PJUllrich

Big-O Time Complexities for Elixir data structures

Map [1]

Operation	Time Complexity
Access	O(log n)
Search	O(log n)
Insertion	O(n) for <= 32 elements, O(log n) for > 32 elements [2]
Deletion	O(n) for <= 32 elements, O(log n) for > 32 elements

justinhchae

# given a dataframe 

def reduce_precision(df):
        import numpy as np
        """
        usage: give a dataframe, this fuction returns an optimized dataframe
        
        df = reduce_precision(df)
        
        reference: https://gist.github.com/enamoria/fa9baa906f23d1636c002e7186516a7b

enamoria

# This function is used to reduce memory of a pandas dataframe
# The idea is cast the numeric type to another more memory-effective type
# For ex: Features "age" should only need type='np.int8'
# Source: https://www.kaggle.com/gemartin/load-data-reduce-memory-usage
def reduce_mem_usage(df):
    """ iterate through all the columns of a dataframe and modify the data type
        to reduce memory usage.        
    """
    start_mem = df.memory_usage().sum() / 1024**2
    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))