Overview

Dataset statistics

Number of variables9
Number of observations637
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.3 KiB
Average record size in memory79.2 B

Variable types

Text1
Numeric7
Categorical1

Dataset

Description농업관련 도시농업정보 및 웰빙정보 컨텐츠
Author농림수산식품교육문화정보원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20141229000000000408

Alerts

1인분칼로리(kcal) is highly overall correlated with 탄수화물(g) and 3 other fieldsHigh correlation
탄수화물(g) is highly overall correlated with 1인분칼로리(kcal) and 1 other fieldsHigh correlation
단백질(g) is highly overall correlated with 1인분칼로리(kcal) and 3 other fieldsHigh correlation
지방(g) is highly overall correlated with 1인분칼로리(kcal) and 3 other fieldsHigh correlation
콜레스트롤(g) is highly overall correlated with 1인분칼로리(kcal) and 2 other fieldsHigh correlation
식이섬유(g) is highly overall correlated with 탄수화물(g)High correlation
나트륨(g) is highly overall correlated with 단백질(g) and 1 other fieldsHigh correlation
탄수화물(g) has 33 (5.2%) zerosZeros
단백질(g) has 33 (5.2%) zerosZeros
지방(g) has 96 (15.1%) zerosZeros
콜레스트롤(g) has 320 (50.2%) zerosZeros
식이섬유(g) has 217 (34.1%) zerosZeros
나트륨(g) has 64 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-11 03:23:53.499199
Analysis finished2023-12-11 03:24:02.017419
Duration8.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

음식명
Text

Distinct628
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-12-11T12:24:02.306969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length4.4835165
Min length1

Characters and Unicode

Total characters2856
Distinct characters375
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique619 ?
Unique (%)97.2%

Sample

1st row가래떡(떡국용)
2nd row가래떡(떡볶이용)
3rd row가자미
4th row가자미구이
5th row가자미조림
ValueCountFrequency (%)
참나물 2
 
0.3%
취나물 2
 
0.3%
소주 2
 
0.3%
숙주나물 2
 
0.3%
고추장아찌 2
 
0.3%
딸기 2
 
0.3%
콩나물 2
 
0.3%
홍차 2
 
0.3%
청주 2
 
0.3%
된장 2
 
0.3%
Other values (619) 619
96.9%
2023-12-11T12:24:02.941991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 133
 
4.7%
( 133
 
4.7%
고 77
 
2.7%
기 70
 
2.5%
치 64
 
2.2%
이 57
 
2.0%
김 46
 
1.6%
어 45
 
1.6%
지 42
 
1.5%
국 42
 
1.5%
Other values (365) 2147
75.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2567
89.9%
Close Punctuation 133
 
4.7%
Open Punctuation 133
 
4.7%
Other Punctuation 21
 
0.7%
Space Separator 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 77
 
3.0%
기 70
 
2.7%
치 64
 
2.5%
이 57
 
2.2%
김 46
 
1.8%
어 45
 
1.8%
지 42
 
1.6%
국 42
 
1.6%
장 41
 
1.6%
무 38
 
1.5%
Other values (361) 2045
79.7%
Close Punctuation
ValueCountFrequency (%)
) 133
100.0%
Open Punctuation
ValueCountFrequency (%)
( 133
100.0%
Other Punctuation
ValueCountFrequency (%)
, 21
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2567
89.9%
Common 289
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 77
 
3.0%
기 70
 
2.7%
치 64
 
2.5%
이 57
 
2.2%
김 46
 
1.8%
어 45
 
1.8%
지 42
 
1.6%
국 42
 
1.6%
장 41
 
1.6%
무 38
 
1.5%
Other values (361) 2045
79.7%
Common
ValueCountFrequency (%)
) 133
46.0%
( 133
46.0%
, 21
 
7.3%
2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2567
89.9%
ASCII 289
 
10.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 133
46.0%
( 133
46.0%
, 21
 
7.3%
2
 
0.7%
Hangul
ValueCountFrequency (%)
고 77
 
3.0%
기 70
 
2.7%
치 64
 
2.5%
이 57
 
2.2%
김 46
 
1.8%
어 45
 
1.8%
지 42
 
1.6%
국 42
 
1.6%
장 41
 
1.6%
무 38
 
1.5%
Other values (361) 2045
79.7%

1인분칼로리(kcal)
Real number (ℝ)

