Skip to content
    geeksforgeeks
    • Interview Prep
      • DSA
      • Interview Corner
      • Aptitude & Reasoning
      • Practice Coding Problems
      • All Courses
    • Tutorials
      • Python
      • Java
      • ML & Data Science
      • Programming Languages
      • Web Development
      • CS Subjects
      • DevOps
      • Software and Tools
      • School Learning
    • Tracks
      • Languages
        • Python
        • C
        • C++
        • Java
        • Advanced Java
        • SQL
        • JavaScript
        • C#
      • Interview Preparation
        • GfG 160
        • GfG 360
        • System Design
        • Core Subjects
        • Interview Questions
        • Interview Puzzles
        • Aptitude and Reasoning
        • Product Management
        • Computer Organisation and Architecture
      • Data Science
        • Python
        • Data Analytics
        • Complete Data Science
        • Gen AI
        • Agentic AI
      • Dev Skills
        • Full-Stack Web Dev
        • DevOps
        • Software Testing
        • CyberSecurity
        • NextJS
        • Git
      • Tools
        • Computer Fundamentals
        • AI Tools
        • MS Excel & Google Sheets
        • MS Word & Google Docs
      • Maths
        • Maths For Computer Science
        • Engineering Mathematics
        • School Maths
    • Python Tutorial
    • Data Types
    • Interview Questions
    • Examples
    • Quizzes
    • DSA Python
    • Data Science
    • NumPy
    • Pandas
    • Practice
    • Django
    • Flask
    • Projects
    Open In App

    Python - Extract String elements from Mixed Matrix

    Last Updated : 30 Apr, 2023
    Comments
    Improve
    Suggest changes
    1 Likes
    Like
    Report
    See More

    Given a Matrix, Extract all the elements that are of string data type.

    Input : test_list = [[5, 6, 3], ["Gfg", 3], [9, "best", 4]] 
    Output : ['Gfg', 'best'] 
    Explanation : All strings are extracted.
    Input : test_list = [["Gfg", 3], [9, "best", 4]] 
    Output : ['Gfg', 'best'] 
    Explanation : All strings are extracted. 

    Method #1 : Using list comprehension + isinstance()

    The combination of above functions can be used to solve this problem. In this, we iterate nested lists using list comprehension and check for string instance using isinstance().

    Python3
    # Python3 code to demonstrate working of
    # Extract String elements from Mixed Matrix
    # Using list comprehension + isinstance()
    
    # initializing lists
    test_list = [[5, 6, 3], ["Gfg", 3, "is"], [9, "best", 4]]
    
    # printing original list
    print("The original list : " + str(test_list))
    
    # strings are extracted using isinstance()
    res = [ele for sub in test_list for ele in sub if isinstance(ele, str)]
    
    # printing result
    print("The String instances : " + str(res))
    

    Output
    The original list : [[5, 6, 3], ['Gfg', 3, 'is'], [9, 'best', 4]]
    The String instances : ['Gfg', 'is', 'best']

    Time Complexity: O(n) where n is the number of elements in the list “test_list”. 
    Auxiliary Space: O(1) additional space is not needed.

    Method #2 :  Using chain.from_iterables() + list comprehension + isinstance()

    This is yet another way in which this task can be performed. Whole Matrix is flattened and then isinstance() is applied over it to check for string elements in flattened list.

    Python3
    # Python3 code to demonstrate working of
    # Extract String elements from Mixed Matrix
    # Using chain.from_iterables + list comprehension + isinstance()
    from itertools import chain
    
    # initializing lists
    test_list = [[5, 6, 3], ["Gfg", 3, "is"], [9, "best", 4]]
    
    # printing original list
    print("The original list : " + str(test_list))
    
    # strings are extracted using isinstance()
    # using chain.from_iterables()
    res = [ele for ele in chain.from_iterable(test_list) if isinstance(ele, str)]
    
    # printing result
    print("The String instances : " + str(res))
    

    Output
    The original list : [[5, 6, 3], ['Gfg', 3, 'is'], [9, 'best', 4]]
    The String instances : ['Gfg', 'is', 'best']

    Method #3 : Using extend() and type() methods

    Python3
    # Python3 code to demonstrate working of
    # Extract String elements from Mixed Matrix
    
