Overview

Dataset statistics

Number of variables12
Number of observations271730
Missing cells0
Missing cells (%)0.0%
Total size in memory14.3 MiB
Average record size in memory55.0 B

Variable types

DateTime1
Categorical6
Text1
Numeric4

Alerts

pricetype has constant value ""Constant
currency has constant value ""Constant
day has constant value ""Constant
market has a high cardinality: 215 distinct valuesHigh cardinality
unit is highly imbalanced (98.9%)Imbalance

Reproduction

Analysis started2023-12-29 02:48:48.531344
Analysis finished2023-12-29 02:48:49.755588
Duration1.22 second
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

date
Date

Distinct202
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Minimum2007-01-15 00:00:00
Maximum2023-11-15 00:00:00
2023-12-29T09:48:50.061930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2023-12-29T09:48:50.577856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

market
Categorical

HIGH CARDINALITY 

Distinct215
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size540.6 KiB
National Average
 
1663
Pasar Tenguyun
 
1443
Pasar Gusher
 
1437
Pasar Pancasila
 
1437
Pasar Segiri
 
1436
Other values (210)
264314 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational Average
2nd rowNational Average
3rd rowNational Average
4th rowNational Average
5th rowNational Average

Common Values

ValueCountFrequency (%)
National Average 1663
 
0.6%
Pasar Tenguyun 1443
 
0.5%
Pasar Gusher 1437
 
0.5%
Pasar Pancasila 1437
 
0.5%
Pasar Segiri 1436
 
0.5%
Pasar Oeba 1435
 
0.5%
Pasar Cikurubuk 1435
 
0.5%
Pasar Pelita 1434
 
0.5%
Pasar Mandonga 1433
 
0.5%
Pasar Kota 1433
 
0.5%
Other values (205) 257144
94.6%

category
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.8 KiB
vegetables and fruits
100694 
meat, fish and eggs
72334 
cereals and tubers
40930 
oil and fats
29639 
miscellaneous food
27891 
Other values (2)
 
242

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcereals and tubers
2nd rowcereals and tubers
3rd rowmeat, fish and eggs
4th rowmeat, fish and eggs
5th rowmeat, fish and eggs

Common Values

ValueCountFrequency (%)
vegetables and fruits 100694
37.1%
meat, fish and eggs 72334
26.6%
cereals and tubers 40930
15.1%
oil and fats 29639
 
10.9%
miscellaneous food 27891
 
10.3%
milk and dairy 158
 
0.1%
non-food 84
 
< 0.1%

Common Values (Plot)

2023-12-29T09:48:50.994212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

commodity
Categorical

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size266.8 KiB
Chili (red)
 
10994
Eggs
 
10993
Oil (vegetable)
 
10992
Chili (bird's eye)
 
10991
Sugar
 
10990
Other values (25)
216770 

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRice
2nd rowWheat flour
3rd rowEggs
4th rowMeat (beef)
5th rowMeat (chicken, broiler)

Common Values

ValueCountFrequency (%)
Chili (red) 10994
 
4.0%
Eggs 10993
 
4.0%
Oil (vegetable) 10992
 
4.0%
Chili (bird's eye) 10991
 
4.0%
Sugar 10990
 
4.0%
Eggs (broiler) 10838
 
4.0%
Garlic (medium) 10838
 
4.0%
Garlic 10836
 
4.0%
Onions (shallot) 10836
 
4.0%
Meat (chicken, broiler) 10821
 
4.0%
Other values (20) 162601
59.8%

unit
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.6 KiB
KG
271330 
L
 
242
385 G
 
158

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KG 271330
99.9%
L 242
 
0.1%
385 G 158
 
0.1%

Common Values (Plot)

2023-12-29T09:48:51.234232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

priceflag
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2023-12-29T09:48:51.399708image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.981639863
Min length6

Characters and Unicode

Total characters2440581
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowactual
2nd rowactual
3rd rowactual
4th rowactual
5th rowactual
ValueCountFrequency (%)
aggregate 270067
99.4%
actual 1663
 
0.6%
2023-12-29T09:48:51.861571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 810201
33.2%
a 543460
22.3%
e 540134
22.1%
t 271730
 
11.1%
r 270067
 
11.1%
c 1663
 
0.1%
u 1663
 
0.1%
l 1663
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2440581
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 810201
33.2%
a 543460
22.3%
e 540134
22.1%
t 271730
 
11.1%
r 270067
 
11.1%
c 1663
 
0.1%
u 1663
 
0.1%
l 1663
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2440581
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 810201
33.2%
a 543460
22.3%
e 540134
22.1%
t 271730
 