HIGH CORRELATION 

Distinct312
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.08273
Minimum0
Maximum794
Zeros3
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-11T12:24:03.144970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q119
median52.5
Q3124
95-th percentile448.56
Maximum794
Range794
Interquartile range (IQR)105

Descriptive statistics

Standard deviation141.18527
Coefficient of variation (CV)1.3062704
Kurtosis3.9977679
Mean108.08273
Median Absolute Deviation (MAD)40.5
Skewness2.0621199
Sum68848.7
Variance19933.281
MonotonicityNot monotonic
2023-12-11T12:24:03.350516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 15
 
2.4%
2.0 14
 
2.2%
6.0 13
 
2.0%
12.0 12
 
1.9%
1.0 9
 
1.4%
11.0 9
 
1.4%
8.0 9
 
1.4%
33.0 8
 
1.3%
13.0 8
 
1.3%
34.0 8
 
1.3%
Other values (302) 532
83.5%
ValueCountFrequency (%)
0.0 3
 
0.5%
1.0 9
1.4%
2.0 14
2.2%
2.4 1
 
0.2%
3.0 15
2.4%
4.0 6
 
0.9%
5.0 7
1.1%
5.5 2
 
0.3%
6.0 13
2.0%
6.3 1
 
0.2%
ValueCountFrequency (%)
794.0 1
0.2%
738.0 1
0.2%
696.5 1
0.2%
660.3 1
0.2%
638.0 1
0.2%
610.4 1
0.2%
599.0 1
0.2%
588.9 1
0.2%
586.0 1
0.2%
583.0 1
0.2%

탄수화물(g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct240
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.256986
Minimum0
Maximum121.9
Zeros33
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-11T12:24:03.552349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3.4
Q312.4
95-th percentile76
Maximum121.9
Range121.9
Interquartile range (IQR)11.4

Descriptive statistics

Standard deviation23.198446
Coefficient of variation (CV)1.7499035
Kurtosis6.389821
Mean13.256986
Median Absolute Deviation (MAD)3
Skewness2.5984847
Sum8444.7
Variance538.1679
MonotonicityNot monotonic
2023-12-11T12:24:03.736129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 33
 
5.2%
0.3 20
 
3.1%
0.1 18
 
2.8%
0.4 15
 
2.4%
0.8 15
 
2.4%
0.9 14
 
2.2%
0.2 14
 
2.2%
3.0 13
 
2.0%
0.5 11
 
1.7%
1.6 10
 
1.6%
Other values (230) 474
74.4%
ValueCountFrequency (%)
0.0 33
5.2%
0.1 18
2.8%
0.2 14
2.2%
0.3 20
3.1%
0.4 15
2.4%
0.5 11
 
1.7%
0.6 8
 
1.3%
0.7 8
 
1.3%
0.8 15
2.4%
0.9 14
2.2%
ValueCountFrequency (%)
121.9 1
0.2%
118.7 1
0.2%
117.2 1
0.2%
113.9 1
0.2%
111.3 1
0.2%
105.2 1
0.2%
104.4 1
0.2%
103.5 1
0.2%
99.8 1
0.2%
96.1 1
0.2%

단백질(g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct188
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5987441
Minimum0
Maximum45.4
Zeros33
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-11T12:24:04.202804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.7
median2.6
Q37.7
95-th percentile20.52
Maximum45.4
Range45.4
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.1189996
Coefficient of variation (CV)1.2715351
Kurtosis4.7819654
Mean5.5987441
Median Absolute Deviation (MAD)2.3
Skewness2.0308929
Sum3566.4
Variance50.680156
MonotonicityNot monotonic
2023-12-11T12:24:04.339461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 33
 
