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Python: Built-in Functions vs. For & If Loops – 5 Programs Explained

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Python’s built-in functions make coding fast and efficient. But understanding how they work under the hood is crucial to mastering Python. This post shows five Python tasks, each implemented in two ways: Using built-in functions Using for loops and if statements ✅ 1. Sum of a List ✅ Using Built-in Function: numbers = [ 10 , 20 , 30 , 40 ] total = sum (numbers) print ( "Sum:" , total) 🔁 Using For Loop: numbers = [ 10 , 20 , 30 , 40 ] total = 0 for num in numbers: total += num print ( "Sum:" , total) ✅ 2. Find Maximum Value ✅ Using Built-in Function: values = [ 3 , 18 , 7 , 24 , 11 ] maximum = max (values) print ( "Max:" , maximum) 🔁 Using For and If: values = [ 3 , 18 , 7 , 24 , 11 ] maximum = values[ 0 ] for val in values: if val > maximum: maximum = val print ( "Max:" , maximum) ✅ 3. Count Vowels in a String ✅ Using Built-ins: text = "hello world" vowel_count = sum ( 1 for ch in text if ch i...
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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...
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