Overview

Dataset statistics

Number of variables7
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory57.9 B

Variable types

Categorical6
Numeric1

Dataset

DescriptionSample
Author(주)제로투원파트너스
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ZTO00000000000000004

Alerts

"YEAR" has constant value ""Constant
"HT_SE_VALUE" is highly overall correlated with "QU_SE_VALUE" and 1 other fieldsHigh correlation
"QU_SE_VALUE" is highly overall correlated with "HT_SE_VALUE" and 1 other fieldsHigh correlation
"YM" is highly overall correlated with "HT_SE_VALUE" and 1 other fieldsHigh correlation
"MENTN_VALUE" has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:36:44.286564
Analysis finished2023-12-10 06:36:48.467006
Duration4.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

"PRDUCT_CTGRY_NM"
Categorical

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
냉장고
48 
에어컨
48 
공기청정기
47 
세탁기/의류건조기
7 

Length

Max length9
Median length3
Mean length3.9066667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
냉장고 48
32.0%
에어컨 48
32.0%
공기청정기 47
31.3%
세탁기/의류건조기 7
 
4.7%

Length

2023-12-10T15:36:48.642157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:48.899156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
냉장고 48
32.0%
에어컨 48
32.0%
공기청정기 47
31.3%
세탁기/의류건조기 7
 
4.7%

"YEAR"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
"2019"
150 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"2019"
2nd row"2019"
3rd row"2019"
4th row"2019"
5th row"2019"

Common Values

ValueCountFrequency (%)
"2019" 150
100.0%

Length

2023-12-10T15:36:49.098653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:49.294046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 150
100.0%

"HT_SE_VALUE"
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
"2019H1"
85 
"2019H2"
65 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"2019H1"
2nd row"2019H1"
3rd row"2019H1"
4th row"2019H1"
5th row"2019H1"

Common Values

ValueCountFrequency (%)
"2019H1" 85
56.7%
"2019H2" 65
43.3%

Length

2023-12-10T15:36:49.517121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:49.688226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019h1 85
56.7%
2019h2 65
43.3%

"QU_SE_VALUE"
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
"2019Q1"
46 
"2019Q2"
39 
"2019Q3"
39 
"2019Q4"
26 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"2019Q1"
2nd row"2019Q1"
3rd row"2019Q1"
4th row"2019Q1"
5th row"2019Q1"

Common Values

ValueCountFrequency (%)
"2019Q1" 46
30.7%
"2019Q2" 39
26.0%
"2019Q3" 39
26.0%
"2019Q4" 26
17.3%

Length

2023-12-10T15:36:49.888600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:50.084187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019q1 46
30.7%
2019q2 39
26.0%
2019q3 39
26.0%
2019q4 26
17.3%

"YM"
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
"201901"
20 
"201905"
15 
"201907"
15 
"201910"
15 
"201902"
14 
Other values (6)
71 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"201901"
2nd row"201901"
3rd row"201901"
4th row"201901"
5th row"201901"

Common Values

ValueCountFrequency (%)
"201901" 20
13.3%
"201905" 15
10.0%
"201907" 15
10.0%
"201910" 15
10.0%
"201902" 14
9.3%
"201903" 12
8.0%
"201904" 12
8.0%
"201906" 12
8.0%
"201908" 12
8.0%
"201909" 12
8.0%

Length

2023-12-10T15:36:50.402526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
201901 20
13.3%
201905 15
10.0%
201907 15
10.0%
201910 15
10.0%
201902 14
9.3%
201903 12
8.0%
201904 12
8.0%
201906 12
8.0%
201908 12
8.0%
201909 12
8.0%

"WEEK_UNIT_ODR"
Categorical

Distinct5
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
W1
35 
W2
35 
W3
34 
W4
33 
W5
13 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW1
2nd rowW2
3rd rowW3
4th rowW4
5th rowW5

Common Values

ValueCountFrequency (%)
W1 35
23.3%
W2 35
23.3%
W3 34
22.7%
W4 33
22.0%
W5 13
 
8.7%

Length

2023-12-10T15:36:50.636199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:36:50.927033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
w1 35
23.3%
w2 35
23.3%
w3 34
22.7%
w4 33
22.0%
w5 13
 
8.7%

"MENTN_VALUE"
Real number (ℝ)

UNIQUE 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17841.753
Minimum1042
Maximum186872
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-10T15:36:51.176400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1042
5-th percentile1746.75
Q14431.75
median11495.5
Q324729.25
95-th percentile56768.1
Maximum186872
Range185830
Interquartile range (IQR)20297.5

Descriptive statistics

Standard deviation21250.658
Coefficient of variation (CV)1.1910633
Kurtosis26.208594
Mean17841.753
Median Absolute Deviation (MAD)8087
Skewness3.9302614
Sum2676263
Variance4.5159048 × 108
MonotonicityNot monotonic
2023-12-10T15:36:51.618974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12259 1
 
