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If you're a job seeker, these well structured resources will help you to know and learn all the real time Python Interview questions with their exact answer. Folks who are having 0-4 years of experience have cracked the interview using this guide!

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NOTE: -Most data aspirants hoard resources without actually opening them even once! The reason for keeping a small price for these resources is to ensure that you value the content available inside this and encourage you to make the best out of it.

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I kept the minimal price so that everyone can afford but many people are misusing it by selling the same resources at higher cost.
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Python Quick Notes Part-1

Thanks for the amazing response on poll ☝️

Let's start the python series on YouTube so that I can explain the concepts with beautiful visuals

Here you can find how to print anything on Python
👇👇
https://youtu.be/3TVm0mzQfXw?si=VKS2xo4aaxbGaKy5

Please watch the complete video to understand it and let me know in comments if you want me to continue it ❤️

I explained it in Hindi Language, let me know in comments if you need similar videos in English as well

Kaafi time lagta hai voiceover and video editing mai, agar thoda sa bhi pasand aata hai to like and comment kardena.
https://youtu.be/3TVm0mzQfXw?si=VKS2xo4aaxbGaKy5

So that I can gradually shift towards more interesting topics 😄❤️

Here are some essential Python Concepts for Data Analyst

Roadmap to learn Python in 50 Days 👇👇
https://www.linkedin.com/posts/sql-analysts_many-people-pay-too-much-to-learn-python-activity-7189472351640576000-8vlW?utm_source=share&utm_medium=member_android

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TOP 10 Python Concepts for Job Interview

1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming

⌨️ Benefits of learning Python Programming

1. Web Development: Python frameworks like Django and Flask are popular for building dynamic websites and web applications.

2. Data Analysis: Python has powerful libraries like Pandas and NumPy for data manipulation and analysis, making it widely used in data science and analytic.

3. Machine Learning: Python's libraries such as TensorFlow, Keras, and Scikit-learn are extensively used for implementing machine learning algorithms and building predictive models.

4. Artificial Intelligence: Python is commonly used in AI development due to its simplicity and extensive libraries for tasks like natural language processing, image recognition, and neural network implementation.

5. Cybersecurity: Python is utilized for tasks such as penetration testing, network scanning, and creating security tools due to its versatility and ease of use.

6. Game Development: Python, along with libraries like Pygame, is used for developing games, prototyping game mechanics, and creating game scripts.

7. Automation: Python's simplicity and versatility make it ideal for automating repetitive tasks, such as scripting, data scraping, and process automation.

Python Programming Interview Questions for Entry Level Data Analyst

1. What is Python, and why is it popular in data analysis?

2. Differentiate between Python 2 and Python 3.

3. Explain the importance of libraries like NumPy and Pandas in data analysis.

4. How do you read and write data from/to files using Python?

5. Discuss the role of Matplotlib and Seaborn in data visualization with Python.

6. What are list comprehensions, and how do you use them in Python?

7. Explain the concept of object-oriented programming (OOP) in Python.


8. Discuss the significance of libraries like SciPy and Scikit-learn in data analysis.

9. How do you handle missing or NaN values in a DataFrame using Pandas?

10. Explain the difference between loc and iloc in Pandas DataFrame indexing.

11. Discuss the purpose and usage of lambda functions in Python.

12. What are Python decorators, and how do they work?

13. How do you handle categorical data in Python using the Pandas library?

14. Explain the concept of data normalization and its importance in data preprocessing.

15. Discuss the role of regular expressions (regex) in data cleaning with Python.

16. What are Python virtual environments, and why are they useful?

17. How do you handle outliers in a dataset using Python?

18. Explain the usage of the map and filter functions in Python.

19. Discuss the concept of recursion in Python programming.

20. How do you perform data analysis and visualization using Jupyter Notebooks?

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Python Interview Questions for Data/Business Analysts:

Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?

Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.

Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?

Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.

Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.

Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.

Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?

Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?

Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.

Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?

Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?

Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?

Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.

Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?

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Exploratory Data Analysis (EDA) in Python involves a variety of techniques and tools to summarize, visualize, and understand the structure of a dataset. Here are some common EDA techniques using Python, along with relevant libraries:

𝐈𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

𝐋𝐨𝐚𝐝𝐢𝐧𝐠 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚𝐬𝐞𝐭:

df = pd.read_csv('your_dataset.csv')

𝐈𝐧𝐢𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧:

1- View the first few rows:
df.head()

2- Summary of the dataset:
df.info()

3- Statistical summary:
df.describe()

𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐌𝐢𝐬𝐬𝐢𝐧𝐠 𝐕𝐚𝐥𝐮𝐞𝐬:

1- Identify missing values:
df.isnull().sum()

2- Visualize missing values:
sns.heatmap(df.isnull(), cbar=False, cmap='viridis')
plt.show()

𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧:

1- Histograms:
df.hist(bins=30, figsize=(20, 15))
plt.show()

2 - Box plots:
plt.figure(figsize=(10, 6))
sns.boxplot(data=df)
plt.xticks(rotation=90)
plt.show()

3- Pair plots:
sns.pairplot(df)
plt.show()

4- Correlation matrix and heatmap:
correlation_matrix = df.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.show()

𝐂𝐚𝐭𝐞𝐠𝐨𝐫𝐢𝐜𝐚𝐥 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬:
Count plots for categorical features:

plt.figure(figsize=(10, 6))
sns.countplot(x='categorical_column', data=df)
plt.show()

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These 6 videos will help you to get the product knowledge and case study solving skill
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https://t.iss.one/caseinterviewscracked/18

Frequently asked Python practice questions and answers in Data Analyst 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}')

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Lists 🆚 Tuples 🆚 Dictionaries

What's the difference?

Lists are mutable.
Tuples are immutable.
Dictionaries are associative.

When should you use each?

Lists:
⟶ When you want to add or remove elements
⟶ When you want to sort elements
⟶ When you want to slice elements

Tuples:
⟶ When you want a constant object
⟶ When you want to send multiple in a function
⟶ When you want to return multiple from a function

Dictionaries:
⟶ When you want to map keys to values
⟶ When you want to loop over the keys
⟶ When you want to validate if key exists

Now, pick your weapon of mass data analysis and become a Python pro!

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