Overview

Dataset statistics

Number of variables7
Number of observations199
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.4 KiB
Average record size in memory58.7 B

Variable types

Categorical4
Text1
Numeric2

Alerts

2020-01-01 has constant value ""Constant
1.1 is highly overall correlated with 50.000000High correlation
50.000000 is highly overall correlated with 1.1 and 1 other fieldsHigh correlation
1 is highly overall correlated with [20-29]High correlation
[20-29] is highly overall correlated with 1High correlation
15 is highly overall correlated with 50.000000High correlation
1 is highly imbalanced (78.9%)Imbalance

Reproduction

Analysis started2023-12-10 06:26:27.444446
Analysis finished2023-12-10 06:26:29.982182
Duration2.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

2020-01-01
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2020-01-01
199 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-01-01
2nd row2020-01-01
3rd row2020-01-01
4th row2020-01-01
5th row2020-01-01

Common Values

ValueCountFrequency (%)
2020-01-01 199
100.0%

Length

2023-12-10T15:26:30.122593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:26:30.281384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-01-01 199
100.0%

떡국
Text

Distinct141
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-10T15:26:30.774978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length5.5929648
Min length2

Characters and Unicode

Total characters1113
Distinct characters250
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

Unique111 ?
Unique (%)55.8%

Sample

1st row 돼지갈비찜
2nd row 쌀밥
3rd row 고구마(찐것)
4th row 잡곡밥
5th row 돼지뒷다리(생것)
ValueCountFrequency (%)
배추김치 8
 
3.5%
쌀밥 6
 
2.6%
떡국 5
 
2.2%
잡곡밥 4
 
1.7%
계란후라이 4
 
1.7%
총각김치 4
 
1.7%
닭고기(통닭 3
 
1.3%
짜파게티 3
 
1.3%
떡만둣국 3
 
1.3%
간장 3
 
1.3%
Other values (156) 187
81.3%
2023-12-10T15:26:31.523831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
230
 
20.7%
치 27
 
2.4%
김 22
 
2.0%
밥 20
 
1.8%
이 20
 
1.8%
( 19
 
1.7%
) 15
 
1.3%
추 14
 
1.3%
국 14
 
1.3%
구 13
 
1.2%
Other values (240) 719
64.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 825
74.1%
Space Separator 230
 
20.7%
Open Punctuation 19
 
1.7%
Close Punctuation 15
 
1.3%
Decimal Number 15
 
1.3%
Lowercase Letter 8
 
0.7%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
치 27
 
3.3%
김 22
 
2.7%
밥 20
 
2.4%
이 20
 
2.4%
추 14
 
1.7%
국 14
 
1.7%
구 13
 
1.6%
라 12
 
1.5%
고 12
 
1.5%
닭 12
 
1.5%
Other values (227) 659
79.9%
Decimal Number
ValueCountFrequency (%)
0 6
40.0%
2 2
 
13.3%
3 2
 
13.3%
1 2
 
13.3%
6 1
 
6.7%
5 1
 
6.7%
8 1
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
m 4
50.0%
l 4
50.0%
Space Separator
ValueCountFrequency (%)
230
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Other Punctuation
ValueCountFrequency (%)
% 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 825
74.1%
Common 280
 
25.2%
Latin 8
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
치 27
 
3.3%
김 22
 
2.7%
밥 20
 
2.4%
이 20
 
2.4%
추 14
 
1.7%
국 14
 
1.7%
구 13
 
1.6%
라 12
 
1.5%
고 12
 
1.5%
닭 12
 
1.5%
Other values (227) 659
79.9%
Common
ValueCountFrequency (%)
230
82.1%
( 19
 
6.8%
) 15
 
5.4%
0 6
 
2.1%
2 2
 
0.7%
3 2
 
0.7%
1 2
 
0.7%
% 1
 
0.4%
6 1
 
0.4%
5 1
 
0.4%
Latin
ValueCountFrequency (%)
m 4
50.0%
l 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 825
74.1%
ASCII 288
 
