Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations8893
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 MiB
Average record size in memory447.3 B

Variable types

Categorical5
Numeric8
Text1
DateTime1
Unsupported1

Alerts

Sales is highly overall correlated with price_per_kg and 2 other fieldsHigh correlation
category is highly overall correlated with price_per_kg and 3 other fieldsHigh correlation
demand_gap is highly overall correlated with sales_to_shipment_ratio and 2 other fieldsHigh correlation
price_per_kg is highly overall correlated with Sales and 3 other fieldsHigh correlation
product_id is highly overall correlated with category and 2 other fieldsHigh correlation
product_name is highly overall correlated with category and 2 other fieldsHigh correlation
sales_to_shipment_ratio is highly overall correlated with Sales and 3 other fieldsHigh correlation
supplier is highly overall correlated with categoryHigh correlation
units_on_hand_kg is highly overall correlated with demand_gap and 2 other fieldsHigh correlation
units_shipped_kg is highly overall correlated with demand_gap and 2 other fieldsHigh correlation
units_sold_kg is highly overall correlated with Sales and 2 other fieldsHigh correlation
month is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-09-20 18:26:42.448604
Analysis finished2025-09-20 18:26:51.678823
Duration9.23 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

product_id
Categorical

High correlation 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size807.8 KiB
57ad2178-9598-4981-b0f7-78a91162821e
 
464
6c8adfc3-2114-4b78-a3e2-2269a6a0dc02
 
454
9bb2d8db-a7ac-4d40-9d3c-0c96cafe7153
 
447
7ba3ccef-363a-40c5-9bbd-a451068846c5
 
446
0985f295-bb82-484f-9cab-76637e8e8bdb
 
443
Other values (23)
6639 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters320148
Distinct characters17
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 rowb12c721e-8301-4b08-8ac3-d435be9b8b32
2nd row4a0f8862-c851-4073-bc17-3a3d93834902
3rd rowbe996df6-1780-4352-8b7d-1f927279aa49
4th row6c8adfc3-2114-4b78-a3e2-2269a6a0dc02
5th row0bd5bf2b-be0a-4ba0-aa92-059021b169e3

Common Values

ValueCountFrequency (%)
57ad2178-9598-4981-b0f7-78a91162821e 464
 
5.2%
6c8adfc3-2114-4b78-a3e2-2269a6a0dc02 454
 
5.1%
9bb2d8db-a7ac-4d40-9d3c-0c96cafe7153 447
 
5.0%
7ba3ccef-363a-40c5-9bbd-a451068846c5 446
 
5.0%
0985f295-bb82-484f-9cab-76637e8e8bdb 443
 
5.0%
b12c721e-8301-4b08-8ac3-d435be9b8b32 442
 
5.0%
4a0f8862-c851-4073-bc17-3a3d93834902 434
 
4.9%
3b87981b-375b-494a-9b11-00bf7144143a 426
 
4.8%
f0fa3446-1398-417a-bda7-de131121a15b 307
 
3.5%
28f45387-03b8-48f9-b67c-d921d29f60b6 296
 
3.3%
Other values (18) 4734
53.2%

Length

2025-09-20T18:26:51.807987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
57ad2178-9598-4981-b0f7-78a91162821e 464
 
5.2%
6c8adfc3-2114-4b78-a3e2-2269a6a0dc02 454
 
5.1%
9bb2d8db-a7ac-4d40-9d3c-0c96cafe7153 447
 
5.0%
7ba3ccef-363a-40c5-9bbd-a451068846c5 446
 
5.0%
0985f295-bb82-484f-9cab-76637e8e8bdb 443
 
5.0%
b12c721e-8301-4b08-8ac3-d435be9b8b32 442
 
5.0%
4a0f8862-c851-4073-bc17-3a3d93834902 434
 
4.9%
3b87981b-375b-494a-9b11-00bf7144143a 426
 
4.8%
f0fa3446-1398-417a-bda7-de131121a15b 307
 
3.5%
28f45387-03b8-48f9-b67c-d921d29f60b6 296
 
3.3%
Other values (18) 4734
53.2%

Most occurring characters

ValueCountFrequency (%)
- 35572
 
11.1%
b 24196
 
7.6%
4 22186
 
6.9%
8 22175
 
6.9%
9 21058
 
6.6%
a 20580
 
6.4%
1 19901
 
6.2%
3 19757
 
6.2%
0 18039
 
5.6%
c 16677
 
5.2%
Other values (7) 100007
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 320148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 35572
 
