pandas.DataFrame.sample — pandas 0.24.0rc1 documentation (original) (raw)

DataFrame. sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)[source]¶

Return a random sample of items from an axis of object.

You can use random_state for reproducibility.

Parameters:	n : int, optional Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None. frac : float, optional Fraction of axis items to return. Cannot be used with n. replace : bool, default False Sample with or without replacement. weights : str or ndarray-like, optional Default ‘None’ results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed. random_state : int or numpy.random.RandomState, optional Seed for the random number generator (if int), or numpy RandomState object. axis : int or string, optional Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames, 1 for Panels).
Returns:	Series or DataFrame A new object of same type as caller containing n items randomly sampled from the caller object.

Parameters:

n : int, optional Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None. frac : float, optional Fraction of axis items to return. Cannot be used with n. replace : bool, default False Sample with or without replacement. weights : str or ndarray-like, optional Default ‘None’ results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed. random_state : int or numpy.random.RandomState, optional Seed for the random number generator (if int), or numpy RandomState object. axis : int or string, optional Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames, 1 for Panels).

Returns:

Series or DataFrame A new object of same type as caller containing n items randomly sampled from the caller object.

Examples

df = pd.DataFrame({'num_legs': [2, 4, 8, 0], ... 'num_wings': [2, 0, 0, 0], ... 'num_specimen_seen': [10, 2, 1, 8]}, ... index=['falcon', 'dog', 'spider', 'fish']) df num_legs num_wings num_specimen_seen falcon 2 2 10 dog 4 0 2 spider 8 0 1 fish 0 0 8

Extract 3 random elements from the Series df['num_legs']: Note that we use random_state to ensure the reproducibility of the examples.

df['num_legs'].sample(n=3, random_state=1) fish 0 spider 8 falcon 2 Name: num_legs, dtype: int64

A random 50% sample of the DataFrame with replacement:

df.sample(frac=0.5, replace=True, random_state=1) num_legs num_wings num_specimen_seen dog 4 0 2 fish 0 0 8

Using a DataFrame column as weights. Rows with larger value in thenum_specimen_seen column are more likely to be sampled.

df.sample(n=2, weights='num_specimen_seen', random_state=1) num_legs num_wings num_specimen_seen falcon 2 2 10 fish 0 0 8