Overview

Dataset statistics

Number of variables13
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.9 KiB
Average record size in memory112.3 B

Variable types

Categorical9
Numeric4

Dataset

DescriptionSample
Author(주)제로투원파트너스
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=ZTO011TOTALCGISTARC

Alerts

MT has constant value ""Constant
201901 has constant value ""Constant
전분/분말류 has constant value ""Constant
전체.1 is highly overall correlated with 전체 and 1 other fieldsHigh correlation
전체 is highly overall correlated with 전체.1 and 1 other fieldsHigh correlation
127 is highly overall correlated with 127.2High correlation
127.1 is highly overall correlated with 123High correlation
127.2 is highly overall correlated with 127High correlation
123 is highly overall correlated with 127.1High correlation
2 is highly overall correlated with 50High correlation
50 is highly overall correlated with 전체 and 2 other fieldsHigh correlation
경상북도 is highly overall correlated with 영주시High correlation
영주시 is highly overall correlated with 경상북도High correlation
2 is highly imbalanced (63.1%)Imbalance

Reproduction

Analysis started2023-12-10 06:40:35.334293
Analysis finished2023-12-10 06:40:39.118092
Duration3.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

MT
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
MT 99
100.0%

Length

2023-12-10T15:40:39.224588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:39.372160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mt 99
100.0%

201901
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
201901
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201901
2nd row201901
3rd row201901
4th row201901
5th row201901

Common Values

ValueCountFrequency (%)
201901 99
100.0%

Length

2023-12-10T15:40:39.532205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:39.684321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201901 99
100.0%

127
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.63636
Minimum27
Maximum1887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:40:39.831154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile52.9
Q1103
median122
Q3157
95-th percentile363.1
Maximum1887
Range1860
Interquartile range (IQR)54

Descriptive statistics

Standard deviation199.05944
Coefficient of variation (CV)1.2239541
Kurtosis58.518607
Mean162.63636
Median Absolute Deviation (MAD)24
Skewness7.001644
Sum16101
Variance39624.662
MonotonicityNot monotonic
2023-12-10T15:40:40.025580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106 4
 
4.0%
86 3
 
3.0%
125 3
 
3.0%
128 3
 
3.0%
110 3
 
3.0%
101 3
 
3.0%
116 3
 
3.0%
103 3
 
3.0%
109 2
 
2.0%
123 2
 
2.0%
Other values (63) 70
70.7%
ValueCountFrequency (%)
27 1
1.0%
34 1
1.0%
38 1
1.0%
47 1
1.0%
52 1
1.0%
53 1
1.0%
54 1
1.0%
56 1
1.0%
61 1
1.0%
62 1
1.0%
ValueCountFrequency (%)
1887 1
1.0%
579 1
1.0%
542 1
1.0%
530 1
1.0%
382 1
1.0%
361 1
1.0%
294 1
1.0%
280 1
1.0%
257 1
1.0%
248 1
1.0%

127.1
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)78.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.35354
Minimum16
Maximum597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:40:40.246638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile40.9
Q197
median126
Q3152
95-th percentile282.4
Maximum597
Range581
Interquartile range (IQR)55

Descriptive statistics

Standard deviation87.450897
Coefficient of variation (CV)0.62754703
Kurtosis9.3673877
Mean139.35354
Median Absolute Deviation (MAD)28
Skewness2.5133916
Sum13796
Variance7647.6595
MonotonicityNot monotonic
2023-12-10T15:40:40.450579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137 5
 
5.1%
115 3
 
3.0%
124 3
 
3.0%
60 2
 
2.0%
127 2
 
2.0%
99 2
 
2.0%
136 2
 
2.0%
150 2
 
2.0%
83 2
 
2.0%
166 2
 
2.0%
Other values (68) 74
74.7%
ValueCountFrequency (%)
16 1
1.0%
17 1
1.0%
23 1
1.0%
28 1
1.0%
40 1
1.0%
41 1
1.0%
42 1
1.0%
46 1
1.0%
53 1
1.0%
60 2
2.0%
ValueCountFrequency (%)
597 1
1.0%
455 1
1.0%
447 1
1.0%
325 1
1.0%
286 1
1.0%
282 1
1.0%
261 1
1.0%
260 1
1.0%
257 1
1.0%
245 1
1.0%

127.2
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.63636
Minimum27
Maximum1887
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:40:40.655638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile52.9
Q1103
median122
Q3157
95-th percentile363.1
Maximum1887
Range1860
Interquartile range (IQR)54

Descriptive statistics

Standard deviation199.05944
Coefficient of variation (CV)1.2239541
Kurtosis58.518607
Mean162.63636
Median Absolute Deviation (MAD)24
Skewness7.001644
Sum16101
Variance39624.662
MonotonicityNot monotonic
2023-12-10T15:40:40.875441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106 4
 
