Overview

Dataset statistics

Number of variables12
Number of observations726
Missing cells5030
Missing cells (%)57.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory72.4 KiB
Average record size in memory102.2 B

Variable types

Categorical2
Numeric6
Text3
Boolean1

Dataset

Description농가경영기록장 시스템에서 사용하는 코드에 대한 정보입니다. 공통코드와 날씨지역정보 및 지역별 우편변호 코드를 포함합니다. 공통코드와 지역코드를 구분컬럼으로 구분하였습니다.
URLhttps://www.data.go.kr/data/15119435/fileData.do

Alerts

사용여부 has constant value ""Constant
코드_구분 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 기관코드 and 6 other fieldsHigh correlation
기관코드 is highly overall correlated with 엑스좌표(X) and 2 other fieldsHigh correlation
엑스좌표(X) is highly overall correlated with 기관코드 and 1 other fieldsHigh correlation
와이좌표(Y) is highly overall correlated with 우편번호_시작 and 2 other fieldsHigh correlation
우편번호_시작 is highly overall correlated with 와이좌표(Y) and 2 other fieldsHigh correlation
우편번호_끝 is highly overall correlated with 기관코드 and 3 other fieldsHigh correlation
순서번호 is highly overall correlated with 구분High correlation
구분 is highly imbalanced (85.5%)Imbalance
기관코드 has 711 (97.9%) missing valuesMissing
지역명 has 711 (97.9%) missing valuesMissing
엑스좌표(X) has 711 (97.9%) missing valuesMissing
와이좌표(Y) has 711 (97.9%) missing valuesMissing
사용여부 has 711 (97.9%) missing valuesMissing
우편번호_시작 has 711 (97.9%) missing valuesMissing
우편번호_끝 has 711 (97.9%) missing valuesMissing
코드_아이디 has 15 (2.1%) missing valuesMissing
코드_이름 has 15 (2.1%) missing valuesMissing
순서번호 has 23 (3.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 14:20:28.932103
Analysis finished2023-12-12 14:20:33.596295
Duration4.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
공통코드
711 
날씨지역코드
 
15

Length

Max length6
Median length4
Mean length4.0413223
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공통코드 711
97.9%
날씨지역코드 15
 
2.1%

Length

2023-12-12T23:20:33.683536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:20:33.786000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공통코드 711
97.9%
날씨지역코드 15
 
2.1%

기관코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)100.0%
Missing711
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean4.3449667 × 109
Minimum4.3 × 109
Maximum4.38 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T23:20:33.872903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.3 × 109
5-th percentile4.30777 × 109
Q14.31135 × 109
median4.372 × 109
Q34.37475 × 109
95-th percentile4.3779 × 109
Maximum4.38 × 109
Range80000000
Interquartile range (IQR)63400000

Descriptive statistics

Standard deviation33636219
Coefficient of variation (CV)0.0077414217
Kurtosis-2.2212036
Mean4.3449667 × 109
Median Absolute Deviation (MAD)8000000
Skewness-0.17099757
Sum6.51745 × 1010
Variance1.1313952 × 1015
MonotonicityStrictly increasing
2023-12-12T23:20:33.978138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
4300000000 1
 
0.1%
4311100000 1
 
0.1%
4311200000 1
 
0.1%
4311300000 1
 
0.1%
4311400000 1
 
0.1%
4313000000 1
 
0.1%
4315000000 1
 
0.1%
4372000000 1
 
0.1%
4373000000 1
 
0.1%
4374000000 1
 
0.1%
Other values (5) 5
 
0.7%
(Missing) 711
97.9%
ValueCountFrequency (%)
4300000000 1
0.1%
4311100000 1
0.1%
4311200000 1
0.1%
4311300000 1
0.1%
4311400000 1
0.1%
4313000000 1
0.1%
4315000000 1
0.1%
4372000000 1
0.1%
4373000000 1
0.1%
4374000000 1
0.1%
ValueCountFrequency (%)
4380000000 1
0.1%
4377000000 1
0.1%
4376000000 1
0.1%
4375000000 1
0.1%
4374500000 1
0.1%
4374000000 1
0.1%
4373000000 1
0.1%
4372000000 1
0.1%
4315000000 1
0.1%
4313000000 1
0.1%

