14. Python - Pandas - Summary Functions & Maps

 

Summary Methods:

data_frame.describe()

data_frame.COLUMNA.mean() // return mean

data_frame.COLUMNA.median() // return median

data_frame.COLUMNA.unique() // returns only unque values

data_frame.COLUMNA.value_counts() // returns value and their count

data_frame.COLUMNA.idxmax() // returns max

data_frame.COLUMNA.sum()


Maps:

data_frame_COLUMNA_mean = data_frame.COLUMNA.mean()

// finds the mean of the column

data_frame.COLUMNA.map(lambda p: p - data_frame_COLUMNA_mean)

// creates a map from COLUMNA. The values of the keys per index will be determined with the lambda function. Lamdba function returns result of COLUMNA's value minus the mean.

// lambda is anonymous function.


data_frame_mean = data_frame.COLUMNA.mean()

data_frame.COLUMNA - data_frame_mean

// returns same result as the two earlier map steps.


data_frame.COLUMNA + "SPACE" + data_frame.COLUMNB

// returns a new map with values that combine COLUMNA and COLUMNB with a string "SPACE" in between


Apply Function: // creates a new series or small data frame of 1 column

def remean_points(row):

    row.points = row.points - review_points_mean

    return row

// this is a new function that takes a row value from data_frame.points and subtract it by the mean and returning the new value


data_frame.apply(remean_points, axis='columns')

// apply method applies the function to every value in the column used in the function

// returns a new whole data_frame with new values for that column



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