Sometimes you need some realistic looking data. This can be useful when testing an app or design. This could also be useful for showing examples such as test API endpoints or. You can also use this data to test data visualizations.
Create a table of mock sales data for a retail shoe company
Sure! Here's a table of mock sales data for a retail shoe company:
Date | Product | Quantity Sold | Price per Unit | Total Sales |
---|---|---|---|---|
2023-01-01 | Sneakers | 50 | $50 | $2,500 |
2023-01-02 | Sandals | 30 | $30 | $900 |
2023-01-03 | Boots | 20 | $80 | $1,600 |
2023-01-04 | Heels | 40 | $60 | $2,400 |
2023-01-05 | Sneakers | 25 | $50 | $1,250 |
2023-01-06 | Loafers | 15 | $70 | $1,050 |
2023-01-07 | Sandals | 35 | $30 | $1,050 |
2023-01-08 | Sneakers | 45 | $50 | $2,250 |
2023-01-09 | Boots | 20 | $80 | $1,600 |
We can change the output format as well. I typically use JSON for software I'm writing and csv when I want to import the data into excel.
Format the table as csv. Make sure to escape any special characters.
Here's the table formatted as a CSV file with special characters escaped:
Date,Product,Quantity Sold,Price per Unit,Total Sales
2023-01-01,Sneakers,50,"$50","$2,500"
2023-01-02,Sandals,30,"$30","$900"
2023-01-03,Boots,20,"$80","$1,600"
2023-01-04,Heels,40,"$60","$2,400"
2023-01-05,Sneakers,25,"$50","$1,250"
2023-01-06,Loafers,15,"$70","$1,050"
2023-01-07,Sandals,35,"$30","$1,050"
2023-01-08,Sneakers,45,"$50","$2,250"
2023-01-09,Boots,20,"$80","$1,600"
2023-01-10,Heels,30,"$60","$1,800"
2023-01-11,Sneakers,40,"$50","$2,000"
2023-01-12,Loafers,20,"$70","$1,400"
2023-01-13,Sandals,25,"$30","$750"
2023-01-14,Sneakers,35,"$50","$1,750"
2023-01-15,Boots,15,"$80","$1,200"
2023-01-16,Heels,25,"$60","$1,500"
2023-01-17,Sneakers,30,"$50","$1,500"
2023-01-18,Loafers,10,"$70","$700"
2023-01-19,Sandals,20,"$30","$600"
2023-01-20,Boots,40,"$80","$3,200"
Now JSON
[
{
"Date": "2023-01-01",
"Product": "Sneakers",
"Quantity Sold": 50,
"Price per Unit": "$50",
"Total Sales": "$2,500"
},
{
"Date": "2023-01-02",
"Product": "Sandals",
"Quantity Sold": 30,
"Price per Unit": "$30",
"Total Sales": "$900"
},
{
"Date": "2023-01-03",
"Product": "Boots",
"Quantity Sold": 20,
"Price per Unit": "$80",
"Total Sales": "$1,600"
},
{
"Date": "2023-01-04",
"Product": "Heels",
"Quantity Sold": 40,
"Price per Unit": "$60",
"Total Sales": "$2,400"
},
{
"Date": "2023-01-05",
"Product": "Sneakers",
"Quantity Sold": 25,
"Price per Unit": "$50",
"Total Sales": "$1,250"
},
{
"Date": "2023-01-06",
"Product": "Loafers",
"Quantity Sold": 15,
"Price per Unit": "$70",
"Total Sales": "$1,050"
},
{
"Date": "2023-01-07",
"Product": "Sandals",
"Quantity Sold": 35,
"Price per Unit": "$30",
"Total Sales": "$1,050"
},
...
]
We can ask for specific columns as well.
Create a table of mock daily weather data. Include columns for temperature, humidity, precipitation, and other important weather data.
Sure! Here's a table of mock daily weather data for a specific location:
Date | Temperature (°C) | Humidity (%) | Precipitation (mm) | Wind Speed (km/h) | Cloud Cover |
---|---|---|---|---|---|
2023-05-01 | 15 | 70 | 0.0 | 10 | 30% |
2023-05-02 | 18 | 65 | 0.0 | 12 | 20% |
2023-05-03 | 21 | 60 | 0.0 | 15 | 10% |
2023-05-04 | 24 | 55 | 0.0 | 18 | 5% |
2023-05-05 | 26 | 50 | 0.0 | 20 | 5% |
2023-05-06 | 28 | 45 | 0.0 | 22 | 5% |
2023-05-07 | 30 | 40 | 0.0 | 25 | 5% |