Overview

Dataset statistics

Number of variables13
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.5 KiB
Average record size in memory105.9 B

Variable types

Categorical11
Text1
Numeric1

Dataset

DescriptionSample
Author(주)제로투원파트너스
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ZTO00000000000000001

Alerts

"PRDUCT_CTGRY_NM" has constant value ""Constant
"YEAR" has constant value ""Constant
"HT_SE_VALUE" has constant value ""Constant
"QU_SE_VALUE" has constant value ""Constant
"YM" has constant value ""Constant
"WEEK_UNIT_ODR" has constant value ""Constant
"SEXDSTN_AGRDE_CODE" has constant value ""Constant
"PRDUCT_LCLAS_NM" has constant value ""Constant
"PRDUCT_MLSFC_NM" has constant value ""Constant
"PRDUCT_SCLAS_NM" has constant value ""Constant

Reproduction

Analysis started2023-12-10 06:41:36.072757
Analysis finished2023-12-10 06:41:36.989128
Duration0.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

"PRDUCT_CTGRY_NM"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
가공식품
150 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가공식품 150
100.0%

Length

2023-12-10T15:41:37.077833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:37.222913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가공식품 150
100.0%

"YEAR"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
"2018"
150 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"2018"
2nd row"2018"
3rd row"2018"
4th row"2018"
5th row"2018"

Common Values

ValueCountFrequency (%)
"2018" 150
100.0%

Length

2023-12-10T15:41:37.381975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:37.504252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 150
100.0%

"BRTC_NM"
Categorical

Distinct14
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
경기도
41 
서울특별시
26 
경상남도
15 
부산광역시
14 
경상북도
11 
Other values (9)
43 

Length

Max length7
Median length5
Mean length4.1733333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 41
27.3%
서울특별시 26
17.3%
경상남도 15
 
10.0%
부산광역시 14
 
9.3%
경상북도 11
 
7.3%
대구광역시 8
 
5.3%
인천광역시 8
 
5.3%
강원도 6
 
4.0%
대전광역시 6
 
4.0%
광주광역시 5
 
3.3%
Other values (4) 10
 
6.7%

Length

2023-12-10T15:41:37.672571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 41
27.3%
서울특별시 26
17.3%
경상남도 15
 
10.0%
부산광역시 14
 
9.3%
경상북도 11
 
7.3%
대구광역시 8
 
5.3%
인천광역시 8
 
5.3%
강원도 6
 
4.0%
대전광역시 6
 
4.0%
광주광역시 5
 
3.3%
Other values (4) 10
 
6.7%

"SIGNGU_NM"
Text

Distinct124
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-10T15:41:38.107202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.56
Min length2

Characters and Unicode

Total characters534
Distinct characters106
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)78.0%

Sample

1st row속초시
2nd row원주시
3rd row전체
4th row철원군
5th row춘천시
ValueCountFrequency (%)
전체 13
 
7.4%
창원시 5
 
2.8%
중구 5
 
2.8%
북구 5
 
2.8%
서구 4
 
2.3%
남구 4
 
2.3%
수원시 4
 
2.3%
고양시 3
 
1.7%
성남시 3
 
1.7%
용인시 3
 
1.7%
Other values (121) 127
72.2%
2023-12-10T15:41:38.709304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 90
 
16.9%
시 64
 
12.0%
26
 
4.9%
원 16
 
3.0%
전 13
 
2.4%
양 13
 
2.4%
성 13
 
2.4%
천 13
 
2.4%
산 13
 
2.4%
체 13
 
2.4%
Other values (96) 260
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 506
94.8%
Space Separator 26
 
4.9%
Other Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 90
 
17.8%
시 64
 
12.6%
원 16
 
3.2%
전 13
 
2.6%
양 13
 
2.6%
성 13
 
2.6%
천 13
 
2.6%
산 13
 
2.6%
체 13
 
2.6%
군 12
 
2.4%
Other values (94) 246
48.6%
Space Separator
ValueCountFrequency (%)
26
100.0%
Other Punctuation
ValueCountFrequency (%)
" 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 506
94.8%
Common 28
 
5.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 90
 
17.8%
시 64
 
12.6%
원 16
 
3.2%
전 13
 
2.6%
양 13
 
2.6%
성 13
 
2.6%
천 13
 
2.6%
산 13
 
2.6%
체 13
 
2.6%
군 12
 
2.4%
Other values (94) 246
48.6%
Common
ValueCountFrequency (%)
26
92.9%
" 2
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 506
94.8%
ASCII 28
 
5.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 90
 
17.8%
시 64
 
12.6%
원 16
 
3.2%
전 13
 
2.6%
양 13
 
2.6%
성 13
 
2.6%
천 13
 
2.6%
산 13
 
2.6%
체 13
 
2.6%
군 12
 
2.4%
Other values (94) 246
48.6%
ASCII
ValueCountFrequency (%)
26
92.9%
" 2
 
7.1%

"CNSMP_WGHTVAL_IDEX_VALUE"
Real number (ℝ)

Distinct109
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.3
Minimum20
Maximum485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:41:38.880440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile52.7
Q1104.25
median128.5
Q3157.75
95-th percentile239.5
Maximum485
Range465
Interquartile range (IQR)53.5

Descriptive statistics

Standard deviation63.356957
Coefficient of variation (CV)0.45811249
Kurtosis6.8178532
Mean138.3
Median Absolute Deviation (MAD)26
Skewness1.8750247
Sum20745
Variance4014.104
MonotonicityNot monotonic
2023-12-10T15:41:39.092345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124 5
 
