Pandas Series: sample() function

Last update on September 15 2022 12:55:32 (UTC/GMT +8 hours)

Random items from an axis of Pandas object

The sample() function is used to get a random sample of items from an axis of object.

Syntax:

Series.sample(self, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)

Parameters:

Name	Description	Type/Default Value	Required / Optional
n	Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.	int	optional
frac	Fraction of axis items to return. Cannot be used with n.	float	optional
replace	Sample with or without replacement.	bool Default Value: False	Required
weights	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.	str or ndarray-like	optional
random_state	Seed for the random number generator (if int), or numpy RandomState object.	int or numpy.random.RandomState,	optional
axis	Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames).	int or string	optional

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

Example - Generate a DataFrame with default index:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
                   'num_wings': [2, 0, 0, 0],
                   'num_specimen_seen': [8, 2, 1, 6]},
                  index=['sparrow', 'cat', 'spider', 'snake'])
df

Output:

       num_legs	num_wings	num_specimen_seen
sparrow	    2	    2	     8
cat	        4	    0	     2
spider	    8	    0	     1
snake	       0	    0	     6

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

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
                   'num_wings': [2, 0, 0, 0],
                   'num_specimen_seen': [8, 2, 1, 6]},
                  index=['sparrow', 'cat', 'spider', 'snake'])
df['num_legs'].sample(n=3, random_state=1)

Output:

snake      0
spider     8
sparrow    2
Name: num_legs, dtype: int64

Example - A random 50% sample of the DataFrame with replacement:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
                   'num_wings': [2, 0, 0, 0],
                   'num_specimen_seen': [8, 2, 1, 6]},
                  index=['sparrow', 'cat', 'spider', 'snake'])
df.sample(frac=0.5, replace=True, random_state=1)

Output:

              num_legs	num_wings	num_specimen_seen
cat	            4	         0    	    2
snake	        0	         0	        6

Example - Using a DataFrame column as weights. Rows with larger value in the num_specimen_seen column are more likely to be sampled:

Python-Pandas Code:

import numpy as np
import pandas as pd
df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
                   'num_wings': [2, 0, 0, 0],
                   'num_specimen_seen': [8, 2, 1, 6]},
                  index=['sparrow', 'cat', 'spider', 'snake'])
df.sample(n=2, weights='num_specimen_seen', random_state=1)

Output:

                   num_legs	num_wings	num_specimen_seen
sparrow	           2	       2	        8
snake	              0	       0	        6

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