In this article we will discuss if a list contains all or any elements of another list.
Suppose we have have two list i.e.
# List of string list1 = ['Hi' , 'hello', 'at', 'this', 'there', 'from'] # List of string list2 = ['there' , 'hello', 'Hi']
Check if list1 contains all elements of list2 using all()
''' check if list1 contains all elements in list2 ''' result = all(elem in list1 for elem in list2) if result: print("Yes, list1 contains all elements in list2") else : print("No, list1 does not contains all elements in list2"
Python all() function checks if all Elements of given Iterable is True. So, convert the list2 to Iterable and for each element in Iterable i.e. list2 check if element exists in list1.
Check if list1 contains any elements of list2 using any()
''' check if list1 contains any elements of list2 ''' result = any(elem in list1 for elem in list2) if result: print("Yes, list1 contains any elements of list2") else : print("No, list1 contains any elements of list2")
Python any() function checks if any Element of given Iterable is True. So, convert the list2 to Iterable and for each element in Iterable i.e. list2 check if any element exists in list1.
Complete example is as follows,
def main(): # List of string list1 = ['Hi' , 'hello', 'at', 'this', 'there', 'from'] # List of string list2 = ['there' , 'hello', 'Hi'] ''' check if list1 contains all elements in list2 ''' result = all(elem in list1 for elem in list2) if result: print("Yes, list1 contains all elements in list2") else : print("No, list1 does not contains all elements in list2") ''' check if list1 contains any elements of list2 ''' result = any(elem in list1 for elem in list2) if result: print("Yes, list1 contains any elements of list2") else : print("No, list1 contains any elements of list2") if __name__ == '__main__': main()
Output:
Yes, list1 contains all elements in list2 Yes, list1 contains any elements of list2
Pandas Tutorials -Learn Data Analysis with Python
-
Pandas Tutorial Part #1 - Introduction to Data Analysis with Python
-
Pandas Tutorial Part #2 - Basics of Pandas Series
-
Pandas Tutorial Part #3 - Get & Set Series values
-
Pandas Tutorial Part #4 - Attributes & methods of Pandas Series
-
Pandas Tutorial Part #5 - Add or Remove Pandas Series elements
-
Pandas Tutorial Part #6 - Introduction to DataFrame
-
Pandas Tutorial Part #7 - DataFrame.loc[] - Select Rows / Columns by Indexing
-
Pandas Tutorial Part #8 - DataFrame.iloc[] - Select Rows / Columns by Label Names
-
Pandas Tutorial Part #9 - Filter DataFrame Rows
-
Pandas Tutorial Part #10 - Add/Remove DataFrame Rows & Columns
-
Pandas Tutorial Part #11 - DataFrame attributes & methods
-
Pandas Tutorial Part #12 - Handling Missing Data or NaN values
-
Pandas Tutorial Part #13 - Iterate over Rows & Columns of DataFrame
-
Pandas Tutorial Part #14 - Sorting DataFrame by Rows or Columns
-
Pandas Tutorial Part #15 - Merging or Concatenating DataFrames
-
Pandas Tutorial Part #16 - DataFrame GroupBy explained with examples
Are you looking to make a career in Data Science with Python?
Data Science is the future, and the future is here now. Data Scientists are now the most sought-after professionals today. To become a good Data Scientist or to make a career switch in Data Science one must possess the right skill set. We have curated a list of Best Professional Certificate in Data Science with Python. These courses will teach you the programming tools for Data Science like Pandas, NumPy, Matplotlib, Seaborn and how to use these libraries to implement Machine learning models.
Checkout the Detailed Review of Best Professional Certificate in Data Science with Python.
Remember, Data Science requires a lot of patience, persistence, and practice. So, start learning today.