Overview

Dataset statistics

Number of variables15
Number of observations30
Missing cells14
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory136.3 B

Variable types

Numeric12
Categorical3

Dataset

Description해외에서 수입되는 재식용식물에 대한 검역장소 관리 현황 및 통계정보로 종자, 구근, 묘/묘목, 삽수 등의 정보를 제공합니다.
Author국제식물검역인증원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220714000000002162

Alerts

컨테이너 20FT(2대 이상) is highly correlated with 컨테이너 40FT(2대 이상) and 1 other fieldsHigh correlation
컨테이너 40FT(1대) is highly correlated with 품목(묘/묘목)High correlation
컨테이너 40FT(2대 이상) is highly correlated with 컨테이너 20FT(2대 이상) and 1 other fieldsHigh correlation
품목(종자) is highly correlated with 컨테이너 20FT(2대 이상) and 1 other fieldsHigh correlation
품목(묘/묘목) is highly correlated with 컨테이너 40FT(1대)High correlation
관리실적(천원) is highly correlated with 컨테이너 기타 내용High correlation
컨테이너 기타 내용 is highly correlated with 관리실적(천원)High correlation
컨테이너 기타 내용 has 14 (46.7%) missing values Missing
컨테이너 20FT(1대) has 10 (33.3%) zeros Zeros
컨테이너 20FT(2대 이상) has 14 (46.7%) zeros Zeros
컨테이너 40FT(1대) has 3 (10.0%) zeros Zeros
컨테이너 40FT(2대 이상) has 4 (13.3%) zeros Zeros
컨테이너 기타 has 14 (46.7%) zeros Zeros
해출발견 건수 has 10 (33.3%) zeros Zeros
품목(종자) has 6 (20.0%) zeros Zeros
품목(구근) has 5 (16.7%) zeros Zeros
품목(묘/묘목) has 11 (36.7%) zeros Zeros
품목(삽수) has 21 (70.0%) zeros Zeros
품목(기타) has 24 (80.0%) zeros Zeros

Reproduction

Analysis started2022-08-12 14:50:02.242637
Analysis finished2022-08-12 14:50:19.528882
Duration17.29 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

검사년도
Real number (ℝ≥0)

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.666667
Minimum2012
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:19.565147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12013
median2015
Q32016
95-th percentile2017
Maximum2017
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.728729518
Coefficient of variation (CV)0.0008580722294
Kurtosis-1.268596788
Mean2014.666667
Median Absolute Deviation (MAD)1.5
Skewness-0.08740459367
Sum60440
Variance2.988505747
MonotonicityDecreasing
2022-08-12T23:50:19.668642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
20176
20.0%
20165
16.7%
20155
16.7%
20145
16.7%
20135
16.7%
20124
13.3%
ValueCountFrequency (%)
20124
13.3%
20135
16.7%
20145
16.7%
20155
16.7%
20165
16.7%
20176
20.0%
ValueCountFrequency (%)
20176
20.0%
20165
16.7%
20155
16.7%
20145
16.7%
20135
16.7%
20124
13.3%

사무소 명
Categorical

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size368.0 B
부산신항
부 산
인 천
광 양
군 산
Other values (5)

Length

Max length4
Median length3
Mean length2.866666667
Min length2

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row부산
2nd row부산신항
3rd row군산
4th row평택
5th row광양

Common Values

ValueCountFrequency (%)
부산신항5
16.7%
부 산4
13.3%
인 천4
13.3%
광 양4
13.3%
군 산4
13.3%
부산2
 
6.7%
군산2
 
6.7%
광양2
 
6.7%
인천2
 
6.7%
평택1
 
3.3%

Length

2022-08-12T23:50:19.776396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-08-12T23:50:20.030738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
8
17.4%
부산신항5
10.9%
4
8.7%
4
8.7%
4
8.7%
4
8.7%
4
8.7%
4
8.7%
부산2
 
4.3%
군산2
 
4.3%
Other values (3)5
10.9%

컨테이너 20FT(1대)
Real number (ℝ≥0)

ZEROS

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.73333333
Minimum0
Maximum173
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:20.154738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median40.5
Q3109
95-th percentile131.55
Maximum173
Range173
Interquartile range (IQR)109