지역명
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing711
Missing (%)97.9%
Memory size5.8 KiB
2023-12-12T23:20:34.134605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.8
Min length4

Characters and Unicode

Total characters132
Distinct characters31
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)100.0%

Sample

1st row충청북도
2nd row충청북도 청주시 상당구
3rd row충청북도 청주시 서원구
4th row충청북도 청주시 흥덕구
5th row충청북도 청주시 청원구
ValueCountFrequency (%)
충청북도 15
45.5%
청주시 4
 
12.1%
상당구 1
 
3.0%
서원구 1
 
3.0%
흥덕구 1
 
3.0%
청원구 1
 
3.0%
충주시 1
 
3.0%
제천시 1
 
3.0%
보은군 1
 
3.0%
옥천군 1
 
3.0%
Other values (6) 6
 
18.2%
2023-12-12T23:20:34.426126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
청 20
15.2%
18
13.6%
충 16
12.1%
북 15
11.4%
도 15
11.4%
군 8
 
6.1%
시 6
 
4.5%
주 5
 
3.8%
구 4
 
3.0%
천 3
 
2.3%
Other values (21) 22
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 114
86.4%
Space Separator 18
 
13.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
청 20
17.5%
충 16
14.0%
북 15
13.2%
도 15
13.2%
군 8
 
7.0%
시 6
 
5.3%
주 5
 
4.4%
구 4
 
3.5%
천 3
 
2.6%
원 2
 
1.8%
Other values (20) 20
17.5%
Space Separator
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 114
86.4%
Common 18
 
13.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
청 20
17.5%
충 16
14.0%
북 15
13.2%
도 15
13.2%
군 8
 
7.0%
시 6
 
5.3%
주 5
 
4.4%
구 4
 
3.5%
천 3
 
2.6%
원 2
 
1.8%
Other values (20) 20
17.5%
Common
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 114
86.4%
ASCII 18
 
13.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
청 20
17.5%
충 16
14.0%
북 15
13.2%
도 15
13.2%
군 8
 
7.0%
시 6
 
5.3%
주 5
 
4.4%
구 4
 
3.5%
천 3
 
2.6%
원 2
 
1.8%
Other values (20) 20
17.5%
ASCII
ValueCountFrequency (%)
18
100.0%

엑스좌표(X)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)66.7%
Missing711
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean72.466667
Minimum67
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T23:20:34.543024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile67.7
Q169
median71
Q374
95-th percentile81.9
Maximum84
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.8235533
Coefficient of variation (CV)0.066562373
Kurtosis1.3140841
Mean72.466667
Median Absolute Deviation (MAD)2
Skewness1.3082537
Sum1087
Variance23.266667
MonotonicityNot monotonic
2023-12-12T23:20:34.652339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
69 4
 
0.6%
71 2
 
0.3%
74 2
 
0.3%
67 1
 
0.1%
76 1
 
0.1%
81 1
 
0.1%
73 1
 
0.1%
68 1
 
0.1%
72 1
 
0.1%
84 1
 
0.1%
(Missing) 711
97.9%
ValueCountFrequency (%)
67 1
 
0.1%
68 1
 
0.1%
69 4
0.6%
71 2
0.3%
72 1
 
0.1%
73 1
 
0.1%
74 2
0.3%
76 1
 
0.1%
81 1
 
0.1%
84 1
 
0.1%
ValueCountFrequency (%)
84 1
 
0.1%
81 1
 
0.1%
76 1
 
0.1%
74 2
0.3%
73 1
 
0.1%
72 1
 
0.1%
71 2
0.3%
69 4
0.6%
68 1
 
0.1%
67 1
 
0.1%

와이좌표(Y)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)73.3%
Missing711
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean108.26667
Minimum97
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T23:20:34.806074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97
5-th percentile98.4
Q1106
median107
Q3112
95-th percentile115.9
Maximum118
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.7875071
Coefficient of variation (CV)0.053456038
Kurtosis-0.14020111
Mean108.26667
Median Absolute Deviation (MAD)4
Skewness-0.35433575
Sum1624
Variance33.495238
MonotonicityNot monotonic
2023-12-12T23:20:34.907816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
107 3
 
