Python for Coder's Day 13: Introduction to NumPy and Pandas for Data Analysis | Egnicode

Welcome to Day 13 of our Python series! 🌟 In this video, we’re introducing two of Python’s most powerful libraries: NumPy and Pandas. These tools are essential for data manipulation, numerical computation, and data analysis, making them must-know libraries for data enthusiasts and developers alike.

🚀 What You'll Learn in This Video:
✅ NumPy Arrays: Learn how to create and manipulate arrays efficiently.
✅ NumPy Operations: Explore numerical computations and matrix operations.
✅ Pandas DataFrames: Work with tabular data for analysis.
✅ Series: Understand one-dimensional labeled data and perform quick operations.

💡 Real-World Applications: These libraries are widely used in data science, machine learning, and scientific computing. By mastering these tools, you'll unlock endless possibilities in data manipulation and analysis.

🔧 Tools Used:

Python 3.x
Libraries: NumPy and Pandas
IDE: Jupyter Notebook, VS Code, or PyCharm
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Code Examples for the Video

1. NumPy Basics: Arrays and Operations
python
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import numpy as np

# Creating a NumPy array
print("Array:", array)

# Array operations
print("Array + 2:", array + 2)
print("Array squared:", array ** 2)

# Matrix multiplication
print("Matrix Multiplication:\n", result)

2. Pandas Basics: DataFrames and Series
python
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import pandas as pd

# Creating a DataFrame
data = {
"Name": ["Alice," "Bob," "Charlie"].
"Age": [25, 30, 35],
"Salary": [50000, 60000, 70000]
}
df = pd.DataFrame(data)
print("DataFrame:\n", df)

# Accessing columns
print("Ages:", df["Age"])

# Adding a new column
df["Bonus"] = df["Salary"] * 0.1
print("Updated DataFrame:\n", df)

3. Working with Pandas Series
python
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# Creating a Series
series = pd.Series([10, 20, 30, 40], index=["a", "b", "c", "d"])
print("Series:\n", series)

# Accessing elements
print("Element with index 'b':", series["b"])

# Performing operations
print("Series multiplied by 2:\n", series * 2)

4. Combining NumPy and Pandas
python
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# NumPy array to DataFrame
df = pd.DataFrame(array, columns=["A", "B", "C"])
print("DataFrame from NumPy array:\n", df)

# Performing column-wise operations
df["A_squared"] = df["A"] ** 2
print("Updated DataFrame:\n", df)
Key Takeaways:
NumPy is ideal for numerical computations with arrays and matrices.
Pandas excels in organizing and analyzing structured data.
Together, they form the backbone of Python's data analysis ecosystem.
By the end of this video, you’ll be equipped to handle numerical computations and perform data analysis tasks efficiently. This is your first step into the exciting world of data science! 💻

Let us know in the comments if you’d like more examples or challenges to practice. Don’t forget to like, share, and subscribe to Egnicode for more awesome content! 🚀

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