    # initializing lists
    test_list = [[5, 6, 3], ["Gfg", 3, "is"], [9, "best", 4]]
    
    # printing original list
    print("The original list : " + str(test_list))
    
    x=[]
    res=[]
    for i in test_list:
        x.extend(i)
    for i in x:
        if(type(i) is str):
            res.append(i)
            
    # printing result
    print("The String instances : " + str(res))
    

    Output
    The original list : [[5, 6, 3], ['Gfg', 3, 'is'], [9, 'best', 4]]
    The String instances : ['Gfg', 'is', 'best']

    Method #4: Using nested loops

    1. Initialize an empty list res.
    2. Loop through each sublist in test_list.
    3. For each sublist, loop through each element.
    4. Check if the element is a string using isinstance() function.
    5. If the element is a string, append it to the res list.
    6. Finally, print the res list.
    Python3
    # Python3 code to demonstrate working of
    # Extract String elements from Mixed Matrix
    
    # initializing lists
    test_list = [[5, 6, 3], ["Gfg", 3, "is"], [9, "best", 4]]
    
    # printing original list
    print("The original list : " + str(test_list))
    
    res = []
    for sublist in test_list:
        for element in sublist:
            if isinstance(element, str):
                res.append(element)
    
    # printing result
    print("The String instances : " + str(res))
    

    Output
    The original list : [[5, 6, 3], ['Gfg', 3, 'is'], [9, 'best', 4]]
    The String instances : ['Gfg', 'is', 'best']

    Time complexity: O(n^2) where n is the length of test_list. 
    Auxiliary space: O(m) where m is the number of string elements in the list.

    Method #5 : Using reduce():

    Algorithm :

    1. Initialize a 2D list named test_list containing three sublists, each with a mix of integer and string elements.
    2. Print the original list.
    3. Initialize an empty list named res to store the string elements.
    4. Iterate over each sublist in test_list.
    5. For each sublist, iterate over each element.
    6. If the element is a string, append it to the res list.
    7. After all elements have been checked, return the res list containing all string elements.
    8. Print the resulting list of string elements.
       
    Python3
    from functools import reduce
    
    # initializing lists
    test_list = [[5, 6, 3], ["Gfg", 3, "is"], [9, "best", 4]]
    
    # printing original list
    print("The original list : " + str(test_list))
    
    # using reduce() to extract string elements
    res = reduce(lambda acc, sublist: acc + [elem for elem in sublist if isinstance(elem, str)], test_list, [])
    
    # printing result
    print("The String instances : " + str(res))
    #This code is contributed by Rayudu
    

    Output
    The original list : [[5, 6, 3], ['Gfg', 3, 'is'], [9, 'best', 4]]
    The String instances : ['Gfg', 'is', 'best']

    The time complexity : O(n*m) where n is the number of sublists in test_list and m is the length of the longest sublist. This is because we are iterating over each element of each sublist using the nested for loops, and the isinstance() function has a constant time complexity.

    The space complexity : O(m) because we are creating a new list to store the resulting string elements, and the size of this list is proportional to the number of string elements in the matrix.

    Method #6 : Using  heapq:

    Algorithm :

    1. Initialize an empty list res to store the string elements.
    2. Use a list comprehension to iterate over each sublist in the input list.
    3. Filter the string elements from each sublist using filter() and a lambda function that checks if an element is an instance of a string.
    4. Merge the filtered sublists using heapq.merge() to create an iterator that yields the string elements in ascending order.
    5. Convert the iterator to a list using list().
    6. Store the resulting list of string elements in the res list.
    7. Return the res list.
    Python3
    import heapq
    
    # initializing lists
    test_list = [[5, 6, 3], ["Gfg", 3, "is"], [9, "best", 4]]
    
    # printing original list
    print("The original list : " + str(test_list))
    
    # using heapq to extract string elements
    res = list(heapq.merge(*[filter(lambda x: isinstance(x, str), sublist) for sublist in test_list]))
    
    # printing result
    print("The String instances : " + str(res))
    #This code is contributed by Vinay pinjala.
    

    Output
    The original list : [[5, 6, 3], ['Gfg', 3, 'is'], [9, 'best', 4]]
    The String instances : ['Gfg', 'best', 'is']
    

    Time Complexity:

    The time complexity of this algorithm is O(NlogN), where N is the total number of elements in the input list. This is because the heapq.merge() method uses a heap data structure that requires O(logN) time to insert and extract elements, and it is performed on each sublist.
    Space Complexity:

    The space complexity of this algorithm is O(N), where N is the total number of elements in the input list. This is because the filter() method creates a new list of filtered elements for each sublist in the input list, and the resulting list of string elements is stored in the res list.

