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Create a record array from a (flat) list of array and fetch arrays using names in Numpy
To create a record array from a (flat) list of array, use the numpy.core.records.fromarrays() method in Python Numpy. The names is set using the "names" parameter. The field names, either specified as a comma-separated string in the form 'col1, col2, col3', or as a list or tuple of strings in the form ['col1', 'col2', 'col3']. An empty list can be used, in that case default field names (‘f0’, ‘f1’, …) are used.
It returns the record array consisting of given arrayList columns. The first parameter is a List of arraylike objects (such as lists, tuples, and ndarrays). The dtype is the valid dtype for all arrays. The formats, names, titles, aligned, byteorder parameters, if dtype is None, these arguments are passed to numpy.format_parser to construct a dtype.
Steps
At first, import the required library −
import numpy as np
Create a new array using the numpy.array() method −
arr1 = np.array([[5, 10, 15], [20, 25, 30]]) arr2 = np.array([[9, 18, 24], [87.5, 65, 23.8]]) arr3 = np.array([['12', 'bbb', 'john'], ['5.6', '29', 'k']])
Display the arrays −
print("Array1...
",arr1)
print("Array2...
",arr2)
print("Array3...
",arr3)Get the type of the arrays −
print("
Array1 type...
", arr1.dtype)
print("
Array2 type...
", arr2.dtype)
print("
Array3 type...
", arr3.dtype)Get the dimensions of the Arrays −
print("
Array1 Dimensions...
", arr1.ndim)
print("
Array2 Dimensions...
", arr2.ndim)
print("
Array3 Dimensions...
", arr3.ndim)To create a record array from a (flat) list of array, use the numpy.core.records.fromarrays() method in Python Numpy. The names is set using the "names" parameter −
rec = np.core.records.fromarrays([arr1,arr2,arr3], names = 'i,j,k')
print("
Record Array...
",rec)Fetching the arrays based on names −
print("
Fetching the array1...
",rec.i)
print("
Fetching the array2...
",rec.j)
print("
Fetching the array3...
",rec.k)Example
import numpy as np
# Create a new array using the numpy.array() method
arr1 = np.array([[5, 10, 15], [20, 25, 30]])
arr2 = np.array([[9, 18, 24], [87.5, 65, 23.8]])
arr3 = np.array([['12', 'bbb', 'john'], ['5.6', '29', 'k']])
# Display the arrays
print("Array1...
",arr1)
print("Array2...
",arr2)
print("Array3...
",arr3)
# Get the type of the arrays
print("
Array1 type...
", arr1.dtype)
print("
Array2 type...
", arr2.dtype)
print("
Array3 type...
", arr3.dtype)
# Get the dimensions of the Arrays
print("
Array1 Dimensions...
", arr1.ndim)
print("
Array2 Dimensions...
", arr2.ndim)
print("
Array3 Dimensions...
", arr3.ndim)
# To create a record array from a (flat) list of array, use the numpy.core.records.fromarrays() method in Python Numpy
# The names is set using the "names" parameter
# The field names, either specified as a comma-separated string in the form 'col1, col2, col3', or as a list or tuple of strings in the form ['col1', 'col2', 'col3'].
# An empty list can be used, in that case default field names (‘f0’, ‘f1’, …) are used.
rec = np.core.records.fromarrays([arr1,arr2,arr3], names = 'i,j,k')
print("
Record Array...
",rec)
# Fetching the arrays based on names
print("
Fetching the array1...
",rec.i)
print("
Fetching the array2...
",rec.j)
print("
Fetching the array3...
",rec.k)Output
Array1... [[ 5 10 15] [20 25 30]] Array2... [[ 9. 18. 24. ] [87.5 65. 23.8]] Array3... [['12' 'bbb' 'john'] ['5.6' '29' 'k']] Array1 type... int64 Array2 type... float64 Array3 type... <U4 Array1 Dimensions... 2 Array2 Dimensions... 2 Array3 Dimensions... 2 Record Array... [[( 5, 9. , '12') (10, 18. , 'bbb') (15, 24. , 'john')] [(20, 87.5, '5.6') (25, 65. , '29') (30, 23.8, 'k')]] Fhing the array1... [[ 5 10 15] [20 25 30]] Fhing the array2... [[ 9. 18. 24. ] [87.5 65. 23.8]] Fhing the array3... [['12' 'bbb' 'john'] ['5.6' '29' 'k']]
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