0.4%
106 2
 
0.3%
111 2
 
0.3%
114 1
 
0.1%
118 1
 
0.1%
103 1
 
0.1%
99 1
 
0.1%
97 1
 
0.1%
110 1
 
0.1%
113 1
 
0.1%
(Missing) 711
97.9%
ValueCountFrequency (%)
97 1
 
0.1%
99 1
 
0.1%
103 1
 
0.1%
106 2
0.3%
107 3
0.4%
110 1
 
0.1%
111 2
0.3%
113 1
 
0.1%
114 1
 
0.1%
115 1
 
0.1%
ValueCountFrequency (%)
118 1
 
0.1%
115 1
 
0.1%
114 1
 
0.1%
113 1
 
0.1%
111 2
0.3%
110 1
 
0.1%
107 3
0.4%
106 2
0.3%
103 1
 
0.1%
99 1
 
0.1%

사용여부
Boolean

CONSTANT  MISSING 

Distinct1
Distinct (%)6.7%
Missing711
Missing (%)97.9%
Memory size1.5 KiB
True
 
15
(Missing)
711 
ValueCountFrequency (%)
True 15
 
2.1%
(Missing) 711
97.9%
2023-12-12T23:20:35.014713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

우편번호_시작
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)73.3%
Missing711
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean27940
Minimum27000
Maximum29100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T23:20:35.113810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27000
5-th percentile27000
Q127450
median28000
Q328100
95-th percentile29030
Maximum29100
Range2100
Interquartile range (IQR)650

Descriptive statistics

Standard deviation682.22326
Coefficient of variation (CV)0.024417439
Kurtosis-0.64131349
Mean27940
Median Absolute Deviation (MAD)400
Skewness0.28425101
Sum419100
Variance465428.57
MonotonicityNot monotonic
2023-12-12T23:20:35.216706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
28100 4
 
0.6%
27000 2
 
0.3%
27300 1
 
0.1%
27100 1
 
0.1%
28900 1
 
0.1%
29000 1
 
0.1%
29100 1
 
0.1%
27900 1
 
0.1%
27800 1
 
0.1%
28000 1
 
0.1%
(Missing) 711
97.9%
ValueCountFrequency (%)
27000 2
0.3%
27100 1
 
0.1%
27300 1
 
0.1%
27600 1
 
0.1%
27800 1
 
0.1%
27900 1
 
0.1%
28000 1
 
0.1%
28100 4
0.6%
28900 1
 
0.1%
29000 1
 
0.1%
ValueCountFrequency (%)
29100 1
 
0.1%
29000 1
 
0.1%
28900 1
 
0.1%
28100 4
0.6%
28000 1
 
0.1%
27900 1
 
0.1%
27800 1
 
0.1%
27600 1
 
0.1%
27300 1
 
0.1%
27100 1
 
0.1%

우편번호_끝
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)80.0%
Missing711
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean28445.667
Minimum27099
Maximum29999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T23:20:35.311839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27099
5-th percentile27239
Q127849
median28899
Q328949
95-th percentile29439
Maximum29999
Range2900
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation820.16259
Coefficient of variation (CV)0.028832602
Kurtosis-0.74834861
Mean28445.667
Median Absolute Deviation (MAD)800
Skewness-0.018666381
Sum426685
Variance672666.67
MonotonicityNot monotonic
2023-12-12T23:20:35.419115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
28899 4
 
