Numpy_in_depth_course_2023_for_Indian_Kagglers.ipynb

{"cells":[{"source":"<a href=\"https://www.kaggle.com/code/mlvprasad/numpy-part-1-of-5-indepth-notebook?scriptVersionId=141316589\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","id":"2e5d05ca","metadata":{"execution":{"iopub.execute_input":"2023-06-30T22:18:07.194461Z","iopub.status.busy":"2023-06-30T22:18:07.194087Z","iopub.status.idle":"2023-06-30T22:18:07.907827Z","shell.execute_reply":"2023-06-30T22:18:07.906605Z","shell.execute_reply.started":"2023-06-30T22:18:07.194432Z"},"papermill":{"duration":0.007462,"end_time":"2023-08-29T06:02:35.939212","exception":false,"start_time":"2023-08-29T06:02:35.93175","status":"completed"},"tags":[]},"source":["![mlv prasad](https://github.com/MlvPrasadOfficial/kaggle_notebooks/raw/main/mlvprasad.png)\n"]},{"cell_type":"markdown","id":"0b0b729c","metadata":{"papermill":{"duration":0.006458,"end_time":"2023-08-29T06:02:35.952607","exception":false,"start_time":"2023-08-29T06:02:35.946149","status":"completed"},"tags":[]},"source":["![pandas](https://github.com/MlvPrasadOfficial/ZZPYCHARM/raw/main/canvapandasnumpy/NUMP11.png)\n"]},{"cell_type":"markdown","id":"7382dd6b","metadata":{"papermill":{"duration":0.006725,"end_time":"2023-08-29T06:02:35.966063","exception":false,"start_time":"2023-08-29T06:02:35.959338","status":"completed"},"tags":[]},"source":["### <font color='289C4E'>TABLE OF CONTENTS<font><a class='anchor' id='top'></a>"]},{"cell_type":"markdown","id":"b1b94ac2","metadata":{"papermill":{"duration":0.006596,"end_time":"2023-08-29T06:02:35.979478","exception":false,"start_time":"2023-08-29T06:02:35.972882","status":"completed"},"tags":[]},"source":["# Table of Contents\n","\n","1. [Chapter 1: Introduction to NumPy](#chapter-1-introduction-to-numpy)\n","2. [Chapter 2: Installing and Importing NumPy](#chapter-2-installing-and-importing-numpy)\n","3. [Chapter 3: NumPy Arrays: Creation and Initialization](#chapter-3-numpy-arrays-creation-and-initialization)\n","4. [Chapter 4: NumPy Array Indexing and Slicing](#chapter-4-numpy-array-indexing-and-slicing)\n","5. [Chapter 5: Understanding Array Shape and Dimensions](#chapter-5-understanding-array-shape-and-dimensions)\n","6. [Chapter 6: Exploring Array Data Types](#chapter-6-exploring-array-data-types)\n","7. [Chapter 7: Basic Operations on NumPy Arrays](#chapter-7-basic-operations-on-numpy-arrays)\n","8. [Chapter 8: Mathematical Functions in NumPy](#chapter-8-mathematical-functions-in-numpy)\n","9. [Chapter 9: Aggregation and Statistical Functions](#chapter-9-aggregation-and-statistical-functions)\n","10. [Chapter 10: Sorting and Searching in Arrays](#chapter-10-sorting-and-searching-in-arrays)\n","11. [Chapter 11: Array Manipulation Techniques](#chapter-11-array-manipulation-techniques)\n","12. [Chapter 12: Broadcasting in NumPy](#chapter-12-broadcasting-in-numpy)\n","13. [Chapter 13: Input and Output Operations with NumPy Arrays](#chapter-13-input-and-output-operations-with-numpy-arrays)\n","14. [Chapter 14: Reading and Writing CSV Files with NumPy](#chapter-14-reading-and-writing-csv-files-with-numpy)\n","15. [Chapter 15: Binary File Operations with NumPy](#chapter-15-binary-file-operations-with-numpy)\n","16. [Chapter 16: Saving and Loading NumPy Arrays](#chapter-16-saving-and-loading-numpy-arrays)\n","17. [Chapter 17: Reshaping Arrays in NumPy](#chapter-17-reshaping-arrays-in-numpy)\n","18. [Chapter 18: Joining and Splitting Arrays](#chapter-18-joining-and-splitting-arrays)\n","19. [Chapter 19: Adding and Removing Elements from Arrays](#chapter-19-adding-and-removing-elements-from-arrays)\n","20. [Chapter 20: Rearranging Array Elements](#chapter-20-rearranging-array-elements)\n"]},{"cell_type":"markdown","id":"d802bb57","metadata":{"papermill":{"duration":0.006533,"end_time":"2023-08-29T06:02:35.992867","exception":false,"start_time":"2023-08-29T06:02:35.986334","status":"completed"},"tags":[]},"source":["<h1 align=\"center\"><font color='red'>Let's Begin</font></h1>\n"]},{"cell_type":"markdown","id":"89711fde","metadata":{"papermill":{"duration":0.006669,"end_time":"2023-08-29T06:02:36.0064","exception":false,"start_time":"2023-08-29T06:02:35.999731","status":"completed"},"tags":[]},"source":["# <h1 align=\"left\"><font color='red'>1 </font></h1>\n","\n","## **1.1** Overview:\n","### Chapter 1 serves as an introduction to NumPy, a powerful numerical computing library in Python. NumPy stands for Numerical Python and is widely used in the scientific and data analysis communities. This chapter provides an overview of NumPy, its features, and the benefits it offers for numerical computations.\n","\n","## 1.2 What is NumPy?\n","### NumPy is a fundamental library in Python for numerical computing. It provides efficient multidimensional array objects, which are known as NumPy arrays or simply arrays. These arrays are the primary data structure in NumPy, and they allow efficient storage and manipulation of large datasets. NumPy also provides a wide range of mathematical functions and tools for working with these arrays.\n","\n","## 1.3 Key Features of NumPy:\n","### **Multidimensional Array Objects** : NumPy arrays are homogeneous and can have any number of dimensions. They are efficient for storing and performing operations on large datasets.\n","### **Mathematical Functions** : NumPy provides a vast collection of mathematical functions, including basic arithmetic operations, trigonometric functions, logarithmic functions, statistical functions, and more.\n","\n","### **Broadcasting**: NumPy allows operations on arrays of different shapes and sizes through broadcasting, which simplifies array operations and eliminates the need for explicit loops.\n","### **Integration with Other Libraries** : NumPy seamlessly integrates with other scientific libraries in Python, such as SciPy (Scientific Python) for advanced scientific computing and Matplotlib for data visualization.\n","### **Efficient Data Processing**: NumPy is implemented in C, which makes it faster than pure Python for numerical computations. It also has optimized algorithms for array operations, making it suitable for high-performance computing.\n","## 1.4 Installing NumPy:\n","### To start using NumPy, you need to install it. The most common way is to use the Python package manager, pip. You can open your command prompt or terminal and run the following command:\n","\n","```\n","pip install numpy\n","```\n","### This command will download and install the latest version of NumPy.\n","\n","\n","## 1.5 Importing NumPy:\n","### Once NumPy is installed, you can import it into your Python script or interactive session using the import statement:\n","\n","```python\n","import numpy as np\n","```\n","### The import statement makes all the functionalities of NumPy accessible under the namespace np. This is a widely used convention in the scientific Python community.\n","\n","## 1.6 Creating NumPy Arrays:\n","### NumPy arrays are the building blocks of numerical computations. There are multiple ways to create NumPy arrays:\n","\n","### **Converting Python Lists** : You can convert a Python list to a NumPy array using the np.array() function.\n","### **Using Built-in Functions** : NumPy provides several functions for creating arrays, such as np.zeros(), np.ones(), np.arange(), and np.linspace().\n","### **Reading from Files** : NumPy can also read data from files like CSV or text files and create arrays from them.\n","\n","## 1.7 Array Attributes: \n","### NumPy arrays have several attributes that provide information about their shape, size, data type, and memory layout. Some commonly used attributes include shape, ndim, dtype, and size.\n","\n","## 1.8 Array Indexing and Slicing:\n","### NumPy arrays support various indexing and slicing techniques, similar to Python lists. You can access individual elements, subsets of elements, or entire rows and columns of an array.\n","\n","## 1.9 Array Operations:\n","### NumPy provides a wide range of mathematical and logical operations on arrays. These include element-wise arithmetic operations, array multiplication and dot product, comparison operators, logical operations, and more. NumPy's vectorized operations are faster and more concise than explicit loops in Python.\n","\n","## 1.10 Universal Functions (ufuncs):\n","### Universal functions, or ufuncs, are functions that operate element-wise on arrays, producing an output array with the same shape. NumPy provides a vast collection of ufuncs, including mathematical functions, trigonometric functions, exponential functions, logarithmic functions, and statistical functions.\n","\n","## 1.11 Conclusion:\n","### **Chapter 1** provides an introduction to NumPy, highlighting its features, benefits, and installation process. It covers the basics of creating NumPy arrays, accessing array attributes, performing array indexing and slicing, and using universal functions. This foundation sets the stage for exploring more advanced topics and applications of NumPy in subsequent chapters.\n","\n","\n","\n"]},{"cell_type":"markdown","id":"6a8180f0","metadata":{"papermill":{"duration":0.006568,"end_time":"2023-08-29T06:02:36.019693","exception":false,"start_time":"2023-08-29T06:02:36.013125","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>2 </font></h1>\n","\n","\n","\n","## 2.1 Introduction:\n","### Chapter 2 focuses on the practical aspects of installing and importing NumPy, a powerful numerical computing library in Python. It provides step-by-step instructions on how to install NumPy on your system and how to import it into your Python environment.\n","\n","## 2.2 Installing NumPy:\n","### Before you can use NumPy, you need to install it on your system. Here are the steps to install NumPy using the Python package manager, pip:\n","\n","#### **Step 1**: Open your command prompt or terminal.