4.0%
86 3
 
3.0%
125 3
 
3.0%
128 3
 
3.0%
110 3
 
3.0%
101 3
 
3.0%
116 3
 
3.0%
103 3
 
3.0%
109 2
 
2.0%
123 2
 
2.0%
Other values (63) 70
70.7%
ValueCountFrequency (%)
27 1
1.0%
34 1
1.0%
38 1
1.0%
47 1
1.0%
52 1
1.0%
53 1
1.0%
54 1
1.0%
56 1
1.0%
61 1
1.0%
62 1
1.0%
ValueCountFrequency (%)
1887 1
1.0%
579 1
1.0%
542 1
1.0%
530 1
1.0%
382 1
1.0%
361 1
1.0%
294 1
1.0%
280 1
1.0%
257 1
1.0%
248 1
1.0%

전분/분말류
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
전분/분말류
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전분/분말류
2nd row전분/분말류
3rd row전분/분말류
4th row전분/분말류
5th row전분/분말류

Common Values

ValueCountFrequency (%)
전분/분말류 99
100.0%

Length

2023-12-10T15:40:41.067979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:41.182328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전분/분말류 99
100.0%

전체
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
63 
튀김가루
36 

Length

Max length4
Median length2
Mean length2.7272727
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 63
63.6%
튀김가루 36
36.4%

Length

2023-12-10T15:40:41.365841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:41.535719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 63
63.6%
튀김가루 36
36.4%

전체.1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
63 
튀김가루
36 

Length

Max length4
Median length2
Mean length2.7272727
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전체 63
63.6%
튀김가루 36
36.4%

Length

2023-12-10T15:40:41.743170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:41.919000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전체 63
63.6%
튀김가루 36
36.4%

2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2
92 
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 92
92.9%
1 7
 
7.1%

Length

2023-12-10T15:40:42.060548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:42.175690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 92
92.9%
1 7
 
7.1%

50
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
60
36 
30
28 
99
16 
20
15 
50
4 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50
2nd row50
3rd row50
4th row50
5th row60

Common Values

ValueCountFrequency (%)
60 36
36.4%
30 28
28.3%
99 16
16.2%
20 15
15.2%
50 4
 
4.0%

Length

2023-12-10T15:40:42.360365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:40:42.489454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
60 36
36.4%
30 28
28.3%
99 16
16.2%
20 15
15.2%
50 4
 
4.0%

경상북도
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size924.0 B
서울특별시
25 
경기도
18 
전라북도
11 
경상남도
10 
부산광역시
10 
Other values (6)
25 

Length

Max length5
Median length4
Mean length4.2828283
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상북도
2nd row경상남도
3rd row경상남도
4th row경상남도
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 25
25.3%
경기도 18
18.2%
전라북도 11
11.1%
경상남도 10
 
10.1%
부산광역시 10
 
10.1%
인천광역시 6
 
6.1%
충청북도 5
 
5.1%
전라남도 5
 
5.1%
경상북도 4
 
4.0%
대구광역시 3
 
3.0%

Length

2023-12-10T15:40:42.696378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 25
25.3%
경기도 18
18.2%
전라북도 11
11.1%
경상남도 10
 
10.1%
부산광역시 10
 
10.1%
인천광역시 6
 
6.1%
충청북도 5
 
5.1%
전라남도 5
 
5.1%
경상북도 4
 
4.0%
대구광역시 3
 
3.0%

영주시
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Memory size924.0 B
전체
19 
중구
7 
송파구
 
4
김해시
 
4
성북구
 
4
Other values (24)
61 

Length

Max length4
Median length3
Mean length2.7878788
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row칠곡군
2nd row김해시
3rd row거제시
4th row양산시
5th row중구

Common Values

ValueCountFrequency (%)
전체 19
19.2%
중구 7
 
7.1%
송파구 4
 
4.0%
김해시 4
 
4.0%
성북구 4
 
4.0%
노원구 4
 
4.0%
강서구 4
 
4.0%
마포구 3
 
3.0%
청주시 3
 
3.0%
양산시 3
 
3.0%
Other values (19) 44
44.4%

Length

2023-12-10T15:40:42.963061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전체 19
19.2%
중구 7
 
7.1%
송파구 4
 
4.0%
김해시 4
 
4.0%
성북구 4
 
4.0%
노원구 4
 
4.0%
강서구 4
 
4.0%
거제시 3
 
3.0%
사상구 3
 
3.0%
의정부시 3
 
3.0%
Other values (19) 44
44.4%

123
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.09091
Minimum24
Maximum236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-10T15:40:43.182123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile60.6
Q1100
median119
Q3138.5
95-th percentile178.5
Maximum236
Range212
Interquartile range (IQR)38.5

Descriptive statistics

Standard deviation35.816736
Coefficient of variation (CV)0.30075122
Kurtosis1.1073854
Mean119.09091
Median Absolute Deviation (MAD)20
Skewness0.17780168
Sum11790
Variance1282.8386
MonotonicityNot monotonic
2023-12-10T15:40:43.398651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122 3
 