11.1%
b 24196
 
7.6%
4 22186
 
6.9%
8 22175
 
6.9%
9 21058
 
6.6%
a 20580
 
6.4%
1 19901
 
6.2%
3 19757
 
6.2%
0 18039
 
5.6%
c 16677
 
5.2%
Other values (7) 100007
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 320148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 35572
 
11.1%
b 24196
 
7.6%
4 22186
 
6.9%
8 22175
 
6.9%
9 21058
 
6.6%
a 20580
 
6.4%
1 19901
 
6.2%
3 19757
 
6.2%
0 18039
 
5.6%
c 16677
 
5.2%
Other values (7) 100007
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 320148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 35572
 
11.1%
b 24196
 
7.6%
4 22186
 
6.9%
8 22175
 
6.9%
9 21058
 
6.6%
a 20580
 
6.4%
1 19901
 
6.2%
3 19757
 
6.2%
0 18039
 
5.6%
c 16677
 
5.2%
Other values (7) 100007
31.2%

product_name
Categorical

High correlation 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size547.2 KiB
Cheese
 
464
Milk
 
454
Butter
 
447
Yogurt
 
446
Chicken
 
443
Other values (23)
6639 

Length

Max length12
Median length11
Mean length5.991454
Min length3

Characters and Unicode

Total characters53282
Distinct characters33
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 rowLamb
2nd rowBeef
3rd rowOranges
4th rowMilk
5th rowBananas

Common Values

ValueCountFrequency (%)
Cheese 464
 
5.2%
Milk 454
 
5.1%
Butter 447
 
5.0%
Yogurt 446
 
5.0%
Chicken 443
 
5.0%
Lamb 442
 
5.0%
Beef 434
 
4.9%
Pork 426
 
4.8%
Oats 307
 
3.5%
Corn 296
 
3.3%
Other values (18) 4734
53.2%

Length

2025-09-20T18:26:51.959564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cheese 464
 
5.2%
milk 454
 
5.1%
butter 447
 
5.0%
yogurt 446
 
5.0%
chicken 443
 
5.0%
lamb 442
 
5.0%
beef 434
 
4.9%
pork 426
 
4.8%
oats 307
 
3.5%
corn 296
 
3.3%
Other values (18) 4734
53.2%

Most occurring characters

ValueCountFrequency (%)
e 8836
16.6%
r 4490
 
8.4%
a 4340
 
8.1%
s 3857
 
7.2%
t 3743
 
7.0%
o 2672
 
5.0%
i 1973
 
3.7%
n 1939
 
3.6%
B 1696
 
3.2%
C 1674
 
3.1%
Other values (23) 18062
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53282
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8836
16.6%
r 4490
 
8.4%
a 4340
 
8.1%
s 3857
 
7.2%
t 3743
 
7.0%
o 2672
 
5.0%
i 1973
 
3.7%
n 1939
 
3.6%
B 1696
 
3.2%
C 1674
 
3.1%
Other values (23) 18062
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53282
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8836
16.6%
r 4490
 
8.4%
a 4340
 
8.1%
s 3857
 
7.2%
t 3743
 
7.0%
o 2672
 
5.0%
i 1973
 
3.7%
n 1939
 
3.6%
B 1696
 
3.2%
C 1674
 
3.1%
Other values (23) 18062
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53282
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8836
16.6%
r 4490
 
8.4%
a 4340
 
8.1%
s 3857
 
7.2%
t 3743
 
7.0%
o 2672
 
5.0%
i 1973
 
3.7%
n 1939
 
3.6%
B 1696
 
3.2%
C 1674
 
3.1%
Other values (23) 18062
33.9%

category
Categorical

High correlation 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size557.5 KiB
Fruits
1846 
Dairy
1811 
Vegetables
1765 
Livestock
1745 
Grains
1726 

Length

Max length10
Median length9
Mean length7.1789048
Min length5

Characters and Unicode

Total characters63842
Distinct characters21
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 rowLivestock
2nd rowLivestock
3rd rowFruits
4th rowDairy
5th rowFruits

Common Values

ValueCountFrequency (%)
Fruits 1846
20.8%
Dairy 1811
20.4%
Vegetables 1765
19.8%
Livestock 1745
19.6%
Grains 1726
19.4%

Length

2025-09-20T18:26:52.107776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-20T18:26:52.250409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
fruits 1846
20.8%
dairy 1811
20.4%
vegetables 1765
19.8%
livestock 1745
19.6%
grains 1726
19.4%

Most occurring characters

ValueCountFrequency (%)
i 7128
 
11.2%
s 7082
 
11.1%
e 7040
 
11.0%
r 5383
 
8.4%
t 5356
 
8.4%
a 5302
 
8.3%
F 1846
 
2.9%
u 1846
 
2.9%
D 1811
 
2.8%
y 1811
 
2.8%
Other values (11) 19237
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63842
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 7128
 