\n","\n","#### **Step 2**: Run the following command:\n","\n","```\n","pip install numpy\n","```\n","\n","### This command will initiate the installation process and download the latest version of NumPy from the Python Package Index (PyPI).\n","\n","#### **Step 3** : Wait for the installation to complete. You will see the progress of the installation in the command prompt or terminal.\n","\n","#### **Step 4** : Once the installation is finished, you can verify if NumPy is successfully installed by importing it in a Python script or interactive session.\n","\n","## 2.3 Importing NumPy:\n","### After installing NumPy, you can import it into your Python environment using the import statement. Here's how to import NumPy:\n","\n","#### **Step 1** : Open a Python script or start an interactive session.\n","\n","#### **Step 2** : Add the following line at the beginning of your script or session to import NumPy:\n","\n","```python\n","import numpy as np\n","```\n","\n","\n","\n","#### **Step 3** : Now you can use the functionalities of NumPy by calling them with the np prefix. For example, you can create a NumPy array using np.array() or perform mathematical operations using NumPy functions.\n","\n","## 2.4 Verifying the Installation:\n","### To verify if NumPy is installed correctly, you can try importing it and printing its version. Follow these steps:\n","\n","#### **Step 1**: Open a Python script or start an interactive session.\n","\n","#### **Step 2** : Import NumPy:\n","\n","```python\n","\n","import numpy as np\n","```\n","#### **Step 3**: Print the NumPy version:\n","\n","```python\n","print(np.__version__)\n","```\n","### **Step 4** : Run the script or session. If NumPy is installed correctly, you will see the version number printed in the output.\n","\n","## 2.5 Troubleshooting:\n","### In case you encounter any issues during the installation or import process, here are a few troubleshooting steps you can try:\n","\n","#### Ensure that you have a compatible version of Python installed on your system. NumPy requires Python 3.7 or above.\n","#### Make sure you have an active internet connection while installing NumPy using pip.\n","#### If you encounter any error messages during installation, carefully read the error message for clues on how to resolve the issue. It may indicate missing dependencies or conflicts with other packages.\n","#### Check if you have multiple Python installations on your system, and verify that you are installing NumPy for the correct Python version.\n","#### If you are working in a virtual environment, ensure that the virtual environment is activated before installing NumPy.\n","## 2.6 Conclusion:\n","#### **Chapter 2** covered the practical aspects of installing and importing NumPy. It explained the steps to install NumPy using pip and how to import it into your Python environment. Additionally, it provided instructions on verifying the installation and troubleshooting common issues. With NumPy successfully installed and imported, you are now ready to explore its powerful numerical computing capabilities in the upcoming chapters.\n","\n","\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"markdown","id":"1c778428","metadata":{"papermill":{"duration":0.006606,"end_time":"2023-08-29T06:02:36.033225","exception":false,"start_time":"2023-08-29T06:02:36.026619","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>3 </font></h1>\n","\n","# **Chapter 3: NumPy Arrays: Creation and Initialization**\n","\n","## **3.1 Introduction:**\n","### Chapter 3 focuses on the creation and initialization of NumPy arrays, which are the fundamental data structure in NumPy. It covers various methods and techniques to create NumPy arrays and initialize their values.\n","\n","## 3.2 Creating NumPy Arrays:\n","### There are multiple ways to create NumPy arrays. Let's explore some of the commonly used methods:\n","\n","## 3.2.1 Converting Python Lists:\n","### One way to create a NumPy array is by converting a Python list using the np.array() function. For example:\n","\n","```python\n","\n","import numpy as np\n","\n","\n","list_data = [1, 2, 3, 4, 5]\n","numpy_array = np.array(list_data)\n","```\n","### In this example, we have a Python list list_data, and by calling np.array(list_data), we create a NumPy array numpy_array with the same elements as the list.\n","\n","### 3.2.2 Using Built-in Functions:\n","### NumPy provides several built-in functions for creating arrays with specific values. Some commonly used ones include:\n","```python\n","np.zeros():\n","#Creates an array filled with zeros.\n","\n","zeros_array = np.zeros((3, 4))\n","# creates a 3x4 array filled with zeros\n","\n","np.ones(): \n","#Creates an array filled with ones.\n","\n","ones_array = np.ones((2, 3)) \n","# creates a 2x3 array filled with ones\n","\n","np.full(): \n","#Creates an array with a specified constant value.\n","\n","constant_array = np.full((2, 2), 5)  \n","# creates a 2x2 array filled with 5\n","\n","np.arange(): \n","#Creates an array with a range of values.\n","\n","range_array = np.arange(0, 10, 2) \n","# creates an array with values [0, 2, 4, 6, 8]\n","\n","np.linspace():\n","#Creates an array with evenly spaced values.\n","\n","linspace_array = np.linspace(0, 1, 5) \n","# creates an array with values [0.0, 0.25, 0.5, 0.75, 1.0]\n","``` \n","## 3.2.3 Reading from Files:\n","### NumPy can also create arrays by reading data from files. For example, you can read data from a CSV file using the np.genfromtxt() function:\n","\n","```python\n","data_array = np.genfromtxt('data.csv', delimiter=',')\n","``` \n","\n","### This function reads the data from the specified CSV file, assuming comma-separated values, and creates a NumPy array data_array with the data.\n","\n","## 3.3 Array Initialization:\n","### Once you have created a NumPy array, you can initialize its values using various techniques:\n","\n","## 3.3.1 Assigning Values:\n","### You can assign values to specific elements or slices of the array using indexing. For example:\n","\n","```python\n","numpy_array[0] = 10  # assigns the value 10 to the first element of the array\n","numpy_array[1:3] = [20, 30]  # assigns the values 20 and 30 to the second and third elements\n","```\n","\n","## 3.3.2 Random Initialization:\n","### NumPy provides functions to initialize arrays with random values. Some commonly used functions include:\n","```python\n","np.random.rand()\n","#Creates an array with random values from a uniform distribution between 0 and 1.\n","    \n","\n","random_array = np.random.rand(3, 3)\n","# creates a 3x3 array with random values\n","\n","np.random.randint()\n","# Creates an array with random integer values within a specified range.\n","\n","random_int_array = np.random.randint(0, 10, size=(2, 3))  \n","# creates a 2x3 array with random integers from 0 to 9\n","```\n","## 3.4 Conclusion:\n","### **Chapter 3** provided an overview of creating and initializing NumPy arrays. It covered methods such as converting Python lists, using built-in functions like np.zeros() and np.ones(), reading from files using np.genfromtxt(), and initializing values using indexing and random functions. By understanding these techniques, you can create NumPy arrays and populate them with the desired values, setting the foundation for performing advanced numerical computations in the subsequent chapters.\n","\n"]},{"cell_type":"markdown","id":"9e67e30b","metadata":{"papermill":{"duration":0.006701,"end_time":"2023-08-29T06:02:36.04687","exception":false,"start_time":"2023-08-29T06:02:36.040169","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>4  </font></h1>"]},{"cell_type":"markdown","id":"e3d01ef1","metadata":{"papermill":{"duration":0.00689,"end_time":"2023-08-29T06:02:36.060862","exception":false,"start_time":"2023-08-29T06:02:36.053972","status":"completed"},"tags":[]},"source":["# Chapter 4: NumPy Array Indexing and Slicing\n","\n","## 4.1 Introduction:\n","### Chapter 4 focuses on array indexing and slicing in NumPy. Indexing allows accessing individual elements of an array, while slicing enables extracting subsets of an array. Understanding these concepts is crucial for working with specific data points or extracting relevant portions of an array.\n","\n","## 4.2 Array Indexing:\n","### NumPy arrays can be indexed to access specific elements. Indexing in NumPy is zero-based, meaning the index of the first element is 0.\n","\n","## 4.2.1 Single Element Indexing:\n","### To access a single element, you can use square brackets [] with the index value. For example:\n","\n","```python\n","import numpy as np\n","\n","arr = np.array([1, 2, 3, 4, 5])\n","\n","print(arr[0])  # Accesses the first element of the array\n","```\n","\n","### In this example, arr[0] retrieves the first element of the array arr (which is 1) because indexing starts at 0.\n","\n","## 4.2.2 Multi-Dimensional Array Indexing:\n","### For multi-dimensional arrays, you can use a comma-separated tuple of indices to access elements in each dimension. For example:\n","\n","```python\n","arr = np.array([[1, 2, 3], [4, 5, 6]])\n","print(arr[1, 2])  # Accesses the element at row 1, column 2\n","```\n","### In this case, arr[1, 2] retrieves the element at row 1 and column 2 of the array arr (which is 6).\n","\n","## 4.3 Array Slicing:\n","### Slicing allows extracting a subset or a range of elements from an array. It is done using the colon : symbol within square brackets [].\n","\n","## 4.3.1 Basic Slicing:\n","### To perform basic slicing, you can specify the start, stop, and step values within the square brackets. The slicing operation extracts elements from the start index up to, but not including, the stop index.\n","\n","```python\n","arr = np.array([1, 2, 3, 4, 5])\n","print(arr[1:4])  # Extracts elements from index 1 to 3 (not including 4)\n","```\n","### This example prints [2, 3, 4], which are the elements in the range from index 1 to 3 of the array arr.