Overview

Dataset statistics

Number of variables16
Number of observations6293
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory854.4 KiB
Average record size in memory139.0 B

Variable types

Categorical3
DateTime2
Text1
Numeric10

Dataset

Description화성시의 각 지역(읍/면/동/리)의 지번 코드를 요청 값으로 사용하여 각 지역 토양의 화학성 성분 상세 정보(최근 5년 이내)
URLhttps://www.data.go.kr/data/15096334/fileData.do

Alerts

데이터기준일 has constant value ""Constant
유효규산 is highly overall correlated with 양이온치환용량High correlation
마그네슘 is highly overall correlated with 칼슘 and 2 other fieldsHigh correlation
칼슘 is highly overall correlated with 마그네슘High correlation
전기전도도 is highly overall correlated with 마그네슘 and 1 other fieldsHigh correlation
나트륨 is highly overall correlated with 마그네슘High correlation
양이온치환용량 is highly overall correlated with 유효규산 and 1 other fieldsHigh correlation
석회소요량 is highly imbalanced (80.6%)Imbalance
전기전도도 is highly skewed (γ1 = 23.33355343)Skewed
유효규산 has 2882 (45.8%) zerosZeros
마그네슘 has 639 (10.2%) zerosZeros
칼슘 has 639 (10.2%) zerosZeros
전기전도도 has 1228 (19.5%) zerosZeros
나트륨 has 1550 (24.6%) zerosZeros

Reproduction

Analysis started2023-12-12 00:53:38.134751
Analysis finished2023-12-12 00:53:56.083685
Duration17.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

채취년도
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.3 KiB
2022
4237 
2023
2056 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 4237
67.3%
2023 2056
32.7%

Length

2023-12-12T09:53:56.165257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:53:56.301090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 4237
67.3%
2023 2056
32.7%
Distinct94
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size49.3 KiB
Minimum2022-01-14 00:00:00
Maximum2023-08-17 00:00:00
2023-12-12T09:53:56.457829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:56.659711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

경지구분
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size49.3 KiB
3668 
1430 
과수
955 
시설
 
226
<NA>
 
13

Length

Max length4
Median length1
Mean length1.194184
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row과수
4th row시설
5th row

Common Values

ValueCountFrequency (%)
3668
58.3%
1430
 
22.7%
과수 955
 
15.2%
시설 226
 
3.6%
<NA> 13
 
0.2%
성토용 1
 
< 0.1%

Length

2023-12-12T09:53:56.857157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:53:57.022991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3668
58.3%
1430
 
22.7%
과수 955
 
15.2%
시설 226
 
3.6%
na 13
 
0.2%
성토용 1
 
< 0.1%
Distinct4431
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Memory size49.3 KiB
2023-12-12T09:53:57.402925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length100
Median length30
Mean length20.296043
Min length13

Characters and Unicode

Total characters127723
Distinct characters181
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2984 ?
Unique (%)47.4%

Sample

1st row경기도 화성시 마도면 송정리 196-2
2nd row경기도 화성시 배양동 13-32
3rd row경기도 화성시 팔탄면 노하리 1010
4th row경기도 화성시 반정동 294-1
5th row경기도 화성시 정남면 오일리 360-1
ValueCountFrequency (%)
경기도 6291
20.0%
화성시 6288
20.0%
우정읍 2333
 
7.4%
장안면 1068
 
3.4%
송산면 853
 
2.7%
화산리 714
 
2.3%
향남읍 478
 
1.5%
호곡리 459
 
1.5%
주곡리 340
 
1.1%
장안리 338
 
1.1%
Other values (3536) 12278
39.1%
2023-12-12T09:53:58.018563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25436
19.9%
7338
 
5.7%
6433
 
5.0%
6339
 
5.0%
6314
 
4.9%
6309
 
4.9%
6292
 
4.9%
6126
 
4.8%
1 4658
 
3.6%
- 3452
 
2.7%
Other values (171) 49026
38.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75093
58.8%
Space Separator 25436
 