5.2%
0.3 24
 
3.8%
0.1 22
 
3.5%
0.7 20
 
3.1%
0.4 19
 
3.0%
0.2 17
 
2.7%
0.5 17
 
2.7%
1.1 16
 
2.5%
0.9 15
 
2.4%
0.8 13
 
2.0%
Other values (178) 441
69.2%
ValueCountFrequency (%)
0.0 33
5.2%
0.1 22
3.5%
0.2 17
2.7%
0.3 24
3.8%
0.4 19
3.0%
0.5 17
2.7%
0.6 13
 
2.0%
0.7 20
3.1%
0.8 13
 
2.0%
0.9 15
2.4%
ValueCountFrequency (%)
45.4 1
0.2%
40.6 1
0.2%
39.8 1
0.2%
35.5 1
0.2%
33.1 1
0.2%
32.4 1
0.2%
31.7 1
0.2%
30.4 1
0.2%
28.4 2
0.3%
28.1 1
0.2%

지방(g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct140
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5130298
Minimum0
Maximum52.4
Zeros96
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-11T12:24:04.502958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median1
Q34.2
95-th percentile15.82
Maximum52.4
Range52.4
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.1807779
Coefficient of variation (CV)1.7593867
Kurtosis18.82602
Mean3.5130298
Median Absolute Deviation (MAD)1
Skewness3.7008358
Sum2237.8
Variance38.202015
MonotonicityNot monotonic
2023-12-11T12:24:04.727089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 96
 
15.1%
0.1 58
 
9.1%
0.2 42
 
6.6%
0.3 28
 
4.4%
0.8 17
 
2.7%
1.1 15
 
2.4%
0.6 14
 
2.2%
0.5 14
 
2.2%
0.4 13
 
2.0%
0.7 13
 
2.0%
Other values (130) 327
51.3%
ValueCountFrequency (%)
0.0 96
15.1%
0.1 58
9.1%
0.2 42
6.6%
0.3 28
 
4.4%
0.4 13
 
2.0%
0.5 14
 
2.2%
0.6 14
 
2.2%
0.7 13
 
2.0%
0.8 17
 
2.7%
0.9 12
 
1.9%
ValueCountFrequency (%)
52.4 1
0.2%
45.5 1
0.2%
44.0 1
0.2%
43.9 1
0.2%
43.7 1
0.2%
32.4 1
0.2%
30.4 1
0.2%
26.0 1
0.2%
22.8 1
0.2%
22.5 1
0.2%

콜레스트롤(g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct262
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.189796
Minimum0
Maximum249.3
Zeros320
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-11T12:24:04.889927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q329.3
95-th percentile119.74
Maximum249.3
Range249.3
Interquartile range (IQR)29.3

Descriptive statistics

Standard deviation41.891715
Coefficient of variation (CV)1.8064719
Kurtosis6.505826
Mean23.189796
Median Absolute Deviation (MAD)0
Skewness2.4751068
Sum14771.9
Variance1754.9158
MonotonicityNot monotonic
2023-12-11T12:24:05.092372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 320
50.2%
0.1 8
 
1.3%
1.8 4
 
0.6%
0.5 4
 
0.6%
28.8 3
 
0.5%
37.6 3
 
0.5%
5.0 3
 
0.5%
7.1 3
 
0.5%
16.0 3
 
0.5%
17.5 3
 
0.5%
Other values (252) 283
44.4%
ValueCountFrequency (%)
0.0 320
50.2%
0.1 8
 
1.3%
0.3 1
 
0.2%
0.4 1
 
0.2%
0.5 4
 
0.6%
0.7 1
 
0.2%
0.8 1
 
0.2%
0.9 1
 
0.2%
1.0 1
 
0.2%
1.4 1
 
0.2%
ValueCountFrequency (%)
249.3 1
0.2%
214.7 1
0.2%
210.7 1
0.2%
207.3 1
0.2%
206.8 1
0.2%
199.0 1
0.2%
198.5 1
0.2%
191.5 1
0.2%
191.2 1
0.2%
189.6 1
0.2%

식이섬유(g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34850863
Minimum0
Maximum7.6
Zeros217
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-11T12:24:05.256510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q30.5
95-th percentile1.12
Maximum7.6
Range7.6
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.55842755
Coefficient of variation (CV)1.6023349
Kurtosis53.093377
Mean0.34850863
Median Absolute Deviation (MAD)0.2
Skewness5.4697122
Sum222
Variance0.31184133
MonotonicityNot monotonic
2023-12-11T12:24:05.420753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0 217
34.1%
0.1 84
 