0.6%
29999 1
 
0.1%
27599 1
 
0.1%
27299 1
 
0.1%
28999 1
 
0.1%
29099 1
 
0.1%
29199 1
 
0.1%
27999 1
 
0.1%
27899 1
 
0.1%
28099 1
 
0.1%
Other values (2) 2
 
0.3%
(Missing) 711
97.9%
ValueCountFrequency (%)
27099 1
 
0.1%
27299 1
 
0.1%
27599 1
 
0.1%
27799 1
 
0.1%
27899 1
 
0.1%
27999 1
 
0.1%
28099 1
 
0.1%
28899 4
0.6%
28999 1
 
0.1%
29099 1
 
0.1%
ValueCountFrequency (%)
29999 1
 
0.1%
29199 1
 
0.1%
29099 1
 
0.1%
28999 1
 
0.1%
28899 4
0.6%
28099 1
 
0.1%
27999 1
 
0.1%
27899 1
 
0.1%
27799 1
 
0.1%
27599 1
 
0.1%

코드_구분
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size5.8 KiB
CROP_C_CD
404 
YEAR_T_CD
52 
CROP_B_CD
49 
BANK_T_CD
 
26
CROP_W_CD
 
19
Other values (37)
176 

Length

Max length13
Median length9
Mean length8.9283747
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
CROP_C_CD 404
55.6%
YEAR_T_CD 52
 
7.2%
CROP_B_CD 49
 
6.7%
BANK_T_CD 26
 
3.6%
CROP_W_CD 19
 
2.6%
PACK_T_CD 17
 
2.3%
<NA> 15
 
2.1%
MT_T_CD 12
 
1.7%
CROP_A_CD 12
 
1.7%
SKY_T_CD 10
 
1.4%
Other values (32) 110
 
15.2%

Length

2023-12-12T23:20:35.555820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
crop_c_cd 404
55.6%
year_t_cd 52
 
7.2%
crop_b_cd 49
 
6.7%
bank_t_cd 26
 
3.6%
crop_w_cd 19
 
2.6%
pack_t_cd 17
 
2.3%
na 15
 
2.1%
mt_t_cd 12
 
1.7%
crop_a_cd 12
 
1.7%
sky_t_cd 10
 
1.4%
Other values (32) 110
 
15.2%

코드_아이디
Text

MISSING 

Distinct545
Distinct (%)76.7%
Missing15
Missing (%)2.1%
Memory size5.8 KiB
2023-12-12T23:20:35.890712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.0407876
Min length1

Characters and Unicode

Total characters2162
Distinct characters31
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique504 ?
Unique (%)70.9%

Sample

1st rowSFCB
2nd rowU
3rd rowV
4th rowD
5th rowE
ValueCountFrequency (%)
1 23
 
3.2%
2 23
 
3.2%
3 18
 
2.5%
4 14
 
2.0%
5 11
 
1.5%
6 7
 
1.0%
7 7
 
1.0%
d 7
 
1.0%
8 6
 
0.8%
9 6
 
0.8%
Other values (535) 589
82.8%
2023-12-12T23:20:36.381747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 327
15.1%
2 326
15.1%
0 313
14.5%
5 312
14.4%
3 224
10.4%
4 222
10.3%
6 110
 
5.1%
7 110
 
5.1%
8 80
 
3.7%
9 74
 
3.4%
Other values (21) 64
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2098
97.0%
Uppercase Letter 64
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 7
 
10.9%
F 6
 
9.4%
B 6
 
9.4%
C 6
 
9.4%
I 5
 
7.8%
A 4
 
6.2%
L 3
 
4.7%
S 3
 
4.7%
H 3
 
4.7%
P 3
 
4.7%
Other values (11) 18
28.1%
Decimal Number
ValueCountFrequency (%)
1 327
15.6%
2 326
15.5%
0 313
14.9%
5 312
14.9%
3 224
10.7%
4 222
10.6%
6 110
 
5.2%
7 110
 
5.2%
8 80
 
3.8%
9 74
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 2098
97.0%
Latin 64
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 7
 