11.2%
s 7082
 
11.1%
e 7040
 
11.0%
r 5383
 
8.4%
t 5356
 
8.4%
a 5302
 
8.3%
F 1846
 
2.9%
u 1846
 
2.9%
D 1811
 
2.8%
y 1811
 
2.8%
Other values (11) 19237
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63842
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 7128
 
11.2%
s 7082
 
11.1%
e 7040
 
11.0%
r 5383
 
8.4%
t 5356
 
8.4%
a 5302
 
8.3%
F 1846
 
2.9%
u 1846
 
2.9%
D 1811
 
2.8%
y 1811
 
2.8%
Other values (11) 19237
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63842
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 7128
 
11.2%
s 7082
 
11.1%
e 7040
 
11.0%
r 5383
 
8.4%
t 5356
 
8.4%
a 5302
 
8.3%
F 1846
 
2.9%
u 1846
 
2.9%
D 1811
 
2.8%
y 1811
 
2.8%
Other values (11) 19237
30.1%

price_per_kg
Real number (ℝ)

High correlation 

Distinct1256
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6414776
Minimum0.4
Maximum14.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2025-09-20T18:26:52.421053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.59
Q10.97
median2.68
Q35.3
95-th percentile10.9
Maximum14.39
Range13.99
Interquartile range (IQR)4.33

Descriptive statistics

Standard deviation3.3063484
Coefficient of variation (CV)0.90796891
Kurtosis0.67029165
Mean3.6414776
Median Absolute Deviation (MAD)1.8
Skewness1.229211
Sum32383.66
Variance10.93194
MonotonicityNot monotonic
2025-09-20T18:26:52.596511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75 64
 
0.7%
0.84 64
 
0.7%
0.86 61
 
0.7%
0.82 61
 
0.7%
0.77 60
 
0.7%
0.83 58
 
0.7%
0.88 56
 
0.6%
0.69 55
 
0.6%
0.92 54
 
0.6%
0.8 53
 
0.6%
Other values (1246) 8307
93.4%
ValueCountFrequency (%)
0.4 11
0.1%
0.41 10
 
0.1%
0.42 19
0.2%
0.43 11
0.1%
0.44 18
0.2%
0.45 23
0.3%
0.46 19
0.2%
0.47 14
0.2%
0.48 21
0.2%
0.49 25
0.3%
ValueCountFrequency (%)
14.39 2
< 0.1%
14.38 3
< 0.1%
14.37 1
 
< 0.1%
14.36 3
< 0.1%
14.35 1
 
< 0.1%
14.34 1
 
< 0.1%
14.32 2
< 0.1%
14.3 1
 
< 0.1%
14.29 2
< 0.1%
14.26 1
 
< 0.1%

units_shipped_kg
Real number (ℝ)

High correlation 

Distinct8143
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25471.786
Minimum1005
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2025-09-20T18:26:52.771373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1005
5-th percentile3349.2
Q113228
median25468
Q337964
95-th percentile47457.4
Maximum50000
Range48995
Interquartile range (IQR)24736

Descriptive statistics

Standard deviation14177.334
Coefficient of variation (CV)0.55658973
Kurtosis-1.2120234
Mean25471.786
Median Absolute Deviation (MAD)12361
Skewness0.0033572976
Sum2.2652059 × 108
Variance2.0099681 × 108
MonotonicityNot monotonic
2025-09-20T18:26:52.947765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26667 4
 
< 0.1%
35708 4
 
< 0.1%
1551 4
 
< 0.1%
23719 3
 
< 0.1%
10942 3
 
< 0.1%
9828 3
 
< 0.1%
6983 3
 
< 0.1%
1523 3
 
< 0.1%
10828 3
 
< 0.1%
48130 3
 
< 0.1%
Other values (8133) 8860
99.6%
ValueCountFrequency (%)
1005 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
1013 1
< 0.1%
1015 1
< 0.1%
1017 1
< 0.1%
1028 1
< 0.1%
1050 1
< 0.1%
1052 1
< 0.1%
1061 1
< 0.1%
ValueCountFrequency (%)
50000 1
< 0.1%
49987 1
< 0.1%
49979 1
< 0.1%
49976 1
< 0.1%
49959 1
< 0.1%
49957 1
< 0.1%
49953 1
< 0.1%
49952 2
< 0.1%
49950 1
< 0.1%
49936 1
< 0.1%

units_sold_kg
Real number (ℝ)