19.9%
Decimal Number 23736
 
18.6%
Dash Punctuation 3452
 
2.7%
Uppercase Letter 3
 
< 0.1%
Other Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7338
 
9.8%
6433
 
8.6%
6339
 
8.4%
6314
 
8.4%
6309
 
8.4%
6292
 
8.4%
6126
 
8.2%
3090
 
4.1%
3026
 
4.0%
2731
 
3.6%
Other values (153) 21095
28.1%
Decimal Number
ValueCountFrequency (%)
1 4658
19.6%
2 3429
14.4%
3 2442
10.3%
4 2254
9.5%
6 2130
9.0%
5 2076
8.7%
7 1888
8.0%
8 1728
 
7.3%
0 1625
 
6.8%
9 1506
 
6.3%
Uppercase Letter
ValueCountFrequency (%)
A 1
33.3%
I 1
33.3%
D 1
33.3%
Other Punctuation
ValueCountFrequency (%)
: 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
25436
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3452
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 75093
58.8%
Common 52627
41.2%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7338
 
9.8%
6433
 
8.6%
6339
 
8.4%
6314
 
8.4%
6309
 
8.4%
6292
 
8.4%
6126
 
8.2%
3090
 
4.1%
3026
 
4.0%
2731
 
3.6%
Other values (153) 21095
28.1%
Common
ValueCountFrequency (%)
25436
48.3%
1 4658
 
8.9%
- 3452
 
6.6%
2 3429
 
6.5%
3 2442
 
4.6%
4 2254
 
4.3%
6 2130
 
4.0%
5 2076
 
3.9%
7 1888
 
3.6%
8 1728
 
3.3%
Other values (5) 3134
 
6.0%
Latin
ValueCountFrequency (%)
A 1
33.3%
I 1
33.3%
D 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 75093
58.8%
ASCII 52630
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25436
48.3%
1 4658
 
8.9%
- 3452
 
6.6%
2 3429
 
6.5%
3 2442
 
4.6%
4 2254
 
4.3%
6 2130
 
4.0%
5 2076
 
3.9%
7 1888
 
3.6%
8 1728
 
3.3%
Other values (8) 3137
 
6.0%
Hangul
ValueCountFrequency (%)
7338
 
9.8%
6433
 
8.6%
6339
 
8.4%
6314
 
8.4%
6309
 
8.4%
6292
 
8.4%
6126
 
8.2%
3090
 
4.1%
3026
 
4.0%
2731
 
3.6%
Other values (153) 21095
28.1%

산도
Real number (ℝ)

Distinct748
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0481503
Minimum0
Maximum38.3
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size55.4 KiB
2023-12-12T09:53:58.196329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.086
Q15.86
median6.45
Q37.1
95-th percentile8.79
Maximum38.3
Range38.3
Interquartile range (IQR)1.24

Descriptive statistics

Standard deviation3.4156134
Coefficient of variation (CV)0.48461132
Kurtosis31.792305
Mean7.0481503
Median Absolute Deviation (MAD)0.62
Skewness5.349562
Sum44354.01
Variance11.666415
MonotonicityNot monotonic
2023-12-12T09:53:58.392711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.5 62
 
1.0%
6.44 45
 
0.7%
6.46 42
 
0.7%
6.51 40
 
0.6%
6.47 39
 
0.6%
6.32 38
 
0.6%
5.83 36
 
0.6%
6.33 36
 
0.6%
6.48 35
 
0.6%
6.3 35
 
0.6%
Other values (738) 5885
93.5%
ValueCountFrequency (%)
0.0 1
< 0.1%
3.36 1
< 0.1%
3.58 1
< 0.1%
3.69 2
< 0.1%
3.73 1
< 0.1%
3.81 1
< 0.1%
3.83 2
< 0.1%
3.84 1
< 0.1%
3.86 1
< 0.1%
3.87 2
< 0.1%
ValueCountFrequency (%)
38.3 1
< 0.1%
38.03 1
< 0.1%
36.82 1
< 0.1%
35.88 1
< 0.1%
35.72 1
< 0.1%
35.0 1
< 0.1%
34.96 1
< 0.1%
34.91 1
< 0.1%
34.79 1
< 0.1%
34.56 1
< 0.1%

유효인산
Real number (ℝ)

