In this article, we will learn How to create a list of Files, Folders, and Sub Folders and then export them to Excel using Python. We will create a list of names and paths using a few folder traversing methods explained below and store them in an Excel sheet by either using openpyxl or pandas module.
Input: The following image represents the structure of the directory.
NOTE:SF2 is an empty directoryTraversing files, folder, and subfolders
The following Functions are methods for traversing folders and stores the name and path of files/folders in lists.
Method 1: Using append_path_name(path, name_list, path_list, glob)
An important function that is used in the following folder traversing functions. The purpose of the function is to check if the given path is of Windows or Linux os as the path separator is different and appends the names and paths of files or folders to name_list and path_list respectively.
Note: Windows uses "\" and Linux uses "/" as path separator, since python treats "\" as an invalid character, we need to use "\\" instead of "\" in the path.
Approach:
- The function will first find if the path contains "\\" using :
# Returns the count if it finds
# any "\\" in the given path.
path.find("\\")
Note: If it returns any number greater than zero, it means the current os is Windows and the first block of code will be executed or else the second block of code will be executed representing Linux os.
- We will split the path according to the current os and store it in a temporary list.
# Windows
temp = path.split("\\")
# Linux
temp = path.split("/")
- We will append the name and path of files or folders to the name_list and path_list respectively.
# temp[-1] gets the last value present in
# the temporary list that represents
# the file or folder name.
name_list.append(temp[-1])
path_list.append(path)
- If the glob variable is True, the parent path will be joined with a regex expression that is required for recursive traversing in glob.iglob() method.
# Windows
path = os.path.join(path, "**\\*")
# Linux
path = os.path.join(path, "**/*")
Example:
Python3
import os
# This function splits the path by checking
# if it is a windows os or linux os path and
# appends the name and path of directory (and
# files only for glob function).
def append_path_name(path, name_list, path_list, glob):
# Checks if it is a windows path or linux
# path
if path.find("\\") > 0:
# Splits the windows path and stores the
# list in a temp list and appends the last
# value of temp_list in name_list as it
# represents the name of file/ folder and
# also appends the path to path_list.
temp = path.split("\\")
name_list.append(temp[-1])
path_list.append(path)
# If this function is called under
# find_using_glob then we return modified
# path so that iglob can recursively
# traverse the folders.
if glob == True:
path = os.path.join(path, "**\\*")
return path, name_list, path_list
else:
# Same explanation as above but the path splitting
# is based on Linux
temp = path.split("/")
name_list.append(temp[-1])
path_list.append(path)
if glob == True:
path = os.path.join(path, "**/*")
return path, name_list, path_list
return name_list, path_list
name_list, path_list = append_path_name("/content/sample_data", [], [], False)
print(name_list)
print(path_list)
Output:
['sample_data', 'anscombe.json', 'california_housing_train.csv', 'F2', 'SF2', 'california_housing_test.csv',
'.ipynb_checkpoints', '.ipynb_checkpoints', 'F1', 'mnist_test.csv', 'README.md', '.ipynb_checkpoints', 'SF1',
'mnist_train_small.csv']
['/content/sample_data', '/content/sample_data/anscombe.json',
'/content/sample_data/california_housing_train.csv', '/content/sample_data/F2',
'/content/sample_data/F2/SF2', '/content/sample_data/F2/SF2/california_housing_test.csv',
'/content/sample_data/F2/.ipynb_checkpoints', '/content/sample_data/.ipynb_checkpoints',
'/content/sample_data/F1', '/content/sample_data/F1/mnist_test.csv', '/content/sample_data/F1/README.md',
'/content/sample_data/F1/.ipynb_checkpoints', '/content/sample_data/F1/SF1',
'/content/sample_data/F1/SF1/mnist_train_small.csv']
Method 2: Using find_using_os_walk(path, name_list, path_list)
This method generates the file names in a directory tree by walking the tree either top-down or bottom-up in the given path.