\n","\n","## 4.3.2 Slicing Multi-Dimensional Arrays:\n","### For multi-dimensional arrays, you can perform slicing on each dimension using a comma-separated tuple. For example:\n","\n","```python\n","arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n","print(arr[1:3, 0:2])  # Extracts a subset of the array from rows 1 to 2 and columns 0 to 1\n","```\n","\n","### In this case, arr[1:3, 0:2] extracts the sub-array [[4, 5], [7, 8]], which includes rows 1 to 2 and columns 0 to 1 of the original array arr.\n","\n","## 4.3.3 Slicing with Steps:\n","### Slicing can also incorporate a step value to skip elements within the specified range. For example:\n","\n","```python\n","\n","arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])\n","print(arr[1:7:2])  # Extracts elements from index 1 to 6 with a step of 2\n","```\n","### In this example, arr[1:7:2] retrieves elements [2, 4, 6] by starting from index 1, stopping at index 7 (not including), and skipping every second element.\n","\n","## 4.4 Conclusion:\n","### **Chapter 4** covered the essential concepts of array indexing and slicing in NumPy. Indexing allows accessing individual elements, while slicing enables extracting subsets of arrays. By understanding how to use indexing and slicing effectively, you can access specific elements or extract relevant portions of arrays for further computations or analysis.\n"]},{"cell_type":"markdown","id":"3c3f0e79","metadata":{"papermill":{"duration":0.006872,"end_time":"2023-08-29T06:02:36.074826","exception":false,"start_time":"2023-08-29T06:02:36.067954","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>5 </font></h1>\n","\n","# Chapter 5: Understanding Array Shape and Dimensions\n","\n","## **5.1 Introduction**:\n","### Chapter 5 focuses on understanding the shape and dimensions of NumPy arrays. The shape and dimensions of an array provide information about its size, structure, and organization. Understanding these concepts is crucial for manipulating arrays and performing various computations.\n","\n","## **5.2 Array Shape** :\n","### The shape of an array refers to the dimensions or the size of each dimension of the array. In NumPy, the shape is represented as a tuple of integers.\n","\n","## 5.2.1 Obtaining Array Shape:\n","### To obtain the shape of a NumPy array, you can use the shape attribute. For example:\n","\n","```python\n","import numpy as np\n","\n","arr = np.array([[1, 2, 3], [4, 5, 6]])\n","print(arr.shape)  # Prints the shape of the array\n","```\n","\n","### In this example, arr.shape returns (2, 3), indicating that the array has 2 rows and 3 columns.\n","\n","## 5.2.2 Modifying Array Shape:\n","### You can modify the shape of an array using the reshape() function. The reshape() function returns a new array with a modified shape without changing the original array.\n","\n","```python\n","arr = np.array([1, 2, 3, 4, 5, 6])\n","new_arr = arr.reshape((2, 3))  # Reshapes the array to have 2 rows and 3 columns\n","```\n","### In this case, the reshape() function transforms the original 1-dimensional array into a 2-dimensional array with shape (2, 3).\n","\n","## 5.3 Array Dimensions:\n","### The dimensions of an array refer to the number of axes or the rank of the array. A 1-dimensional array has a single dimension, a 2-dimensional array has two dimensions, and so on.\n","\n","## 5.3.1 Obtaining Array Dimensions:\n","### To obtain the number of dimensions of a NumPy array, you can use the ndim attribute. For example:\n","\n","```python\n","arr = np.array([1, 2, 3, 4, 5])\n","print(arr.ndim)  # Prints the number of dimensions of the array\n","```\n","\n","### In this example, arr.ndim returns 1, indicating that the array has a single dimension.\n","\n","## 5.3.2 Modifying Array Dimensions:\n","### You can modify the dimensions of an array using the reshape() function, as mentioned earlier. Additionally, you can change the dimensions using the np.newaxis keyword or the expand_dims() function.\n","\n","```python\n","arr = np.array([1, 2, 3, 4, 5])\n","new_arr = arr[:, np.newaxis]  # Adds a new axis to convert the array to a 2-dimensional column vector\n","```\n","### In this case, arr[:, np.newaxis] adds a new axis to the original array, transforming it into a 2-dimensional column vector.\n","\n","## 5.4 Conclusion:\n","### Chapter 5 provided an understanding of array shape and dimensions in NumPy. The shape of an array refers to the size of each dimension, which can be obtained using the shape attribute. The dimensions of an array represent the number of axes or the rank of the array, which can be obtained using the ndim attribute.\n","### Additionally, the shape and dimensions of an array can be modified using functions like reshape(), np.newaxis, or expand_dims(). Having a clear understanding of array shape and dimensions is essential for working with arrays effectively and performing various operations and computations.\n"]},{"cell_type":"markdown","id":"538d6b89","metadata":{"papermill":{"duration":0.006565,"end_time":"2023-08-29T06:02:36.088443","exception":false,"start_time":"2023-08-29T06:02:36.081878","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>6 </font></h1>\n","\n","# Chapter 6: Exploring Array Data Types\n","\n","## 6.1 Introduction:\n","### Chapter 6 focuses on exploring the data types of NumPy arrays. Data types determine the kind of values that can be stored in an array and the memory representation of those values. Understanding array data types is crucial for working with different types of data and performing computations efficiently.\n","\n","## 6.2 NumPy Data Types:\n","### NumPy provides a wide range of data types to handle different kinds of data. Some commonly used data types include:\n","```python\n","int: Integer values.\n","float: Floating-point values.\n","bool: Boolean values (True or False).\n","complex: Complex numbers with real and imaginary parts.\n","str: Strings.\n","object: Python objects.\n","datetime: Dates and times.\n","timedelta: Differences between dates and times.\n","```\n","## 6.3 Determining Array Data Type:\n","### To determine the data type of a NumPy array, you can use the dtype attribute. For example:\n","\n","```python \n","import numpy as np\n","\n","arr = np.array([1, 2, 3])\n","\n","print(arr.dtype)  # Prints the data type of the array\n","```\n","### In this example, arr.dtype returns int64, indicating that the data type of the array is 64-bit integer.\n","\n","## 6.4 Specifying Array Data Type:\n","### You can specify the data type of an array explicitly by using the dtype parameter when creating the array. For example:\n","\n","```python \n","arr = np.array([1, 2, 3], dtype=float)  # Creates an array with float data type\n","```\n","### In this case, the array arr is created with the data type float.\n","\n","## 6.5 Changing Array Data Type:\n","### You can change the data type of an existing array using the astype() method. The astype() method returns a new array with the specified data type.\n","\n","```python \n","arr = np.array([1, 2, 3])\n","new_arr = arr.astype(float)  # Changes the data type of the array to float\n","In this example, arr.astype(float) returns a new array new_arr with the data type float.\n","```\n","\n","## 6.6 Conclusion:\n","### **Chapter 6** explored the concept of array data types in NumPy. Understanding the data types available in NumPy and how to determine, specify, and change the data type of arrays is essential for handling different kinds of data and performing efficient computations. By utilizing the appropriate data types, you can ensure data integrity and optimize memory usage in NumPy arrays.\n","\n"]},{"cell_type":"markdown","id":"dffaabc3","metadata":{"papermill":{"duration":0.006927,"end_time":"2023-08-29T06:02:36.102192","exception":false,"start_time":"2023-08-29T06:02:36.095265","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>7 </font></h1>"]},{"cell_type":"markdown","id":"2e5bca26","metadata":{"papermill":{"duration":0.006888,"end_time":"2023-08-29T06:02:36.115992","exception":false,"start_time":"2023-08-29T06:02:36.109104","status":"completed"},"tags":[]},"source":["# Chapter 7: Basic Operations on NumPy Arrays\n","\n","## 7.1 Introduction:\n","### Chapter 7 focuses on the basic operations that can be performed on NumPy arrays. These operations include arithmetic operations, element-wise operations, aggregation functions, and broadcasting. Understanding these operations is essential for manipulating and analyzing data efficiently using NumPy arrays.\n","\n","## 7.2 Arithmetic Operations:\n","### NumPy arrays support various arithmetic operations such as addition, subtraction, multiplication, and division. These operations can be performed element-wise on arrays of the same shape or between arrays and scalar values.\n","\n","## 7.2.1 Element-wise Arithmetic Operations:\n","### Element-wise arithmetic operations are performed on corresponding elements of two arrays. For example:\n","\n","```python\n","import numpy as np\n","\n","arr1 = np.array([1, 2, 3])\n","arr2 = np.array([4, 5, 6])\n","\n","# Addition\n","result_add = arr1 + arr2\n","\n","# Subtraction\n","result_sub = arr1 - arr2\n","\n","# Multiplication\n","result_mul = arr1 * arr2\n","\n","# Division\n","result_div = arr1 / arr2\n","```\n","\n","### In this example, element-wise addition, subtraction, multiplication, and division are performed between arr1 and arr2, resulting in result_add, result_sub, result_mul, and result_div, respectively.\n","\n","## 7.2.2 Arithmetic Operations with Scalars:\n","### Arithmetic operations can also be performed between arrays and scalar values. The scalar value is applied element-wise to the array. For example:\n","\n","```python\n","arr = np.array([1, 2, 3])\n","\n","# Scalar addition\n","result_add_scalar = arr + 5\n","\n","# Scalar multiplication\n","result_mul_scalar = arr * 2\n","```\n","\n","### In this case, scalar addition and multiplication are performed on the array arr, resulting in result_add_scalar and result_mul_scalar, respectively.