    Create Quiz

    M

    manjeet_04
    Improve

    M

    manjeet_04
    Improve
    Article Tags :
    • Python
    • Python list-programs
    • Python string-programs

    Explore

      Python Fundamentals

      Python Introduction

      2 min read

      Input and Output in Python

      4 min read

      Python Variables

      4 min read

      Python Operators

      4 min read

      Python Keywords

      2 min read

      Python Data Types

      8 min read

      Conditional Statements in Python

      3 min read

      Loops in Python - For, While and Nested Loops

      5 min read

      Python Functions

      5 min read

      Recursion in Python

      4 min read

      Python Lambda Functions

      5 min read

      Python Data Structures

      Python String

      5 min read

      Python Lists

      4 min read

      Python Tuples

      4 min read

      Python Dictionary

      3 min read

      Python Sets

      6 min read

      Python Arrays

      7 min read

      List Comprehension in Python

      4 min read

      Advanced Python

      Python OOP Concepts

      11 min read

      Python Exception Handling

      5 min read

      File Handling in Python

      4 min read

      Python Database Tutorial

      4 min read

      Python MongoDB Tutorial

      3 min read

      Python MySQL

      9 min read

      Python Packages

      10 min read

      Python Modules

      3 min read

      Python DSA Libraries

      15 min read

      List of Python GUI Library and Packages

      3 min read

      Data Science with Python

      NumPy Tutorial - Python Library

      3 min read

      Pandas Tutorial

      4 min read

      Matplotlib Tutorial

      5 min read

      Python Seaborn Tutorial

      3 min read

      StatsModel Library - Tutorial

      3 min read

      Learning Model Building in Scikit-learn

      6 min read

      TensorFlow Tutorial

      2 min read

      PyTorch Tutorial

      6 min read

      Web Development with Python

      Flask Tutorial

      8 min read

      Django Tutorial | Learn Django Framework

      7 min read

      Django ORM - Inserting, Updating & Deleting Data

      4 min read

      Templating With Jinja2 in Flask

      6 min read

      Django Templates

      5 min read

      Build a REST API using Flask - Python

      3 min read

      Building a Simple API with Django REST Framework

      3 min read

      Python Practice

      Python Quiz

      1 min read

      Python Coding Practice

      1 min read

      Python Interview Questions and Answers

      15+ min read
    top_of_element && top_of_screen < bottom_of_element) || (bottom_of_screen > articleRecommendedTop && top_of_screen < articleRecommendedBottom) || (top_of_screen > articleRecommendedBottom)) { if (!isfollowingApiCall) { isfollowingApiCall = true; setTimeout(function(){ if (loginData && loginData.isLoggedIn) { if (loginData.userName !== $('#followAuthor').val()) { is_following(); } else { $('.profileCard-profile-picture').css('background-color', '#E7E7E7'); } } else { $('.follow-btn').removeClass('hideIt'); } }, 3000); } } }); } $(".accordion-header").click(function() { var arrowIcon = $(this).find('.bottom-arrow-icon'); arrowIcon.toggleClass('rotate180'); }); }); window.isReportArticle = false; function report_article(){ if (!loginData || !loginData.isLoggedIn) { const loginModalButton = $('.login-modal-btn') if (loginModalButton.length) { loginModalButton.click(); } return; } if(!window.isReportArticle){ //to add loader $('.report-loader').addClass('spinner'); jQuery('#report_modal_content').load(gfgSiteUrl+'wp-content/themes/iconic-one/report-modal.php', { PRACTICE_API_URL: practiceAPIURL, PRACTICE_URL:practiceURL },function(responseTxt, statusTxt, xhr){ if(statusTxt == "error"){ alert("Error: " + xhr.status + ": " + xhr.statusText); } }); }else{ window.scrollTo({ top: 0, behavior: 'smooth' }); $("#report_modal_content").show(); } } function closeShareModal() { const shareOption = document.querySelector('[data-gfg-action="share-article"]'); shareOption.classList.remove("hover_share_menu"); let shareModal = document.querySelector(".hover__share-modal-container"); shareModal && shareModal.remove(); } function openShareModal() { closeShareModal(); // Remove existing modal if any