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‎contrib/numpy/datatypes.md

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

‎contrib/numpy/index.md

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- [Installing NumPy](installing-numpy.md)
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- [Introduction](introduction.md)
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- [NumPy Data Types](datatypes.md)

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