Python | Панды Series.sample ()
Серия Pandas - это одномерный массив ndarray с метками осей. Этикетки не обязательно должны быть уникальными, но должны быть хешируемого типа. Объект поддерживает индексирование как на основе целых чисел, так и на основе меток и предоставляет множество методов для выполнения операций, связанных с индексом.
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 pdimport pandas as pd # Creating the Seriessr = pd.Series(["New York", "Chicago", "Toronto", "Lisbon", "Rio", "Moscow"]) # Create the Datetime Indexindex_ = ["City 1", "City 2", "City 3", "City 4", "City 5", "City 6"] # set the indexsr.index = index_ # Print the seriesprint(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 valuesselected_cities = sr.sample(n = 3) # Print the returned Series objectprint(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 pdimport pandas as pd # Creating the Seriessr = pd.Series([100, 25, 32, 118, 24, 65]) # Create the Indexindex_ = ["Coca Cola", "Sprite", "Coke", "Fanta", "Dew", "ThumbsUp"] # set the indexsr.index = index_ # Print the seriesprint(sr) |
Выход :

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 objectselected_items = sr.sample(frac = 0.25) # Print the returned Series objectprint(selected_items) |
Выход :

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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