\n","\n","## 7.3 Aggregation Functions:\n","### NumPy provides various aggregation functions to compute summary statistics of an array, such as the sum, mean, minimum, maximum, and standard deviation.\n","\n","```python \n","arr = np.array([1, 2, 3, 4, 5])\n","\n","# Sum\n","result_sum = np.sum(arr)\n","\n","# Mean\n","result_mean = np.mean(arr)\n","\n","# Minimum\n","result_min = np.min(arr)\n","\n","# Maximum\n","result_max = np.max(arr)\n","\n","# Standard deviation\n","result_std = np.std(arr)\n","```\n","\n","### In this example, the sum, mean, minimum, maximum, and standard deviation of the array arr are computed using the corresponding NumPy aggregation functions.\n","\n","## 7.4 Broadcasting:\n","### **Broadcasting** is a powerful feature in NumPy that allows performing operations between arrays of different shapes. Broadcasting automatically adjusts the dimensions of arrays to match for element-wise operations.\n","\n","```python\n","arr1 = np.array([1, 2, 3])\n","arr2 = np.array([4, 5, 6])\n","\n","\n","# Broadcasting addition\n","result_broadcast_add = arr1 + 2\n","\n","# Broadcasting multiplication\n","result_broadcast_mul = arr2 * 3\n","```\n","## In this case, broadcasting is used to perform addition between arr1 and a scalar value of 2, and multiplication between arr2 and a scalar value of 3.\n","\n","## 7.5 Conclusion:\n","### Chapter 7 covered the basic operations that can be performed on NumPy arrays. It explored arithmetic operations, including element-wise operations and operations with scalar values. Additionally, it introduced aggregation functions for computing summary statistics of arrays and demonstrated the broadcasting feature for performing operations between arrays of different shapes. By mastering these basic operations, you can efficiently manipulate and analyze data using NumPy arrays in a variety of scientific and numerical applications."]},{"cell_type":"markdown","id":"514ee6d8","metadata":{"papermill":{"duration":0.006476,"end_time":"2023-08-29T06:02:36.12939","exception":false,"start_time":"2023-08-29T06:02:36.122914","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>8 </font></h1>\n","\n","# Chapter 8: Mathematical Functions in NumPy\n","\n","## 8.1 Introduction:\n","### Chapter 8 focuses on the mathematical functions available in NumPy. NumPy provides a wide range of mathematical functions that operate element-wise on arrays, allowing for efficient numerical computations. Understanding and utilizing these functions is essential for performing mathematical operations on arrays in various scientific and numerical applications.\n","\n","## 8.2 Common Mathematical Functions:\n","### NumPy offers a comprehensive set of mathematical functions, including:\n","```python\n","Trigonometric functions: sin(), cos(), tan(), arcsin(), arccos(), arctan(), etc.\n","Exponential and logarithmic functions: exp(), log(), log10(), log2(), sqrt(), etc.\n","Hyperbolic functions: sinh(), cosh(), tanh(), etc.\n","Rounding functions: round(), floor(), ceil(), etc.\n","Absolute value function: abs().\n","Power and logarithmic functions: power(), logaddexp(), logaddexp2(), etc.\n","```\n","### These are just a few examples of the mathematical functions available in NumPy. You can explore the NumPy documentation for a complete list of functions.\n","\n","## 8.3 Applying Mathematical Functions to Arrays:\n","### Mathematical functions in NumPy can be applied directly to arrays, operating element-wise on each element of the array. For example:\n","\n","```python\n","import numpy as np\n","\n","arr = np.array([1, 2, 3, 4, 5])\n","\n","\n","# Applying mathematical functions to array\n","result_sin = np.sin(arr)\n","result_exp = np.exp(arr)\n","result_sqrt = np.sqrt(arr)\n","\n","```\n","### In this example, the sin(), exp(), and sqrt() functions are applied to the array arr, resulting in result_sin, result_exp, and result_sqrt, respectively.\n","\n","## 8.4 Mathematical Operations with Aggregation Functions:\n","### Mathematical functions in NumPy can also be combined with aggregation functions to perform calculations on arrays and obtain summary statistics. For example:\n","\n","```python\n","arr = np.array([1, 2, 3, 4, 5])\n","\n","### Applying mathematical functions with aggregation functions\n","result_sum_exp = np.sum(np.exp(arr))\n","result_mean_sqrt = np.mean(np.sqrt(arr))\n","```\n","\n","### In this case, the exp() and sqrt() functions are applied to the array arr inside the sum() and mean() functions, respectively, resulting in result_sum_exp and result_mean_sqrt.\n","\n","## 8.5 Conclusion:\n","### **Chapter 8** introduced the mathematical functions available in NumPy and demonstrated how to apply them to arrays for efficient numerical computations. These functions cover a wide range of mathematical operations, including trigonometric functions, exponential and logarithmic functions, rounding functions, and more. By utilizing these mathematical functions, you can perform complex calculations on arrays and obtain valuable insights in scientific and numerical applications.\n"]},{"cell_type":"markdown","id":"c6aadce0","metadata":{"papermill":{"duration":0.006726,"end_time":"2023-08-29T06:02:36.143","exception":false,"start_time":"2023-08-29T06:02:36.136274","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>9 </font></h1>\n","\n","# Chapter 9: Aggregation and Statistical Functions\n","\n","## 9.1 Introduction:\n","### Chapter 9 focuses on aggregation and statistical functions available in NumPy. These functions allow you to compute various summary statistics, such as mean, median, variance, and percentiles, from NumPy arrays. Understanding and utilizing these functions is essential for data analysis and statistical computations.\n","\n","## 9.2 Aggregation Functions:\n","### NumPy provides several aggregation functions that allow you to compute summary statistics of an array. Some commonly used aggregation functions include:\n","\n","```python\n","sum(): Computes the sum of array elements.\n","mean(): Computes the mean (average) of array elements.\n","median(): Computes the median of array elements.\n","min(): Computes the minimum value in the array.\n","max(): Computes the maximum value in the array.\n","```\n","### These functions can be applied to an entire array or along a specific axis, depending on the desired aggregation.\n","\n","## 9.3 Statistical Functions:\n","### NumPy offers a wide range of statistical functions for analyzing data. Some commonly used statistical functions include:\n","\n","```python\n","var(): Computes the variance of array elements.\n","std(): Computes the standard deviation of array elements.\n","percentile(): Computes the nth percentile of array elements.\n","corrcoef(): Computes the correlation coefficient between two arrays.\n","histogram(): Computes the histogram of array values.\n","```\n","\n","### These functions provide valuable insights into the distribution, variability, and relationships within the data.\n","\n","## 9.4 Applying Aggregation and Statistical Functions:\n","### Aggregation and statistical functions in NumPy can be applied directly to arrays. For example:\n","\n","```python \n","import numpy as np\n","\n","arr = np.array([1, 2, 3, 4, 5])\n","\n","# Aggregation functions\n","result_sum = np.sum(arr)\n","result_mean = np.mean(arr)\n","result_median = np.median(arr)\n","result_min = np.min(arr)\n","result_max = np.max(arr)\n","\n","# Statistical functions\n","result_var = np.var(arr)\n","result_std = np.std(arr)\n","result_percentile = np.percentile(arr, 75)\n","```\n","\n","### In this example, aggregation functions such as sum(), mean(), median(), min(), and max() are applied to the array arr, resulting in result_sum, result_mean, result_median, result_min, and result_max, respectively. Additionally, statistical functions such as var(), std(), and percentile() are used to compute the variance, standard deviation, and 75th percentile of the array.\n","\n","## 9.5 Conclusion:\n","### Chapter 9 introduced aggregation and statistical functions available in NumPy. These functions allow you to compute summary statistics, analyze data distributions, and explore relationships within arrays. By utilizing these functions, you can gain valuable insights and perform advanced statistical computations on your data. Understanding and applying these functions is essential for data analysis and scientific computations using NumPy arrays.\n","\n"]},{"cell_type":"markdown","id":"76afb461","metadata":{"papermill":{"duration":0.006993,"end_time":"2023-08-29T06:02:36.157115","exception":false,"start_time":"2023-08-29T06:02:36.150122","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>10 </font></h1>\n","\n","# Chapter 10: Sorting and Searching in Arrays\n","\n","## 10.1 Introduction:\n","### Chapter 10 focuses on sorting and searching operations on NumPy arrays. Sorting allows you to arrange the elements of an array in a specific order, while searching helps locate the position of specific elements within an array. These operations are fundamental for data analysis, optimization, and efficient information retrieval.\n","\n","## 10.2 Sorting Arrays:\n","### NumPy provides functions to sort arrays in ascending or descending order. Some commonly used sorting functions include:\n","\n","```python \n","sort(): Sorts the elements of an array in place along the last axis.\n","argsort(): Returns the indices that would sort an array.\n","lexsort(): Performs a stable sort on multiple arrays, sorting by one array at a time.