From b605661bcd3dbd0bd4407194320e6a6a9382a45e Mon Sep 17 00:00:00 2001 From: Ro-shni <112896479+Ro-shni@users.noreply.github.com> Date: Wed, 4 Oct 2023 18:42:37 +0530 Subject: [PATCH 1/6] Update Intro-to-Numpy.jsx --- .../Numpy/Intro-to-Numpy.jsx | 74 +++++++++++++++++-- 1 file changed, 68 insertions(+), 6 deletions(-) diff --git a/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx b/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx index 31986e7..4645fe3 100644 --- a/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx +++ b/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx @@ -1,13 +1,75 @@ +import { color } from "framer-motion"; import React from "react"; -const NumpyBasics = () => { +const PandasBasics = () => { return ( -
-

Introduction to NumPy

+
+
+

NumPy

+
+ +
+

Exploring the World of Numerical Computing with NumPy

+
+
+
+

+ In the vast realm of data science and scientific computing, the ability to efficiently manipulate and process large datasets is of paramount importance. This is where NumPy, short for Numerical Python, comes into play. NumPy is a powerful library for Python that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. In this blog post, we'll dive into the world of NumPy, exploring its features and benefits. +

-

Development can start for NumPy from here

+ +
    +
  • + • Efficient Array Operations: +
      +
    • NumPy's primary feature is the ndarray (n-dimensional array) object, which enables you to perform efficient array operations. These arrays are more memory-efficient and faster to process than traditional Python lists.
      • +
    • The ability to perform element-wise operations simplifies complex mathematical calculations.
      • +
    +
    • +
  • + • Broadcasting: +
      +
    • NumPy allows for broadcasting, which means you can perform operations on arrays of different shapes.
      • +
    • NumPy automatically broadcasts smaller arrays to match the shape of larger ones, making element-wise operations possible in many scenarios.
      • +
    +
    • +
  • + • Mathematical Functions: +
      +
    • NumPy provides a vast array of mathematical functions and operations for tasks such as linear algebra, Fourier analysis, statistics, and more.
      • +
    • It's a go-to tool for numerical computations.
      • +
    +
    • +
  • + • Interoperability: +
      +
    • NumPy seamlessly integrates with other data analysis libraries like Pandas and visualization libraries like Matplotlib.
      • +
    • This interoperability allows data scientists to work efficiently with data and produce meaningful visualizations.
      • +
    +
    • +
+ + + Applications of NumPy +

+ NumPy is the backbone of many scientific and data analysis tasks. Its applications include data manipulation, statistical analysis, image processing, machine learning, and more. Whether you're working with large datasets or complex mathematical models, NumPy plays a crucial role in making these tasks manageable and efficient. +

+ +

More About NumPy

+

NumPy is the foundation of many Python libraries used in scientific and data-intensive applications. It supports a wide range of data types, including numeric, logical, and complex data, and includes tools for integrating with databases and performing file I/O. NumPy's flexibility and efficiency make it the ideal choice for numerical and data manipulation tasks.