Distinct5045
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.0413
Minimum-31.62
Maximum4737.37
Zeros1
Zeros (%)< 0.1%
Negative2
Negative (%)< 0.1%
Memory size55.4 KiB
2023-12-12T09:53:58.581981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-31.62
5-th percentile3.948
Q132.82
median64
Q3270.52
95-th percentile985.78
Maximum4737.37
Range4768.99
Interquartile range (IQR)237.7

Descriptive statistics

Standard deviation364.27771
Coefficient of variation (CV)1.5974199
Kurtosis14.011875
Mean228.0413
Median Absolute Deviation (MAD)43.46
Skewness3.0576251
Sum1435063.9
Variance132698.25
MonotonicityNot monotonic
2023-12-12T09:53:58.752351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.12 19
 
0.3%
0.13 17
 
0.3%
34.0 15
 
0.2%
23.0 14
 
0.2%
25.0 14
 
0.2%
37.0 14
 
0.2%
46.0 13
 
0.2%
28.0 13
 
0.2%
0.22 13
 
0.2%
21.0 12
 
0.2%
Other values (5035) 6149
97.7%
ValueCountFrequency (%)
-31.62 1
 
< 0.1%
-21.92 1
 
< 0.1%
0.0 1
 
< 0.1%
0.1 2
 
< 0.1%
0.11 6
 
0.1%
0.12 19
0.3%
0.13 17
0.3%
0.14 9
0.1%
0.15 9
0.1%
0.16 9
0.1%
ValueCountFrequency (%)
4737.37 1
< 0.1%
3589.47 1
< 0.1%
3279.71 1
< 0.1%
2859.83 1
< 0.1%
2853.65 1
< 0.1%
2771.44 1
< 0.1%
2766.24 1
< 0.1%
2620.31 1
< 0.1%
2615.23 1
< 0.1%
2592.75 1
< 0.1%

유효규산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2905
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.401894
Minimum0
Maximum1526.05
Zeros2882
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size55.4 KiB
2023-12-12T09:53:58.911504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.06
Q3181.49
95-th percentile340.738
Maximum1526.05
Range1526.05
Interquartile range (IQR)181.49

Descriptive statistics

Standard deviation136.89472
Coefficient of variation (CV)1.4200419
Kurtosis10.125194
Mean96.401894
Median Absolute Deviation (MAD)1.06
Skewness2.1808969
Sum606657.12
Variance18740.166
MonotonicityNot monotonic
2023-12-12T09:53:59.434779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2882
45.8%
0.52 11
 
0.2%
0.49 8
 
0.1%
0.58 8
 
0.1%
0.44 7
 
0.1%
0.62 6
 
0.1%
0.27 6
 
0.1%
0.65 6
 
0.1%
0.64 6
 
0.1%
0.71 6
 
0.1%
Other values (2895) 3347
53.2%
ValueCountFrequency (%)
0.0 2882
45.8%
0.16 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 2
 
< 0.1%
0.21 1
 
< 0.1%
0.22 3
 
< 0.1%
0.23 1
 
< 0.1%
0.24 2
 
< 0.1%
0.25 1
 
< 0.1%
0.27 6
 
0.1%
ValueCountFrequency (%)
1526.05 1
< 0.1%
1493.49 1
< 0.1%
1384.69 1
< 0.1%
1247.28 1
< 0.1%
1233.12 1
< 0.1%
1204.59 1
< 0.1%
1182.78 1
< 0.1%
1065.42 1
< 0.1%
1030.93 1
< 0.1%
955.86 1
< 0.1%

유기물
Real number (ℝ)

Distinct3083
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.229012
Minimum0
Maximum399.23
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size55.4 KiB
2023-12-12T09:53:59.620460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.846
Q113.59
median19.93
Q326.58
95-th percentile46.972
Maximum399.23
Range399.23
Interquartile range (IQR)12.99

Descriptive statistics

Standard deviation16.871368
Coefficient of variation (CV)0.75897969
Kurtosis80.743085
Mean22.229012
Median Absolute Deviation (MAD)6.51
Skewness5.8971034
Sum139887.17
Variance284.64307
MonotonicityNot monotonic
2023-12-12T09:53:59.799984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.0 50
 