13.2%
0.2 64
 
10.0%
0.4 51
 
8.0%
0.3 39
 
6.1%
0.5 39
 
6.1%
0.6 38
 
6.0%
0.7 26
 
4.1%
0.8 22
 
3.5%
0.9 13
 
2.0%
Other values (17) 44
 
6.9%
ValueCountFrequency (%)
0.0 217
34.1%
0.1 84
 
13.2%
0.2 64
 
10.0%
0.3 39
 
6.1%
0.4 51
 
8.0%
0.5 39
 
6.1%
0.6 38
 
6.0%
0.7 26
 
4.1%
0.8 22
 
3.5%
0.9 13
 
2.0%
ValueCountFrequency (%)
7.6 1
 
0.2%
4.5 1
 
0.2%
4.0 1
 
0.2%
2.9 1
 
0.2%
2.5 3
0.5%
2.2 1
 
0.2%
2.1 3
0.5%
1.9 1
 
0.2%
1.8 2
0.3%
1.7 1
 
0.2%

나트륨(g)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct357
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean279.34882
Minimum0
Maximum4067
Zeros64
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2023-12-11T12:24:05.608390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median114
Q3368
95-th percentile987.6
Maximum4067
Range4067
Interquartile range (IQR)361

Descriptive statistics

Standard deviation441.19725
Coefficient of variation (CV)1.5793775
Kurtosis20.648747
Mean279.34882
Median Absolute Deviation (MAD)113
Skewness3.7002471
Sum177945.2
Variance194655.02
MonotonicityNot monotonic
2023-12-11T12:24:05.789825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 64
 
10.0%
1.0 35
 
5.5%
2.0 24
 
3.8%
5.0 13
 
2.0%
7.0 8
 
1.3%
18.0 7
 
1.1%
3.0 7
 
1.1%
4.0 6
 
0.9%
11.0 6
 
0.9%
9.0 6
 
0.9%
Other values (347) 461
72.4%
ValueCountFrequency (%)
0.0 64
10.0%
0.2 1
 
0.2%
1.0 35
5.5%
2.0 24
 
3.8%
3.0 7
 
1.1%
4.0 6
 
0.9%
5.0 13
 
2.0%
6.0 5
 
0.8%
7.0 8
 
1.3%
8.0 4
 
0.6%
ValueCountFrequency (%)
4067.0 1
0.2%
3489.0 1
0.2%
3344.0 1
0.2%
2933.0 1
0.2%
2775.0 1
0.2%
2321.0 1
0.2%
2190.0 1
0.2%
1945.0 1
0.2%
1878.0 1
0.2%
1653.0 1
0.2%

등록일
Categorical

Distinct9
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2012-01-04
318 
<NA>
163 
2012-01-17
102 
2012-01-06
 
28
2012-01-16
 
13
Other values (4)
 
13

Length

Max length10
Median length10
Mean length8.4646782
Min length4

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row2012-01-06
5th row2012-01-04

Common Values

ValueCountFrequency (%)
2012-01-04 318
49.9%
<NA> 163
25.6%
2012-01-17 102
 
16.0%
2012-01-06 28
 
4.4%
2012-01-16 13
 
2.0%
2012-01-11 6
 
0.9%
2012-01-18 5
 
0.8%
2012-01-12 1
 
0.2%
2011-12-27 1
 
0.2%

Length

2023-12-11T12:24:05.965441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:24:06.113690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2012-01-04 318
49.9%
na 163
25.6%
2012-01-17 102
 
