12-13T04:16:11.336174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:16:11.409414image/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
0충청북도05
1충청북도11258
2충청북도252
3충청북도310
4충청북도424
5충청북도530
6충청북도613
7충청북도725
8충청북도88
9충청북도918
시도명시험회차관리대상자수
21충청북도21259
22충청북도22247
23충청북도23253
24충청북도24263
25충청북도25245
26충청북도26391
27충청북도27483
28충청북도28430
29충청북도29297
30충청북도30421