11.1%
r 270067
 
11.1%
c 1663
 
0.1%
u 1663
 
0.1%
l 1663
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2440581
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 810201
33.2%
a 543460
22.3%
e 540134
22.1%
t 271730
 
11.1%
r 270067
 
11.1%
c 1663
 
0.1%
u 1663
 
0.1%
l 1663
 
0.1%

pricetype
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.6 KiB
Retail
271730 

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Retail 271730
100.0%

Common Values (Plot)

2023-12-29T09:48:52.063904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

currency
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size265.6 KiB
IDR
271730 

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
IDR 271730
100.0%

Common Values (Plot)

2023-12-29T09:48:52.228138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

price
Real number (ℝ)

Distinct51294
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38058.65811
Minimum1630.65
Maximum215000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-12-29T09:48:52.517287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1630.65
5-th percentile10850
Q114900
median27050
Q341049.405
95-th percentile125000
Maximum215000
Range213369.35
Interquartile range (IQR)26149.405

Descriptive statistics

Standard deviation34198.11955
Coefficient of variation (CV)0.898563461
Kurtosis2.488586273
Mean38058.65811
Median Absolute Deviation (MAD)12550
Skewness1.844404869
Sum1.034167917 × 1010
Variance1169511381
MonotonicityNot monotonic
2023-12-29T09:48:52.846901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14000 3812
 
1.4%
120000 3513
 
1.3%
15000 3385
 
1.2%
130000 2768
 
1.0%
13000 2480
 
0.9%
14500 2031
 
0.7%
110000 1892
 
0.7%
13500 1889
 
0.7%
16000 1777
 
0.7%
12000 1766
 
0.6%
Other values (51284) 246417
90.7%
ValueCountFrequency (%)
1630.65 2
 
< 0.1%
1750 2
 
< 0.1%
1975.81 2
 
< 0.1%
2000 8
< 0.1%
2619.05 1
 
< 0.1%
ValueCountFrequency (%)
215000 1
< 0.1%
212879.55 2
< 0.1%
210833.33 1
< 0.1%
204404.76 1
< 0.1%
201708.33 1
< 0.1%

year
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.913223
Minimum2007
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-12-29T09:48:53.102522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2016
Q12020
median2021
Q32022
95-th percentile2023
Maximum2023
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.006299482
Coefficient of variation (CV)0.0009927687441
Kurtosis4.09518174
Mean2020.913223
Median Absolute Deviation (MAD)1
Skewness-1.689812983
Sum549142750
Variance4.025237611
MonotonicityNot monotonic
2023-12-29T09:48:53.354990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2022 63536
23.4%
2021 63349
23.3%
2020 60714
22.3%
2023 56480
20.8%
2016 22054
 
8.1%
2017 4194
 
1.5%
2008 132
 
< 0.1%
2007 132
 
< 0.1%
2012 132
 
< 0.1%
2011 132
 
< 0.1%
Other values (7) 875
 
0.3%
ValueCountFrequency (%)
2007 132
< 0.1%
2008 132
< 0.1%
2009 132
< 0.1%
2010 132
< 0.1%
2011 132
< 0.1%
ValueCountFrequency (%)
2023 56480
20.8%
2022 63536
23.4%
2021 63349
23.3%
2020 60714
22.3%
2019 120
 
< 0.1%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.585360468
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-12-29T09:48:53.595968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.412121397
Coefficient of variation (CV)0.5181373767
Kurtosis-1.217954182
Mean6.585360468
Median Absolute Deviation (MAD)3
Skewness-0.05698628547
Sum1789440
Variance11.64257243
MonotonicityNot monotonic
2023-12-29T09:48:53.826685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 25114
9.2%
11 24686
9.1%
10 24570
9.0%
9 24378
9.0%
8 24107
8.9%
7 23962
8.8%
4 21053
7.7%
5 21027
7.7%
6 21017
7.7%
1 20981
7.7%
Other values (2) 40835
15.0%
ValueCountFrequency (%)
1 20981
7.7%
2 20959
7.7%
3 25114
9.2%
4 21053
7.7%
5 21027
7.7%
ValueCountFrequency (%)
12 19876
7.3%
11 24686
9.1%
10 24570
9.0%
9 24378
9.0%
8 24107
8.9%

day
Real number (ℝ)

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15
Minimum15
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2023-12-29T09:48:54.051687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q115
median15
Q315
95-th percentile15
Maximum15
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0
Coefficient of variation (CV)0
Kurtosis0
Mean15
Median Absolute Deviation (MAD)0
Skewness0
Sum4075950
Variance0
MonotonicityIncreasing
2023-12-29T09:48:54.254458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
15 271730
100.0%
ValueCountFrequency (%)
15 271730
100.0%
ValueCountFrequency (%)
15 271730
100.0%