High correlation 

Distinct7630
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12545.278
Minimum0
Maximum49726
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2025-09-20T18:26:53.326783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile634.2
Q13642
median9643
Q318832
95-th percentile34673.4
Maximum49726
Range49726
Interquartile range (IQR)15190

Descriptive statistics

Standard deviation10772.529
Coefficient of variation (CV)0.85869191
Kurtosis0.24092725
Mean12545.278
Median Absolute Deviation (MAD)6872
Skewness0.9846334
Sum1.1156516 × 108
Variance1.1604738 × 108
MonotonicityNot monotonic
2025-09-20T18:26:53.504813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1232 6
 
0.1%
5248 5
 
0.1%
585 5
 
0.1%
1641 4
 
< 0.1%
979 4
 
< 0.1%
3525 4
 
< 0.1%
2990 4
 
< 0.1%
938 4
 
< 0.1%
438 4
 
< 0.1%
1196 4
 
< 0.1%
Other values (7620) 8849
99.5%
ValueCountFrequency (%)
0 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
11 2
< 0.1%
13 1
< 0.1%
17 2
< 0.1%
18 1
< 0.1%
21 2
< 0.1%
ValueCountFrequency (%)
49726 1
< 0.1%
48817 1
< 0.1%
48618 1
< 0.1%
48267 1
< 0.1%
48263 1
< 0.1%
48124 1
< 0.1%
47949 1
< 0.1%
47392 1
< 0.1%
47346 1
< 0.1%
47328 1
< 0.1%

units_on_hand_kg
Real number (ℝ)

High correlation 

Distinct7632
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12926.507
Minimum0
Maximum49818
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2025-09-20T18:26:53.675346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile649.6
Q13890
median9805
Q319622
95-th percentile35070
Maximum49818
Range49818
Interquartile range (IQR)15732

Descriptive statistics

Standard deviation10996.125
Coefficient of variation (CV)0.85066482
Kurtosis0.037624223
Mean12926.507
Median Absolute Deviation (MAD)7077
Skewness0.92809867
Sum1.1495543 × 108
Variance1.2091476 × 108
MonotonicityNot monotonic
2025-09-20T18:26:53.879584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
935 5
 
0.1%
661 5
 
0.1%
8589 4
 
< 0.1%
2290 4
 
< 0.1%
502 4
 
< 0.1%
1087 4
 
< 0.1%
9905 4
 
< 0.1%
2452 4
 
< 0.1%
1206 4
 
< 0.1%
3348 4
 
< 0.1%
Other values (7622) 8851
99.5%
ValueCountFrequency (%)
0 2
< 0.1%
3 3
< 0.1%
5 1
 
< 0.1%
6 2
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
14 2
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
49818 1
< 0.1%
48668 1
< 0.1%
48401 1
< 0.1%
48351 1
< 0.1%
48339 1
< 0.1%
48169 1
< 0.1%
48101 1
< 0.1%
48035 1
< 0.1%
47929 1
< 0.1%
47896 1
< 0.1%

supplier
Categorical

High correlation 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size647.2 KiB
Citrus World Inc.
 
492
Dairyland Co-op
 
483
Orchard Lane Farms
 
464
Fresh Dairy Farms
 
462
Organic Meats Co.
 
454
Other values (15)
6538 

Length

Max length26
Median length21
Mean length17.510064
Min length12

Characters and Unicode

Total characters155717
Distinct characters41
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 rowOrganic Meats Co.
2nd rowOrganic Meats Co.
3rd rowBerry Fields
4th rowFresh Dairy Farms
5th rowGolden Orchards

Common Values

ValueCountFrequency (%)
Citrus World Inc. 492
 
5.5%
Dairyland Co-op 483
 
5.4%
Orchard Lane Farms 464
 
5.2%
Fresh Dairy Farms 462
 
5.2%
Organic Meats Co. 454
 
5.1%
Midwest Grains Co. 453
 
5.1%
Fresh Harvest Co. 449
 
5.0%
Golden Orchards 446
 
5.0%
Nature's Best Produce 446
 
5.0%
Ranchers Pride 445
 
5.0%
Other values (10) 4299
48.3%

Length

2025-09-20T18:26:54.063171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
co 1356
 
5.7%
farms 1355
 
5.6%
fresh 1335
 
5.6%
meats 1300
 
5.4%
organic 1299
 
5.4%
dairy 906
 
3.8%
harvest 875
 
3.6%
golden 872
 
3.6%
agriculture 867
 
3.6%
valley 851
 
3.5%
Other values (28) 12982
54.1%

Most occurring characters

ValueCountFrequency (%)
r 17325
 
11.1%
15105
 
9.7%
e 13124
 
8.4%
a 11985
 
7.7%
s 9799
 
6.3%
i 8399
 
5.4%
n 7497
 
4.8%
o 5847
 
3.8%
t 5726
 
3.7%
l 5706
 
3.7%
Other values (31) 55204
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 155717
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 17325
 
