Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
sample() is used to generate a sample random row or column from the function caller data frame.
DataFrame.sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)
n: int value, Number of random rows to generate.
frac: Float value, Returns (float value * length of data frame values ). frac cannot be used with n.
replace: Boolean value, return sample with replacement if True.
random_state: int value or numpy.random.RandomState, optional. if set to a particular integer, will return same rows as sample in every iteration.
axis: 0 or ‘row’ for Rows and 1 or ‘column’ for Columns.
Return type: New object of same type as caller.
To download the CSV file used, Click Here.
Example #1: Random row from Data frame
In this example, two random rows are generated by the .sample() method and compared later.
As shown in the output image, the two random sample rows generated are different from each other.
Example #2: Generating 25% sample of data frame
In this example, 25% random sample data is generated out of the Data frame.
As shown in the output image, the length of sample generated is 25% of data frame. Also the sample is generated randomly.
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