From 54eec70ca9001137fb9d80d442c3fd7d31f7b1ce Mon Sep 17 00:00:00 2001
From: Pranshu shah <97401387+shahpranshu27@users.noreply.github.com>
Date: Sat, 25 May 2024 07:25:26 +0530
Subject: [PATCH] create map-function.md

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 contrib/advanced-python/map-function.md | 54 +++++++++++++++++++++++++
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diff --git a/contrib/advanced-python/index.md b/contrib/advanced-python/index.md
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 - [Regular Expressions in Python](regular_expressions.md)
 - [JSON module](json-module.md)
 - [OOPs](OOPs.md)
+- [Map Function](map-function.md)
diff --git a/contrib/advanced-python/map-function.md b/contrib/advanced-python/map-function.md
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+The `map()` function in Python is a built-in function used for applying a given function to each item of an iterable (like a list, tuple, or dictionary) and returning a new iterable with the results. It's a powerful tool for transforming data without the need for explicit loops. Let's break down its syntax, explore examples, and discuss various use cases.
+
+### Syntax:
+
+```python
+map(function, iterable1, iterable2, ...)
+```
+
+- `function`: The function to apply to each item in the iterables.
+- `iterable1`, `iterable2`, ...: One or more iterable objects whose items will be passed as arguments to `function`.
+
+### Examples:
+
+#### Example 1: Doubling the values in a list
+
+```python
+# Define the function
+def double(x):
+    return x * 2
+
+# Apply the function to each item in the list using map
+original_list = [1, 2, 3, 4, 5]
+doubled_list = list(map(double, original_list))
+print(doubled_list)  # Output: [2, 4, 6, 8, 10]
+```
+
+#### Example 2: Converting temperatures from Celsius to Fahrenheit
+
+```python
+# Define the function
+def celsius_to_fahrenheit(celsius):
+    return (celsius * 9/5) + 32
+
+# Apply the function to each Celsius temperature using map
+celsius_temperatures = [0, 10, 20, 30, 40]
+fahrenheit_temperatures = list(map(celsius_to_fahrenheit, celsius_temperatures))
+print(fahrenheit_temperatures)  # Output: [32.0, 50.0, 68.0, 86.0, 104.0]
+```
+
+### Use Cases:
+
+1. **Data Transformation**: When you need to apply a function to each item of a collection and obtain the transformed values, `map()` is very handy.
+
+2. **Parallel Processing**: In some cases, `map()` can be utilized in parallel processing scenarios, especially when combined with `multiprocessing` or `concurrent.futures`.
+
+3. **Cleaning and Formatting Data**: It's often used in data processing pipelines for tasks like converting data types, normalizing values, or applying formatting functions.
+
+4. **Functional Programming**: In functional programming paradigms, `map()` is frequently used along with other functional constructs like `filter()` and `reduce()` for concise and expressive code.
+
+5. **Generating Multiple Outputs**: You can use `map()` to generate multiple outputs simultaneously by passing multiple iterables. The function will be applied to corresponding items in the iterables.
+
+6. **Lazy Evaluation**: In Python 3, `map()` returns an iterator rather than a list. This means it's memory efficient and can handle large datasets without loading everything into memory at once.
+
+Remember, while `map()` is powerful, it's essential to balance its use with readability and clarity. Sometimes, a simple loop might be more understandable than a `map()` call.
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