Descriptive statistics

Standard deviation56.76201579
Coefficient of variation (CV)1.056365058
Kurtosis-1.299305701
Mean53.73333333
Median Absolute Deviation (MAD)40.5
Skewness0.4867698149
Sum1612
Variance3221.926437
MonotonicityNot monotonic
2022-08-12T23:50:20.253489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
010
33.3%
22
 
6.7%
1262
 
6.7%
11
 
3.3%
531
 
3.3%
1121
 
3.3%
501
 
3.3%
1321
 
3.3%
971
 
3.3%
601
 
3.3%
Other values (9)9
30.0%
ValueCountFrequency (%)
010
33.3%
11
 
3.3%
22
 
6.7%
211
 
3.3%
311
 
3.3%
501
 
3.3%
531
 
3.3%
591
 
3.3%
601
 
3.3%
961
 
3.3%
ValueCountFrequency (%)
1731
3.3%
1321
3.3%
1311
3.3%
1262
6.7%
1231
3.3%
1171
3.3%
1121
3.3%
1001
3.3%
971
3.3%
961
3.3%

컨테이너 20FT(2대 이상)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.833333333
Minimum0
Maximum33
Zeros14
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:20.346538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q36
95-th percentile18.55
Maximum33
Range33
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.625200205
Coefficient of variation (CV)1.577627629
Kurtosis5.687843295
Mean4.833333333
Median Absolute Deviation (MAD)1.5
Skewness2.259813965
Sum145
Variance58.14367816
MonotonicityNot monotonic
2022-08-12T23:50:20.428283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
014
46.7%
63
 
10.0%
22
 
6.7%
42
 
6.7%
52
 
6.7%
141
 
3.3%
181
 
3.3%
131
 
3.3%
191
 
3.3%
71
 
3.3%
Other values (2)2
 
6.7%
ValueCountFrequency (%)
014
46.7%
11
 
3.3%
22
 
6.7%
42
 
6.7%
52
 
6.7%
63
 
10.0%
71
 
3.3%
131
 
3.3%
141
 
3.3%
181
 
3.3%
ValueCountFrequency (%)
331
 
3.3%
191
 
3.3%
181
 
3.3%
141
 
3.3%
131
 
3.3%
71
 
3.3%
63
10.0%
52
6.7%
42
6.7%
22
6.7%

컨테이너 40FT(1대)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240.6666667
Minimum0
Maximum1163
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:20.523700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.75
median81.5
Q3219.75
95-th percentile1088.55
Maximum1163
Range1163
Interquartile range (IQR)215

Descriptive statistics

Standard deviation388.2405345
Coefficient of variation (CV)1.613187817
Kurtosis1.367462373
Mean240.6666667
Median Absolute Deviation (MAD)80
Skewness1.724887036
Sum7220
Variance150730.7126
MonotonicityNot monotonic
2022-08-12T23:50:20.627783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
03
 
10.0%
22
 
6.7%
892
 
6.7%
12
 
6.7%
122
 
6.7%
271
 
3.3%
2441
 
3.3%
111
 
3.3%
9831
 
3.3%
1091
 
3.3%
Other values (14)14
46.7%
ValueCountFrequency (%)
03
10.0%
12
6.7%
22
6.7%
41
 
3.3%
71
 
3.3%
111
 
3.3%
122
6.7%
161
 
3.3%
271
 
3.3%
741
 
3.3%
ValueCountFrequency (%)
11631
3.3%
11071
3.3%
10661
3.3%
10461
3.3%
9831
3.3%
2871
3.3%
2441
3.3%
2321
3.3%
1831
3.3%
1771
3.3%

컨테이너 40FT(2대 이상)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.06666667
Minimum0
Maximum251
Zeros4
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:20.731162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.25
median28
Q369.75
95-th percentile160.7
Maximum251
Range251
Interquartile range (IQR)63.5

Descriptive statistics

Standard deviation62.1898835
Coefficient of variation (CV)1.19442798
Kurtosis2.358280709
Mean52.06666667
Median Absolute Deviation (MAD)26.5
Skewness1.584725473
Sum1562
Variance3867.581609
MonotonicityNot monotonic
2022-08-12T23:50:20.847388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
04
 