11.1%
15105
 
9.7%
e 13124
 
8.4%
a 11985
 
7.7%
s 9799
 
6.3%
i 8399
 
5.4%
n 7497
 
4.8%
o 5847
 
3.8%
t 5726
 
3.7%
l 5706
 
3.7%
Other values (31) 55204
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 155717
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 17325
 
11.1%
15105
 
9.7%
e 13124
 
8.4%
a 11985
 
7.7%
s 9799
 
6.3%
i 8399
 
5.4%
n 7497
 
4.8%
o 5847
 
3.8%
t 5726
 
3.7%
l 5706
 
3.7%
Other values (31) 55204
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 155717
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 17325
 
11.1%
15105
 
9.7%
e 13124
 
8.4%
a 11985
 
7.7%
s 9799
 
6.3%
i 8399
 
5.4%
n 7497
 
4.8%
o 5847
 
3.8%
t 5726
 
3.7%
l 5706
 
3.7%
Other values (31) 55204
35.5%
Distinct8862
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size630.5 KiB
2025-09-20T18:26:54.357869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length26
Mean length15.580457
Min length8

Characters and Unicode

Total characters138557
Distinct characters60
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

Unique8831 ?
Unique (%)99.3%

Sample

1st rowO'Reillyboro, OR
2nd rowLake Nora, AR
3rd rowEast Friedaside, NM
4th rowEast Javon, FL
5th rowLake D'angelo, MN
ValueCountFrequency (%)
lake 506
 
2.3%
south 494
 
2.2%
fort 468
 
2.1%
east 460
 
2.1%
west 450
 
2.0%
new 439
 
2.0%
north 439
 
2.0%
port 409
 
1.9%
nh 206
 
0.9%
sc 203
 
0.9%
Other values (7259) 17882
81.4%
2025-09-20T18:26:54.806288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13063
 
9.4%
e 10630
 
7.7%
, 8893
 
6.4%
a 8129
 
5.9%
r 8077
 
5.8%
t 7640
 
5.5%
o 7518
 
5.4%
n 5731
 
4.1%
i 5412
 
3.9%
l 4819
 
3.5%
Other values (50) 58645
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 138557
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13063
 
9.4%
e 10630
 
7.7%
, 8893
 
6.4%
a 8129
 
5.9%
r 8077
 
5.8%
t 7640
 
5.5%
o 7518
 
5.4%
n 5731
 
4.1%
i 5412
 
3.9%
l 4819
 
3.5%
Other values (50) 58645
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 138557
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13063
 
9.4%
e 10630
 
7.7%
, 8893
 
6.4%
a 8129
 
5.9%
r 8077
 
5.8%
t 7640
 
5.5%
o 7518
 
5.4%
n 5731
 
4.1%
i 5412
 
3.9%
l 4819
 
3.5%
Other values (50) 58645
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 138557
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13063
 
9.4%
e 10630
 
7.7%
, 8893
 
6.4%
a 8129
 
5.9%
r 8077
 
5.8%
t 7640
 
5.5%
o 7518
 
5.4%
n 5731
 
4.1%
i 5412
 
3.9%
l 4819
 
3.5%
Other values (50) 58645
42.3%
Distinct730
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Memory size69.6 KiB
Minimum2022-01-01 00:00:00
Maximum2023-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-20T18:26:55.006639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:55.178063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size529.9 KiB
2022
4454 
2023
4439 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters35572
Distinct characters3
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 row2022
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2022 4454
50.1%
2023 4439
49.9%

Length

2025-09-20T18:26:55.331494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-20T18:26:55.448371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 4454
50.1%
2023 4439
49.9%

Most occurring characters

ValueCountFrequency (%)
2 22240
62.5%
0 8893
 
25.0%
3 4439
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35572
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 22240
62.5%
0 8893
 
25.0%
3 4439
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35572
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 22240
62.5%
0 8893
 
25.0%
3 4439
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35572
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 22240
62.5%
0 8893
 
25.0%
3 4439
 
12.5%

month
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size69.6 KiB

day
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.643765
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2025-09-20T18:26:55.573257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29.4
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7531207
Coefficient of variation (CV)0.55952776
Kurtosis-1.1760833
Mean15.643765
Median Absolute Deviation (MAD)8
Skewness0.020528964
Sum139120
Variance76.617122
MonotonicityNot monotonic
2025-09-20T18:26:55.727776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 338
 
