Sometimes, while working with Python records, we can have a problem in which we need to extract count of any data type occurred in tuple. This can have application in various domains such as day-day programming and web development. Let’s discuss certain ways in which this task can be performed.
Input : test_tuple = (5, ‘Gfg’, 2, 8.8, 1.2, ‘is’), data_type = int
Output : 2Input : test_tuple = (5, ‘Gfg’, 2, 8.8, 1.2, ‘is’), data_type = str
Output : 2
Method #1 : Using loop + isinstance() The combination of above functions can be used to solve this problem. In this, we perform the task of checking for data type using isinstance() and run a counter to increment on match.
Python3
# Python3 code to demonstrate working of # Data type frequency in tuple# Using loop + isinstance()# initializing tuplestest_tuple = (5, 'Gfg', 2, 8.8, 1.2, 'is')# printing original tupleprint("The original tuple : " + str(test_tuple))# initializing data typedata_type = float# Data type frequency in tuple# Using loop + isinstance()count = 0for ele in test_tuple: if isinstance(ele, float): count = count + 1# printing result print("The data type frequency : " + str(count)) |
The original tuple : (5, 'Gfg', 2, 8.8, 1.2, 'is') The data type frequency : 2
Method #2 : Using sum() + isinstance() The combination of above functions can also be used to solve this problem. This used similar way of solving as above method, just in shorthand way using sum() for counting.
Python3
# Python3 code to demonstrate working of # Data type frequency in tuple# Using sum() + isinstance()# initializing tuplestest_tuple = (5, 'Gfg', 2, 8.8, 1.2, 'is')# printing original tupleprint("The original tuple : " + str(test_tuple))# initializing data typedata_type = float# Data type frequency in tuple# Using sum() + isinstance()count = sum(1 for ele in test_tuple if isinstance(ele, data_type))# printing result print("The data type frequency : " + str(count)) |
The original tuple : (5, 'Gfg', 2, 8.8, 1.2, 'is') The data type frequency : 2
Method #3 : Using type() method. type() method returns the datatype of variable.
Python3
# Python3 code to demonstrate working of# Data type frequency in tuple# initializing tuplestest_tuple = (5, 'Gfg', 2, 8.8, 1.2, 'is')# printing original tupleprint("The original tuple : " + str(test_tuple))# initializing data typedata_type = float# Data type frequency in tuplecount = 0for ele in test_tuple: if type(ele) is data_type: count+=1# printing resultprint("The data type frequency : " + str(count)) |
The original tuple : (5, 'Gfg', 2, 8.8, 1.2, 'is') The data type frequency : 2
Method#4: Using filter()
Python3
# Python3 code to demonstrate working of# Data type frequency in tuple# initializing tuplestest_tuple = (5, 'Gfg', 2, 8.8, 1.2, 'is')# printing original tupleprint("The original tuple : " + str(test_tuple))# initializing data typedata_type = float# Data type frequency in tuple using filtercount = len(list(filter(lambda ele: type(ele) is data_type, test_tuple)))# printing resultprint("The data type frequency : " + str(count))#This code is contributed by Vinay Pinjala. |
The original tuple : (5, 'Gfg', 2, 8.8, 1.2, 'is') The data type frequency : 2
Time complexity: O(n)
Space complexity: O(1)
Method#5: Using Recursive method.
Python3
# Python3 code to demonstrate working of# Data type frequency in tuple#using recursive methoddef count_data_type(tup, data_type, count=0): if not tup: #base condition return count if type(tup[0]) is data_type: count += 1 return count_data_type(tup[1:], data_type, count) # initializing tuplestest_tuple = (5, 'Gfg', 2, 8.8, 1.2, 'is')# printing original tupleprint("The original tuple : " + str(test_tuple))# initializing data typedata_type = floatcount = count_data_type(test_tuple, data_type)# printing resultprint("The data type frequency:", count)#this code contributed by tvsk. |
The original tuple : (5, 'Gfg', 2, 8.8, 1.2, 'is') The data type frequency: 2
Time Complexity: O(n)
Auxiliary Space: O(n)
Method #6: Using a reduce() function and lambda function
Algorithm:
1.Import the reduce function from the functools module.
2.Initialize the input tuple and the data type whose frequency is to be counted.
3.Use the reduce() function to count the number of elements in the tuple that are of the specified data type. The reduce() function applies the 4.lambda function to each element of the tuple, which checks whether the element is of the specified data type. If it is, it increments the count by 1.
5.Print the count of the specified data type in the tuple.
Python3
from functools import reduce# initializing tuplestest_tuple = (5, 'Gfg', 2, 8.8, 1.2, 'is')# printing original tupleprint("The original tuple : " + str(test_tuple))# initializing data typedata_type = float# Data type frequency in tuple using reducecount = reduce(lambda x, y: x + isinstance(y, data_type), test_tuple, 0)# printing resultprint("The data type frequency : " + str(count))#This code is contributed by Jyothi pinjala |
The original tuple : (5, 'Gfg', 2, 8.8, 1.2, 'is') The data type frequency : 2
Time Complexity: O(n), where n is the number of elements in the tuple. The reduce() function iterates over each element of the tuple once.
Auxiliary Space: O(1), since only constant extra space is used to store the count variable.
Method #7: Using list comprehension and the built-in function type():
Step-by-step approach:
- Initialize a tuple test_tuple.
- Print the original tuple.
- Initialize the data type data_type as float.
- Use a list comprehension to create a list of all elements in test_tuple that have the same data type as data_type.
- Find the length of the list generated in step 4 using the built-in function len().
- Print the result.
Python3
# initializing tuplestest_tuple = (5, 'Gfg', 2, 8.8, 1.2, 'is')# printing original tupleprint("The original tuple : " + str(test_tuple))# initializing data typedata_type = float# Data type frequency in tuple using list comprehension and type()count = len([x for x in test_tuple if type(x) == data_type])# printing resultprint("The data type frequency : " + str(count)) |
The original tuple : (5, 'Gfg', 2, 8.8, 1.2, 'is') The data type frequency : 2
Time complexity: O(n), where n is the length of the tuple.
Auxiliary space: O(k), where k is the number of elements in the tuple that have the same data type as data_type.