10.9%
F 6
 
9.4%
B 6
 
9.4%
C 6
 
9.4%
I 5
 
7.8%
A 4
 
6.2%
L 3
 
4.7%
S 3
 
4.7%
H 3
 
4.7%
P 3
 
4.7%
Other values (11) 18
28.1%
Common
ValueCountFrequency (%)
1 327
15.6%
2 326
15.5%
0 313
14.9%
5 312
14.9%
3 224
10.7%
4 222
10.6%
6 110
 
5.2%
7 110
 
5.2%
8 80
 
3.8%
9 74
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 327
15.1%
2 326
15.1%
0 313
14.5%
5 312
14.4%
3 224
10.4%
4 222
10.3%
6 110
 
5.1%
7 110
 
5.1%
8 80
 
3.7%
9 74
 
3.4%
Other values (21) 64
 
3.0%

코드_이름
Text

MISSING 

Distinct684
Distinct (%)96.2%
Missing15
Missing (%)2.1%
Memory size5.8 KiB
2023-12-12T23:20:36.693291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length3.4205345
Min length1

Characters and Unicode

Total characters2432
Distinct characters437
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique668 ?
Unique (%)94.0%

Sample

1st rowSFCB
2nd row수정
3rd row보기
4th row삭제
5th row표출
ValueCountFrequency (%)
기타 9
 
1.2%
대변 3
 
0.4%
추가 3
 
0.4%
현금 3
 
0.4%
차변 3
 
0.4%
흐리고 3
 
0.4%
가끔 3
 
0.4%
과세 2
 
0.3%
비 2
 
0.3%
눈 2
 
0.3%
Other values (675) 687
95.4%
2023-12-12T23:20:37.184976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
류 70
 
2.9%
리 55
 
2.3%
기 51
 
2.1%
2 50
 
2.1%
0 49
 
2.0%
아 47
 
1.9%
타 43
 
1.8%
1 41
 
1.7%
스 37
 
1.5%
9 36
 
1.5%
Other values (427) 1953
80.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2113
86.9%
Decimal Number 225
 
9.3%
Open Punctuation 28
 
1.2%
Close Punctuation 28
 
1.2%
Other Punctuation 10
 
0.4%
Lowercase Letter 10
 
0.4%
Space Separator 9
 
0.4%
Uppercase Letter 9
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
류 70
 
3.3%
리 55
 
2.6%
기 51
 
2.4%
아 47
 
2.2%
타 43
 
2.0%
스 37
 
1.8%
나 36
 
1.7%
무 30
 
1.4%
라 28
 
1.3%
고 27
 
1.3%
Other values (399) 1689
79.9%
Decimal Number
ValueCountFrequency (%)
2 50
22.2%
0 49
21.8%
1 41
18.2%
9 36
16.0%
8 16
 
7.1%
3 8
 
3.6%
4 7
 
3.1%
5 6
 
2.7%
7 6
 
2.7%
6 6
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
S 2
22.2%
C 2
22.2%
L 1
11.1%
Y 1
11.1%
N 1
11.1%
F 1
11.1%
B 1
11.1%
Lowercase Letter
ValueCountFrequency (%)
m 3
30.0%
g 3
30.0%
k 2
20.0%
l 1
 
10.0%
w 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
/ 6
60.0%
. 3
30.0%
, 1
 
10.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2111
86.8%
Common 300
 
12.3%
Latin 19
 
0.8%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
류 70
 
3.3%
리 55
 
2.6%
기 51
 
2.4%
아 47
 
2.2%
타 43
 
2.0%
스 37
 
1.8%
나 36
 
1.7%
무 30
 
1.4%
라 28
 
1.3%
고 27
 
1.3%
Other values (397) 1687
79.9%
Common
ValueCountFrequency (%)
2 50
16.7%
0 49
16.3%
1 41
13.7%
9 36
12.0%
( 28
9.3%
) 28
9.3%
8 16
 
