Specify how many rows of data you want to generate.
Configure your dataset columns and their data generation properties.
| Column Name | Type | Options | Actions |
|---|---|---|---|
| id | Number | min: 1, max: 100, decimals: 0 | |
| name | Text | minLength: 5, maxLength: 10, casing: title | |
| date | Date | startDate: 2023-01-01, endDate: 2025-12-31, format: iso |
Random data generation is the process of creating synthetic datasets with characteristics you specify. These datasets can be used for:
While randomly generated data is great for testing and prototyping, remember that it may not exhibit the same statistical properties, correlations, or edge cases as real-world data.
For machine learning or statistical analysis, consider augmenting random data with realistic constraints or using data simulation techniques that preserve important statistical properties.
Generates random text strings. You can control the length, casing (uppercase, lowercase, title case), and add prefixes or suffixes.
Generates random numerical values. You can set minimum and maximum values and specify the number of decimal places.
Generates random dates within a specified range. You can choose different date formats (ISO, US, EU, or full date strings).
Generates true/false values. You can adjust the probability of true values and choose different formats (true/false, yes/no, 1/0).
Generates values from a predefined list of categories. You can specify the categories and optionally assign different weights to control their frequency in the generated data.
After generating your random dataset, you can:
If you need to generate data programmatically, here's an example using Python:
import pandas as pd
import numpy as np
import random
from datetime import datetime, timedelta
# Generate random dataset
def generate_random_dataset(rows=100):
# Date range
start_date = datetime(2023, 1, 1)
end_date = datetime(2025, 12, 31)
date_range = (end_date - start_date).days
# Categories
regions = ['North', 'South', 'East', 'West']
products = ['Widgets', 'Gadgets', 'Tools', 'Supplies']
# Generate data
data = {
'id': list(range(1, rows + 1)),
'date': [start_date + timedelta(days=random.randint(0, date_range)) for _ in range(rows)],
'region': [random.choice(regions) for _ in range(rows)],
'product': [random.choice(products) for _ in range(rows)],
'sales': [round(random.uniform(100, 5000), 2) for _ in range(rows)],
'units': [random.randint(1, 100) for _ in range(rows)],
'is_promotion': [random.choice([True, False]) for _ in range(rows)]
}
# Create DataFrame
df = pd.DataFrame(data)
return df
# Generate and export
df = generate_random_dataset(1000)
df.to_csv('random_dataset.csv', index=False)
print(f"Generated dataset with {len(df)} rows")