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# Python – Extract Particular data type rows

• Last Updated : 08 Mar, 2023

Given A Matrix, extract all the rows which have all the elements with particular data type.

Input : test_list = [[4, 5, “Hello”], [2, 6, 7], [“g”, “f”, “g”], [9, 10, 11]], data_type = int
Output : [[2, 6, 7], [9, 10, 11]]
Explanation : All lists with integer are extracted.

Input : test_list = [[4, 5, “Hello”], [2, 6, 7], [“g”, “f”, “g”], [9, 10, 11]], data_type = str
Output : [[“g”, “f”, “g”]]
Explanation : All lists with strings are extracted.

Method #1 : Using isinstance() + all() + list comprehension

In this, we check for data type using isinstance(), all() checks for all the elements being of particular data type.

## Python3

 `# Python3 code to demonstrate working of``# Extract Particular data type rows``# Using isinstance() + all() + list comprehension` `# initializing list``test_list ``=` `[[``4``, ``5``, ``"Hello"``], [``2``, ``6``, ``7``], [``"g"``, ``"f"``, ``"g"``], [``9``, ``10``, ``11``]]` `# printing original list``print``(``"The original list is : "` `+` `str``(test_list))` `# initializing data type``data_type ``=` `int` `# checking data type using isinstance``res ``=` `[row ``for` `row ``in` `test_list ``if` `all``(``    ``isinstance``(ele, data_type) ``for` `ele ``in` `row)]` `# printing result``print``(``"Filtered Rows : "` `+` `str``(res))`

Output:

The original list is : [[4, 5, ‘Hello’], [2, 6, 7], [‘g’, ‘f’, ‘g’], [9, 10, 11]] Filtered Rows : [[2, 6, 7], [9, 10, 11]]

Time Complexity: O(n*n)
Auxiliary Space: O(1)

Method #2 : Using filter() + lambda + isinstance()

In this, we perform task of filtering using filter() and lambda, isinstance(), is used to perform task of type check as in case of above method.

## Python3

 `# Python3 code to demonstrate working of``# Extract Particular data type rows``# Using filter() + lambda + isinstance()` `# initializing list``test_list ``=` `[[``4``, ``5``, ``"Hello"``], [``2``, ``6``, ``7``], [``"g"``, ``"f"``, ``"g"``], [``9``, ``10``, ``11``]]` `# printing original list``print``(``"The original list is : "` `+` `str``(test_list))` `# initializing data type``data_type ``=` `int` `# checking data type using isinstance``# filter() used to get filter``res ``=` `list``(``filter``(``lambda` `row: ``all``(``isinstance``(ele, data_type)``                                  ``for` `ele ``in` `row), test_list))` `# printing result``print``(``"Filtered Rows : "` `+` `str``(res))`

Output:

The original list is : [[4, 5, ‘Hello’], [2, 6, 7], [‘g’, ‘f’, ‘g’], [9, 10, 11]] Filtered Rows : [[2, 6, 7], [9, 10, 11]]

Time Complexity: O(n), where n is the length of the input list. This is because we’re using the built-in filter() + lambda + isinstance() which has a time complexity of O(n) in the worst case.
Auxiliary Space: O(n), where n is the length of the input list as we’re using additional space other than the input list itself.

Method #3: Using type().type() returns the data type of variable.

## Python3

 `# Python3 code to demonstrate working of``# Extract Particular data type rows` `# initializing list``test_list ``=` `[[``4``, ``5``, ``"Hello"``], [``2``, ``6``, ``7``], [``"g"``, ``"f"``, ``"g"``], [``9``, ``10``, ``11``]]` `# printing original list``print``(``"The original list is : "` `+` `str``(test_list))` `# initializing data type``data_type ``=` `int``res``=``[]``# checking data type``for` `i ``in` `test_list:``    ``c``=``0``    ``for` `j ``in` `i:``        ``if``(``type``(j) ``is` `data_type):``            ``c``+``=``1``    ``if``(c``=``=``len``(i)):``        ``res.append(i)` `# printing result``print``(``"Filtered Rows : "` `+` `str``(res))`

Output

```The original list is : [[4, 5, 'Hello'], [2, 6, 7], ['g', 'f', 'g'], [9, 10, 11]]
Filtered Rows : [[2, 6, 7], [9, 10, 11]]```

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