\n","#Here's an example of using the sort() and argsort() functions:\n","\n","import numpy as np\n","\n","arr = np.array([3, 1, 5, 2, 4])\n","\n","# Sort the array in ascending order\n","np.sort(arr)  # Returns a sorted copy of the array\n","arr.sort()  # Sorts the array in place\n","\n","# Get the indices that would sort the array\n","np.argsort(arr)  # Returns the indices of the sorted elements\n","```\n","\n","### In this example, the sort() function is used to sort the array arr in ascending order, both by returning a sorted copy of the array and by sorting it in place. The argsort() function returns the indices that would sort the array.\n","\n","## 10.3 Searching Arrays:\n","### NumPy provides functions for searching specific elements within an array. Some commonly used searching functions include:\n","\n","\n","```python \n","where(): Returns the indices of elements that satisfy a given condition.\n","argwhere(): Returns the indices of elements that are non-zero.\n","searchsorted(): Finds the indices where elements should be inserted to maintain sorted order.\n","\n","#Here's an example of using the where() and searchsorted() functions:\n","\n","import numpy as np\n","\n","arr = np.array([1, 2, 3, 4, 5])\n","\n","# Find the indices where the array elements are greater than 3\n","np.where(arr > 3)\n","\n","# Find the indices where elements should be inserted to maintain sorted order\n","np.searchsorted(arr, 3)\n","```\n","### In this example, the where() function is used to find the indices where the array elements are greater than 3. The searchsorted() function determines the indices where the value 3 should be inserted to maintain the sorted order of the array.\n","\n","## 10.4 Conclusion:\n","### Chapter 10 explored sorting and searching operations on NumPy arrays. Sorting functions allow you to arrange the elements of an array in a specific order, while searching functions help locate specific elements or determine the appropriate positions for insertion. These operations are vital for data analysis, optimization, and information retrieval tasks. By utilizing the sorting and searching functions in NumPy, you can efficiently organize and access data within arrays, enabling more effective data analysis and computations.\n","\n"]},{"cell_type":"markdown","id":"5c31f7e4","metadata":{"papermill":{"duration":0.006879,"end_time":"2023-08-29T06:02:36.17128","exception":false,"start_time":"2023-08-29T06:02:36.164401","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>11 </font></h1>\n","\n","# Chapter 11: Array Manipulation Techniques\n","\n","## 11.1 Introduction:\n","### Chapter 11 focuses on array manipulation techniques in NumPy. Array manipulation involves reshaping, concatenating, splitting, and other operations that modify the shape and structure of NumPy arrays. Understanding these techniques is crucial for manipulating and transforming data efficiently.\n","\n","## 11.2 Reshaping Arrays:\n","### Reshaping arrays allows you to change the shape and dimensions of an array without modifying the underlying data. NumPy provides the following functions for reshaping arrays:\n","```python \n","reshape(): Reshapes an array into a specified shape.\n","resize(): Changes the shape and size of an array in-place.\n","flatten(): Returns a copy of the array collapsed into one dimension.\n","# Here's an example of using these functions:\n","\n","\n","import numpy as np\n","\n","arr = np.array([[1, 2, 3],\n","                [4, 5, 6]])\n","\n","# Reshape the array to a 1D array\n","arr_1d = arr.reshape(6)\n","\n","# Resize the array to a different shape\n","arr.resize((3, 2))\n","\n","# Flatten the array into a 1D array\n","arr_flat = arr.flatten()\n","```\n","### In this example, the reshape() function is used to reshape the array arr into a 1D array with 6 elements. The resize() function modifies the shape of the array in-place to a 3x2 shape. The flatten() function returns a copy of the array collapsed into a 1D array.\n","\n","## 11.3 Concatenating Arrays:\n","### Concatenating arrays allows you to combine multiple arrays along a specified axis. NumPy provides the following functions for concatenation:\n","```python\n","\n","concatenate(): Concatenates arrays along a specified axis.\n","vstack(): Vertically stacks arrays.\n","hstack(): Horizontally stacks arrays.\n","# Here's an example of using these functions:\n","\n","\n","import numpy as np\n","\n","arr1 = np.array([1, 2, 3])\n","arr2 = np.array([4, 5, 6])\n","\n","# Concatenate the arrays horizontally\n","result_hstack = np.hstack((arr1, arr2))\n","\n","# Concatenate the arrays vertically\n","result_vstack = np.vstack((arr1, arr2))\n","\n","# Concatenate the arrays along the first axis\n","result_concat = np.concatenate((arr1, arr2))\n","```\n","\n","### In this example, the hstack() function horizontally concatenates arr1 and arr2, resulting in result_hstack. The vstack() function vertically concatenates the arrays, resulting in result_vstack. The concatenate() function concatenates the arrays along the first axis, resulting in result_concat.\n","\n","## 11.4 Splitting Arrays:\n","### Splitting arrays allows you to divide an array into multiple smaller arrays along a specified axis. NumPy provides the following functions for array splitting:\n","\n","```python \n","split(): Splits an array into multiple sub-arrays.\n","vsplit(): Vertically splits an array into multiple sub-arrays.\n","hsplit(): Horizontally splits an array into multiple sub-arrays.\n","# Here's an example of using these functions:\n","\n","\n","import numpy as np\n","\n","arr = np.array([1, 2, 3, 4, 5, 6])\n","\n","# Split the array into three sub-arrays\n","result_split = np.split(arr, 3)\n","\n","# Vertically split the array into two sub-arrays\n","result_vsplit = np.vsplit(arr, 2)\n","\n","# Horizontally split the array into two sub-arrays\n","result_hsplit = np.hsplit(arr, 2)\n","```\n","### In this example, the split() function splits the array arr into three sub-arrays, resulting in result_split. The vsplit() function vertically splits the array into two sub-arrays, and the hsplit() function horizontally splits the array into two sub-arrays.\n","\n","## 11.5 Conclusion:\n","### **Chapter 11** explored various array manipulation techniques in NumPy. Reshaping arrays allows you to change their shape and dimensions, while concatenating arrays combines multiple arrays along a specified axis. Splitting arrays divides an array into smaller sub-arrays. These techniques are crucial for manipulating and transforming data efficiently. By utilizing these array manipulation techniques in NumPy, you can reshape, combine, and split arrays to suit your specific data processing needs."]},{"cell_type":"markdown","id":"65448655","metadata":{"papermill":{"duration":0.007615,"end_time":"2023-08-29T06:02:36.185989","exception":false,"start_time":"2023-08-29T06:02:36.178374","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>12 </font></h1>\n","\n","# Chapter 12: Broadcasting in NumPy\n","\n","## 12.1 Introduction:\n","### Chapter 12 focuses on broadcasting in NumPy. Broadcasting is a powerful feature in NumPy that allows for performing arithmetic and logical operations on arrays with different shapes and sizes. It eliminates the need for explicit looping and enables efficient element-wise computations on arrays.\n","\n","## 12.2 Broadcasting Rules:\n","### NumPy follows a set of rules to determine how arrays with different shapes are broadcasted to perform element-wise operations. These rules are as follows:\n","```python\n","Rule 1: If the arrays have different numbers of dimensions, the array with fewer dimensions is padded with ones on its left side until the dimensions match.\n","Rule 2: If the arrays' shapes don't match in any dimension, the array with shape equal to 1 in that dimension is stretched to match the other array's shape.\n","Rule 3: If the arrays have different shapes and neither shape is equal to 1 in any dimension, a \"ValueError\" is raised, indicating that the arrays are not compatible for broadcasting.\n","```\n","\n","## 12.3 Broadcasting Examples:\n","### Here are some examples illustrating broadcasting in NumPy:\n","\n","```python\n","import numpy as np\n","\n","# Broadcasting example 1\n","arr1 = np.array([1, 2, 3])\n","arr2 = np.array([[4], [5], [6]])\n","result1 = arr1 + arr2\n","\n","# Broadcasting example 2\n","arr3 = np.array([1, 2, 3])\n","arr4 = np.array([[4, 5, 6], [7, 8, 9]])\n","result2 = arr3 * arr4\n","\n","# Broadcasting example 3\n","arr5 = np.array([[1, 2, 3], [4, 5, 6]])\n","arr6 = np.array([10, 20, 30])\n","result3 = arr5 + arr6\n","```\n","\n","### In the first example, the arrays arr1 and arr2 have different shapes, but they can still be added together through broadcasting. The 1D array arr1 is broadcasted to match the shape of arr2, resulting in result1 with shape (3, 1).\n","\n","### In the second example, the array arr3 of shape (3,) is multiplied element-wise with arr4 of shape (2, 3). The 1D array arr3 is broadcasted to match the shape of arr4, resulting in result2 with the same shape as arr4.\n","\n","### In the third example, the array arr5 of shape (2, 3) is added to the 1D array arr6 of shape (3,). The 1D array arr6 is broadcasted to match the shape of arr5, resulting in result3 with the same shape as arr5.\n","\n","## 12.4 Broadcasting Benefits:\n","### Broadcasting in NumPy offers several benefits, including:\n","\n","### **Simplified syntax** : Broadcasting eliminates the need for explicit looping or manual expansion of arrays, leading to cleaner and more concise code.\n","### **Efficient computation** : Broadcasting allows for efficient element-wise operations on arrays without the need for creating additional copies or expanding dimensions.\n","### **Enhanced flexibility**: With broadcasting, operations can be performed on arrays with different shapes and sizes, providing greater flexibility in data manipulation.