This tool is built with pandas.DataFrame.sample, numpy.random.choice, and numpy.random.randint functions to help you sample data from a dataset. You can select and configure a sampling method to generate a sample from your data. The tool provides summary statistics and visual comparisons between the original and sampled data. You can also export the sampled data to a CSV file.
Upload your dataset or use our sample data
Statistical sampling is a process of selecting a subset of individuals from a population to estimate characteristics of the whole population. It's widely used in research, quality control, auditing, and data science when analysis of an entire population is impractical. Here is a visual guide to common sampling methods:
Each element in the population has an equal chance of selection.
Population divided into distinct groups (strata), then samples taken from each stratum.
Elements selected at regular intervals after a random start.
Population divided into clusters, and entire clusters are randomly selected.
Cluster sampling selects entire groups (clusters) rather than individual elements:
Clusters are randomly selected, then elements are sampled within each selected cluster. Basically, simple random sampling after cluster sampling.
Elements selected with probability proportional to a weight value.
Sampling with replacement to create multiple resamples from the original dataset.
This tool is built with pandas.DataFrame.sample, numpy.random.choice, and numpy.random.randint functions to help you sample data from a dataset. You can select and configure a sampling method to generate a sample from your data. The tool provides summary statistics and visual comparisons between the original and sampled data. You can also export the sampled data to a CSV file.
Upload your dataset or use our sample data
Statistical sampling is a process of selecting a subset of individuals from a population to estimate characteristics of the whole population. It's widely used in research, quality control, auditing, and data science when analysis of an entire population is impractical. Here is a visual guide to common sampling methods:
Each element in the population has an equal chance of selection.
Population divided into distinct groups (strata), then samples taken from each stratum.
Elements selected at regular intervals after a random start.
Population divided into clusters, and entire clusters are randomly selected.
Cluster sampling selects entire groups (clusters) rather than individual elements:
Clusters are randomly selected, then elements are sampled within each selected cluster. Basically, simple random sampling after cluster sampling.
Elements selected with probability proportional to a weight value.
Sampling with replacement to create multiple resamples from the original dataset.
