pandas.DataFrame.sample#

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

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

You can userandom_state for reproducibility.

Parameters:

nint, optional

Number of items from axis to return. Cannot be used withfrac.Default = 1 iffrac = None.

fracfloat, optional

Fraction of axis items to return. Cannot be used withn.

replacebool, default False

Allow or disallow sampling of the same row more than once.

weightsstr or ndarray-like, optional

Default ‘None’ results in equal probability weighting.If passed a Series, will align with target object on index. Indexvalues in weights not found in sampled object will be ignored andindex values in sampled object not in weights will be assignedweights of zero.If called on a DataFrame, will accept the name of a columnwhen axis = 0.Unless weights are a Series, weights must be same length as axisbeing 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_stateint, array-like, BitGenerator, np.random.RandomState, np.random.Generator, optional

If int, array-like, or BitGenerator, seed for random number generator.If np.random.RandomState or np.random.Generator, use as given.

Changed in version 1.4.0:np.random.Generator objects now accepted

axis{0 or ‘index’, 1 or ‘columns’, None}, default None

Axis to sample. Accepts axis number or name. Default is stat axisfor given data type. ForSeries this parameter is unused and defaults toNone.

ignore_indexbool, default False

If True, the resulting index will be labeled 0, 1, …, n - 1.

Added in version 1.3.0.

Returns:

Series or DataFrame

A new object of same type as caller containingn items randomlysampled from the caller object.

See also

DataFrameGroupBy.sample

Generates random samples from each group of a DataFrame object.

SeriesGroupBy.sample

Generates random samples from each group of a Series object.

numpy.random.choice

Generates a random sample from a given 1-D numpy array.

Notes

Iffrac > 1,replacement should be set toTrue.

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_seenfalcon         2          2                 10dog            4          0                  2spider         8          0                  1fish           0          0                  8

Extract 3 random elements from theSeriesdf['num_legs']:Note that we userandom_state to ensure the reproducibility ofthe examples.

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

A random 50% sample of theDataFrame with replacement:

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

An upsample sample of theDataFrame with replacement:Note thatreplace parameter has to beTrue forfrac parameter > 1.

>>>df.sample(frac=2,replace=True,random_state=1)        num_legs  num_wings  num_specimen_seendog            4          0                  2fish           0          0                  8falcon         2          2                 10falcon         2          2                 10fish           0          0                  8dog            4          0                  2fish           0          0                  8dog            4          0                  2

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_seenfalcon         2          2                 10fish           0          0                  8