sample(self: 'NDFrameT', n: 'int | None' = None, frac: 'float | None' = None, replace: 'bool_t' = False, weights=None, random_state: 'RandomState | None' = None, axis: 'Axis | None' = None, ignore_index: 'bool_t' = False) -> 'NDFrameT'
You can use random_state
for reproducibility.
If frac
> 1, :None:None:`replacement` should be set to :None:None:`True`.
Number of items from axis to return. Cannot be used with frac
. Default = 1 if frac
= None.
Fraction of axis items to return. Cannot be used with n.
Allow or disallow sampling of the same row more than once.
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.
If int, array-like, or BitGenerator, seed for random number generator. If np.random.RandomState or np.random.Generator, use as given.
array-like and BitGenerator object now passed to np.random.RandomState() as seed
np.random.Generator objects now accepted
Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames).
If True, the resulting index will be labeled 0, 1, …, n - 1.
A new object of same type as caller containing n items randomly sampled from the caller object.
Return a random sample of items from an axis of object.
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.
>>> 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
An upsample sample of the DataFrame
with replacement: Note that :None:None:`replace` parameter has to be :None:None:`True` for frac
parameter > 1.
>>> df.sample(frac=2, replace=True, random_state=1) num_legs num_wings num_specimen_seen dog 4 0 2 fish 0 0 8 falcon 2 2 10 falcon 2 2 10 fish 0 0 8 dog 4 0 2 fish 0 0 8 dog 4 0 2
Using a DataFrame column as weights. Rows with larger value in the :None:None:`num_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 8See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.sample.process_sampling_size
pandas.core.sample.preprocess_weights
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