0.8%
17.0 44
 
0.7%
19.0 44
 
0.7%
16.0 43
 
0.7%
20.0 41
 
0.7%
21.0 37
 
0.6%
22.0 35
 
0.6%
18.0 35
 
0.6%
23.0 31
 
0.5%
24.0 30
 
0.5%
Other values (3073) 5903
93.8%
ValueCountFrequency (%)
0.0 3
< 0.1%
0.69 2
< 0.1%
0.71 1
 
< 0.1%
0.72 1
 
< 0.1%
0.77 1
 
< 0.1%
0.88 1
 
< 0.1%
0.95 1
 
< 0.1%
0.97 1
 
< 0.1%
0.98 1
 
< 0.1%
0.99 1
 
< 0.1%
ValueCountFrequency (%)
399.23 1
< 0.1%
293.3 1
< 0.1%
286.21 1
< 0.1%
278.42 1
< 0.1%
221.56 1
< 0.1%
202.59 1
< 0.1%
182.33 1
< 0.1%
162.48 1
< 0.1%
158.13 1
< 0.1%
152.18 1
< 0.1%

마그네슘
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct754
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6184443
Minimum0
Maximum29
Zeros639
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size55.4 KiB
2023-12-12T09:54:00.017902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.38
median2.44
Q33.59
95-th percentile5.534
Maximum29
Range29
Interquartile range (IQR)2.21

Descriptive statistics

Standard deviation1.9408659
Coefficient of variation (CV)0.74122863
Kurtosis23.28821
Mean2.6184443
Median Absolute Deviation (MAD)1.11
Skewness2.6908488
Sum16477.87
Variance3.7669604
MonotonicityNot monotonic
2023-12-12T09:54:00.214637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 639
 
10.2%
2.15 26
 
0.4%
3.71 22
 
0.3%
2.84 22
 
0.3%
1.95 22
 
0.3%
2.37 22
 
0.3%
2.77 22
 
0.3%
2.71 21
 
0.3%
2.05 21
 
0.3%
1.04 21
 
0.3%
Other values (744) 5455
86.7%
ValueCountFrequency (%)
0.0 639
10.2%
0.01 1
 
< 0.1%
0.05 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.1 3
 
< 0.1%
0.11 1
 
< 0.1%
0.15 1
 
< 0.1%
0.16 1
 
< 0.1%
0.18 1
 
< 0.1%
ValueCountFrequency (%)
29.0 1
< 0.1%
28.71 1
< 0.1%
27.09 1
< 0.1%
26.6 1
< 0.1%
19.21 1
< 0.1%
18.62 1
< 0.1%
17.72 1
< 0.1%
17.25 1
< 0.1%
16.1 1
< 0.1%
15.27 1
< 0.1%

칼륨
Real number (ℝ)

Distinct586
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.022471
Minimum0
Maximum19.7
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size55.4 KiB
2023-12-12T09:54:00.399928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17
Q10.32
median0.55
Q30.92
95-th percentile4.744
Maximum19.7
Range19.7
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation1.5641585
Coefficient of variation (CV)1.5297827
Kurtosis17.188868
Mean1.022471
Median Absolute Deviation (MAD)0.26
Skewness3.8112975
Sum6434.41
Variance2.4465917
MonotonicityNot monotonic
2023-12-12T09:54:00.606907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 219
 
3.5%
0.4 166
 
2.6%
0.2 165
 
2.6%
0.5 131
 
2.1%
0.6 92
 
1.5%
0.23 87
 
1.4%
0.25 81
 
1.3%
0.7 80
 
1.3%
0.26 78
 
1.2%
0.36 77
 
1.2%
Other values (576) 5117
81.3%
ValueCountFrequency (%)
0.0 4
 
0.1%
0.03 1
 
< 0.1%
0.04 5
 
0.1%
0.05 3
 
< 0.1%
0.06 4
 
0.1%
0.07 3
 
< 0.1%
0.08 11
 
0.2%
0.09 13
 
0.2%
0.1 46
0.7%
0.11 27
0.4%
ValueCountFrequency (%)
19.7 1
< 0.1%
16.46 1
< 0.1%
12.24 1
< 0.1%
12.06 1
< 0.1%
11.85 1
< 0.1%
11.76 1
< 0.1%
11.0 1
< 0.1%
10.88 1
< 0.1%
10.61 1
< 0.1%
10.58 1
< 0.1%