+ +
  • + Numpy Web +
    • +
  • + Latest development documentation +
    • + + + {/* Add more content here */} +
    +
    ); }; - -export default NumpyBasics; \ No newline at end of file +export default PandasBasics; From 145f574f6e081a48468cb5f408358dabd8525e10 Mon Sep 17 00:00:00 2001 From: Ro-shni <112896479+Ro-shni@users.noreply.github.com> Date: Thu, 12 Oct 2023 09:20:43 +0530 Subject: [PATCH 2/6] Update Intro-to-Numpy.jsx --- src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx b/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx index 4645fe3..e310804 100644 --- a/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx +++ b/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx @@ -1,7 +1,7 @@ import { color } from "framer-motion"; import React from "react"; -const PandasBasics = () => { +const NumpyBasics = () => { return (
    @@ -72,4 +72,4 @@ const PandasBasics = () => {
    ); }; -export default PandasBasics; +export default NumpyBasics; From 3e67771289b5038d2bec90d93e3f4a3e90d8f911 Mon Sep 17 00:00:00 2001 From: Ro-shni <112896479+Ro-shni@users.noreply.github.com> Date: Thu, 12 Oct 2023 11:00:03 +0530 Subject: [PATCH 3/6] Update Intro-to-Numpy.jsx --- .../Numpy/Intro-to-Numpy.jsx | 169 +++++++++++++----- 1 file changed, 128 insertions(+), 41 deletions(-) diff --git a/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx b/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx index e310804..890812f 100644 --- a/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx +++ b/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx @@ -1,75 +1,162 @@ -import { color } from "framer-motion"; -import React from "react"; +import React from 'react'; const NumpyBasics = () => { + const containerStyle = { + fontFamily: 'Arial, sans-serif', + color: '#333', + maxWidth: '800px', + margin: '0 auto', + padding: '20px', + }; + + const heading1Style = { + textAlign: 'center', + fontSize: '36px', + fontWeight: 'bold', + margin: '20px 0', + }; + + const heading2Style = { + fontSize: '28px', + fontWeight: 'bold', + margin: '20px 0', + }; + + const heading3Style = { + fontSize: '24px', + fontWeight: 'bold', + margin: '20px 0', + }; + + const paragraphStyle = { + fontSize: '18px', + lineHeight: '1.6', + margin: '20px 0', + }; + + const listStyle = { + fontSize: '18px', + lineHeight: '1.6', + margin: '20px 0', + }; + + const linkStyle = { + color: '#007BFF', + textDecoration: 'none', + fontWeight: 'bold', + }; + + const italicStyle = { + fontStyle: 'italic', + }; + return ( -
    -
    -

    NumPy

    -
    - -
    -

    Exploring the World of Numerical Computing with NumPy

    +
    +
    +

    NumPy: Exploring the World of Numerical Computing

    +
    -

    - In the vast realm of data science and scientific computing, the ability to efficiently manipulate and process large datasets is of paramount importance. This is where NumPy, short for Numerical Python, comes into play. NumPy is a powerful library for Python that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays. In this blog post, we'll dive into the world of NumPy, exploring its features and benefits. +

    Efficient Numerical Computing

    +

    + In the realm of data science and scientific computing, efficiently manipulating and processing large datasets is paramount. Enter NumPy, short for Numerical Python, a powerful Python library that supports large, multi-dimensional arrays and matrices. It also provides a wide range of mathematical functions for array operations. In this blog post, we'll explore NumPy's features and benefits.

    - -
      +
      • - • Efficient Array Operations: -
          -
        • NumPy's primary feature is the ndarray (n-dimensional array) object, which enables you to perform efficient array operations. These arrays are more memory-efficient and faster to process than traditional Python lists.
          • -
        • The ability to perform element-wise operations simplifies complex mathematical calculations.
          • + Efficient Array Operations: +
            +
          • NumPy's ndarray (n-dimensional array) object enables efficient array operations, making it faster and more memory-efficient than traditional Python lists.
            • +
          • Element-wise operations simplify complex mathematical calculations.
        • - • Broadcasting: -
            -
          • NumPy allows for broadcasting, which means you can perform operations on arrays of different shapes.
            • -
          • NumPy automatically broadcasts smaller arrays to match the shape of larger ones, making element-wise operations possible in many scenarios.
            • + Broadcasting: +
              +
            • NumPy allows operations on arrays of different shapes through broadcasting.
              • +
            • It automatically aligns smaller arrays with larger ones, enabling element-wise operations.
          • - • Mathematical Functions: -
              -
            • NumPy provides a vast array of mathematical functions and operations for tasks such as linear algebra, Fourier analysis, statistics, and more.
              • -
            • It's a go-to tool for numerical computations.
              • + Mathematical Functions: +
                +
              • NumPy provides a vast array of mathematical functions for tasks such as linear algebra, Fourier analysis, and statistics.
                • +
              • It's an essential tool for numerical computations.
            • - • Interoperability: -
                -
              • NumPy seamlessly integrates with other data analysis libraries like Pandas and visualization libraries like Matplotlib.
                • -
              • This interoperability allows data scientists to work efficiently with data and produce meaningful visualizations.
                • + Interoperability: +
                  +
                • NumPy seamlessly integrates with data analysis libraries like Pandas and visualization libraries like Matplotlib.
                  • +
                • This enables efficient data processing and meaningful visualizations.
              +