13.3%
282
 
6.7%
62
 
6.7%
482
 
6.7%
152
 
6.7%
121
 
3.3%
571
 
3.3%
101
 
3.3%
11
 
3.3%
471
 
3.3%
Other values (13)13
43.3%
ValueCountFrequency (%)
04
13.3%
11
 
3.3%
21
 
3.3%
62
6.7%
71
 
3.3%
101
 
3.3%
121
 
3.3%
152
6.7%
251
 
3.3%
282
6.7%
ValueCountFrequency (%)
2511
3.3%
1671
3.3%
1531
3.3%
1381
3.3%
1261
3.3%
1031
3.3%
1021
3.3%
721
3.3%
631
3.3%
571
3.3%

컨테이너 기타
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6
Minimum0
Maximum49
Zeros14
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:20.938771image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34.75
95-th percentile28.6
Maximum49
Range49
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation11.1590384
Coefficient of variation (CV)1.992685428
Kurtosis8.419434606
Mean5.6
Median Absolute Deviation (MAD)1
Skewness2.856911758
Sum168
Variance124.5241379
MonotonicityNot monotonic
2022-08-12T23:50:21.026369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
014
46.7%
52
 
6.7%
12
 
6.7%
42
 
6.7%
32
 
6.7%
22
 
6.7%
81
 
3.3%
221
 
3.3%
91
 
3.3%
341
 
3.3%
Other values (2)2
 
6.7%
ValueCountFrequency (%)
014
46.7%
12
 
6.7%
22
 
6.7%
32
 
6.7%
42
 
6.7%
52
 
6.7%
81
 
3.3%
91
 
3.3%
161
 
3.3%
221
 
3.3%
ValueCountFrequency (%)
491
3.3%
341
3.3%
221
3.3%
161
3.3%
91
3.3%
81
3.3%
52
6.7%
42
6.7%
32
6.7%
22
6.7%

컨테이너 기타 내용
Categorical

HIGH CORRELATION
MISSING

Distinct14
Distinct (%)87.5%
Missing14
Missing (%)46.7%
Memory size368.0 B
5 (20피트, 40피트 혼합)
1 (20피트, 40피트 혼합)
8 (20피트, 40피트 혼합)
4 (20피트, 40피트 혼합)
3 (20피트, 40피트 혼합)
Other values (9)

Length

Max length33
Median length32
Mean length19.5625
Min length4

Unique

Unique12 ?
Unique (%)75.0%

Sample

1st row8 (20피트, 40피트 혼합)
2nd row5 (20피트, 40피트 혼합)
3rd row1 (20피트, 40피트 혼합)
4th row1 (20피트, 40피트 혼합)
5th row5 (20피트, 40피트 혼합)

Common Values

ValueCountFrequency (%)
5 (20피트, 40피트 혼합)2
 
6.7%
1 (20피트, 40피트 혼합)2
 
6.7%
8 (20피트, 40피트 혼합)1
 
3.3%
4 (20피트, 40피트 혼합) 1
 
3.3%
3 (20피트, 40피트 혼합)1
 
3.3%
2 (20피트, 40피트 혼합)1
 
3.3%
3(20피트,40피트 혼합)1
 
3.3%
10(20피트,40피트 혼합), 12(벌크)1
 
3.3%
4(20피트,40피트 혼합)1
 
3.3%
(20피트·40피트 혼합 : 8) 벌크 11
 
3.3%
Other values (4)4
 
13.3%
(Missing)14
46.7%

Length

2022-08-12T23:50:21.152458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
혼합15
22.1%
40피트8
11.8%
20피트8
11.8%
6
 
8.8%
벌크4
 
5.9%
84
 
5.9%
20피트·40피트4
 
5.9%
13
 
4.4%
52
 
2.9%
22
 
2.9%
Other values (12)12
17.6%

관리실적(천원)
Categorical

HIGH CORRELATION

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size368.0 B
0
 
2
2,720
 
1
960
 
1
1,545
 
1
120
 
1
Other values (24)
24 

Length

Max length6
Median length5
Mean length3.966666667
Min length1

Unique

Unique28 ?
Unique (%)93.3%

Sample

1st row18,705
2nd row2,720
3rd row960
4th row1,545
5th row120

Common Values

ValueCountFrequency (%)
02
 
6.7%
2,7201
 
3.3%
9601
 
3.3%
1,5451
 
3.3%
1201
 
3.3%
23,2001
 
3.3%
43301
 
3.3%
17401
 
3.3%
269701
 
3.3%
2401
 
3.3%
Other values (19)19
63.3%

Length

2022-08-12T23:50:21.275621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02
 