칼슘
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1338
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1694629
Minimum0
Maximum53.26
Zeros639
Zeros (%)10.2%
Negative0
Negative (%)0.0%
Memory size55.4 KiB
2023-12-12T09:54:00.783670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.98
median5.84
Q37.87
95-th percentile13.004
Maximum53.26
Range53.26
Interquartile range (IQR)3.89

Descriptive statistics

Standard deviation4.1116524
Coefficient of variation (CV)0.66645224
Kurtosis10.71461
Mean6.1694629
Median Absolute Deviation (MAD)1.95
Skewness1.9167449
Sum38824.43
Variance16.905685
MonotonicityNot monotonic
2023-12-12T09:54:00.952952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 639
 
10.2%
5.46 17
 
0.3%
7.32 16
 
0.3%
6.01 16
 
0.3%
5.96 15
 
0.2%
6.52 15
 
0.2%
6.19 15
 
0.2%
6.29 15
 
0.2%
4.87 15
 
0.2%
6.63 15
 
0.2%
Other values (1328) 5515
87.6%
ValueCountFrequency (%)
0.0 639
10.2%
0.09 1
 
< 0.1%
0.14 2
 
< 0.1%
0.29 1
 
< 0.1%
0.34 1
 
< 0.1%
0.42 1
 
< 0.1%
0.48 1
 
< 0.1%
0.61 1
 
< 0.1%
0.65 1
 
< 0.1%
0.7 1
 
< 0.1%
ValueCountFrequency (%)
53.26 1
< 0.1%
43.85 1
< 0.1%
41.07 1
< 0.1%
39.51 1
< 0.1%
39.03 1
< 0.1%
38.08 1
< 0.1%
36.68 1
< 0.1%
34.24 1
< 0.1%
33.11 1
< 0.1%
32.96 1
< 0.1%

전기전도도
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct938
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2512315
Minimum0
Maximum1326
Zeros1228
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size55.4 KiB
2023-12-12T09:54:01.143838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.34
median1.22
Q32.64
95-th percentile8.264
Maximum1326
Range1326
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation31.427266
Coefficient of variation (CV)7.3925087
Kurtosis721.40881
Mean4.2512315
Median Absolute Deviation (MAD)1.05
Skewness23.333553
Sum26753
Variance987.67305
MonotonicityNot monotonic
2023-12-12T09:54:01.334439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1228
 
19.5%
0.37 31
 
0.5%
0.78 28
 
0.4%
0.4 28
 
0.4%
0.61 27
 
0.4%
1.04 27
 
0.4%
0.68 27
 
0.4%
0.47 26
 
0.4%
0.67 25
 
0.4%
0.96 24
 
0.4%
Other values (928) 4822
76.6%
ValueCountFrequency (%)
0.0 1228
19.5%
0.06 3
 
< 0.1%
0.07 4
 
0.1%
0.08 5
 
0.1%
0.09 4
 
0.1%
0.1 2
 
< 0.1%
0.11 5
 
0.1%
0.12 8
 
0.1%
0.13 5
 
0.1%
0.14 13
 
0.2%
ValueCountFrequency (%)
1326.0 1
 
< 0.1%
795.6 2
 
< 0.1%
663.0 2
 
< 0.1%
397.8 7
 
0.1%
265.2 10
0.2%
265.0 2
 
< 0.1%
133.0 1
 
< 0.1%
132.6 19
0.3%
64.86 1
 
< 0.1%
64.15 1
 
< 0.1%

나트륨
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1057
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.184988
Minimum-0.13
Maximum1500
Zeros1550
Zeros (%)24.6%
Negative30
Negative (%)0.5%
Memory size55.4 KiB
2023-12-12T09:54:01.501011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.13
5-th percentile0
Q10
median0.4
Q31.65
95-th percentile145.832
Maximum1500
Range1500.13
Interquartile range (IQR)1.65