3.0%
112 3
 
3.0%
147 3
 
3.0%
116 3
 
3.0%
119 3
 
3.0%
75 3
 
3.0%
111 3
 
3.0%
123 2
 
2.0%
132 2
 
2.0%
156 2
 
2.0%
Other values (62) 72
72.7%
ValueCountFrequency (%)
24 1
 
1.0%
28 1
 
1.0%
45 1
 
1.0%
54 1
 
1.0%
57 1
 
1.0%
61 1
 
1.0%
67 1
 
1.0%
75 3
3.0%
77 1
 
1.0%
79 2
2.0%
ValueCountFrequency (%)
236 1
1.0%
210 1
1.0%
194 1
1.0%
190 1
1.0%
183 1
1.0%
178 1
1.0%
168 1
1.0%
167 1
1.0%
165 1
1.0%
162 1
1.0%

Interactions

2023-12-10T15:40:38.082300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:36.262783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:36.899790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:37.471112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:38.204934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:36.421870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:37.046210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:37.637457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:38.340049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:36.602110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:37.179485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:37.785220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:38.490146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:36.774536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:37.321132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:40:37.941029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:40:43.532532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
127127.1127.2전체전체.1250경상북도영주시123
1271.0000.4621.0000.0000.0000.0000.2160.2180.0000.216
127.10.4621.0000.4620.2420.2420.4470.4510.3440.2710.674
127.21.0000.4621.0000.0000.0000.0000.2160.2180.0000.216
전체0.0000.2420.0001.0000.9990.2100.7780.0000.0000.272
전체.10.0000.2420.0000.9991.0000.2100.7780.0000.0000.272
20.0000.4470.0000.2100.2101.0000.5230.3880.0000.087
500.2160.4510.2160.7780.7780.5231.0000.3190.0000.460
경상북도0.2180.3440.2180.0000.0000.3880.3191.0000.9490.120
영주시0.0000.2710.0000.0000.0000.0000.0000.9491.0000.380
1230.2160.6740.2160.2720.2720.0870.4600.1200.3801.000
2023-12-10T15:40:44.082428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전체.12경상북도50영주시전체
전체.11.0000.1340.0000.8970.0000.978
20.1341.0000.3530.6240.0000.134
경상북도0.0000.3531.0000.1730.6480.000
500.8970.6240.1731.0000.0000.897
영주시0.0000.0000.6480.0001.0000.000
전체0.9780.1340.0000.8970.0001.000
2023-12-10T15:40:44.256576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
127127.1127.2123전체전체.1250경상북도영주시
1271.0000.3671.0000.4300.0000.0000.0000.1760.1240.000
127.10.3671.0000.3670.6260.1740.1740.3250.2910.1630.080
127.21.0000.3671.0000.4300.0000.0000.0000.1760.1240.000
1230.4300.6260.4301.0000.1980.1980.0560.2000.0380.115
전체0.0000.1740.0000.1981.0000.9780.1340.8970.0000.000
전체.10.0000.1740.0000.1980.9781.0000.1340.8970.0000.000
20.0000.3250.0000.0560.1340.1341.0000.6240.3530.000
500.1760.2910.1760.2000.8970.8970.6241.0000.1730.000
경상북도0.1240.1630.1240.0380.0000.0000.3530.1731.0000.648
영주시0.0000.0800.0000.1150.0000.0000.0000.0000.6481.000

Missing values

2023-12-10T15:40:38.676617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:40:38.998119image/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

MT201901127127.1127.2전분/분말류전체전체.1250경상북도영주시123
0MT201901706470전분/분말류전체전체250경상북도칠곡군91
1MT20190110583105전분/분말류전체전체250경상남도김해시102
2MT20190113787137전분/분말류전체전체250경상남도거제시128
3MT201901115110115전분/분말류전체전체250경상남도양산시108
4MT2019018410684전분/분말류전체전체260서울특별시중구92
5MT201901111107111전분/분말류전체전체260서울특별시용산구110
6MT201901106126106전분/분말류전체전체260서울특별시성북구147
7MT201901110121110전분/분말류전체전체260서울특별시노원구116
8MT201901179118179전분/분말류전체전체260서울특별시마포구103
9MT201901106109106전분/분말류전체전체260서울특별시강서구107
MT201901127127.1127.2전분/분말류전체전체.1250경상북도영주시123
89MT20190115694156전분/분말류튀김가루튀김가루230경기도부천시106
90MT20190110395103전분/분말류튀김가루튀김가루230경기도평택시113
91MT2019018611586전분/분말류튀김가루튀김가루230경기도남양주시105
92MT201901225166225전분/분말류튀김가루튀김가루230경기도오산시158
93MT2019016714967전분/분말류튀김가루튀김가루230경기도군포시79
94MT201901106112106전분/분말류튀김가루튀김가루230충청북도청주시125
95MT201901126146126전분/분말류튀김가루튀김가루230충청북도제천시119
96MT201901100136100전분/분말류튀김가루튀김가루230전라북도전체122
97MT201901148196148전분/분말류튀김가루튀김가루230전라북도익산시183
98MT2019013816438전분/분말류튀김가루튀김가루230전라북도정읍시54