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+# Numpy Data Types
+In NumPy, data types play a crcial role in representing and manipulating numerical data.
+
+Numpy supports the following data types:
+
+- `i` - integer 
+- `b` - boolean
+- `u` - unsigned integer
+- `f` - float
+- `c` - complex float
+- `m` - timedelta
+- `M` - datetime
+- `O` - object
+- `S` - string
+- `U` - unicode string
+
+
+_Referred from: W3schools_
+
+## dtype() Function
+The `dtype()` function returns the type of the NumPy array object.
+
+Example 1
+``` python
+    import numpy as np
+
+    arr = np.array([1, 2, 3, 4])
+
+    print(arr.dtype)
+
+    # Output: int64
+```
+
+Example 2
+``` python
+    import numpy as np
+
+    arr = np.array(['apple', 'banana', 'cherry'])
+
+    print(arr.dtype)
+
+    # Output: <U6
+```
+## Example for integer type
+The NumPy integer array can be defined in two ways.
+
+Way 1: Using function `int_()`
+``` python
+    import numpy as np
+
+    arr = np.int_([2,4,6])
+    # Size: int8, int16, int32, int64
+
+    print(arr.dtype())
+
+    # Output: int64
+```
+
+Way 2: Using `dtype()`
+``` python
+    import numpy as np
+
+    arr = np.array([2,4,6], dtype='i4')
+    # Size: i1, i2, i4, i8
+
+    print(arr.dtype)
+
+    # Output: int32
+```
+
+Note: `np.intc()` has the same function as `int32()`.
+## Example for float type
+
+Way 1: Using function `float_()`
+``` python
+    import numpy as np
+
+    arr = np.float_(1)
+    # Size: float8, float16, float32, float64
+
+    print(arr)
+    print(arr.dtype())
+
+    # Output: 
+    # 1.0
+    # float64
+```
+
+Way 2: Using `dtype()`
+``` python
+    import numpy as np
+
+    arr = np.array([2,4,6], dtype='f4')
+    # Size: f1, f2, f4, f8
+
+    print(arr)
+    print(arr.dtype)
+
+    # Output:
+    # [1. 2. 3. 4.]
+    # float32
+```
+
+Note: `np.single()` has the same function as `float32()`.
+
+## Example for boolean type
+
+``` python
+    import numpy as np
+
+    x = np.bool_(1)
+
+    print(x)
+    print(x.dtype)
+
+    # Output: 
+    # True
+    # bool
+```
+## Example for unsigned integer type
+
+``` python
+    import numpy as np
+
+    x = np.uintc(1)
+
+    print(x)
+    print(x.dtype)
+
+    # Output: 
+    # 1
+    # uint32
+```
+
+## Example for complex type
+Complex type is a combination of real number + imaginary number. The `complex_()` is used to define the complex type NumPy object.
+``` python
+    import numpy as np
+
+    x = np.complex_(1)
+    # Size: complex64, complex128
+
+    print(x)
+    print(x.dtype)
+
+    # Output: 
+    # (1+0j)
+    # complex128
+```
+
+## Example for datetime type
+The `datetime64()` is used to define the date, month and year.
+
+``` python
+    import numpy as np
+
+    x = np.datetime64('2024-05')
+    y = np.datetime64('2024-05-20')
+    z = np.datetime64('2024')
+    
+    print(x,x.dtype)
+    print(y,y.dtype)
+    print(z,z.dtype)
+
+    # Output: 
+    # 2024-05 datetime64[M]
+    # 2024-20-05 datetime64[D]
+    # 2024 datetime64[Y]
+```
+
+## Example for string type
+``` python
+    import numpy as np
+
+    arr = np.str_("roopa")
+
+    print(arr.dtype)
+
+    # Output: <U5
+```
+
+## Example for object type
+``` python
+    import numpy as np
+
+    arr = np.object_([1, 2, 3, 4])
+
+    print(arr)
+    print(arr.dtype)
+
+    # Output: 
+    # [1, 2, 3, 4]
+    # object
+```
+## Example for unicode string type
+``` python
+    import numpy as np
+
+    arr = np.array(['apple', 'banana', 'cherry'])
+
+    print(arr.dtype)
+
+    # Output: <U6
+```
+## Example for timedelta type
+The `timedelta64()` used to find the difference between the `datetime64()`. The arguments for timedelta64 are a number, to represent the number of units, and a date/time unit, such as (D)ay, (M)onth, (Y)ear, (h)ours, (m)inutes, or (s)econds. The timedelta64 data type also accepts the string “NAT” in place of the number for a “Not A Time” value.
+
+``` python
+    import numpy as np
+
+    x = np.datetime64('2024-05-20')
+    y = np.datetime64('2023-05-20')
+    res = x - y
+
+    print(res)
+    print(res.dtype)
+
+    # Output: 
+    # 366 days
+    # timedelta64[D]
+```
+## Additional Data Type (`longdouble`)
+`longdouble` is a data type that provides higher precision than the standard double-precision floating-point (`float64`) type.
+
+``` python
+    import numpy as np
+
+    arr = np.longdouble([1.222222, 4.44, 45.55])
+
+    print(arr, arr.dtype)
+
+    # Output: 
+    # [1.222222 4.44 45.55] float128
+```
+
+# Data Type Conversion
+`astype()` function is used to the NumPy object from one type to another type.
+
+It creates a copy of the array and allows to specify the data type of our choice.
+
+## Example 1
+
+``` python
+    import numpy as np
+
+    x = np.array([1.2, 3.4, 5.6])
+    y = x.astype(int)
+
+    print(y,y.dtype)
+
+    # Output: 
+    # [1 3 5] int64
+```
+
+## Example 2
+
+``` python
+    import numpy as np
+
+    x = np.array([1, 3, 0])
+    y = x.astype(bool)
+
+    print(y,y.dtype)
+
+    # Output: 
+    # [True True False] bool
+```
diff --git a/contrib/numpy/index.md b/contrib/numpy/index.md
index ff0f25b..b38006c 100644
--- a/contrib/numpy/index.md
+++ b/contrib/numpy/index.md
@@ -2,3 +2,4 @@
 
 - [Installing NumPy](installing-numpy.md)
 - [Introduction](introduction.md)
+- [NumPy Data Types](datatypes.md)