25.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
230
79.9%
( 19
 
6.6%
) 15
 
5.2%
0 6
 
2.1%
m 4
 
1.4%
l 4
 
1.4%
2 2
 
0.7%
3 2
 
0.7%
1 2
 
0.7%
% 1
 
0.3%
Other values (3) 3
 
1.0%
Hangul
ValueCountFrequency (%)
치 27
 
3.3%
김 22
 
2.7%
밥 20
 
2.4%
이 20
 
2.4%
추 14
 
1.7%
국 14
 
1.7%
구 13
 
1.6%
라 12
 
1.5%
고 12
 
1.5%
닭 12
 
1.5%
Other values (227) 659
79.9%

1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
1
180 
2
 
10
튀김)
 
3
양념장
 
1
호상
 
1
Other values (4)
 
4

Length

Max length5
Median length2
Mean length2.0703518
Min length2

Unique

Unique6 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 180
90.5%
2 10
 
5.0%
튀김) 3
 
1.5%
양념장 1
 
0.5%
호상 1
 
0.5%
삶은것) 1
 
0.5%
3 1
 
0.5%
삶은것 1
 
0.5%
4 1
 
0.5%

Length

2023-12-10T15:26:31.753788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:26:31.926712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 180
90.5%
2 10
 
5.0%
튀김 3
 
1.5%
삶은것 2
 
1.0%
양념장 1
 
0.5%
호상 1
 
0.5%
3 1
 
0.5%
4 1
 
0.5%

1.1
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0552764
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:26:32.139238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q39
95-th percentile16
Maximum21
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.7696388
Coefficient of variation (CV)0.78768309
Kurtosis0.2285222
Mean6.0552764
Median Absolute Deviation (MAD)3
Skewness0.9871926
Sum1205
Variance22.749454
MonotonicityNot monotonic
2023-12-10T15:26:32.355439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 34
17.1%
2 24
12.1%
3 22
11.1%
4 16
8.0%
5 15
7.5%
6 13
 
6.5%
7 12
 
6.0%
9 10
 
5.0%
8 9
 
4.5%
10 8
 
4.0%
Other values (11) 36
18.1%
ValueCountFrequency (%)
1 34
17.1%
2 24
12.1%
3 22
11.1%
4 16
8.0%
5 15
7.5%
6 13
 
6.5%
7 12
 
6.0%
8 9
 
4.5%
9 10
 
5.0%
10 8
 
4.0%
ValueCountFrequency (%)
21 1
 
0.5%
20 1
 
0.5%
19 1
 
0.5%
18 2
 
1.0%
17 3
1.5%
16 3
1.5%
15 3
1.5%
14 5
2.5%
13 6
3.0%
12 5
2.5%

50.000000
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.893755
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-10T15:26:32.584957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.166667
Q15.263158
median9.090909
Q314.285714
95-th percentile34
Maximum100
Range99
Interquartile range (IQR)9.022556

Descriptive statistics

Standard deviation15.879268
Coefficient of variation (CV)1.1429069
Kurtosis15.879269
Mean13.893755
Median Absolute Deviation (MAD)3.827751
Skewness3.6211716
Sum2764.8571
Variance252.15117
MonotonicityNot monotonic
2023-12-10T15:26:32.786474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
10.0 19
 
9.5%
4.166667 19
 
9.5%
5.263158 17
 
8.5%
5.0 15
 
7.5%
7.142857 13
 
6.5%
7.692308 13
 
6.5%
20.0 13
 
6.5%
6.666667 13
 
6.5%
14.285714 12
 
6.0%
33.333333 11
 
5.5%
Other values (17) 54
27.1%
ValueCountFrequency (%)
1.0 2
 
1.0%
2.0 1
 
0.5%
4.0 2
 
1.0%
4.166667 19
9.5%
5.0 15
7.5%
5.263158 17
8.5%
6.666667 13
6.5%
7.142857 13
6.5%
7.692308 13
6.5%
8.0 1
 
0.5%
ValueCountFrequency (%)
100.0 4
 
2.0%
50.0 5
 
2.5%
40.0 1
 
0.5%
33.333333 11
5.5%
28.571429 1
 
0.5%
25.0 7
3.5%
20.0 13
6.5%
18.181818 1
 
0.5%
16.666667 1
 
0.5%
14.285714 12
6.0%

[20-29]
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
[0-19]
102 
[20-29]
59 
[50-59]
31 
25.000000
 