              + Applications of NumPy +
              + NumPy is indispensable in scientific and data analysis tasks, including data manipulation, statistical analysis, image processing, and machine learning. It's a crucial tool for working with large datasets and complex mathematical models. +

              + +

              Exploring Further

              +

              + NumPy serves as the foundation for many Python libraries used in scientific and data-intensive applications. It supports various data types, integrates with databases, and handles file I/O efficiently. Its flexibility and efficiency make it the ideal choice for numerical and data manipulation tasks. +

              - Applications of NumPy -

              - NumPy is the backbone of many scientific and data analysis tasks. Its applications include data manipulation, statistical analysis, image processing, machine learning, and more. Whether you're working with large datasets or complex mathematical models, NumPy plays a crucial role in making these tasks manageable and efficient. +

              +

              Example: Working with NumPy

              +

              + Here's an example of working with NumPy in a Python script. The script creates a NumPy array, performs a simple operation, and displays the result.

              -

              More About NumPy

              -

              NumPy is the foundation of many Python libraries used in scientific and data-intensive applications. It supports a wide range of data types, including numeric, logical, and complex data, and includes tools for integrating with databases and performing file I/O. NumPy's flexibility and efficiency make it the ideal choice for numerical and data manipulation tasks.

              + 
              +            {`# Python script to work with NumPy
+import numpy as np
+
+# Create a NumPy array
+arr = np.array([1, 2, 3, 4, 5])
 
-          
            • 
-          Numpy Web
-        
              • 
-        
            • 
-          Latest development documentation
-        
              • 
+# Perform a simple operation (multiply by 2)
+result = arr * 2
 
+# Print the result
+print("Original array:")
+print(arr)
+print("\\nResult after multiplying by 2:")
+print(result)
+`}
+
              - {/* Add more content here */} +

              + To run this script and see the output, execute the Python script in your Python environment with NumPy installed. Make sure to include the necessary import statement for NumPy in your script. +

              + +

              + If you haven't already installed NumPy, you can do so using pip: +

              + + 
              +            {'pip install numpy'}
+
    + +
    +

    + NumPy is an essential tool for numerical computing and scientific data analysis. Its efficiency, extensive features, and wide-ranging applications make it a must-know for data scientists and Python developers. Install NumPy and unlock the potential of numerical computing in Python! +

    +
    ); }; + export default NumpyBasics; From 1011b0efea511dcb39d3a2d336225cc736d3c91f Mon Sep 17 00:00:00 2001 From: Ro-shni <112896479+Ro-shni@users.noreply.github.com> Date: Thu, 12 Oct 2023 11:03:32 +0530 Subject: [PATCH 4/6] Update Intro-to-Numpy.jsx --- src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx b/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx index 890812f..15260fd 100644 --- a/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx +++ b/src/Python_Library_Pages/Numpy/Intro-to-Numpy.jsx @@ -1,4 +1,4 @@ -import React from 'react'; +import React from "react"; const NumpyBasics = () => { const containerStyle = { From ca5b931c05d221484a2aae7313e9a8720888b78e Mon Sep 17 00:00:00 2001 From: Ro-shni <112896479+Ro-shni@users.noreply.github.com> Date: Tue, 24 Oct 2023 08:02:28 +0530 Subject: [PATCH 5/6] Update Intro-to-Pandas.jsx --- .../Pandas/Intro-to-Pandas.jsx | 128 +++++++++++++++++- 1 file changed, 124 insertions(+), 4 deletions(-) diff --git a/src/Python_Library_Pages/Pandas/Intro-to-Pandas.jsx b/src/Python_Library_Pages/Pandas/Intro-to-Pandas.jsx index 9e2102a..0438fe8 100644 --- a/src/Python_Library_Pages/Pandas/Intro-to-Pandas.jsx +++ b/src/Python_Library_Pages/Pandas/Intro-to-Pandas.jsx @@ -1,13 +1,133 @@ import React from "react"; const PandasBasics = () => { + const containerStyle = { + fontFamily: 'Arial, sans-serif', + color: '#333', + maxWidth: '800px', + margin: '0 auto', + padding: '20px', + }; + + const heading1Style = { + textAlign: 'center', + fontSize: '36px', + fontWeight: 'bold', + margin: '20px 0', + }; + + const heading2Style = { + fontSize: '28px', + fontWeight: 'bold', + margin: '20px 0', + }; + + const paragraphStyle = { + fontSize: '18px', + lineHeight: '1.6', + margin: '20px 0', + }; + + const linkStyle = { + color: '#007BFF', + textDecoration: 'none', + fontWeight: 'bold', + }; + + const italicStyle = { + fontStyle: 'italic', + }; + return ( -
    -

