Python | Pandas Series.sample ()

Series.sample() Pandas Series.sample() returns a random sample of items from the object axis. 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 () to draw a random sample of values ​​from a given Series object.

< p> # import pandas as pd import pandas as pd   # Create a series sr = pd.Series ([ ’New York’ , ’ Chicago’ , ’Toronto’ , ’ Lisbon’ , ’Rio’ , ’ Moscow’ ])   # Create date index and time index_ = [ ’City 1’ , ’ City 2’ , ’City 3’ , ’City 4’ , ’ City 5’ , ’City 6’ ]   # set index sr.index = index_   # Print series print (sr)

Output:

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

# Draw a 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, Series.sample () successfully returned a random sample of 3 values ​​from the given Series object.

Example # 2: Use Series.sample () to draw a random you a collection of values ​​from this Series object.

# import pandas as pd import pandas as pd   # Create Series sr = pd .Series ([ 100 , 25 , 32 , 118 , 24 , 65 ])   # Create index index_ = [ ’ Coca Cola’ , ’Sprite’ , ’ Coke’ , ’Fanta’ , ’ Dew’ , ’ThumbsUp’ ]   # set index sr.index = index_   # Print series print (sr)

Output:

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

# Draw a random sample size 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, Series.sample () successfully returned a random selection 2 values ​​from this Series object, which is 25% of the size of the original series object.