3.8%
17 322
 
3.6%
13 321
 
3.6%
6 315
 
3.5%
21 307
 
3.5%
7 307
 
3.5%
19 304
 
3.4%
15 303
 
3.4%
10 303
 
3.4%
5 299
 
3.4%
Other values (21) 5774
64.9%
ValueCountFrequency (%)
1 292
3.3%
2 283
3.2%
3 287
3.2%
4 291
3.3%
5 299
3.4%
6 315
3.5%
7 307
3.5%
8 295
3.3%
9 280
3.1%
10 303
3.4%
ValueCountFrequency (%)
31 178
2.0%
30 267
3.0%
29 273
3.1%
28 258
2.9%
27 261
2.9%
26 295
3.3%
25 286
3.2%
24 273
3.1%
23 296
3.3%
22 279
3.1%

Sales
Real number (ℝ)

High correlation 

Distinct8873
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46506.475
Minimum0
Maximum624033.78
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2025-09-20T18:26:55.892951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile956.032
Q16682.32
median19691.14
Q354909.18
95-th percentile186860.5
Maximum624033.78
Range624033.78
Interquartile range (IQR)48226.86

Descriptive statistics

Standard deviation69996.142
Coefficient of variation (CV)1.5050838
Kurtosis11.780069
Mean46506.475
Median Absolute Deviation (MAD)16212.4
Skewness3.0409936
Sum4.1358208 × 108
Variance4.8994599 × 109
MonotonicityNot monotonic
2025-09-20T18:26:56.068997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45360 2
 
< 0.1%
1929.84 2
 
< 0.1%
28.38 2
 
< 0.1%
2187.84 2
 
< 0.1%
2288.5 2
 
< 0.1%
1673.28 2
 
< 0.1%
2610.27 2
 
< 0.1%
2252.88 2
 
< 0.1%
2367.42 2
 
< 0.1%
1082.52 2
 
< 0.1%
Other values (8863) 8873
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
13.42 1
< 0.1%
15.48 1
< 0.1%
15.98 1
< 0.1%
18.56 1
< 0.1%
24.38 1
< 0.1%
26.28 1
< 0.1%
27.6 1
< 0.1%
28.38 2
< 0.1%
29.25 1
< 0.1%
ValueCountFrequency (%)
624033.78 1
< 0.1%
617437.3 1
< 0.1%
609006.72 1
< 0.1%
575388 1
< 0.1%
548701.62 1
< 0.1%
525627.69 1
< 0.1%
521278.18 1
< 0.1%
513912 1
< 0.1%
512689.04 1
< 0.1%
505744.84 1
< 0.1%

sales_to_shipment_ratio
Real number (ℝ)

High correlation 

Distinct8891
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49418005
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2025-09-20T18:26:56.242161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.053968688
Q10.24922242
median0.48809936
Q30.73995272
95-th percentile0.94727438
Maximum1
Range1
Interquartile range (IQR)0.4907303

Descriptive statistics

Standard deviation0.28555068
Coefficient of variation (CV)0.57782722
Kurtosis-1.1884831
Mean0.49418005
Median Absolute Deviation (MAD)0.24560505
Skewness0.03462041
Sum4394.7432
Variance0.081539193
MonotonicityNot monotonic
2025-09-20T18:26:56.421941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2
 
< 0.1%
0.1839622642 2
 
< 0.1%
0.5254707132 1
 
< 0.1%
0.7419945879 1
 
< 0.1%
0.3850308642 1
 
< 0.1%
0.53329791 1
 
< 0.1%
0.7067765709 1
 
< 0.1%
0.4061856158 1
 
< 0.1%
0.03084688727 1
 
< 0.1%
0.9555860806 1
 
< 0.1%
Other values (8881) 8881
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
0.0007499062617 1
< 0.1%
0.0009411272613 1
< 0.1%
0.001047394606 1
< 0.1%
0.001208702659 1
< 0.1%
0.001333218648 1
< 0.1%
0.001363255034 1
< 0.1%
0.001387390165 1
< 0.1%
0.001419686317 1
< 0.1%
0.001696065129 1
< 0.1%
ValueCountFrequency (%)
1 2
< 0.1%
0.9999239968 1
< 0.1%
0.9998210664 1
< 0.1%
0.9997298132 1
< 0.1%
0.9995487365 1
< 0.1%
0.9994681558 1
< 0.1%
0.9991992932 1
< 0.1%
0.9990557377 1
< 0.1%
0.9990482234 1
< 0.1%
0.9990364099 1
< 0.1%

demand_gap
Real number (ℝ)

