Create loading_arrays_from_files.md

Added Introduction
Added numpy.loadtxt method
Added numpy.genfromtxt method
Added numpy.fromfile method
Added numpy.load method

contrib/numpy/loading_arrays_from_files.md

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+# Loading Arrays From Files
+The ability to load data from various file formats is a critical feature for scientific computing and data analysis. 
+NumPy provides several functions to read data from different file types and convert them into ndarrays. 
+This section will cover how to load ndarrays from common file formats, including CSV, TSV, and binary files.
+
+### Here are the methods available:
+
+`numpy.loadtxt`: The loadtxt function allows you to load data from a text file.You can specify various parameters such as the file name, data type, delimiter,
+and more. It reads the file line by line, splits it at the specified delimiter, and converts the values into an array.
+
+- **Syntax:**
+  ```python
+  numpy.loadtxt(fname, dtype = float, delimiter=None, converters=None, skiprows=0, usecols=None)
+  ```
+
+  `fname` : Name of the file <br>
+  `dtype` : Data type of the resulting array. (By default is float) <br>
+  `delimiter`: String or character separating columns; default is any whitespace. <br>
+  `converters`: Dictionary mapping column number to a function to convert that column's string to a float. <br>
+  `skiprows`: Number of lines to skip at the beginning of the file. <br>
+  `usecols`: Which columns to read starting from 0.
+
+- **Example for `loadtxt`:**
+
+  **example.txt** <br>
+  
+  ![image](https://github.com/Santhosh-Siddhardha/learn-python/assets/103999924/a0148d29-5fba-45fa-b3f4-058406b3016b)
+
+  **Code** <br>
+  ```python
+  import numpy as np 
+  arr = np.loadtxt("loadtxt.txt", dtype=int) 
+  print(arr) 
+  ```
+
+  **Output**<br>
+  ```python
+  [1 2 3 4 5]
+  ```
+  
+
+`numpy.genfromtxt`:  The genfromtxt function is similar to loadtxt but provides more flexibility. It handles missing values (such as NaNs), allows custom converters
+for data parsing, and can handle different data types within the same file. It’s particularly useful for handling complex data formats.
+
+- **Syntax:**
+  ```python
+  numpy.genfromtxt(fname, dtype=float, delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None)
+  ```
+
+  `fname` : Name of the file <br>
+  `dtype` : Data type of the resulting array. (By default is float) <br>
+  `delimiter`: String or character separating columns; default is any whitespace. <br>
+  `skip_header`: Number of lines to skip at the beginning of the file.<br>
+  `skip_footer`: Number of lines to skip at the end of the file.<br>
+  `converters`: Dictionary mapping column number to a function to convert that column's string to a float. <br>
+  `missing_values`: Set of strings corresponding to missing data.<br>
+  `filling_values`: Value used to fill in missing data. Default is NaN.<br>
+  `usecols`: Which columns to read starting from 0.
+
+- **Examples for `genfromtxt`:**
+
+
+`numpy.fromfile`: The fromfile function reads binary data directly from a file into a NumPy array. It doesn’t assume any specific format or delimiter;
+instead, it interprets the raw binary data according to the specified data type.
+
+`numpy.load`: Load arrays saved in NumPy’s native binary format (.npy or .npz). These files preserve the array structure, data types, and metadata.
+It’s an efficient way to store and load large arrays.