Python | Pandas Series.sample ()

NumPy | Python Methods and Functions

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.





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