High correlation 

Distinct7632
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12926.507
Minimum0
Maximum49818
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2025-09-20T18:26:56.588130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile649.6
Q13890
median9805
Q319622
95-th percentile35070
Maximum49818
Range49818
Interquartile range (IQR)15732

Descriptive statistics

Standard deviation10996.125
Coefficient of variation (CV)0.85066482
Kurtosis0.037624223
Mean12926.507
Median Absolute Deviation (MAD)7077
Skewness0.92809867
Sum1.1495543 × 108
Variance1.2091476 × 108
MonotonicityNot monotonic
2025-09-20T18:26:56.766466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
935 5
 
0.1%
661 5
 
0.1%
8589 4
 
< 0.1%
2290 4
 
< 0.1%
502 4
 
< 0.1%
1087 4
 
< 0.1%
9905 4
 
< 0.1%
2452 4
 
< 0.1%
1206 4
 
< 0.1%
3348 4
 
< 0.1%
Other values (7622) 8851
99.5%
ValueCountFrequency (%)
0 2
< 0.1%
3 3
< 0.1%
5 1
 
< 0.1%
6 2
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
14 2
< 0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
49818 1
< 0.1%
48668 1
< 0.1%
48401 1
< 0.1%
48351 1
< 0.1%
48339 1
< 0.1%
48169 1
< 0.1%
48101 1
< 0.1%
48035 1
< 0.1%
47929 1
< 0.1%
47896 1
< 0.1%

Interactions

2025-09-20T18:26:50.295688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:43.365087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:44.389229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:45.459181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:46.375106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:47.291928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:48.218854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:49.363952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:50.416852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:43.488911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:44.513771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:45.577711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:46.497819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:47.409052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:48.505791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:49.488673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:50.525896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:43.605401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:44.756808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:45.688890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:46.610701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:47.517085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:48.624693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:49.600761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:50.635797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:43.739425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:44.868587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:45.798057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:46.726072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:47.627516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:48.756578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:49.714262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:50.748009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:43.916854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:44.982655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:45.907439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:46.842508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:47.740670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:48.882811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:49.830217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:50.861377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:44.035408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:45.108003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:46.028559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:46.957035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:47.851671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:49.012761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:49.951449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:50.985202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:44.153466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:45.230127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:46.149245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:47.072072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:47.987284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:49.127970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:50.074095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:51.102604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:44.271344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:45.346935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:46.263332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:47.184441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:48.108437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:49.244291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2025-09-20T18:26:50.185942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2025-09-20T18:26:56.891598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SalesYearcategorydaydemand_gapprice_per_kgproduct_idproduct_namesales_to_shipment_ratiosupplierunits_on_hand_kgunits_shipped_kgunits_sold_kg
Sales1.0000.0000.2470.0040.0060.6440.2240.2240.5140.1640.0060.4750.743
Year0.0001.0000.0100.0080.0120.0250.0560.0560.0050.0050.0120.0170.000
category0.2470.0101.0000.0210.0120.5170.9990.9990.0000.9990.0120.0000.022
day0.0040.0080.0211.000-0.0010.0130.0160.016-0.0060.015-0.001-0.001-0.004
demand_gap0.0060.0120.012-0.0011.000-0.0050.0060.006-0.6770.0001.0000.668-0.015
price_per_kg0.6440.0250.5170.013-0.0051.0000.6470.6470.0200.344-0.0050.0120.019
product_id0.2240.0560.9990.0160.0060.6471.0001.0000.0000.4570.0060.0000.022
product_name0.2240.0560.9990.0160.0060.6471.0001.0000.0000.4570.0060.0000.022
sales_to_shipment_ratio0.5140.0050.000-0.006-0.6770.0200.0000.0001.0000.012-0.677-0.0100.682
supplier0.1640.0050.9990.0150.0000.3440.4570.4570.0121.0000.0000.0150.000
units_on_hand_kg0.0060.0120.012-0.0011.000-0.0050.0060.006-0.6770.0001.0000.668-0.015
units_shipped_kg0.4750.0170.000-0.0010.6680.0120.0000.000-0.0100.0150.6681.0000.649
units_sold_kg0.7430.0000.022-0.004-0.0150.0190.0220.0220.6820.000-0.0150.6491.000

