python: refine notes
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@ -89,7 +89,7 @@ print(people)
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# Output: [['Alice', 30], ['Bob', 25], ['Clare', 35], ['Dave', 28]]
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```
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### Updating a value within an element of a list of lists
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### Updating an inner value within a multidimensional list
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In the following example we have a list of the following structure:
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@ -101,12 +101,18 @@ data = [
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]
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```
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Below we use [list comprehension](/Programming_Languages/Python/Syntax/List_comprehension.md) to convert the first element of each iner list from a Unix timestamp to a readable string:
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Below we use [map](/Programming_Languages/Python/Syntax/Map_and_filter_in_Python.md) and a [lambda function](/Programming_Languages/Python/Syntax/Lambdas_in_Python.md) to convert the first element of each iner list from a Unix timestamp to a readable string:
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```py
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readable_date = list(map(lambda i: [convert_timestamp(i[0])] + i[1:], date))
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```
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We could also use [list comprehension](/Programming_Languages/Python/Syntax/List_comprehension_etc.md) to achieve the same outcome:
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```py
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readable_date = [[convert_timestamp(i[0])] + i[1:] for i in data]
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```
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### Filter elements in a multidimensional list
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Say we have the following data structure:
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@ -1,23 +0,0 @@
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---
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categories:
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- Programming Languages
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tags: [python]
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---
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# List comprehension
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> List comprehension is an older feature of Python. Now the same functionality can be achieved with greater concision using functional methods like `map` and `filter`. But you may see it used in older code.
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```python
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values = [1, 2, 4, 6, 8, 9]
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new_values = [i + 1 for i in values]
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print('new_values', new_values)
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# new_values [2, 3, 5, 7, 9, 10]
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new_list = [item + 1 for item in values if item % 2 == 0]
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print('new_list:', new_list)
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# new_list: [3, 5, 7, 9]
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```
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// TODO: Compare with how the same would be achieved with lambdas
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@ -0,0 +1,74 @@
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---
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categories:
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- Programming Languages
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tags: [python]
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---
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# List comprehension
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List comprehension is a syntactic shorthand for applying a function to each element in a list without explicitly using loop syntax.
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Since its introduction to the language, the same functionality has become achievable by using functional methods like [`map` and `filter`](/Programming_Languages/Python/Syntax/Map_and_filter_in_Python.md) utilising [lambdas](/Programming_Languages/Python/Syntax/Lambdas_in_Python.md) however list comprehension is often more straightforward and easier to read.
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## Syntax
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Here is a basic example which applies `+ 1` to each integer in a list:
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```python
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values = [1, 2, 4, 6, 8, 9]
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new_values = [i + 1 for i in values]
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print('new_values', new_values)
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# new_values [2, 3, 5, 7, 9, 10]
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```
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The basic syntax is as follows:
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```
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new_list = [expression for each member in an iterable]
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```
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- The _expression_ can be the member itself, a call to a method, or any other valid expression that returns a value. In the example above, the expression `i + i` adds one to each member value.
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- The _member_ is the object or value in the list or iterable. In the example above, the member value is _i_.
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- The _iterable_ is a list, set, dictionary or any other object that can return its elements one at a time. In the example above, the iterable is each value in `values`.
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This is a much more condensed way of achieving the same outcome with a traditional loop:
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```py
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values = [1, 2, 4, 6, 8, 9]
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new_list = []
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for i in values:
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values.append(i+1)
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```
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### Another example
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In the following example, we apply list comprehension with a `in range` loop structure:
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```py
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new_list = [i * i for i in range(10) ]
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print(new_list)
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# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
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```
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### Adding a condition
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We can apply a
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```py
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new_list = [item + 1 for item in values if item % 2 == 0]
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print('new_list:', new_list)
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# new_list: [3, 5, 7, 9]
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```
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// Add example of how a simple filter could be applied without any execution on each element
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// TODO: Compare with how the same would be achieved with lambdas
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## Dictionary comprehension
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// TODO: Add note on dictionary and set comprehension
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