\n","## 12.5 Conclusion:\n","### Chapter 12 introduced broadcasting in NumPy, a powerful feature that enables element-wise operations on arrays with different shapes and sizes. By following specific rules, NumPy automatically adjusts the shapes of arrays to facilitate efficient computations. Broadcasting simplifies code, improves performance, and provides flexibility in manipulating arrays. Understanding and utilizing broadcasting in NumPy allows for more concise and efficient data processing and analysis.\n","\n","\n","\n"]},{"cell_type":"markdown","id":"3202e1dc","metadata":{"papermill":{"duration":0.006807,"end_time":"2023-08-29T06:02:36.199845","exception":false,"start_time":"2023-08-29T06:02:36.193038","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>13 </font></h1>\n","\n","# Chapter 13: Input and Output Operations with NumPy Arrays\n","\n","## 13.1 Introduction:\n","### Chapter 13 focuses on input and output (I/O) operations with NumPy arrays. NumPy provides various functions and methods for reading data from files and writing data to files, allowing for seamless integration with external data sources and formats.\n","\n","## 13.2 Reading Data from Files:\n","### NumPy provides functions to read data from different file formats, including text files and binary files. Some commonly used functions for reading data are:\n","\n","```python\n","loadtxt(): Reads data from a text file and returns a NumPy array.\n","genfromtxt(): Reads data from a text file with missing values and returns a NumPy array.\n","fromfile(): Reads data from a binary file and returns a NumPy array.\n","# Here's an example of reading data from a text file using loadtxt():\n","\n","import numpy as np\n","\n","data = np.loadtxt('data.txt')\n","```\n","### In this example, the loadtxt() function is used to read the data from the file named 'data.txt' and store it in the data variable as a NumPy array.\n","\n","## 13.3 Writing Data to Files:\n","### NumPy also provides functions for writing data from NumPy arrays to files. Some commonly used functions for writing data are:\n","\n","```python\n","savetxt(): Writes data from a NumPy array to a text file.\n","tofile(): Writes data from a NumPy array to a binary file.\n","# Here's an example of writing data to a text file using savetxt():\n","\n","\n","import numpy as np\n","\n","data = np.array([[1, 2, 3],\n","                 [4, 5, 6]])\n","\n","np.savetxt('output.txt', data)\n","```\n","\n","### In this example, the savetxt() function is used to write the data from the NumPy array data to a text file named 'output.txt'.\n","\n","## 13.4 Additional I/O Options:\n","### NumPy also provides additional options for customizing the input and output operations, such as specifying delimiters, handling missing values, and controlling formatting. These options ensure compatibility with different file formats and data requirements.\n","\n","## 13.5 Conclusion:\n","### Chapter 13 explored input and output operations with NumPy arrays, allowing for seamless integration with external data sources and formats. Functions like loadtxt() and savetxt() enable reading data from text files and writing data to text files, while fromfile() and tofile() provide similar functionality for binary files. NumPy's I/O capabilities enhance data manipulation, analysis, and sharing, enabling efficient data integration with various file formats. Understanding and utilizing these I/O functions expands the versatility and interoperability of NumPy arrays in practical data processing tasks.\n","\n","\n","\n"]},{"cell_type":"markdown","id":"0576e2bc","metadata":{"papermill":{"duration":0.006646,"end_time":"2023-08-29T06:02:36.213377","exception":false,"start_time":"2023-08-29T06:02:36.206731","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>14</font></h1>\n","\n","# Chapter 14: Reading and Writing CSV Files with NumPy\n","\n","## 14.1 Introduction:\n","### Chapter 14 focuses on reading and writing CSV (Comma-Separated Values) files with NumPy. CSV files are a common format for storing tabular data, and NumPy provides convenient functions for working with this format.\n","\n","## 14.2 Reading CSV Files:\n","### NumPy provides the genfromtxt() function to read data from CSV files. This function is flexible and can handle various data types, missing values, and different delimiters. Here's an example of reading a CSV file using genfromtxt():\n","\n","\n","```python\n","import numpy as np\n","\n","data = np.genfromtxt('data.csv', delimiter=',', skip_header=1)\n","```\n","\n","### In this example, the genfromtxt() function is used to read the data from the CSV file named 'data.csv'. The delimiter parameter specifies that the values are separated by commas, and the skip_header parameter indicates that the first row should be skipped as it contains headers.\n","\n","## 14.3 Writing CSV Files:\n","### NumPy provides the savetxt() function to write data to CSV files. This function allows you to specify the delimiter, formatting options, and handle missing values. Here's an example of writing data to a CSV file using savetxt():\n","\n","```python\n","import numpy as np\n","\n","data = np.array([[1, 2, 3],\n","                 [4, 5, 6]])\n","\n","np.savetxt('output.csv', data, delimiter=',')\n","```\n","### In this example, the savetxt() function is used to write the data from the NumPy array data to a CSV file named 'output.csv'. The delimiter parameter specifies that the values should be separated by commas.\n","\n","## 14.4 Handling Missing Values:\n","### CSV files often contain missing values, represented by special placeholders like 'NaN' or 'None'. NumPy provides options to handle missing values while reading CSV files. You can specify the placeholder value using the missing_values parameter in genfromtxt(), and choose how to handle missing values using the filling_values parameter.\n","\n","## 14.5 Additional CSV Options:\n","### NumPy's CSV functions offer additional options to customize the reading and writing process. You can control data types, skip specific columns, specify header names, and more using the available parameters in genfromtxt() and savetxt().\n","\n","## 14.6 Conclusion:\n","### Chapter 14 explored reading and writing CSV files with NumPy. The genfromtxt() function allows for reading CSV files with flexibility in handling different data types and missing values. The savetxt() function provides convenient options for writing data to CSV files with customizable delimiters and formatting. By leveraging these CSV functions in NumPy, you can seamlessly integrate CSV data into your data processing workflows, facilitating efficient analysis and manipulation of tabular data.\n","\n"]},{"cell_type":"markdown","id":"65a73f68","metadata":{"papermill":{"duration":0.006819,"end_time":"2023-08-29T06:02:36.227073","exception":false,"start_time":"2023-08-29T06:02:36.220254","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>15 </font></h1>\n","\n","# Chapter 15: Binary File Operations with NumPy\n","\n","## 15.1 Introduction:\n","### Chapter 15 focuses on binary file operations with NumPy. Binary files contain data in a compact and efficient format, and NumPy provides functions to read and write data from/to binary files. Working with binary files is particularly useful for storing and manipulating large datasets.\n","\n","## 15.2 Reading Binary Files:\n","### NumPy provides the fromfile() function to read data from binary files. This function allows you to specify the data type and shape of the array to be read. Here's an example of reading data from a binary file using fromfile():\n","\n","```python \n","import numpy as np\n","\n","data = np.fromfile('data.bin', dtype=np.float64)\n","```\n","\n","### In this example, the fromfile() function is used to read the data from the binary file named 'data.bin'. The dtype parameter specifies that the data in the file is of type np.float64.\n","\n","## 15.3 Writing Binary Files:\n","### NumPy also provides the tofile() function to write data to binary files. This function allows you to specify the data type of the array to be written. Here's an example of writing data to a binary file using tofile():\n","\n","```python \n","import numpy as np\n","\n","data = np.array([1, 2, 3, 4, 5], dtype=np.int32)\n","data.tofile('output.bin')\n","```\n","\n","### In this example, the tofile() function is used to write the data from the NumPy array data to a binary file named 'output.bin'. The dtype parameter specifies that the data should be written as np.int32 type.\n","\n","## 15.4 Binary File Formats:\n","### When working with binary files, it's important to consider the format and structure of the data being read or written. NumPy allows for customizing the data type, shape, and other properties of the arrays, ensuring compatibility with the specific binary file format.\n","\n","## 15.5 Additional Binary File Options:\n","### NumPy provides additional options for binary file operations, such as specifying the endianness (byte order) of the data, handling complex numbers, and more. These options offer flexibility and control when working with binary files.\n","\n","## 15.6 Conclusion:\n","### Chapter 15 explored binary file operations with NumPy, allowing for efficient reading and writing of data in a compact format. The fromfile() function enables reading data from binary files, and the tofile() function facilitates writing data to binary files. By leveraging these binary file functions in NumPy, you can handle large datasets and optimize data storage and retrieval. Understanding and utilizing binary file operations in NumPy expands the capabilities of working with data in various formats and enhances the efficiency of data processing tasks.\n","\n","\n"]},{"cell_type":"markdown","id":"9534a4e7","metadata":{"papermill":{"duration":0.006445,"end_time":"2023-08-29T06:02:36.240368","exception":false,"start_time":"2023-08-29T06:02:36.233923","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>16 </font></h1>\n","\n","# Chapter 16: Saving and Loading NumPy Arrays\n","\n","## 16.1 Introduction:\n","### Chapter 16 focuses on saving and loading NumPy arrays. Saving arrays allows you to store the data in a persistent format for future use, while loading arrays enables you to retrieve the saved data back into memory. NumPy provides functions for saving and loading arrays in various file formats.