Python Data Science Handbook

Python Data Science Handbook: full text in Jupyter Notebooks. This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks.

Creator: Jake Vanderplas
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Repo: https://github.com/jakevdp/PythonDataScienceHandbook

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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

React ❤️ for More

9 tips to improve your code:

- Declare variables close to usage
- Functions do 1 thing
- Avoid long functions
- Avoid long lines
- Don't repeat code
- Use descriptive variable/function names
- Use few arguments
- Simplify conditions (return age >17;)
- Remove unused code

Without errors, No-one can become a good programmer.
Errors are the most important phase of learning to code.

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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⚔ Codewars.com
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🌀 Codeforces.com
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