    Introduction to Pandas

    +
    +
    +

    Pandas: Data Manipulation Made Easy

    +
    + +
    +
    +

    Efficient Data Manipulation

    +

    + When it comes to data manipulation and analysis in Python, Pandas is a go-to library. It provides easy-to-use data structures and data analysis tools. In this blog post, we'll explore Pandas and its features. +

    + +

    + Key Features of Pandas +
    + Pandas offers a range of features that make data manipulation efficient and intuitive: +

    + +
      +
    • + Data Structures: Pandas introduces two primary data structures, Series (1D) and DataFrame (2D), for handling data. +
      • +
    • + Data Cleaning: It provides methods for handling missing data and cleaning messy datasets. +
      • +
    • + Data Transformation: You can perform various operations like merging, reshaping, and pivoting data effortlessly. +
      • +
    • + Data Analysis: Pandas offers tools for data exploration, aggregation, and statistical analysis. +
      • +
    + +

    + Whether you are dealing with structured data, time series, or messy real-world datasets, Pandas simplifies the process. +

    + +

    Exploring Further

    +

    + Pandas is widely used in data analysis, scientific research, and machine learning projects. It seamlessly integrates with libraries like NumPy and Matplotlib for advanced data analysis and visualization. +

    + + + +

    Example: Data Manipulation with Pandas

    +

    + Here's a simple example of using Pandas to load a CSV file, perform basic operations, and display the data. +

    + + 
    +            {`# Python script to work with Pandas
+import pandas as pd
+
+# Load a CSV file
+data = pd.read_csv('example.csv')
+
+# Display the first few rows
+print(data.head())
+`}
+
    + +

    + To run this script, ensure you have Pandas installed in your Python environment and replace 'example.csv' with the path to your CSV file. +

    + +

    + If you haven't already installed Pandas, you can do so using pip: +

    + + 
    +            {'pip install pandas'}
+
    +
    +
    -

    Development can start for Pandas from here

    +
    ); }; -export default PandasBasics; \ No newline at end of file +export default PandasBasics; From 6b7c8ef39d224cdf90a536196d7053d818c7f141 Mon Sep 17 00:00:00 2001 From: Ro-shni <112896479+Ro-shni@users.noreply.github.com> Date: Tue, 24 Oct 2023 08:16:55 +0530 Subject: [PATCH 6/6] Intro-to-Pandas.jsx --- src/Python_Library_Pages/Pandas/Intro-to-Pandas.jsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/Python_Library_Pages/Pandas/Intro-to-Pandas.jsx b/src/Python_Library_Pages/Pandas/Intro-to-Pandas.jsx index 0438fe8..97ffce7 100644 --- a/src/Python_Library_Pages/Pandas/Intro-to-Pandas.jsx +++ b/src/Python_Library_Pages/Pandas/Intro-to-Pandas.jsx @@ -1,5 +1,4 @@ import React from "react"; - const PandasBasics = () => { const containerStyle = { fontFamily: 'Arial, sans-serif', @@ -131,3 +130,4 @@ print(data.head()) }; export default PandasBasics; +