\n","\n","## 16.2 Saving Arrays:\n","### NumPy provides the save() function to save arrays to disk in a binary format. This function allows you to specify the filename and the array(s) to be saved. Here's an example of saving an array using save():\n","\n","```python\n","import numpy as np\n","\n","arr = np.array([1, 2, 3, 4, 5])\n","np.save('array.npy', arr)\n","```\n","\n","### In this example, the save() function is used to save the array arr to a file named 'array.npy'. The file is saved in a binary format that preserves the data type and shape of the array.\n","\n","## 16.3 Loading Arrays:\n","### NumPy provides the load() function to load arrays from a saved file. This function allows you to specify the filename and returns the loaded array(s). Here's an example of loading an array using load():\n","\n","```python\n","import numpy as np\n","\n","arr = np.load('array.npy')\n","print(arr)\n","```\n","### In this example, the load() function is used to load the array from the file 'array.npy' into the variable arr. The loaded array is then printed.\n","\n","## 16.4 Saving and Loading Multiple Arrays:\n","### NumPy also supports saving and loading multiple arrays simultaneously. You can pass multiple arrays as arguments to the save() function and specify a filename for each array. Similarly, you can use the load() function to load multiple arrays from a saved file. The loaded arrays are returned as a tuple. Here's an example:\n","\n","```python \n","import numpy as np\n","\n","arr1 = np.array([1, 2, 3])\n","arr2 = np.array([4, 5, 6])\n","\n","np.savez('arrays.npz', array1=arr1, array2=arr2)\n","\n","loaded_data = np.load('arrays.npz')\n","arr1_loaded = loaded_data['array1']\n","arr2_loaded = loaded_data['array2']\n","```\n","\n","### In this example, the np.savez() function is used to save two arrays, arr1 and arr2, to a file named 'arrays.npz'. The arrays are given names (array1 and array2) to be used as keys when loading the data. The np.load() function is then used to load the arrays, and they can be accessed using the corresponding keys.\n","\n","## 16.5 Additional Saving and Loading Options:\n","### NumPy provides additional options for saving and loading arrays, such as specifying a different file format (e.g., text format using savetxt() and loadtxt()), compressing the saved file, and handling different data types. These options allow for customization and compatibility with specific requirements.\n","\n","## 16.6 Conclusion:\n","### Chapter 16 explored saving and loading NumPy arrays, providing a means to persistently store and retrieve data. The save() function enables saving arrays to disk in a binary format, while the load() function allows for loading the saved arrays back into memory. Additionally, NumPy supports saving and loading multiple arrays simultaneously using the .npz file format. Understanding and utilizing these saving and loading functions in NumPy enhances the flexibility and efficiency of working with array data, facilitating data sharing, long-term storage, and reproducibility in data analysis and scientific computing.\n","\n","\n"]},{"cell_type":"markdown","id":"49c407e8","metadata":{"papermill":{"duration":0.006648,"end_time":"2023-08-29T06:02:36.253714","exception":false,"start_time":"2023-08-29T06:02:36.247066","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>17 </font></h1>\n","\n","# Chapter 17: Reshaping Arrays in NumPy\n","\n","## 17.1 Introduction:\n","### Chapter 17 focuses on reshaping arrays in NumPy. Reshaping allows you to change the shape and dimensions of an array without modifying its data. NumPy provides functions to reshape arrays, enabling efficient data manipulation and transformation.\n","\n","## 17.2 Reshaping Arrays:\n","### NumPy provides the reshape() function to change the shape of an array. This function takes the desired shape as an argument and returns a new view of the original array with the specified shape. Here's an example of reshaping an array using reshape():\n","\n","```python\n","import numpy as np\n","\n","arr = np.array([1, 2, 3, 4, 5, 6])\n","reshaped_arr = arr.reshape(2, 3)\n","print(reshaped_arr)\n","```\n","\n","### In this example, the reshape() function is used to reshape the array arr into a 2x3 matrix. The reshaped array is then printed.\n","\n","## 17.3 Reshaping with -1:\n","### NumPy also allows you to use the value -1 as one of the dimensions when reshaping an array. When -1 is used, NumPy automatically calculates the appropriate value based on the size of the array and the other specified dimensions. This is useful when you want to reshape an array while preserving its total number of elements. Here's an example:\n","\n","```python\n","import numpy as np\n","\n","arr = np.array([1, 2, 3, 4, 5, 6])\n","reshaped_arr = arr.reshape(2, -1)\n","print(reshaped_arr)\n","```\n","\n","### n this example, the array arr is reshaped into a 2-row matrix, and the number of columns is automatically calculated by NumPy.\n","\n","## 17.4 Flattening Arrays:\n","### Flattening an array means converting a multi-dimensional array into a 1-dimensional array. NumPy provides the flatten() method to flatten an array. Here's an example:\n","\n","```python \n","import numpy as np\n","\n","arr = np.array([[1, 2, 3], [4, 5, 6]])\n","flattened_arr = arr.flatten()\n","print(flattened_arr)\n","\n","```\n","### In this example, the flatten() method is used to flatten the 2-dimensional array arr into a 1-dimensional array. The flattened array is then printed.\n","\n","## 17.5 Transposing Arrays:\n","### Transposing an array means interchanging its rows with columns. NumPy provides the transpose() method or the T attribute to transpose an array. Here's an example:\n","```python \n","import numpy as np\n","\n","arr = np.array([[1, 2, 3], [4, 5, 6]])\n","transposed_arr = arr.transpose()\n","print(transposed_arr)\n","```\n","### In this example, the transpose() method is used to transpose the array arr. The transposed array is then printed.\n","\n","## 17.6 Conclusion:\n","### Chapter 17 explored reshaping arrays in NumPy, providing flexibility in changing the shape and dimensions of arrays. The reshape() function allows for explicit reshaping of arrays, with the option to automatically calculate dimensions using -1. The flatten() method and the T attribute enable flattening and transposing arrays, respectively. Understanding and utilizing these reshaping techniques in NumPy empower efficient data manipulation, transformation, and analysis, ensuring compatibility with various algorithms and computational requirements.\n","\n","\n"]},{"cell_type":"markdown","id":"eee66a6e","metadata":{"papermill":{"duration":0.006478,"end_time":"2023-08-29T06:02:36.266916","exception":false,"start_time":"2023-08-29T06:02:36.260438","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>18 </font></h1>\n","\n","\n","# Chapter 18: Joining and Splitting Arrays\n","\n","## 18.1 Introduction:\n","### Chapter 18 focuses on joining and splitting arrays in NumPy. Joining arrays allows you to combine multiple arrays into a single array, while splitting arrays enables you to divide a single array into multiple smaller arrays. These operations are useful for data manipulation and analysis.\n","\n","## 18.2 Joining Arrays:\n","### NumPy provides several functions to join arrays together. The most commonly used functions for joining arrays are concatenate(), stack(), and hstack().\n","\n","## 18.2.1 concatenate():\n","### The concatenate() function allows you to concatenate arrays along a specified axis. By default, it concatenates arrays along the first axis (axis=0). Here's an example:\n","\n","```python \n","import numpy as np\n","\n","arr1 = np.array([1, 2, 3])\n","arr2 = np.array([4, 5, 6])\n","joined_arr = np.concatenate((arr1, arr2))\n","print(joined_arr)\n","```\n","### In this example, the concatenate() function is used to concatenate arr1 and arr2 into a single array. The resulting array is then printed.\n","\n","## 18.2.2 stack():\n","### The stack() function allows you to stack arrays along a new axis. It creates a new axis and joins the arrays along that axis. Here's an example:\n","\n","```python \n","import numpy as np\n","\n","arr1 = np.array([1, 2, 3])\n","arr2 = np.array([4, 5, 6])\n","stacked_arr = np.stack((arr1, arr2))\n","print(stacked_arr)\n","``` \n","\n","### In this example, the stack() function is used to stack arr1 and arr2 along a new axis. The resulting array is then printed.\n","\n","## 18.2.3 hstack():\n","### The hstack() function allows you to horizontally stack arrays. It joins arrays along the second axis (axis=1). Here's an example:\n","\n","```python\n","import numpy as np\n","\n","arr1 = np.array([1, 2, 3])\n","arr2 = np.array([4, 5, 6])\n","hstacked_arr = np.hstack((arr1, arr2))\n","print(hstacked_arr)\n","```\n","\n","### In this example, the hstack() function is used to horizontally stack arr1 and arr2. The resulting array is then printed.\n","\n","## 18.3 Splitting Arrays:\n","### NumPy provides functions to split arrays into smaller arrays. The most commonly used functions for splitting arrays are split(), hsplit(), and vsplit().\n","\n","## 18.3.1 split():\n","### The split() function allows you to split an array into multiple smaller arrays along a specified axis. Here's an example:\n","\n","```python\n","import numpy as np\n","\n","arr = np.array([1, 2, 3, 4, 5, 6])\n","split_arr = np.split(arr, 3)\n","print(split_arr)\n","```\n","### In this example, the split() function is used to split arr into three smaller arrays. The resulting arrays are then printed.