16.0%
2012-01-06 28
 
4.4%
2012-01-16 13
 
2.0%
2012-01-11 6
 
0.9%
2012-01-18 5
 
0.8%
2012-01-12 1
 
0.2%
2011-12-27 1
 
0.2%

Interactions

2023-12-11T12:24:00.725243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:54.170741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:55.100902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:56.464977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:57.492740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:58.585924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:59.671283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:24:00.891910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:54.321341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:55.262285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:56.634853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:57.665369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:58.719147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:59.837842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:24:01.015039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:54.455410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:55.401806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:56.770461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:57.851653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:58.936116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:59.993710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:24:01.122330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:54.571091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:55.526711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:56.907211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:57.995895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:59.107591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:24:00.122143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:24:01.221024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:54.703820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:55.953938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:57.050681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:58.146765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:59.233185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:24:00.274619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:24:01.338474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:54.836654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:56.121133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:57.191896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:58.289350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:59.363718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:24:00.401382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:24:01.489089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:54.972320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:56.271978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:57.353211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:58.435353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:23:59.500626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:24:00.561817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:24:06.255737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1인분칼로리(kcal)탄수화물(g)단백질(g)지방(g)콜레스트롤(g)식이섬유(g)나트륨(g)등록일
1인분칼로리(kcal)1.0000.8840.8050.6840.5960.5180.8180.000
탄수화물(g)0.8841.0000.7040.3570.4400.4170.7350.000
단백질(g)0.8050.7041.0000.6200.7730.5040.8110.000
지방(g)0.6840.3570.6201.0000.5200.3010.3590.000
콜레스트롤(g)0.5960.4400.7730.5201.0000.1100.5880.000
식이섬유(g)0.5180.4170.5040.3010.1101.0000.6110.039
나트륨(g)0.8180.7350.8110.3590.5880.6111.0000.000
등록일0.0000.0000.0000.0000.0000.0390.0001.000
2023-12-11T12:24:06.417486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1인분칼로리(kcal)탄수화물(g)단백질(g)지방(g)콜레스트롤(g)식이섬유(g)나트륨(g)등록일
1인분칼로리(kcal)1.0000.6560.7450.7360.5120.2140.4470.000
탄수화물(g)0.6561.0000.2860.2870.0210.5170.3200.000
단백질(g)0.7450.2861.0000.7200.7390.2390.5960.000
지방(g)0.7360.2870.7201.0000.6210.1990.5520.000
콜레스트롤(g)0.5120.0210.7390.6211.000-0.0510.4810.000
식이섬유(g)0.2140.5170.2390.199-0.0511.0000.3940.021
나트륨(g)0.4470.3200.5960.5520.4810.3941.0000.000
등록일0.0000.0000.0000.0000.0000.0210.0001.000

Missing values

2023-12-11T12:24:01.677910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:24:01.929328image/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

음식명1인분칼로리(kcal)탄수화물(g)단백질(g)지방(g)콜레스트롤(g)식이섬유(g)나트륨(g)등록일
0가래떡(떡국용)311.068.35.31.00.00.0231.0<NA>
1가래떡(떡볶이용)96.021.01.60.30.00.071.0<NA>
2가자미58.00.19.91.744.60.0104.0<NA>
3가자미구이76.00.211.23.150.10.0255.02012-01-06
4가자미조림45.42.46.61.227.00.4183.02012-01-04
5가지6.01.60.30.00.30.21.02012-01-06
6가지나물19.02.90.80.90.40.4278.02012-01-16
7간장(왜간장)8.00.71.20.00.00.0879.0<NA>
8갈비구이586.014.328.143.787.40.31093.02012-01-04
9갈비찜262.23.513.720.043.20.1413.02012-01-04
음식명1인분칼로리(kcal)탄수화물(g)단백질(g)지방(g)콜레스트롤(g)식이섬유(g)나트륨(g)등록일
627홍차3.00.30.30.20.00.03.02012-01-04
628홍차음료64.016.60.00.00.00.04.02012-01-17
629홍합14.00.81.90.29.80.052.02012-01-04
630홍합미역국28.02.81.82.20.80.2824.02012-01-17
631회덮밥696.5121.939.85.551.22.5892.02012-01-06
632효모0.00.00.00.00.00.00.02012-01-04
633후렌치후라이367.047.24.419.70.04.0223.02012-01-04
634후르츠샐러드(칵테일)29.07.70.10.20.00.21.02012-01-04
635후추3.00.70.10.00.00.10.0<NA>
636흑미밥387.285.06.70.70.00.47.02012-01-04