0.7%
4955 1
 
0.7%
12722 1
 
0.7%
33118 1
 
0.7%
11641 1
 
0.7%
9453 1
 
0.7%
5648 1
 
0.7%
5834 1
 
0.7%
6521 1
 
0.7%
7074 1
 
0.7%
Other values (140) 140
93.3%
ValueCountFrequency (%)
1042 1
0.7%
1156 1
0.7%
1254 1
0.7%
1372 1
0.7%
1464 1
0.7%
1498 1
0.7%
1616 1
0.7%
1650 1
0.7%
1865 1
0.7%
2417 1
0.7%
ValueCountFrequency (%)
186872 1
0.7%
67019 1
0.7%
63057 1
0.7%
62630 1
0.7%
62554 1
0.7%
59045 1
0.7%
58619 1
0.7%
57534 1
0.7%
55832 1
0.7%
54503 1
0.7%

Interactions

2023-12-10T15:36:47.836882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:36:51.790440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
"PRDUCT_CTGRY_NM""HT_SE_VALUE""QU_SE_VALUE""YM""WEEK_UNIT_ODR""MENTN_VALUE"
"PRDUCT_CTGRY_NM"1.0000.2020.3250.0000.0000.428
"HT_SE_VALUE"0.2021.0001.0001.0000.0000.000
"QU_SE_VALUE"0.3251.0001.0001.0000.0000.118
"YM"0.0001.0001.0001.0000.0000.206
"WEEK_UNIT_ODR"0.0000.0000.0000.0001.0000.000
"MENTN_VALUE"0.4280.0000.1180.2060.0001.000
2023-12-10T15:36:51.969742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
"WEEK_UNIT_ODR""HT_SE_VALUE""PRDUCT_CTGRY_NM""QU_SE_VALUE""YM"
"WEEK_UNIT_ODR"1.0000.0000.0000.0000.000
"HT_SE_VALUE"0.0001.0000.1320.9930.969
"PRDUCT_CTGRY_NM"0.0000.1321.0000.1310.000
"QU_SE_VALUE"0.0000.9930.1311.0000.976
"YM"0.0000.9690.0000.9761.000
2023-12-10T15:36:52.281615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
"MENTN_VALUE""PRDUCT_CTGRY_NM""HT_SE_VALUE""QU_SE_VALUE""YM""WEEK_UNIT_ODR"
"MENTN_VALUE"1.0000.3610.0000.0970.1140.000
"PRDUCT_CTGRY_NM"0.3611.0000.1320.1310.0000.000
"HT_SE_VALUE"0.0000.1321.0000.9930.9690.000
"QU_SE_VALUE"0.0970.1310.9931.0000.9760.000
"YM"0.1140.0000.9690.9761.0000.000
"WEEK_UNIT_ODR"0.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T15:36:48.140362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:36:48.371591image/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""HT_SE_VALUE""QU_SE_VALUE""YM""WEEK_UNIT_ODR""MENTN_VALUE"
0공기청정기"2019""2019H1""2019Q1""201901"W112259
1공기청정기"2019""2019H1""2019Q1""201901"W226078
2공기청정기"2019""2019H1""2019Q1""201901"W335736
3공기청정기"2019""2019H1""2019Q1""201901"W421452
4공기청정기"2019""2019H1""2019Q1""201901"W530982
5공기청정기"2019""2019H1""2019Q1""201902"W113934
6공기청정기"2019""2019H1""2019Q1""201902"W234661
7공기청정기"2019""2019H1""2019Q1""201902"W326698
8공기청정기"2019""2019H1""2019Q1""201902"W428016
9공기청정기"2019""2019H1""2019Q1""201903"W1186872
"PRDUCT_CTGRY_NM""YEAR""HT_SE_VALUE""QU_SE_VALUE""YM""WEEK_UNIT_ODR""MENTN_VALUE"
140에어컨"2019""2019H2""2019Q4""201911"W24254
141에어컨"2019""2019H2""2019Q4""201911"W33571
142에어컨"2019""2019H2""2019Q4""201911"W41254
143세탁기/의류건조기"2019""2019H1""2019Q1""201901"W11156
144세탁기/의류건조기"2019""2019H1""2019Q1""201901"W21464
145세탁기/의류건조기"2019""2019H1""2019Q1""201901"W31865
146세탁기/의류건조기"2019""2019H1""2019Q1""201901"W41616
147세탁기/의류건조기"2019""2019H1""2019Q1""201901"W51372
148세탁기/의류건조기"2019""2019H1""2019Q1""201902"W11042
149세탁기/의류건조기"2019""2019H1""2019Q1""201902"W21498