\n","\n","## 18.3.2 hsplit():\n","### The hsplit() function allows you to horizontally split an array into multiple smaller arrays. It splits the array along the second axis (axis=1). Here's an example:\n","\n","```python \n","import numpy as np\n","\n","arr = np.array([[1, 2, 3], [4, 5, 6]])\n","hsplit_arr = np.hsplit(arr, 3)\n","print(hsplit_arr)\n","```\n","### In this example, the hsplit() function is used to horizontally split arr into three smaller arrays. The resulting arrays are then printed.\n","\n","## 18.3.3 vsplit():\n","### The vsplit() function allows you to vertically split an array into multiple smaller arrays. It splits the array along the first axis (axis=0). Here's an example:\n","\n","```python \n","import numpy as np\n","\n","arr = np.array([[1, 2, 3], [4, 5, 6]])\n","vsplit_arr = np.vsplit(arr, 2)\n","print(vsplit_arr)\n","```\n","### In this example, the vsplit() function is used to vertically split arr into two smaller arrays. The resulting arrays are then printed.\n","\n","## 18.4 Conclusion:\n","### Chapter 18 explored joining and splitting arrays in NumPy, providing flexibility in combining and dividing arrays for data manipulation and analysis. The concatenate() function enables joining arrays along a specified axis, while the stack() and hstack() functions allow for stacking arrays along new axes and horizontally joining arrays, respectively. On the other hand, the split(), hsplit(), and vsplit() functions facilitate splitting arrays into smaller arrays along specified axes. Understanding and utilizing these joining and splitting operations in NumPy enhance the versatility and efficiency of working with arrays, enabling seamless data integration and decomposition for various computational tasks.\n","\n","\n","\n"]},{"cell_type":"markdown","id":"62ab5552","metadata":{"papermill":{"duration":0.006803,"end_time":"2023-08-29T06:02:36.280358","exception":false,"start_time":"2023-08-29T06:02:36.273555","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>19 </font></h1>\n","\n","# Chapter 19: Adding and Removing Elements from Arrays\n","\n","## 19.1 Introduction:\n","### Chapter 19 focuses on adding and removing elements from arrays in NumPy. Adding and removing elements allows you to modify the size and content of an array dynamically. NumPy provides functions and methods to perform these operations efficiently.\n","\n","## 19.2 Adding Elements to Arrays:\n","### NumPy provides functions such as append(), insert(), and concatenate() to add elements to arrays.\n","\n","## 19.2.1 append():\n","### The append() function allows you to append elements to the end of an array. It takes the array and the elements to be appended as arguments. Here's an example:\n","\n","```python \n","import numpy as np\n","\n","arr = np.array([1, 2, 3])\n","new_arr = np.append(arr, [4, 5, 6])\n","print(new_arr)\n","```\n","\n","### In this example, the append() function is used to append elements [4, 5, 6] to the end of the array arr. The resulting array is then printed.\n","\n","## 19.2.2 insert():\n","### The insert() function allows you to insert elements at a specific position in an array. It takes the array, the index position, and the elements to be inserted as arguments. Here's an example:\n","\n","```python \n","import numpy as np\n","\n","arr = np.array([1, 2, 3])\n","new_arr = np.insert(arr, 1, [4, 5])\n","print(new_arr)\n","```\n","### In this example, the insert() function is used to insert elements [4, 5] at index position 1 in the array arr. The resulting array is then printed.\n","\n","## 19.2.3 concatenate():\n","### The concatenate() function allows you to concatenate arrays along a specified axis, effectively adding elements from one array to another. Here's an example:\n","\n","```python\n","import numpy as np\n","\n","arr1 = np.array([1, 2, 3])\n","arr2 = np.array([4, 5, 6])\n","new_arr = np.concatenate((arr1, arr2))\n","print(new_arr)\n","``` \n","### In this example, the concatenate() function is used to concatenate arr1 and arr2, effectively adding elements from arr2 to arr1. The resulting array is then printed.\n","\n","## 19.3 Removing Elements from Arrays:\n","### NumPy provides functions such as delete() and resize() to remove elements from arrays.\n","\n","## 19.3.1 delete():\n","### The delete() function allows you to remove elements from an array along a specified axis and index position. It takes the array, the index position, and the axis as arguments. Here's an example:\n","\n","```python\n","import numpy as np\n","\n","arr = np.array([1, 2, 3, 4, 5])\n","new_arr = np.delete(arr, 2)\n","print(new_arr)\n","```\n","### In this example, the delete() function is used to remove the element at index position 2 from the array arr. The resulting array is then printed.\n","\n","## 19.3.2 resize():\n","### The resize() function allows you to resize an array, effectively adding or removing elements. It takes the array and the new shape as arguments. Here's an example:\n","\n","```python\n","import numpy as np\n","\n","arr = np.array([1, 2, 3])\n","new_arr = np.resize(arr, (5,))\n","print(new_arr)\n","```\n","### In this example, the resize() function is used to resize the array arr to a new shape of (5,). The resulting array is then printed. If the new shape is larger than the original size, the elements are repeated to fill the new shape. If the new shape is smaller, elements are removed.\n","\n","## 19.4 Conclusion:\n","### Chapter 19 explored adding and removing elements from arrays in NumPy, providing flexibility in modifying the size and content of arrays. The append() function enables appending elements to the end of an array, while the insert() function allows for inserting elements at specific positions. The concatenate() function facilitates adding elements from one array to another. On the other hand, the delete() function allows for removing elements from an array, and the resize() function enables resizing arrays by adding or removing elements. Understanding and utilizing these operations in NumPy enhance the dynamic nature of working with arrays, enabling efficient data manipulation and customization for various computational tasks.\n","\n","\n","\n"]},{"cell_type":"markdown","id":"fbf8eac1","metadata":{"papermill":{"duration":0.00659,"end_time":"2023-08-29T06:02:36.293795","exception":false,"start_time":"2023-08-29T06:02:36.287205","status":"completed"},"tags":[]},"source":["\n","<h1 align=\"left\"><font color='red'>20 </font></h1>\n","\n","# Chapter 20: Rearranging Array Elements\n","\n","## 20.1 Introduction:\n","### Chapter 20 focuses on rearranging array elements in NumPy. Rearranging elements allows you to change the order or organization of elements within an array. NumPy provides functions and methods to perform these operations efficiently.\n","\n","## 20.2 Rearranging Elements in Arrays:\n","### NumPy provides functions such as reshape(), flatten(), transpose(), and swapaxes() to rearrange elements in arrays.\n","\n","## 20.2.1 reshape():\n","### The reshape() function allows you to change the shape of an array without modifying its data. It takes the desired shape as an argument and returns a new view of the original array with the specified shape. Here's an example:\n","\n","```python \n","import numpy as np\n","\n","arr = np.array([1, 2, 3, 4, 5, 6])\n","reshaped_arr = arr.reshape(2, 3)\n","print(reshaped_arr)\n","```\n","### In this example, the reshape() function is used to reshape the array arr into a 2x3 matrix. The resulting reshaped array is then printed.\n","\n","## 20.2.2 flatten():\n","### The flatten() method allows you to convert a multi-dimensional array into a 1-dimensional array by flattening it. Here's an example:\n","\n","```python\n","import numpy as np\n","\n","arr = np.array([[1, 2, 3], [4, 5, 6]])\n","flattened_arr = arr.flatten()\n","print(flattened_arr)\n","```\n","### In this example, the flatten() method is used to flatten the 2-dimensional array arr into a 1-dimensional array. The resulting flattened array is then printed.\n","\n","## 20.2.3 transpose():\n","### The transpose() method or the T attribute allows you to transpose an array, interchanging its rows with columns. Here's an example:\n","\n","```python \n","import numpy as np\n","\n","arr = np.array([[1, 2, 3], [4, 5, 6]])\n","transposed_arr = arr.transpose()\n","print(transposed_arr)\n","```\n","### In this example, the transpose() method is used to transpose the array arr. The resulting transposed array is then printed.\n","\n","## 20.2.4 swapaxes():\n","### The swapaxes() function allows you to swap the axes of an array. It takes the indices of the two axes to be swapped as arguments. Here's an example:\n","\n","```python \n","import numpy as np\n","\n","arr = np.array([[1, 2, 3], [4, 5, 6]])\n","swapped_arr = np.swapaxes(arr, 0, 1)\n","print(swapped_arr)\n","```\n","### In this example, the swapaxes() function is used to swap the axes of the array arr between axis 0 and axis 1. The resulting swapped array is then printed.\n","\n","## 20.3 Conclusion:\n","### Chapter 20 explored rearranging array elements in NumPy, providing flexibility in changing the order or organization of elements within arrays. The reshape() function allows for explicit reshaping of arrays, while the flatten() method converts multi-dimensional arrays into 1-dimensional arrays. The transpose() method and the swapaxes() function enable transposing and swapping axes of arrays, respectively. Understanding and utilizing these rearrangement techniques in NumPy empower efficient data manipulation, transformation, and analysis, ensuring compatibility with various algorithms and computational requirements.\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.10"},"papermill":{"default_parameters":{},"duration":12.520639,"end_time":"2023-08-29T06:02:36.923482","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-08-29T06:02:24.402843","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}