Frequently asked Python practice questions and answers in Data Analytics Interview:
1.Temperature Conversion: Write a program that converts a given temperature from Celsius to Fahrenheit or from Fahrenheit to Celsius based on user input.
temp = float(input('Enter the temperature: '))
unit = input('Enter the unit (C/F): ').upper()
if unit == 'C':
converted = (temp * 9/5) + 32
print(f'Temperature in Fahrenheit: {converted}')
elif unit == 'F':
converted = (temp - 32) * 5/9
print(f'Temperature in Celsius: {converted}')
else:
print('Invalid unit')
2.Multiplication Table: Write a program that prints the multiplication table of a given number using a while loop.
num = int(input('Enter a number: '))
i = 1
while i <= 10:
print(f'{num} x {i} = {num * i}')
i += 1
3.Greatest of Three Numbers: Write a program that takes three numbers as input and prints the greatest of the three.
num1 = float(input('Enter first number: '))
num2 = float(input('Enter second number: '))
num3 = float(input('Enter third number: '))
if num1 >= num2 and num1 >= num3:
print(f'The greatest number is {num1}')
elif num2 >= num1 and num2 >= num3:
print(f'The greatest number is {num2}')
else:
print(f'The greatest number is {num3}')
4.Sum of Even Numbers: Write a program that calculates the sum of all even numbers between 1 and a given number using a while loop.
num = int(input('Enter a number: '))
total = 0
i = 2
while i <= num:
total += i
i += 2
print(f'The sum of even numbers up to {num} is {total}')
5.Check Armstrong Number: Write a program that checks if a given number is an Armstrong number.
num = int(input('Enter a number: '))
sum_of_digits = 0
original_num = num
while num > 0:
digit = num % 10
sum_of_digits += digit ** 3
num //= 10
if sum_of_digits == original_num:
print(f'{original_num} is an Armstrong number')
else:
print(f'{original_num} is not an Armstrong number')
6.Reverse a Number: Write a program that reverses the digits of a given number using a while loop.
num = int(input('Enter a number: '))
reversed_num = 0
while num > 0:
digit = num % 10
reversed_num = reversed_num * 10 + digit
num //= 10
print(f'The reversed number is {reversed_num}')
7.Count Vowels and Consonants: Write a program that counts the number of vowels and consonants in a given string.
string = input('Enter a string: ').lower()
vowels = 'aeiou'
vowel_count = 0
consonant_count = 0
for char in string:
if char.isalpha():
if char in vowels:
vowel_count += 1
else:
consonant_count += 1
print(f'Number of vowels: {vowel_count}')
print(f'Number of consonants: {consonant_count}')
Python Interview Q&A: https://topmate.io/coding/898340
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1.Temperature Conversion: Write a program that converts a given temperature from Celsius to Fahrenheit or from Fahrenheit to Celsius based on user input.
temp = float(input('Enter the temperature: '))
unit = input('Enter the unit (C/F): ').upper()
if unit == 'C':
converted = (temp * 9/5) + 32
print(f'Temperature in Fahrenheit: {converted}')
elif unit == 'F':
converted = (temp - 32) * 5/9
print(f'Temperature in Celsius: {converted}')
else:
print('Invalid unit')
2.Multiplication Table: Write a program that prints the multiplication table of a given number using a while loop.
num = int(input('Enter a number: '))
i = 1
while i <= 10:
print(f'{num} x {i} = {num * i}')
i += 1
3.Greatest of Three Numbers: Write a program that takes three numbers as input and prints the greatest of the three.
num1 = float(input('Enter first number: '))
num2 = float(input('Enter second number: '))
num3 = float(input('Enter third number: '))
if num1 >= num2 and num1 >= num3:
print(f'The greatest number is {num1}')
elif num2 >= num1 and num2 >= num3:
print(f'The greatest number is {num2}')
else:
print(f'The greatest number is {num3}')
4.Sum of Even Numbers: Write a program that calculates the sum of all even numbers between 1 and a given number using a while loop.
num = int(input('Enter a number: '))
total = 0
i = 2
while i <= num:
total += i
i += 2
print(f'The sum of even numbers up to {num} is {total}')
5.Check Armstrong Number: Write a program that checks if a given number is an Armstrong number.
num = int(input('Enter a number: '))
sum_of_digits = 0
original_num = num
while num > 0:
digit = num % 10
sum_of_digits += digit ** 3
num //= 10
if sum_of_digits == original_num:
print(f'{original_num} is an Armstrong number')
else:
print(f'{original_num} is not an Armstrong number')
6.Reverse a Number: Write a program that reverses the digits of a given number using a while loop.
num = int(input('Enter a number: '))
reversed_num = 0
while num > 0:
digit = num % 10
reversed_num = reversed_num * 10 + digit
num //= 10
print(f'The reversed number is {reversed_num}')
7.Count Vowels and Consonants: Write a program that counts the number of vowels and consonants in a given string.
string = input('Enter a string: ').lower()
vowels = 'aeiou'
vowel_count = 0
consonant_count = 0
for char in string:
if char.isalpha():
if char in vowels:
vowel_count += 1
else:
consonant_count += 1
print(f'Number of vowels: {vowel_count}')
print(f'Number of consonants: {consonant_count}')
Python Interview Q&A: https://topmate.io/coding/898340
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🔐"Key Python Libraries for Data Science:
Numpy: Core for numerical operations and array handling.
SciPy: Complements Numpy with scientific computing features like optimization.
Pandas: Crucial for data manipulation, offering powerful DataFrames.
Matplotlib: Versatile plotting library for creating various visualizations.
Keras: High-level neural networks API for quick deep learning prototyping.
TensorFlow: Popular open-source ML framework for building and training models.
Scikit-learn: Efficient tools for data mining and statistical modeling.
Seaborn: Enhances data visualization with appealing statistical graphics.
Statsmodels: Focuses on estimating and testing statistical models.
NLTK: Library for working with human language data.
These libraries empower data scientists across tasks, from preprocessing to advanced machine learning."
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Numpy: Core for numerical operations and array handling.
SciPy: Complements Numpy with scientific computing features like optimization.
Pandas: Crucial for data manipulation, offering powerful DataFrames.
Matplotlib: Versatile plotting library for creating various visualizations.
Keras: High-level neural networks API for quick deep learning prototyping.
TensorFlow: Popular open-source ML framework for building and training models.
Scikit-learn: Efficient tools for data mining and statistical modeling.
Seaborn: Enhances data visualization with appealing statistical graphics.
Statsmodels: Focuses on estimating and testing statistical models.
NLTK: Library for working with human language data.
These libraries empower data scientists across tasks, from preprocessing to advanced machine learning."
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Python is a popular programming language in the field of data analysis due to its versatility, ease of use, and extensive libraries for data manipulation, visualization, and analysis. Here are some key Python skills that are important for data analysts:
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
#Python
1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.
4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.
5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.
6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.
7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.
8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.
9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.
10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.
By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.
#Python
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