Descriptive statistics

Standard deviation65.219655
Coefficient of variation (CV)4.0296387
Kurtosis75.752804
Mean16.184988
Median Absolute Deviation (MAD)0.4
Skewness6.6646511
Sum101852.13
Variance4253.6034
MonotonicityNot monotonic
2023-12-12T09:54:01.705256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1550
 
24.6%
0.03 78
 
1.2%
0.08 72
 
1.1%
0.11 70
 
1.1%
0.06 69
 
1.1%
0.1 67
 
1.1%
0.09 66
 
1.0%
0.05 64
 
1.0%
0.07 62
 
1.0%
0.12 61
 
1.0%
Other values (1047) 4134
65.7%
ValueCountFrequency (%)
-0.13 1
 
< 0.1%
-0.08 2
 
< 0.1%
-0.06 2
 
< 0.1%
-0.05 1
 
< 0.1%
-0.04 3
 
< 0.1%
-0.03 7
 
0.1%
-0.02 7
 
0.1%
-0.01 7
 
0.1%
0.0 1550
24.6%
0.01 36
 
0.6%
ValueCountFrequency (%)
1500.0 1
< 0.1%
963.99 1
< 0.1%
904.19 1
< 0.1%
858.75 1
< 0.1%
604.33 1
< 0.1%
565.43 1
< 0.1%
545.97 1
< 0.1%
538.69 1
< 0.1%
538.19 1
< 0.1%
535.52 1
< 0.1%

석회소요량
Categorical

IMBALANCE 

Distinct23
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size49.3 KiB
0
5555 
133
 
219
132.6
 
199
265
 
80
265.2
 
61
Other values (18)
 
179

Length

Max length6
Median length1
Mean length1.3472112
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row265
3rd row530
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5555
88.3%
133 219
 
3.5%
132.6 199
 
3.2%
265 80
 
1.3%
265.2 61
 
1.0%
397.8 38
 
0.6%
398 23
 
0.4%
1326 19
 
0.3%
530.4 19
 
0.3%
663 17
 
0.3%
Other values (13) 63
 
1.0%

Length

2023-12-12T09:54:01.927991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 5555
88.3%
133 219
 
3.5%
132.6 199
 
3.2%
265 80
 
1.3%
265.2 61
 
1.0%
397.8 38
 
0.6%
398 23
 
0.4%
1326 19
 
0.3%
530.4 19
 
0.3%
663 17
 
0.3%
Other values (12) 61
 
1.0%

양이온치환용량
Real number (ℝ)

HIGH CORRELATION 

Distinct4926
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.18838
Minimum0
Maximum1583.53
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size55.4 KiB
2023-12-12T09:54:02.110358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.186
Q120.08
median42.98
Q3207.67
95-th percentile368.528
Maximum1583.53
Range1583.53
Interquartile range (IQR)187.59

Descriptive statistics

Standard deviation139.19386
Coefficient of variation (CV)1.1391743
Kurtosis9.5722859
Mean122.18838
Median Absolute Deviation (MAD)34.51
Skewness2.1167429
Sum768931.47
Variance19374.931
MonotonicityNot monotonic
2023-12-12T09:54:02.282394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.3 16
 