2
7.142857
 
1
Other values (4)
 
4

Length

Max length10
Median length7
Mean length7.5477387
Min length7

Unique

Unique5 ?
Unique (%)2.5%

Sample

1st row [20-29]
2nd row [20-29]
3rd row [20-29]
4th row [20-29]
5th row [20-29]

Common Values

ValueCountFrequency (%)
[0-19] 102
51.3%
[20-29] 59
29.6%
[50-59] 31
 
15.6%
25.000000 2
 
1.0%
7.142857 1
 
0.5%
20.000000 1
 
0.5%
10.000000 1
 
0.5%
33.333333 1
 
0.5%
4.166667 1
 
0.5%

Length

2023-12-10T15:26:32.986880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:26:33.223011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0-19 102
51.3%
20-29 59
29.6%
50-59 31
 
15.6%
25.000000 2
 
1.0%
7.142857 1
 
0.5%
20.000000 1
 
0.5%
10.000000 1
 
0.5%
33.333333 1
 
0.5%
4.166667 1
 
0.5%

15
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
18
41 
12
37 
19
30 
08
16 
13
10 
Other values (16)
65 

Length

Max length8
Median length3
Mean length3.1507538
Min length3

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row 15
2nd row 12
3rd row 12
4th row 12
5th row 12

Common Values

ValueCountFrequency (%)
18 41
20.6%
12 37
18.6%
19 30
15.1%
08 16
 
8.0%
13 10
 
5.0%
22 9
 
4.5%
11 8
 
4.0%
14 7
 
3.5%
09 5
 
2.5%
[0-19] 5
 
2.5%
Other values (11) 31
15.6%

Length

2023-12-10T15:26:33.442904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
18 41
20.6%
12 37
18.6%
19 30
15.1%
08 16
 
8.0%
13 10
 
5.0%
22 9
 
4.5%
11 8
 
4.0%
14 7
 
3.5%
0-19 5
 
2.5%
17 5
 
2.5%
Other values (11) 31
15.6%

Interactions

2023-12-10T15:26:28.945773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:26:28.557276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:26:29.154281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:26:28.771626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:26:33.613263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
11.150.000000[20-29]15
11.0000.0000.0000.9660.716
1.10.0001.0000.3650.0000.000
50.0000000.0000.3651.0000.0000.947
[20-29]0.9660.0000.0001.0000.833
150.7160.0000.9470.8331.000
2023-12-10T15:26:33.856127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1[20-29]15
11.0000.6930.360
[20-29]0.6931.0000.495
150.3600.4951.000
2023-12-10T15:26:34.172117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1.150.0000001[20-29]15
1.11.000-0.5950.0000.0000.000
50.000000-0.5951.0000.0000.0000.759
10.0000.0001.0000.6930.360
[20-29]0.0000.0000.6931.0000.495
150.0000.7590.3600.4951.000

Missing values

2023-12-10T15:26:29.684408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:26:29.884323image/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

2020-01-01떡국11.150.000000[20-29]15
02020-01-01돼지갈비찜1250.0[20-29]15
12020-01-01쌀밥117.142857[20-29]12
22020-01-01고구마(찐것)127.142857[20-29]12
32020-01-01잡곡밥137.142857[20-29]12
42020-01-01돼지뒷다리(생것)147.142857[20-29]12
52020-01-01된장찌개157.142857[20-29]12
62020-01-01배추김치167.142857[20-29]12
72020-01-01청포도177.142857[20-29]12
82020-01-01삶은계란187.142857[20-29]12
92020-01-01닭가슴살197.142857[20-29]12
2020-01-01떡국11.150.000000[20-29]15
1892020-01-01라면1174.166667[0-19]19
1902020-01-01연근조림1184.166667[0-19]19
1912020-01-01배추김치1194.166667[0-19]19
1922020-01-01깻잎장아찌1204.166667[0-19]19
1932020-01-01간장1214.166667[0-19]19
1942020-01-01떡국1125.0[0-19]10
1952020-01-01쌀밥1225.0[0-19]10
1962020-01-01갈비탕1325.0[0-19]10
1972020-01-01완두콩삶은것14.025.000000[0-19]
1982020-01-01피자4116.666667[0-19]17