6.7%
18,7051
 
3.3%
26551
 
3.3%
92701
 
3.3%
147601
 
3.3%
9201
 
3.3%
293251
 
3.3%
901
 
3.3%
289351
 
3.3%
10951
 
3.3%
Other values (19)19
63.3%

해출발견 건수
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.933333333
Minimum0
Maximum28
Zeros10
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:21.382563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q318
95-th percentile26.55
Maximum28
Range28
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.730624722
Coefficient of variation (CV)1.089249036
Kurtosis-1.00645729
Mean8.933333333
Median Absolute Deviation (MAD)5
Skewness0.6929733044
Sum268
Variance94.68505747
MonotonicityNot monotonic
2022-08-12T23:50:21.595574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
010
33.3%
112
 
6.7%
42
 
6.7%
202
 
6.7%
182
 
6.7%
12
 
6.7%
92
 
6.7%
271
 
3.3%
21
 
3.3%
261
 
3.3%
Other values (5)5
16.7%
ValueCountFrequency (%)
010
33.3%
12
 
6.7%
21
 
3.3%
42
 
6.7%
61
 
3.3%
92
 
6.7%
101
 
3.3%
112
 
6.7%
182
 
6.7%
202
 
6.7%
ValueCountFrequency (%)
281
3.3%
271
3.3%
261
3.3%
221
3.3%
211
3.3%
202
6.7%
182
6.7%
112
6.7%
101
3.3%
92
6.7%

품목(종자)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.6666667
Minimum0
Maximum626
Zeros6
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:21.717101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.25
median52.5
Q3118
95-th percentile424.7
Maximum626
Range626
Interquartile range (IQR)114.75

Descriptive statistics

Standard deviation159.8952962
Coefficient of variation (CV)1.358883537
Kurtosis2.693805338
Mean117.6666667
Median Absolute Deviation (MAD)52.5
Skewness1.777932376
Sum3530
Variance25566.50575
MonotonicityNot monotonic
2022-08-12T23:50:21.832349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
06
 
20.0%
4581
 
3.3%
451
 
3.3%
171
 
3.3%
271
 
3.3%
11
 
3.3%
1111
 
3.3%
921
 
3.3%
351
 
3.3%
801
 
3.3%
Other values (15)15
50.0%
ValueCountFrequency (%)
06
20.0%
11
 
3.3%
21
 
3.3%
71
 
3.3%
171
 
3.3%
181
 
3.3%
271
 
3.3%
351
 
3.3%
431
 
3.3%
451
 
3.3%
ValueCountFrequency (%)
6261
3.3%
4581
3.3%
3841
3.3%
3481
3.3%
3441
3.3%
2391
3.3%
1681
3.3%
1191
3.3%
1151
3.3%
1111
3.3%

품목(구근)
Real number (ℝ≥0)

ZEROS

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.2
Minimum0
Maximum82
Zeros5
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:21.961556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median11
Q341.5
95-th percentile56.85
Maximum82
Range82
Interquartile range (IQR)40.25

Descriptive statistics

Standard deviation23.5158054
Coefficient of variation (CV)1.109236104
Kurtosis-0.3509300748
Mean21.2
Median Absolute Deviation (MAD)11
Skewness0.8445257437
Sum636
Variance552.9931034
MonotonicityNot monotonic
2022-08-12T23:50:22.073515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
05
16.7%
24
13.3%
13
 
10.0%
42
 
6.7%
192
 
6.7%
472
 
6.7%
131
 
3.3%
601
 
3.3%
161
 
3.3%
441
 
3.3%
Other values (8)8
26.7%
ValueCountFrequency (%)
05
16.7%
13
10.0%
24
13.3%
42
 