Syntax : os.walk(path)
Approach:
1. Initiate a for loop using os.walk(path) method, it generates a tuple containing the path of the current directory in root and the file list in files.
for root, _, files in os.walk(path):
2. Call the append_path_name function to store the names and paths of directories bypassing the current directory path.
name_list, path_list = append_path_name(
root, name_list, path_list, False)
3. Iterate the files and store the names and paths of the files found inside a folder.
# Joins the folder path and the
# file name to generate file path
file_path = os.path.join(root, file_name)
# Appends file name and file path to
# name_list and path_list respectively.
name_list.append(file_name)
path_list.append(file_path)
Example:
Python3
import os
# This Function uses os.walk method to traverse folders
# recursively and appends the name and path of file/
# folders in name_list and path_list respectively.
def find_using_os_walk(path, name_list, path_list):
for root, _, files in os.walk(path):
# Function returns modified name_list and
# path_list.
name_list, path_list = append_path_name(
root, name_list, path_list, False)
for file_name in files:
file_path = os.path.join(root, file_name)
# Appends file name and file path to
# name_list and path_list respectively.
name_list.append(file_name)
path_list.append(file_path)
return name_list, path_list
name_list, path_list = find_using_os_walk("/content/sample_data", [], [])
print(name_list)
print(path_list)
Output:
['sample_data', 'anscombe.json', 'california_housing_train.csv', 'F2', 'SF2', 'california_housing_test.csv',
'.ipynb_checkpoints', '.ipynb_checkpoints', 'F1', 'mnist_test.csv', 'README.md', '.ipynb_checkpoints', 'SF1',
'mnist_train_small.csv']
['/content/sample_data', '/content/sample_data/anscombe.json',
'/content/sample_data/california_housing_train.csv', '/content/sample_data/F2',
'/content/sample_data/F2/SF2', '/content/sample_data/F2/SF2/california_housing_test.csv',
'/content/sample_data/F2/.ipynb_checkpoints', '/content/sample_data/.ipynb_checkpoints',
'/content/sample_data/F1', '/content/sample_data/F1/mnist_test.csv', '/content/sample_data/F1/README.md',
'/content/sample_data/F1/.ipynb_checkpoints', '/content/sample_data/F1/SF1',
'/content/sample_data/F1/SF1/mnist_train_small.csv']
Method 3: Using find_using_scandir(path, name_list, path_list)
This Function returns an iterator of os.DirEntry objects corresponding to the entries in the directory given by path.
Syntax : os.scandir(path)
Approach:
1. Call the append_path_name function to store the names and paths of directories by passing the current directory path.
name_list, path_list = append_path_name(
path, name_list, path_list, False)
2. Initiate a for loop using os.scandir(path) method that returns an object containing the current name and path of the file/folder.
for curr_path_obj in os.scandir(path):
3. If the current path is a directory then the function calls itself to recursively traverse the folders and store the folder names and paths from step 1.
if curr_path_obj.is_dir() == True:
file_path = curr_path_obj.path
find_using_scandir(file_path, name_list, path_list)
4. Else the file names and paths are stored in name_list and path_list respectively.
file_name = curr_path_obj.name
file_path = curr_path_obj.path
name_list.append(file_name)
path_list.append(file_path)
Example:
Python3
import os
# This Function uses os.scandir method to traverse
# folders recursively and appends the name and path of
# file/folders in name_list and path_list respectively.
def find_using_scandir(path, name_list, path_list):
# Function returns modified name_list and path_list.
name_list, path_list = append_path_name(
path, name_list, path_list, False)
for curr_path_obj in os.scandir(path):
# If the current path is a directory then the
# function calls itself with the directory path
# and goes on until a file is found.
if curr_path_obj.is_dir() == True:
file_path = curr_path_obj.path
find_using_scandir(file_path, name_list, path_list)
else:
# Appends file name and file path to
# name_list and path_list respectively.
file_name = curr_path_obj.name
file_path = curr_path_obj.path
name_list.append(file_name)
path_list.append(file_path)
return name_list, path_list
name_list, path_list = find_using_scandir("/content/sample_data", [], [])
print(name_list)
print(path_list)
Output:
['sample_data', 'anscombe.json', 'california_housing_train.csv', 'F2', 'SF2', 'california_housing_test.csv',
'.ipynb_checkpoints', '.ipynb_checkpoints', 'F1', 'mnist_test.csv', 'README.md', '.ipynb_checkpoints', 'SF1',
'mnist_train_small.csv']
['/content/sample_data', '/content/sample_data/anscombe.json',
'/content/sample_data/california_housing_train.csv', '/content/sample_data/F2',
'/content/sample_data/F2/SF2', '/content/sample_data/F2/SF2/california_housing_test.csv',
'/content/sample_data/F2/.ipynb_checkpoints', '/content/sample_data/.ipynb_checkpoints',
'/content/sample_data/F1', '/content/sample_data/F1/mnist_test.csv', '/content/sample_data/F1/README.md',
'/content/sample_data/F1/.ipynb_checkpoints', '/content/sample_data/F1/SF1',
'/content/sample_data/F1/SF1/mnist_train_small.csv']
Method 4: Using find_using_listdir(path, name_list, path_list)
This Function Gets the list of all files and directories in the given path.
Syntax : os.listdir(path)
Approach:
1. Call the append_path_name function to store the names and paths of directories by passing the current directory path.
name_list, path_list = append_path_name(
path, name_list, path_list, False)
2. Initiate a for loop using os.listdir(path) method that returns a list of files and folder names present in the current path.
for curr_name in os.listdir(path):
3. Join the name of folder or file with the current path.
curr_path = os.path.join(path, curr_name)
4. If the current path is a directory then the function calls itself to recursively traverse the folders and store the folder names and paths from step 1.
if os.path.isdir(curr_path) == True:
find_using_listdir(curr_path, name_list, path_list)
5. Else the file names and paths are stored in name_list and path_list respectively.
name_list.append(curr_name)
path_list.append(curr_path)
Code for the above-mentioned function:
Python3
import os
# This Function uses os.listdir method to traverse
# folders recursively and appends the name and path of
# file/folders in name_list and path_list respectively.
def find_using_listdir(path, name_list, path_list):
# Function appends folder name and folder path to
# name_list and path_list respectively.
name_list, path_list = append_path_name(path,
name_list, path_list, False)
for curr_name in os.listdir(path):
curr_path = os.path.join(path, curr_name)
# Checks if the current path is a directory.
if os.path.isdir(curr_path) == True:
# If the current path is a directory then the
# function calls itself with the directory path
# and goes on until a file is found
find_using_listdir(curr_path, name_list, path_list)
else:
# Appends file name and file path to
# name_list and path_list respectively.
name_list.append(curr_name)
path_list.append(curr_path)
return name_list, path_list
name_list, path_list = find_using_listdir("/content/sample_data", [], [])
print(name_list)
print(path_list)
Output:
['sample_data', 'anscombe.json', 'california_housing_train.csv', 'F2', 'SF2', 'california_housing_test.csv',
'.ipynb_checkpoints', '.ipynb_checkpoints', 'F1', 'mnist_test.csv', 'README.md', '.ipynb_checkpoints', 'SF1',
'mnist_train_small.csv']
['/content/sample_data', '/content/sample_data/anscombe.json',
'/content/sample_data/california_housing_train.csv', '/content/sample_data/F2',
'/content/sample_data/F2/SF2', '/content/sample_data/F2/SF2/california_housing_test.csv',
'/content/sample_data/F2/.ipynb_checkpoints', '/content/sample_data/.ipynb_checkpoints',
'/content/sample_data/F1', '/content/sample_data/F1/mnist_test.csv', '/content/sample_data/F1/README.md',
'/content/sample_data/F1/.ipynb_checkpoints', '/content/sample_data/F1/SF1',
'/content/sample_data/F1/SF1/mnist_train_small.csv']
Method 5: Using find_using_glob(path, name_list, path_list)
This Function returns an iterator that yields the same values as glob() without actually storing them all simultaneously.
