Python | Pandas Series.sample()
Last Updated :
07 Feb, 2019
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.
import pandas as pd
sr = pd.Series([ 'New York' , 'Chicago' , 'Toronto' , 'Lisbon' , 'Rio' , 'Moscow' ])
index_ = [ 'City 1' , 'City 2' , 'City 3' , 'City 4' , 'City 5' , 'City 6' ]
sr.index = index_
print (sr)
|
Output :
Now we will use Series.sample()
function to draw a random sample of values from the given Series object.
selected_cities = sr.sample(n = 3 )
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.
import pandas as pd
sr = pd.Series([ 100 , 25 , 32 , 118 , 24 , 65 ])
index_ = [ 'Coca Cola' , 'Sprite' , 'Coke' , 'Fanta' , 'Dew' , 'ThumbsUp' ]
sr.index = index_
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.
selected_items = sr.sample(frac = 0.25 )
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.
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