3.3%
146 4
 
2.7%
135 3
 
2.0%
99 3
 
2.0%
129 3
 
2.0%
115 3
 
2.0%
148 3
 
2.0%
112 3
 
2.0%
125 3
 
2.0%
153 3
 
2.0%
Other values (99) 117
78.0%
ValueCountFrequency (%)
20 1
0.7%
29 1
0.7%
31 1
0.7%
40 1
0.7%
42 1
0.7%
47 1
0.7%
48 1
0.7%
50 1
0.7%
56 1
0.7%
62 1
0.7%
ValueCountFrequency (%)
485 1
0.7%
350 1
0.7%
334 1
0.7%
333 1
0.7%
306 1
0.7%
284 1
0.7%
277 1
0.7%
244 1
0.7%
234 1
0.7%
233 1
0.7%

"HT_SE_VALUE"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
"2018H1"
150 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"2018H1"
2nd row"2018H1"
3rd row"2018H1"
4th row"2018H1"
5th row"2018H1"

Common Values

ValueCountFrequency (%)
"2018H1" 150
100.0%

Length

2023-12-10T15:41:39.281672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:39.463765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018h1 150
100.0%

"QU_SE_VALUE"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
"2018Q1"
150 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"2018Q1"
2nd row"2018Q1"
3rd row"2018Q1"
4th row"2018Q1"
5th row"2018Q1"

Common Values

ValueCountFrequency (%)
"2018Q1" 150
100.0%

Length

2023-12-10T15:41:39.620591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:39.779650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018q1 150
100.0%

"YM"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
"201801"
150 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"201801"
2nd row"201801"
3rd row"201801"
4th row"201801"
5th row"201801"

Common Values

ValueCountFrequency (%)
"201801" 150
100.0%

Length

2023-12-10T15:41:39.949112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:40.156842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201801 150
100.0%

"WEEK_UNIT_ODR"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
W2
150 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW2
2nd rowW2
3rd rowW2
4th rowW2
5th rowW2

Common Values

ValueCountFrequency (%)
W2 150
100.0%

Length

2023-12-10T15:41:40.336732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:40.531653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
w2 150
100.0%

"SEXDSTN_AGRDE_CODE"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
전체
150 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 150
100.0%

Length

2023-12-10T15:41:40.691242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:40.850972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 150
100.0%

"PRDUCT_LCLAS_NM"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
제과류
150 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제과류 150
100.0%

Length

2023-12-10T15:41:41.022799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:41.170070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제과류 150
100.0%

"PRDUCT_MLSFC_NM"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
스낵류
150 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
스낵류 150
100.0%

Length

2023-12-10T15:41:41.316452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:41.473785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
스낵류 150
100.0%

"PRDUCT_SCLAS_NM"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
기타스낵류
150 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타스낵류 150
100.0%

Length

2023-12-10T15:41:41.616165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:41:41.762078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타스낵류 150
100.0%

Interactions

2023-12-10T15:41:36.440573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:41:41.846786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
"BRTC_NM""CNSMP_WGHTVAL_IDEX_VALUE"
"BRTC_NM"1.0000.129
"CNSMP_WGHTVAL_IDEX_VALUE"0.1291.000
2023-12-10T15:41:41.979189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
"CNSMP_WGHTVAL_IDEX_VALUE""BRTC_NM"
"CNSMP_WGHTVAL_IDEX_VALUE"1.0000.047
"BRTC_NM"0.0471.000

Missing values

2023-12-10T15:41:36.628084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:41:36.894913image/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

"PRDUCT_CTGRY_NM""YEAR""BRTC_NM""SIGNGU_NM""CNSMP_WGHTVAL_IDEX_VALUE""HT_SE_VALUE""QU_SE_VALUE""YM""WEEK_UNIT_ODR""SEXDSTN_AGRDE_CODE""PRDUCT_LCLAS_NM""PRDUCT_MLSFC_NM""PRDUCT_SCLAS_NM"
0가공식품"2018"강원도속초시131"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
1가공식품"2018"강원도원주시152"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
2가공식품"2018"강원도전체125"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
3가공식품"2018"강원도철원군175"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
4가공식품"2018"강원도춘천시120"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
5가공식품"2018"강원도횡성군350"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
6가공식품"2018"경기도고양시 덕양구110"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
7가공식품"2018"경기도고양시 일산동구50"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
8가공식품"2018"경기도고양시 일산서구108"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
9가공식품"2018"경기도과천시72"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
"PRDUCT_CTGRY_NM""YEAR""BRTC_NM""SIGNGU_NM""CNSMP_WGHTVAL_IDEX_VALUE""HT_SE_VALUE""QU_SE_VALUE""YM""WEEK_UNIT_ODR""SEXDSTN_AGRDE_CODE""PRDUCT_LCLAS_NM""PRDUCT_MLSFC_NM""PRDUCT_SCLAS_NM"
140가공식품"2018"인천광역시계양구129"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
141가공식품"2018"인천광역시남동구81"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
142가공식품"2018"인천광역시미추홀구182"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
143가공식품"2018"인천광역시부평구102"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
144가공식품"2018"인천광역시서구147"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
145가공식품"2018"인천광역시연수구111"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
146가공식품"2018"인천광역시전체117"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
147가공식품"2018"인천광역시중구158"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
148가공식품"2018"전라남도광양시88"2018H1""2018Q1""201801"W2전체제과류스낵류기타스낵류
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