Syntax : glob.iglob(path, recursive=True)
Approach:
1. Call the append_path_name function to store the name and path of parent directory and also return the modified path required for glob method since the last parameter is True.
path, name_list, path_list = append_path_name(
path, name_list, path_list, True)
2. Initiate a for loop using glob.iglob(path, recursive=True) method that recursively traverses the folders and returns the current path of file/folder.
for curr_path in glob.iglob(path, recursive=True):
3. Call the append_path_name function to store the names and paths of files/folders by passing the current path.
name_list, path_list = append_path_name(
curr_path, name_list, path_list, False)
Code for the above-mentioned function:
Python3
import glob
# This Function uses glob.iglob method to traverse
# folders recursively and appends the name and path of
# file/folders in name_list and path_list respectively.
def find_using_glob(path, name_list, path_list):
# Appends the Parent Directory name and path
# and modifies the parent path so that iglob
# can traverse recursively.
path, name_list, path_list = append_path_name(
path, name_list, path_list, True)
# glob.iglob with recursive set to True will
# get all the file/folder paths.
for curr_path in glob.iglob(path, recursive=True):
# Appends file/folder name and path to
# name_list and path_list respectively.
name_list, path_list = append_path_name(
curr_path, name_list, path_list, False)
return name_list, path_list
name_list, path_list = find_using_glob("/content/sample_data", [], [])
print(name_list)
print(path_list)
Output:
['sample_data', 'anscombe.json', 'california_housing_train.csv', 'F2', 'SF2', 'california_housing_test.csv',
'.ipynb_checkpoints', '.ipynb_checkpoints', 'F1', 'mnist_test.csv', 'README.md', '.ipynb_checkpoints', 'SF1',
'mnist_train_small.csv']
['/content/sample_data', '/content/sample_data/anscombe.json',
'/content/sample_data/california_housing_train.csv', '/content/sample_data/F2',
'/content/sample_data/F2/SF2', '/content/sample_data/F2/SF2/california_housing_test.csv',
'/content/sample_data/F2/.ipynb_checkpoints', '/content/sample_data/.ipynb_checkpoints',
'/content/sample_data/F1', '/content/sample_data/F1/mnist_test.csv', '/content/sample_data/F1/README.md',
'/content/sample_data/F1/.ipynb_checkpoints', '/content/sample_data/F1/SF1',
'/content/sample_data/F1/SF1/mnist_train_small.csv']
Storing data in Excel Sheet
Method 1: Using openpyxl
This module is used to read/write data to excel. It has a wide range of features but here we will use it to just create and write data to excel. You need to install openpyxl via pip in your system.
pip install openpyxl
Approach:
1. Import the required modules
# imports workbook from openpyxl module
from openpyxl import Workbook
2. Create a workbook object
work_book = Workbook()
3. Get the workbook active sheet object and initiate the following variables with 0.
row, col1_width, col2_width = 0, 0, 0
work_sheet = work_book.active
4. Iterate the rows to the maximum length of name_list as these many entries will be written to the excel sheet
while row <= len(name_list):
5. Get the cell objects of column 1 and column 2 of the same row and store the values of name_list and path_list to the respective cells.
name = work_sheet.cell(row=row+1, column=1)
path = work_sheet.cell(row=row+1, column=2)
# This block will execute only once and
# add the Heading of column 1 and column 2
if row == 0:
name.value = "Name"
path.value = "Path"
row += 1
continue
# Storing the values from name_list and path_list
# to the specified cell objects
name.value = name_list[row-1]
path.value = path_list[row-1]
6. (Optional) Adjusting the width of cells in Excel sheet using openpyxl's column dimensions.