6.7%
91
 
3.3%
131
 
3.3%
161
 
3.3%
192
 
6.7%
371
 
3.3%
391
 
3.3%
ValueCountFrequency (%)
821
3.3%
601
3.3%
531
3.3%
501
3.3%
472
6.7%
441
3.3%
421
3.3%
401
3.3%
391
3.3%
371
3.3%

품목(묘/묘목)
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.5333333
Minimum0
Maximum1135
Zeros11
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:22.184518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23.5
Q355.75
95-th percentile1071.65
Maximum1135
Range1135
Interquartile range (IQR)55.75

Descriptive statistics

Standard deviation392.9647465
Coefficient of variation (CV)1.959598137
Kurtosis1.519701468
Mean200.5333333
Median Absolute Deviation (MAD)23.5
Skewness1.811129322
Sum6016
Variance154421.292
MonotonicityNot monotonic
2022-08-12T23:50:22.295920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
011
36.7%
332
 
6.7%
3541
 
3.3%
11
 
3.3%
9431
 
3.3%
521
 
3.3%
10371
 
3.3%
571
 
3.3%
241
 
3.3%
231
 
3.3%
Other values (9)9
30.0%
ValueCountFrequency (%)
011
36.7%
11
 
3.3%
41
 
3.3%
61
 
3.3%
231
 
3.3%
241
 
3.3%
321
 
3.3%
332
 
6.7%
351
 
3.3%
381
 
3.3%
ValueCountFrequency (%)
11351
3.3%
10911
3.3%
10481
3.3%
10371
3.3%
9431
3.3%
3541
3.3%
701
3.3%
571
3.3%
521
3.3%
381
3.3%

품목(삽수)
Real number (ℝ≥0)

ZEROS

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5
Minimum0
Maximum103
Zeros21
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:22.407762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile47.05
Maximum103
Range103
Interquartile range (IQR)1

Descriptive statistics

Standard deviation22.07471638
Coefficient of variation (CV)2.597025457
Kurtosis11.81595299
Mean8.5
Median Absolute Deviation (MAD)0
Skewness3.292215509
Sum255
Variance487.2931034
MonotonicityNot monotonic
2022-08-12T23:50:22.495032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
021
70.0%
12
 
6.7%
171
 
3.3%
411
 
3.3%
311
 
3.3%
521
 
3.3%
1031
 
3.3%
21
 
3.3%
71
 
3.3%
ValueCountFrequency (%)
021
70.0%
12
 
6.7%
21
 
3.3%
71
 
3.3%
171
 
3.3%
311
 
3.3%
411
 
3.3%
521
 
3.3%
1031
 
3.3%
ValueCountFrequency (%)
1031
 
3.3%
521
 
3.3%
411
 
3.3%
311
 
3.3%
171
 
3.3%
71
 
3.3%
21
 
3.3%
12
 
6.7%
021
70.0%

품목(기타)
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9
Minimum0
Maximum70
Zeros24
Zeros (%)80.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2022-08-12T23:50:22.594991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile67.55
Maximum70
Range70
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.89405886
Coefficient of variation (CV)2.543784318
Kurtosis3.488660061
Mean9
Median Absolute Deviation (MAD)0
Skewness2.284519622
Sum270
Variance524.137931
MonotonicityNot monotonic
2022-08-12T23:50:22.695733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
024
80.0%
41
 
