Given a Matrix, the task is to write a python program to extract rows with has no repeated data types.
Examples:
Input : test_list = [[4, 3, 1], [“gfg”, 3, {4:2}], [3, 1, “jkl”], [9, (2, 3)]]
Output : [[‘gfg’, 3, {4: 2}], [9, (2, 3)]]
Explanation : [4, 3, 1] are all integers hence omitted. [9, (2, 3)] has integer and tuple, different data types, hence included in results.
Input : test_list = [[4, 3, 1], [“gfg”, 3, {4:2}, 4], [3, 1, “jkl”], [9, (2, 3)]]
Output : [[9, (2, 3)]]
Explanation : [4, 3, 1] are all integers hence omitted. [9, (2, 3)] has integer and tuple, different data types, hence included in results.
Method : Using type() + list comprehension
In this, we use type() to check for data types of each element of rows, and if the data type repeats, the row is not included in the result.
Python3
# Python3 code to demonstrate working of# Distinct Data Type Rows# Using type() + list comprehension # initializing listtest_list = [[4, 3, 1], ["gfg", 3, {4: 2}], [3, 1, "jkl"], [9, (2, 3)]] # printing original listprint("The original list is : " + str(test_list)) res = []for sub in test_list: # get Distinct types size type_size = len(list(set([type(ele) for ele in sub]))) # if equal get result if len(sub) == type_size: res.append(sub) # printing resultprint("The Distinct data type rows : " + str(res)) |
Output:
The original list is : [[4, 3, 1], [‘gfg’, 3, {4: 2}], [3, 1, ‘jkl’], [9, (2, 3)]]
The Distinct data type rows : [[‘gfg’, 3, {4: 2}], [9, (2, 3)]]
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