col1_width = max(col1_width, len(name_list[row-1]))
col2_width = max(col2_width, len(path_list[row-1]))
work_sheet.column_dimensions["A"].width = col1_width
work_sheet.column_dimensions["B"].width = col2_width
7. Save the workbook with a file name after the iteration is over with a filename.
work_book.save(filename="Final.xlsx")
Example:
Python3
# Function will create an excel file and
# write the file/ folder names and their
# path using openpyxl
def create_excel_using_openpyxl(name_list, path_list,
path):
# Creates a workbook object and gets an
# active sheet
work_book = Workbook()
work_sheet = work_book.active
# Writing the data in excel sheet
row, col1_width, col2_width = 0, 0, 0
while row <= len(name_list):
name = work_sheet.cell(row=row+1, column=1)
path = work_sheet.cell(row=row+1, column=2)
# Writing the Heading i.e Name and Path
if row == 0:
name.value = "Name"
path.value = "Path"
row += 1
continue
# Writing the data from specified lists to columns
name.value = name_list[row-1]
path.value = path_list[row-1]
# Adjusting width of Column in excel sheet
col1_width = max(col1_width, len(name_list[row-1]))
col2_width = max(col2_width, len(path_list[row-1]))
work_sheet.column_dimensions["A"].width = col1_width
work_sheet.column_dimensions["B"].width = col2_width
row += 1
# Saving the workbook
work_book.save(filename="Final.xlsx")
create_excel_using_openpyxl(name_list, path_list, path)
Output:
Method 2: Using pandas
1. Create a frame (a dictionary) with the keys as 'Name' and 'Path' and values as name_list and path_list respectively:
frame = {'Name': name_list,
'Path': path_list
}
2. Before exporting we need to create a dataframe called df_data with columns as Name and Path.
df_data = pd.DataFrame(frame)
3. Export the data to excel using the following code:
df_data.to_excel('Final.xlsx', index=False)
Code for the above-mentioned function:
Python3
# Function will create a data frame using pandas and
# write File/Folder, and their path to excel file.
def create_excel_using_pandas_dataframe(name_list,
path_list, path):
# Default Frame (a dictionary) is created with
# File/Folder names and their path with the given lists
frame = {'Name': name_list,
'Path': path_list
}
# Creates the dataframe using pandas with the given
# dictionary
df_data = pd.DataFrame(frame)
# Creates and saves the data to an excel file
df_data.to_excel('Final.xlsx', index=False)
create_excel_using_pandas_dataframe(name_list,
path_list, path)
Output:
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Python PackagesPython packages are a way to organize and structure code by grouping related modules into directories. A package is essentially a folder that contains an __init__.py file and one or more Python files (modules). This organization helps manage and reuse code effectively, especially in larger projects.
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Python ModulesPython Module is a file that contains built-in functions, classes,its and variables. There are many Python modules, each with its specific work.In this article, we will cover all about Python modules, such as How to create our own simple module, Import Python modules, From statements in Python, we c
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Python DSA LibrariesData Structures and Algorithms (DSA) serve as the backbone for efficient problem-solving and software development. Python, known for its simplicity and versatility, offers a plethora of libraries and packages that facilitate the implementation of various DSA concepts. In this article, we'll delve in
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List of Python GUI Library and PackagesGraphical User Interfaces (GUIs) play a pivotal role in enhancing user interaction and experience. Python, known for its simplicity and versatility, has evolved into a prominent choice for building GUI applications. With the advent of Python 3, developers have been equipped with lots of tools and li
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Data Science with Python
NumPy Tutorial - Python LibraryNumPy (short for Numerical Python ) is one of the most fundamental libraries in Python for scientific computing. It provides support for large, multi-dimensional arrays and matrices along with a collection of mathematical functions to operate on arrays.At its core it introduces the ndarray (n-dimens
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Pandas TutorialPandas is an open-source software library designed for data manipulation and analysis. It provides data structures like series and DataFrames to easily clean, transform and analyze large datasets and integrates with other Python libraries, such as NumPy and Matplotlib. It offers functions for data t
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Matplotlib TutorialMatplotlib is an open-source visualization library for the Python programming language, widely used for creating static, animated and interactive plots. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, Qt, GTK and wxPython. It
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Python Seaborn TutorialSeaborn is a library mostly used for statistical plotting in Python. It is built on top of Matplotlib and provides beautiful default styles and color palettes to make statistical plots more attractive.In this tutorial, we will learn about Python Seaborn from basics to advance using a huge dataset of