5.3%
9
 
3.0%
3 8
 
2.7%
4 7
 
2.3%
Other values (6) 28
9.3%
Latin
ValueCountFrequency (%)
m 3
15.8%
g 3
15.8%
S 2
10.5%
C 2
10.5%
k 2
10.5%
l 1
 
5.3%
w 1
 
5.3%
L 1
 
5.3%
Y 1
 
5.3%
N 1
 
5.3%
Other values (2) 2
10.5%
Han
ValueCountFrequency (%)
蜂 1
50.0%
羊 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2110
86.8%
ASCII 319
 
13.1%
CJK 2
 
0.1%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
류 70
 
3.3%
리 55
 
2.6%
기 51
 
2.4%
아 47
 
2.2%
타 43
 
2.0%
스 37
 
1.8%
나 36
 
1.7%
무 30
 
1.4%
라 28
 
1.3%
고 27
 
1.3%
Other values (396) 1686
79.9%
ASCII
ValueCountFrequency (%)
2 50
15.7%
0 49
15.4%
1 41
12.9%
9 36
11.3%
( 28
8.8%
) 28
8.8%
8 16
 
5.0%
9
 
2.8%
3 8
 
2.5%
4 7
 
2.2%
Other values (18) 47
14.7%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%
CJK
ValueCountFrequency (%)
蜂 1
50.0%
羊 1
50.0%

순서번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct407
Distinct (%)57.9%
Missing23
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean127.1394
Minimum0
Maximum416
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2023-12-12T23:20:37.355444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median56
Q3240.5
95-th percentile380.9
Maximum416
Range416
Interquartile range (IQR)231.5

Descriptive statistics

Standard deviation134.24
Coefficient of variation (CV)1.0558489
Kurtosis-0.9492711
Mean127.1394
Median Absolute Deviation (MAD)54
Skewness0.72682817
Sum89379
Variance18020.377
MonotonicityNot monotonic
2023-12-12T23:20:37.483111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 40
 
5.5%
2 39
 
5.4%
3 26
 
3.6%
4 19
 
2.6%
5 17
 
2.3%
6 13
 
1.8%
7 11
 
1.5%
8 9
 
1.2%
9 9
 
1.2%
10 9
 
1.2%
Other values (397) 511
70.4%
(Missing) 23
 
3.2%
ValueCountFrequency (%)
0 1
 
0.1%
1 40
5.5%
2 39
5.4%
3 26
3.6%
4 19
2.6%
5 17
2.3%
6 13
 
1.8%
7 11
 
1.5%
8 9
 
1.2%
9 9
 
1.2%
ValueCountFrequency (%)
416 1
0.1%
415 1
0.1%
414 1
0.1%
413 1
0.1%
412 1
0.1%
411 1
0.1%
410 1
0.1%
409 1
0.1%
408 1
0.1%
407 1
0.1%