Missing values

2025-09-20T18:26:51.273726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-20T18:26:51.553052image/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

product_idproduct_namecategoryprice_per_kgunits_shipped_kgunits_sold_kgunits_on_hand_kgsupplierfarm_locationsale_dateYearmonthdaySalessales_to_shipment_ratiodemand_gap
0b12c721e-8301-4b08-8ac3-d435be9b8b32LambLivestock14.1019224.014905.04319.0Organic Meats Co.O'Reillyboro, OR2022-07-0420222022-074210160.500.7753334319
14a0f8862-c851-4073-bc17-3a3d93834902BeefLivestock11.3729504.04550.024954.0Organic Meats Co.Lake Nora, AR2023-10-2120232023-102151733.500.15421624954
2be996df6-1780-4352-8b7d-1f927279aa49OrangesFruits2.591838.01722.0116.0Berry FieldsEast Friedaside, NM2023-12-2420232023-12244459.980.936888116
36c8adfc3-2114-4b78-a3e2-2269a6a0dc02MilkDairy1.346750.01232.05518.0Fresh Dairy FarmsEast Javon, FL2023-12-2520232023-12251650.880.1825195518
40bd5bf2b-be0a-4ba0-aa92-059021b169e3BananasFruits1.7848729.047020.01709.0Golden OrchardsLake D'angelo, MN2023-06-2020232023-062083695.600.9649281709
51c48eb53-85bc-4b76-92ef-97d240a8a227PotatoesVegetables0.9222600.011477.011123.0Nature's Best ProduceDanielville, FL2023-01-2220232023-012210558.840.50783211123
61c48eb53-85bc-4b76-92ef-97d240a8a227PotatoesVegetables0.8614543.05208.09335.0Fresh Harvest Co.Bridgettown, IA2023-09-0620232023-0964478.880.3581109335
73b87981b-375b-494a-9b11-00bf7144143aPorkLivestock7.088442.07570.0872.0Ranchers PrideShainafurt, PA2022-11-1420222022-111453595.600.896707872
8921530a1-c4d1-4e94-b8ad-11f6fbcc4fcaRiceGrains0.9923374.07546.015828.0Midwest Grains Co.New Bruceton, AL2023-12-1220232023-12127470.540.32283715828
96c8adfc3-2114-4b78-a3e2-2269a6a0dc02MilkDairy1.0941999.036611.05388.0Happy Cows DairyEffertzchester, CO2022-01-0220222022-01239905.990.8717115388
product_idproduct_namecategoryprice_per_kgunits_shipped_kgunits_sold_kgunits_on_hand_kgsupplierfarm_locationsale_dateYearmonthdaySalessales_to_shipment_ratiodemand_gap
88839bb2d8db-a7ac-4d40-9d3c-0c96cafe7153ButterDairy4.7639681.031413.08268.0Organic ValleyNew Kailynville, WY2023-05-0220232023-052149525.880.7916388268
8884c0e9f888-f1d2-4b5b-a6e9-3c349f68aed0BarleyGrains0.8011179.07739.03440.0Farmers Grain UnionBorerworth, WA2022-08-2320222022-08236191.200.6922803440
8885403d5331-41ba-4a1a-86d2-e1599b965c1bStrawberriesFruits5.9537915.02729.035186.0Golden OrchardsPredovicstad, AZ2022-08-1720222022-081716237.550.07197735186
88861c48eb53-85bc-4b76-92ef-97d240a8a227PotatoesVegetables0.7249145.02636.046509.0Nature's Best ProduceReillyboro, OR2022-06-0220222022-0621897.920.05363746509
88879bb2d8db-a7ac-4d40-9d3c-0c96cafe7153ButterDairy5.0720572.02561.018011.0Organic ValleyGuaynabo, DE2022-01-0420222022-01412984.270.12449018011
8888c81f400e-c19f-417f-99fb-039a19506e85OnionsVegetables0.8510543.01330.09213.0Fresh Harvest Co.Elyria, MS2022-12-3020222022-12301130.500.1261509213
88896c8adfc3-2114-4b78-a3e2-2269a6a0dc02MilkDairy1.3628336.018644.09692.0Organic ValleySanta Cruz, ND2023-07-2020232023-072025355.840.6579629692
88906c8adfc3-2114-4b78-a3e2-2269a6a0dc02MilkDairy1.2034945.06793.028152.0Organic ValleyCleveland, MN2022-05-2320222022-05238151.600.19439128152
8891857e56ee-a962-479e-99f0-983999e858bdCabbageVegetables0.7526470.012316.014154.0Nature's Best ProduceWaelchiberg, MN2023-01-0620232023-0169237.000.46528114154
88920985f295-bb82-484f-9cab-76637e8e8bdbChickenLivestock3.4227434.010750.016684.0Country Fresh MeatsSouthfield, CT2022-06-2620222022-062636765.000.39185016684