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Mastering flat_map in Python with List Comprehension

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Introduction In Python, when working with nested lists or iterables, one common challenge is flattening them into a single list while applying transformations. Many programming languages provide a built-in flatMap function, but Python does not have an explicit flat_map method. However, Python’s powerful list comprehensions offer an elegant way to achieve the same functionality. This article examines implementation behavior using Python’s list comprehensions and other methods. What is flat_map ? Functional programming  flatMap is a combination of map and flatten . It transforms the collection's element and flattens the resulting nested structure into a single sequence. For example, given a list of lists, flat_map applies a function to each sublist and returns a single flattened list. Example in a Functional Programming Language: List(List(1, 2), List(3, 4)).flatMap(x => x.map(_ * 2)) // Output: List(2, 4, 6, 8) Implementing flat_map in Python Using List Comprehension Python’...

How to Fill Nulls in Pandas: bfill and ffill

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In Pandas, bfill and ffill are two important methods used for filling missing values in a DataFrame or Series by propagating the previous (forward fill) or next (backward fill) valid values respectively. These methods are particularly useful when dealing with time series data or other ordered data where missing values need to be filled based on the available adjacent values. ffill (forward fill): When you use the ffill method on a DataFrame or Series, it fills missing values with the previous non-null value in the same column. It propagates the last known value forward. This method is often used to carry forward the last observed value for a specific column, making it a good choice for time series data when the assumption is that the value doesn't change abruptly. Example: import pandas as pd data = {'A': [1, 2, None, 4, None, 6],         'B': [None, 'X', 'Y', None, 'Z', 'W']} df = pd.DataFrame(data) print(df) # Output: #      A     B...