Python | Pandas Series.sample()

Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.sample() function return a random sample of items from an axis of object. We can also use random_state for reproducibility.

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

Parameter :
n : Number of items from axis to return.
frac : Fraction of axis items to return.
replace : Sample with or without replacement.
weights : Default ‘None’ results in equal probability weighting.
random_state : Seed for the random number generator (if int), or numpy RandomState object.
axis : Axis to sample.

Returns : Series or DataFrame

Example #1: Use Series.sample() function to draw random sample of the values from the given Series object.

# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio', 'Moscow'])

# Create the Datetime Index
index_ = ['City 1', 'City 2', 'City 3', 'City 4', 'City 5', 'City 6']

# set the index
sr.index = index_

# Print the series
print(sr)

Output :

Now we will use Series.sample() function to draw a random sample of values from the given Series object.

# Draw random sample of 3 values
selected_cities = sr.sample(n = 3)

# Print the returned Series object
print(selected_cities)

Output :

As we can see in the output, the Series.sample() function has successfully returned a random sample of 3 values from the given Series object.

Example #2: Use Series.sample() function to draw random sample of the values from the given Series object.

# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series([100, 25, 32, 118, 24, 65])

# Create the Index
index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']

# set the index
sr.index = index_

# Print the series
print(sr)

Output :

Now we will use Series.sample() function to select a random sample of size equivalent to 25% of the size of the given Series object.

# Draw random sample of size of 25 % of the original object
selected_items = sr.sample(frac = 0.25)

# Print the returned Series object
print(selected_items)

Output :

As we can see in the output, the Series.sample() function has successfully returned a random sample of 2 values from the given Series object, which is 25% of the size of the original series object.