Overview

Dataset statistics

Number of variables3
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory27.3 B

Variable types

Categorical1
Text1
Numeric1

Dataset

DescriptionSample
Author(주)제로투원파트너스
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ZTO016LPOINTINDEX

Alerts

YM has constant value ""Constant
PRDLST_NM has unique valuesUnique
PCINDX_VALUE has unique valuesUnique
PCINDX_VALUE has 1 (1.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:22:40.047802
Analysis finished2023-12-10 06:22:40.708711
Duration0.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

YM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
201907
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201907
2nd row201907
3rd row201907
4th row201907
5th row201907

Common Values

ValueCountFrequency (%)
201907 99
100.0%

Length

2023-12-10T15:22:40.813855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:22:40.987926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201907 99
100.0%

PRDLST_NM
Text

UNIQUE 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2023-12-10T15:22:41.454041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.0707071
Min length1

Characters and Unicode

Total characters304
Distinct characters164
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

Unique99 ?
Unique (%)100.0%

Sample

1st row쌀
2nd row혼식곡
3rd row토마토
4th row포도
5th row국산쇠고기
ValueCountFrequency (%)
쌀 1
 
1.0%
기타문구 1
 
1.0%
양념소스 1
 
1.0%
아이스크림 1
 
1.0%
식용유 1
 
1.0%
우유 1
 
1.0%
생선통조림 1
 
1.0%
어묵 1
 
1.0%
소시지 1
 
1.0%
케이크 1
 
1.0%
Other values (89) 89
89.9%
2023-12-10T15:22:42.245398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 12
 
3.9%
자 10
 
3.3%
장 9
 
3.0%
스 8
 
2.6%
품 8
 
2.6%
동 7
 
2.3%
의 7
 
2.3%
주 6
 
2.0%
구 6
 
2.0%
화 6
 
2.0%
Other values (154) 225
74.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 304
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 12
 
3.9%
자 10
 
3.3%
장 9
 
3.0%
스 8
 
2.6%
품 8
 
2.6%
동 7
 
2.3%
의 7
 
2.3%
주 6
 
2.0%
구 6
 
2.0%
화 6
 
2.0%
Other values (154) 225
74.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 304
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 12
 
3.9%
자 10
 
3.3%
장 9
 
3.0%
스 8
 
2.6%
품 8
 
2.6%
동 7
 
2.3%
의 7
 
2.3%
주 6
 
2.0%
구 6
 
2.0%
화 6
 
2.0%
Other values (154) 225
74.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 304
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 12
 
3.9%
자 10
 
3.3%
장 9
 
3.0%
스 8
 
2.6%
품 8
 
2.6%
동 7
 
2.3%
의 7
 
2.3%
주 6
 
2.0%
구 6
 
2.0%
화 6
 
2.0%
Other values (154) 225
74.0%

PCINDX_VALUE
Real number (ℝ)

UNIQUE  ZEROS 

Distinct99
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005412711
Minimum-0.15197749
Maximum0.74209114
Zeros1
Zeros (%)1.0%
Negative54
Negative (%)54.5%
Memory size1023.0 B
2023-12-10T15:22:42.571903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.15197749
5-th percentile-0.08720732
Q1-0.021597905
median-0.001927734
Q30.019897384
95-th percentile0.071543404
Maximum0.74209114
Range0.89406863
Interquartile range (IQR)0.041495288

Descriptive statistics

Standard deviation0.089915536
Coefficient of variation (CV)16.611923
Kurtosis46.409448
Mean0.005412711
Median Absolute Deviation (MAD)0.021596437
Skewness5.6625154
Sum0.53585838
Variance0.0080848036
MonotonicityNot monotonic
2023-12-10T15:22:42.903407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.093869381 1
 
1.0%
-0.016540031 1
 
1.0%
-0.022867697 1
 
1.0%
-0.00851572 1
 
1.0%
-0.025279878 1
 
1.0%
-0.002947415 1
 
1.0%
0.014145518 1
 
1.0%
-0.008326885 1
 
1.0%
0.020126065 1
 
1.0%
-0.00732272 1
 
1.0%
Other values (89) 89
89.9%
ValueCountFrequency (%)
-0.15197749 1
1.0%
-0.13825257 1
1.0%
-0.135693976 1
1.0%
-0.105659037 1
1.0%
-0.095515957 1
1.0%
-0.086284138 1
1.0%
-0.080187833 1
1.0%
-0.071811372 1
1.0%
-0.065152495 1
1.0%
-0.061985001 1
1.0%
ValueCountFrequency (%)
0.742091144 1
1.0%
0.203562059 1
1.0%
0.093949307 1
1.0%
0.093869381 1
1.0%
0.076942185 1
1.0%
0.07094354 1
1.0%
0.065944098 1
1.0%
0.065820821 1
1.0%
0.065651859 1
1.0%
0.061324867 1
1.0%

Interactions

2023-12-10T15:22:40.315194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:22:43.043460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRDLST_NMPCINDX_VALUE
PRDLST_NM1.0001.000
PCINDX_VALUE1.0001.000

Missing values

2023-12-10T15:22:40.505143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:22:40.654119image/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

YMPRDLST_NMPCINDX_VALUE
0201907쌀0.093869
1201907혼식곡0.093949
2201907토마토0.033897
3201907포도-0.036774
4201907국산쇠고기-0.034933
5201907닭고기-0.004518
6201907달걀0.052695
7201907꿀-0.00857
8201907미역-0.004844
9201907두부0.055481
YMPRDLST_NMPCINDX_VALUE
89201907남자하의-0.005661
90201907청바지0.742091
91201907운동복-0.086284
92201907장갑-0.019108
93201907침구0.010649
94201907커튼0.065944
95201907기초화장품0.050856
96201907운동화-0.00074
97201907실내화-0.047944
98201907주택수선재료-0.000485