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StatsModel Library- TutorialStatsmodels is a useful Python library for doing statistics and hypothesis testing. It provides tools for fitting various statistical models, performing tests and analyzing data. It is especially used for tasks in data science ,economics and other fields where understanding data is important. It is
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Learning Model Building in Scikit-learnBuilding machine learning models from scratch can be complex and time-consuming. Scikit-learn which is an open-source Python library which helps in making machine learning more accessible. It provides a straightforward, consistent interface for a variety of tasks like classification, regression, clu
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TensorFlow TutorialTensorFlow is an open-source machine-learning framework developed by Google. It is written in Python, making it accessible and easy to understand. It is designed to build and train machine learning (ML) and deep learning models. It is highly scalable for both research and production.It supports CPUs
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PyTorch TutorialPyTorch is an open-source deep learning framework designed to simplify the process of building neural networks and machine learning models. With its dynamic computation graph, PyTorch allows developers to modify the networkâs behavior in real-time, making it an excellent choice for both beginners an
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Web Development with Python
Flask TutorialFlask is a lightweight and powerful web framework for Python. Itâs often called a "micro-framework" because it provides the essentials for web development without unnecessary complexity. Unlike Django, which comes with built-in features like authentication and an admin panel, Flask keeps things mini
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Django Tutorial | Learn Django FrameworkDjango is a Python framework that simplifies web development by handling complex tasks for you. It follows the "Don't Repeat Yourself" (DRY) principle, promoting reusable components and making development faster. With built-in features like user authentication, database connections, and CRUD operati
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Django ORM - Inserting, Updating & Deleting DataDjango's Object-Relational Mapping (ORM) is one of the key features that simplifies interaction with the database. It allows developers to define their database schema in Python classes and manage data without writing raw SQL queries. The Django ORM bridges the gap between Python objects and databas
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Templating With Jinja2 in FlaskFlask is a lightweight WSGI framework that is built on Python programming. WSGI simply means Web Server Gateway Interface. Flask is widely used as a backend to develop a fully-fledged Website. And to make a sure website, templating is very important. Flask is supported by inbuilt template support na
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Django TemplatesTemplates are the third and most important part of Django's MVT Structure. A Django template is basically an HTML file that can also include CSS and JavaScript. The Django framework uses these templates to dynamically generate web pages that users interact with. Since Django primarily handles the ba
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Python | Build a REST API using FlaskPrerequisite: Introduction to Rest API REST stands for REpresentational State Transfer and is an architectural style used in modern web development. It defines a set or rules/constraints for a web application to send and receive data. In this article, we will build a REST API in Python using the Fla
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How to Create a basic API using Django Rest Framework ?Django REST Framework (DRF) is a powerful extension of Django that helps you build APIs quickly and easily. It simplifies exposing your Django models as RESTfulAPIs, which can be consumed by frontend apps, mobile clients or other services.Before creating an API, there are three main steps to underst
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Python Practice
Python QuizThese Python quiz questions are designed to help you become more familiar with Python and test your knowledge across various topics. From Python basics to advanced concepts, these topic-specific quizzes offer a comprehensive way to practice and assess your understanding of Python concepts. These Pyt
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Python Coding Practice ProblemsThis collection of Python coding practice problems is designed to help you improve your overall programming skills in Python.The links below lead to different topic pages, each containing coding problems, and this page also includes links to quizzes. You need to log in first to write your code. Your
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Python Interview Questions and AnswersPython is the most used language in top companies such as Intel, IBM, NASA, Pixar, Netflix, Facebook, JP Morgan Chase, Spotify and many more because of its simplicity and powerful libraries. To crack their Online Assessment and Interview Rounds as a Python developer, we need to master important Pyth
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