Essential NumPy Functions for Data Analysis

Array Creation:

np.array() - Create an array from a list.

np.zeros((rows, cols)) - Create an array filled with zeros.

np.ones((rows, cols)) - Create an array filled with ones.

np.arange(start, stop, step) - Create an array with a range of values.


Array Operations:

np.sum(array) - Calculate the sum of array elements.

np.mean(array) - Compute the mean.

np.median(array) - Calculate the median.

np.std(array) - Compute the standard deviation.


Indexing and Slicing:

array[start:stop] - Slice an array.

array[row, col] - Access a specific element.

array[:, col] - Select all rows for a column.


Reshaping and Transposing:

array.reshape(new_shape) - Reshape an array.

array.T - Transpose an array.


Random Sampling:

np.random.rand(rows, cols) - Generate random numbers in [0, 1).

np.random.randint(low, high, size) - Generate random integers.


Mathematical Operations:

np.dot(A, B) - Compute the dot product.

np.linalg.inv(A) - Compute the inverse of a matrix.

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Roadmap to become a Python Developer:

📂 Learn Python Basics (Syntax, Data Types, Loops)
∟📂 Learn Data Structures (Lists, Tuples, Dicts, Sets)
∟📂 Learn Functions & Modules
∟📂 Learn File Handling & Exceptions
∟📂 Learn OOP Concepts
∟📂 Learn Libraries (Pandas, NumPy, etc.)
∟📂 Learn Web Development (Flask / Django)
∟📂 Learn APIs & Database Integration
∟📂 Build Projects & Portfolio
∟✅ Apply for Job

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- Declare variables close to usage
- Functions do 1 thing
- Avoid long functions
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What are the common built-in data types in Python?

Python supports the below-mentioned built-in data types:

Immutable data types:

👉Number
👉String
👉Tuple

Mutable data types:

👉List
👉Dictionary
👉set

Python Most Important Interview Questions

Question 1: Calculate the average stock price for Company X over the last 6 months.

Question 2: Identify the month with the highest total sales for Company Y using their monthly sales data.

Question 3: Find the maximum and minimum stock price for Company Z on any given day in the last year.

Question 4: Create a column in the DataFrame showing the percentage change in stock price from the previous day for Company X.

Question 5: Determine the number of days when the stock price of Company Y was above its 30-day moving average. Question

6: Compare the average stock price of Companies X and Z in the first quarter of the year.

#Data#
----------------------------------------------
import pandas as pd
data = {   'Date': pd.date_range(start='2023-01-01', periods=180, freq='D'),   'CompanyX_StockPrice': pd.np.random.randint(50, 150, 180),   'CompanyY_Sales': pd.np.random.randint(20000, 50000, 180),   'CompanyZ_StockPrice': pd.np.random.randint(70, 200, 180) }

df = pd.DataFrame(data)

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Steps to learn Data Structures and Algorithms (DSA) with Python

1. Learn Python: If you're not already familiar with Python, start by learning the basics of the language. There are many online resources and tutorials available for free.

2. Understand the Basics: Before diving into DSA, make sure you have a good grasp of Python's syntax, data types, and basic programming concepts. Use free resources from @dsabooks to help you in learning journey.

3. Pick Good Learning Resources: Choose a good book, online course, or tutorial series on DSA with Python. Most of the free stuff is already posted on the channel @crackingthecodinginterview

4. Data Structures: Begin with fundamental data structures like lists, arrays, stacks, queues, linked lists, trees, graphs, and hash tables. Understand their properties, operations, and when to use them.

5. Algorithms: Study common algorithms such as searching (binary search, linear search), sorting (quick sort, merge sort), and dynamic programming. Learn about their time and space complexity.

6. Practice: The key to mastering DSA is practice. Solve a wide variety of problems to apply your knowledge. Websites like LeetCode and HackerRank provide a vast collection of problems.

7. Analyze Complexity: Learn how to analyze the time and space complexity of algorithms. Big O notation is a crucial concept in DSA.

8. Implement Algorithms: Implement algorithms and data structures from scratch in Python. This hands-on experience will deepen your understanding.

9. Project Work: Apply DSA to real projects. This could be building a simple game, a small web app, or any software that requires efficient data handling. Check channel @programming_experts if you need project ideas.

10. Seek Help and Collaborate: Don't hesitate to ask for help when you're stuck. Engage in coding communities, forums, or collaborate with others to gain new insights.

11. Review and Revise: Periodically review what you've learned. Reinforce your understanding by revisiting data structures and algorithms you've studied.

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