3.3%
11
 
3.3%
601
 
3.3%
701
 
3.3%
681
 
3.3%
671
 
3.3%
ValueCountFrequency (%)
024
80.0%
11
 
3.3%
41
 
3.3%
601
 
3.3%
671
 
3.3%
681
 
3.3%
701
 
3.3%
ValueCountFrequency (%)
701
 
3.3%
681
 
3.3%
671
 
3.3%
601
 
3.3%
41
 
3.3%
11
 
3.3%
024
80.0%

Interactions

2022-08-12T23:50:17.745810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:02.654262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:04.256917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:05.573124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:06.978541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:08.678315image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:10.445562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:11.802904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:12.951954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:14.170529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:15.369683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:16.687655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:17.832868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:02.846045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:04.363202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:05.673927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:07.099412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:08.845482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:10.554761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:11.908242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:13.038891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:14.253045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:15.457429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:16.770748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:17.929413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:03.001462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:04.462831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:05.782856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:07.234077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:08.991789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:10.688178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:12.017425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:13.130183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:14.354428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:15.557996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:16.863351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:18.025915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:03.148251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:04.555328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:05.899463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:07.354548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:09.158521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:10.840373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:12.109682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:13.227369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:14.450312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:15.672847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:16.951429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:18.237114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:03.303956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:04.662133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:06.012566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:07.602357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:09.300368image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:10.965766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:12.200198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:13.442635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:14.553430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:15.783941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:17.045149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:18.325429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:03.431974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:04.751522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:06.128251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:07.718537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:09.458275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:11.062796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:12.284662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:13.528516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:14.645961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:15.892090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:17.146883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:18.432025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:03.547148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:04.841072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:06.235556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:07.830364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:09.582095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:11.166121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:12.373940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:13.626180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:14.726815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:15.991724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:17.230083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:18.522271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:03.661927image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:04.931526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:06.360093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:07.935825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:09.697622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:11.256285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:12.471314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:13.719133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:14.809467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:16.076225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:17.313944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:18.618671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:03.880402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:05.036044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:06.483726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:08.067338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:09.992418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:11.341233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:12.583452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:13.811729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:15.014214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:16.162575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:17.402883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:18.719722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:03.973209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:05.130612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:06.598964image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:08.186718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:10.101197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:11.421046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:12.686031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:13.894556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:15.095579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:16.240536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:17.484193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:18.816245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:04.072257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:05.236811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:06.725842image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:08.384328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:10.239540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:11.504090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:12.769093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:13.990398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:15.182476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:16.338333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:17.566285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:18.905864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:04.163751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:05.324901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:06.869173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:08.504440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:10.338363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:11.589411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:12.855036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:14.080009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:15.276831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:16.442153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-08-12T23:50:17.653960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-08-12T23:50:22.794122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-08-12T23:50:22.964270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-08-12T23:50:23.261381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-08-12T23:50:23.477923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2022-08-12T23:50:23.709440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2022-08-12T23:50:19.090250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-08-12T23:50:19.308782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-08-12T23:50:19.413659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

검사년도사무소 명컨테이너 20FT(1대)컨테이너 20FT(2대 이상)컨테이너 40FT(1대)컨테이너 40FT(2대 이상)컨테이너 기타컨테이너 기타 내용관리실적(천원)해출발견 건수품목(종자)품목(구근)품목(묘/묘목)품목(삽수)품목(기타)
02017부산1261423216788 (20피트, 40피트 혼합)18,70527458503504
12017부산신항312741255 (20피트, 40피트 혼합)2,7201143473301
22017군산0011100<NA>9600174000
32017평택2012150<NA>1,5454272000
42017광양00210<NA>120211100
52017인천11249834711 (20피트, 40피트 혼합)23,20026111169431760
62016부산신항502109280<NA>43302892445210
72016군산0012250<NA>17400352000
82016인천132610666311 (20피트, 40피트 혼합)2697020804010374170
92016광양00020<NA>240020000

Last rows

검사년도사무소 명컨테이너 20FT(1대)컨테이너 20FT(2대 이상)컨테이너 40FT(1대)컨테이너 40FT(2대 이상)컨테이너 기타컨테이너 기타 내용관리실적(천원)해출발견 건수품목(종자)품목(구근)품목(묘/묘목)품목(삽수)품목(기타)
202014인 천131711634844(20피트,40피트 혼합)281451097211355267
212013부산신항2101660<NA>109511819600
222013인 천117510461029(20피트·40피트 혼합 : 8) 벌크 12893518119910481030
232013광 양20202벌크 290000420
242013부 산1733328725134(20피트·40피트 혼합 : 17)파렛트 500kg : 17293259626827000
252013군 산004150<NA>9200019000
262012부 산96411910316(20피트·40피트 혼합 : 8) (파렛트, 벌크 : 8)147606239603270
272012인 천5312445749(20피트·40피트 혼합 : 7)(파렛트, 벌크 42)9270445435410
282012광 양00100<NA>20001000
292012군 산00000<NA>0000000