0.3%
19.3 16
 
0.3%
21.3 13
 
0.2%
20.3 13
 
0.2%
18.3 13
 
0.2%
22.3 12
 
0.2%
25.4 11
 
0.2%
16.2 11
 
0.2%
13.2 11
 
0.2%
14.3 11
 
0.2%
Other values (4916) 6166
98.0%
ValueCountFrequency (%)
0.0 2
< 0.1%
0.96 1
< 0.1%
1.0 1
< 0.1%
1.25 1
< 0.1%
1.37 1
< 0.1%
1.38 1
< 0.1%
1.42 1
< 0.1%
1.44 1
< 0.1%
1.46 1
< 0.1%
1.49 1
< 0.1%
ValueCountFrequency (%)
1583.53 1
< 0.1%
1508.71 1
< 0.1%
1400.87 1
< 0.1%
1273.07 1
< 0.1%
1244.09 1
< 0.1%
1235.48 1
< 0.1%
1219.16 1
< 0.1%
1089.12 1
< 0.1%
1047.95 1
< 0.1%
975.21 1
< 0.1%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size49.3 KiB
Minimum2023-08-22 00:00:00
Maximum2023-08-22 00:00:00
2023-12-12T09:54:02.438846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:54:02.578416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T09:53:54.130990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:41.184806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:42.489259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:44.160614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:45.562051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:46.827908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:48.130965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.443660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.814758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.726985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.271685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:41.300793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:42.611132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:44.321527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:45.704729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:46.931755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:48.263728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.590798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.310279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.872081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.421442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:41.410935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:42.744400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:44.479345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:45.831698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:47.039981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:48.366178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.717791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.428690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.009985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.549803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:41.537328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:42.892443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:44.636119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:45.962221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:47.168353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:48.477899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.852963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.598786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.170871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.686759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:41.657794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:43.004474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:44.763229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:46.088916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:47.283734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:48.626178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.997751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.757035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.346920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.837857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:41.779532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:43.124890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:44.893340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:46.198199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:47.392849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:48.758178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.150017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:51.908912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.471800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:54.990363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:41.914544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:43.304934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:45.036327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:46.323772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:47.525105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:48.872289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.261206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.052636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.605481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:55.119138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:42.035211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:43.764376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:45.142160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:46.471427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:47.665031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:48.993968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.383678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.193112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.743669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:55.250837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:42.183291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:43.915700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:45.257716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:46.582604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:47.840934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.135961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.519014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.331486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.854941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:55.406902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:42.316688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:44.035560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:45.387696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:46.713904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:48.010644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:49.287976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:50.678072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:52.557124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:53:53.999527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:54:02.712333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
채취년도토양검정일경지구분산도유효인산유효규산유기물마그네슘칼륨칼슘전기전도도나트륨석회소요량양이온치환용량
채취년도1.0001.0000.0860.1830.0360.1590.0000.1360.1410.1970.0470.2060.2890.194
토양검정일1.0001.0000.7960.6890.5830.5800.3540.6520.6950.7930.0000.3860.6910.602
경지구분0.0860.7961.0000.2340.4180.5460.1630.1860.2060.3860.0740.1410.4090.575
산도0.1830.6890.2341.0000.0540.3320.2670.0820.6720.3410.0000.0310.0000.343
유효인산0.0360.5830.4180.0541.0000.2070.5310.4630.4420.3970.0000.0000.1900.225
유효규산0.1590.5800.5460.3320.2071.0000.0280.1580.2750.3770.0000.0910.1440.995
유기물0.0000.3540.1630.2670.5310.0281.0000.2500.4400.2930.0000.0000.0000.118
마그네슘0.1360.6520.1860.0820.4630.1580.2501.0000.2410.5700.0000.0000.0940.171
칼륨0.1410.6950.2060.6720.4420.2750.4400.2411.0000.2530.0000.0000.0000.310
칼슘0.1970.7930.3860.3410.3970.3770.2930.5700.2531.0000.0000.1290.0910.354
전기전도도0.0470.0000.0740.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
나트륨0.2060.3860.1410.0310.0000.0910.0000.0000.0000.1290.0001.0000.0000.104
석회소요량0.2890.6910.4090.0000.1900.1440.0000.0940.0000.0910.0000.0001.0000.167
양이온치환용량0.1940.6020.5750.3430.2250.9950.1180.1710.3100.3540.0000.1040.1671.000
2023-12-12T09:54:02.942051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
석회소요량채취년도경지구분
석회소요량1.0000.2510.214
채취년도0.2511.0000.105
경지구분0.2140.1051.000
2023-12-12T09:54:03.108982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산도유효인산유효규산유기물마그네슘칼륨칼슘전기전도도나트륨양이온치환용량채취년도경지구분석회소요량
산도1.0000.1130.078-0.0730.0610.2750.230-0.045-0.1600.0870.1400.0990.000
유효인산0.1131.000-0.4600.1900.2030.3060.4360.072-0.052-0.2720.0360.2570.074
유효규산0.078-0.4601.0000.1080.2420.0610.0230.3710.3530.8790.1220.2570.053
유기물-0.0730.1900.1081.0000.2460.3700.2880.2040.1060.4360.0000.0950.000
마그네슘0.0610.2030.2420.2461.0000.3360.6250.5680.5490.3390.1040.1140.038
칼륨0.2750.3060.0610.3700.3361.0000.1690.373-0.0050.2780.1410.1190.000
칼슘0.2300.4360.0230.2880.6250.1691.0000.3470.3050.1470.1510.1700.034
전기전도도-0.0450.0720.3710.2040.5680.3730.3471.0000.4190.5000.0340.0500.000
나트륨-0.160-0.0520.3530.1060.549-0.0050.3050.4191.0000.2890.1540.0870.000
양이온치환용량0.087-0.2720.8790.4360.3390.2780.1470.5000.2891.0000.1490.2750.062
채취년도0.1400.0360.1220.0000.1040.1410.1510.0340.1540.1491.0000.1050.251
경지구분0.0990.2570.2570.0950.1140.1190.1700.0500.0870.2750.1051.0000.214
석회소요량0.0000.0740.0530.0000.0380.0000.0340.0000.0000.0620.2510.2141.000