let shareModal = document.querySelector(".three_dot_dropdown_share"); shareModal.appendChild(Object.assign(document.createElement("div"), { className: "hover__share-modal-container" })); document.querySelector(".hover__share-modal-container").append( Object.assign(document.createElement('div'), { className: "share__modal" }), ); document.querySelector(".share__modal").append(Object.assign(document.createElement('h1'), { className: "share__modal-heading" }, { textContent: "Share to" })); const socialOptions = ["LinkedIn", "WhatsApp","Twitter", "Copy Link"]; socialOptions.forEach((socialOption) => { const socialContainer = Object.assign(document.createElement('div'), { className: "social__container" }); const icon = Object.assign(document.createElement("div"), { className: `share__icon share__${socialOption.split(" ").join("")}-icon` }); const socialText = Object.assign(document.createElement("span"), { className: "share__option-text" }, { textContent: `${socialOption}` }); const shareLink = (socialOption === "Copy Link") ? Object.assign(document.createElement('div'), { role: "button", className: "link-container CopyLink" }) : Object.assign(document.createElement('a'), { className: "link-container" }); if (socialOption === "LinkedIn") { shareLink.setAttribute('href', `https://www.linkedin.com/sharing/share-offsite/?url=${window.location.href}`); shareLink.setAttribute('target', '_blank'); } if (socialOption === "WhatsApp") { shareLink.setAttribute('href', `https://api.whatsapp.com/send?text=${window.location.href}`); shareLink.setAttribute('target', "_blank"); } if (socialOption === "Twitter") { shareLink.setAttribute('href', `https://twitter.com/intent/tweet?url=${window.location.href}`); shareLink.setAttribute('target', "_blank"); } shareLink.append(icon, socialText); socialContainer.append(shareLink); document.querySelector(".share__modal").appendChild(socialContainer); //adding copy url functionality if(socialOption === "Copy Link") { shareLink.addEventListener("click", function() { var tempInput = document.createElement("input"); tempInput.value = window.location.href; document.body.appendChild(tempInput); tempInput.select(); tempInput.setSelectionRange(0, 99999); // For mobile devices document.execCommand('copy'); document.body.removeChild(tempInput); this.querySelector(".share__option-text").textContent = "Copied" }) } }); // document.querySelector(".hover__share-modal-container").addEventListener("mouseover", () => document.querySelector('[data-gfg-action="share-article"]').classList.add("hover_share_menu")); } function toggleLikeElementVisibility(selector, show) { document.querySelector(`.${selector}`).style.display = show ? "block" : "none"; } function closeKebabMenu(){ document.getElementById("myDropdown").classList.toggle("show"); }
geeksforgeeks-footer-logo
Corporate & Communications Address:
A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)
Registered Address:
K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305
GFG App on Play Store GFG App on App Store
  • Company
  • About Us
  • Legal
  • Privacy Policy
  • Contact Us
  • Advertise with us
  • GFG Corporate Solution
  • Campus Training Program
  • Explore
  • POTD
  • Job-A-Thon
  • Blogs
  • Nation Skill Up
  • Tutorials
  • Programming Languages
  • DSA
  • Web Technology
  • AI, ML & Data Science
  • DevOps
  • CS Core Subjects
  • Interview Preparation
  • Software and Tools
  • Courses
  • ML and Data Science
  • DSA and Placements
  • Web Development
  • Programming Languages
  • DevOps & Cloud
  • GATE
  • Trending Technologies
  • Videos
  • DSA
  • Python
  • Java
  • C++
  • Web Development
  • Data Science
  • CS Subjects
  • Preparation Corner
  • Interview Corner
  • Aptitude
  • Puzzles
  • GfG 160
  • System Design
@GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved
Lightbox
Improvement
Suggest Changes
Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.
geeksforgeeks-suggest-icon
Create Improvement
Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.
geeksforgeeks-improvement-icon
Suggest Changes
min 4 words, max Words Limit:1000

Thank You!

Your suggestions are valuable to us.

What kind of Experience do you want to share?

Interview Experiences
Admission Experiences
Career Journeys
Work Experiences
Campus Experiences
Competitive Exam Experiences