Interactions

2023-12-12T23:20:32.643065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:29.604434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:30.246498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:30.808444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:31.577189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:32.102916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:32.727106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:29.732003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:30.354218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:30.917457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:31.670801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:32.214976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:32.801007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:29.843720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:30.466839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:31.004113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:31.760764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:32.295119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:32.875146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:29.944900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:30.540431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:31.347649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:31.832034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:32.373791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:32.968503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:30.062161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:30.634732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:31.421378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:31.926591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:32.464450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:33.036694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:30.164815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:30.723344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:31.508387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:32.027754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:20:32.570778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:20:37.573490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분기관코드지역명엑스좌표(X)와이좌표(Y)우편번호_시작우편번호_끝코드_구분순서번호
구분1.000NaNNaNNaNNaNNaNNaNNaNNaN
기관코드NaN1.0001.0000.9220.9180.6290.383NaNNaN
지역명NaN1.0001.0001.0001.0001.0001.000NaNNaN
엑스좌표(X)NaN0.9221.0001.0000.8290.9040.844NaNNaN
와이좌표(Y)NaN0.9181.0000.8291.0000.9890.930NaNNaN
우편번호_시작NaN0.6291.0000.9040.9891.0000.924NaNNaN
우편번호_끝NaN0.3831.0000.8440.9300.9241.000NaNNaN
코드_구분NaNNaNNaNNaNNaNNaNNaN1.0000.510
순서번호NaNNaNNaNNaNNaNNaNNaN0.5101.000
2023-12-12T23:20:37.686421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코드_구분구분
코드_구분1.0001.000
구분1.0001.000
2023-12-12T23:20:37.769968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관코드엑스좌표(X)와이좌표(Y)우편번호_시작우편번호_끝순서번호구분코드_구분
기관코드1.0000.5020.375-0.148-0.501NaN1.0000.000
엑스좌표(X)0.5021.0000.443-0.275-0.447NaN1.0000.000
와이좌표(Y)0.3750.4431.000-0.863-0.904NaN1.0000.000
우편번호_시작-0.148-0.275-0.8631.0000.644NaN1.0000.000
우편번호_끝-0.501-0.447-0.9040.6441.000NaN1.0000.000
순서번호NaNNaNNaNNaNNaN1.0001.0000.183
구분1.0001.0001.0001.0001.0001.0001.0001.000
코드_구분0.0000.0000.0000.0000.0000.1831.0001.000

Missing values

2023-12-12T23:20:33.162176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:20:33.309218image/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.
2023-12-12T23:20:33.461524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분기관코드지역명엑스좌표(X)와이좌표(Y)사용여부우편번호_시작우편번호_끝코드_구분코드_아이디코드_이름순서번호
0날씨지역코드4300000000충청북도69107Y2700029999<NA><NA><NA><NA>
1날씨지역코드4311100000충청북도 청주시 상당구69106Y2810028899<NA><NA><NA><NA>
2날씨지역코드4311200000충청북도 청주시 서원구69107Y2810028899<NA><NA><NA><NA>
3날씨지역코드4311300000충청북도 청주시 흥덕구67106Y2810028899<NA><NA><NA><NA>
4날씨지역코드4311400000충청북도 청주시 청원구69107Y2810028899<NA><NA><NA><NA>
5날씨지역코드4313000000충청북도 충주시76114Y2730027599<NA><NA><NA><NA>
6날씨지역코드4315000000충청북도 제천시81118Y2710027299<NA><NA><NA><NA>
7날씨지역코드4372000000충청북도 보은군73103Y2890028999<NA><NA><NA><NA>
8날씨지역코드4373000000충청북도 옥천군7199Y2900029099<NA><NA><NA><NA>
9날씨지역코드4374000000충청북도 영동군7497Y2910029199<NA><NA><NA><NA>
구분기관코드지역명엑스좌표(X)와이좌표(Y)사용여부우편번호_시작우편번호_끝코드_구분코드_아이디코드_이름순서번호
716공통코드<NA><NA><NA><NA><NA><NA><NA>CROP_C_CD3306연133
717공통코드<NA><NA><NA><NA><NA><NA><NA>CROP_C_CD3307토란134
718공통코드<NA><NA><NA><NA><NA><NA><NA>CROP_C_CD3308기타근채류135
719공통코드<NA><NA><NA><NA><NA><NA><NA>CROP_C_CD3401양파136
720공통코드<NA><NA><NA><NA><NA><NA><NA>CROP_C_CD3402파137
721공통코드<NA><NA><NA><NA><NA><NA><NA>CROP_C_CD3403마늘138
722공통코드<NA><NA><NA><NA><NA><NA><NA>CROP_C_CD3404생강139
723공통코드<NA><NA><NA><NA><NA><NA><NA>CROP_C_CD3405고추냉이140
724공통코드<NA><NA><NA><NA><NA><NA><NA>CROP_C_CD3406기타조미채소류141
725공통코드<NA><NA><NA><NA><NA><NA><NA>CROP_C_CD3501고들빼기142