Missing values

2023-12-12T09:53:55.613899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:53:55.970012image/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

채취년도토양검정일경지구분대상지 지번주소산도유효인산유효규산유기물마그네슘칼륨칼슘전기전도도나트륨석회소요량양이온치환용량데이터기준일
020222022-01-14경기도 화성시 마도면 송정리 196-27.631125.770.028.574.341.8311.050.880.14034.742023-08-22
120222022-01-14경기도 화성시 배양동 13-325.36985.580.018.130.551.073.070.23-0.0226519.762023-08-22
220222022-01-14과수경기도 화성시 팔탄면 노하리 10104.65149.390.03.511.160.542.651.721.365305.212023-08-22
320222022-01-14시설경기도 화성시 반정동 294-17.67702.870.023.241.931.119.03.380.64026.282023-08-22
420222022-01-14경기도 화성시 정남면 오일리 360-16.54495.350.024.294.271.0411.991.190.11029.62023-08-22
520222022-01-14경기도 화성시 기안동 457-487.39123.340.07.341.250.516.940.29-0.0209.12023-08-22
620222022-01-14경기도 화성시 배양동 57-17.4735.84151.132.771.380.276.530.620.220155.552023-08-22
720222022-01-14경기도 화성시 배양동 57-27.6735.04232.73.350.950.326.420.560.080237.322023-08-22
820222022-01-14경기도 화성시 배양동 57-37.4619.39156.711.831.140.264.350.350.070159.932023-08-22
920222022-01-14경기도 화성시 배양동 57-46.8418.08111.71.241.70.34.050.320.070114.942023-08-22
채취년도토양검정일경지구분대상지 지번주소산도유효인산유효규산유기물마그네슘칼륨칼슘전기전도도나트륨석회소요량양이온치환용량데이터기준일
628320222022-07-20경기도 화성시 우정읍 주곡리 548-46.2652.57126.2418.743.721.05.223.62.960149.712023-08-22
628420222022-07-20경기도 화성시 우정읍 주곡리 596-27.1590.01554.5727.932.751.19.431.320.90586.352023-08-22
628520222022-07-20경기도 화성시 우정읍 주곡리 538-36.1638.0164.1723.33.760.746.095.423.840191.972023-08-22
628620222022-07-20경기도 화성시 우정읍 주곡리 584-67.1866.87381.6519.533.830.856.921.141.240405.852023-08-22
628720222022-07-20경기도 화성시 우정읍 주곡리 570-46.5723.61548.4735.294.110.4210.673.431.840588.282023-08-22
628820222022-07-20경기도 화성시 우정읍 주곡리 5716.8741.49402.8718.233.160.497.651.771.320424.752023-08-22
628920222022-07-20경기도 화성시 우정읍 주곡리 629-26.5817.06318.5315.013.080.525.720.940.930337.142023-08-22
629020222022-07-20경기도 화성시 우정읍 주곡리 629-37.6322.45319.3211.822.050.467.140.820.70333.652023-08-22
629120222022-07-20경기도 화성시 우정읍 주곡리 632-67.6321.64335.4811.891.980.446.860.810.680349.792023-08-22
629220222022-07-20경기도 화성시 우정읍 주곡리 632-77.5821.1305.7410.981.970.456.890.760.680319.142023-08-22