使用 NumPy 从文本形式的记录列表创建 RecArray 并使用名称获取列
要从文本形式的记录列表创建 RecArray,请在 Python NumPy 中使用 **numpy.core.records.fromrecords()** 方法。名称是使用“**names**”参数设置的。字段名称,可以以 'col1, col2, col3' 形式的逗号分隔字符串指定,也可以以 ['col1', 'col2', 'col3'] 形式的字符串列表或元组指定。可以使用空列表,在这种情况下使用默认字段名称('f0'、'f1'、…)。
第一个参数是数据,同一字段中的数据可能是异构的——它们将被提升到最高数据类型。dtype 是所有数组的有效 dtype。格式、名称、标题、对齐、字节序参数、f dtype 为 None,这些参数将传递给 numpy.format_parser 以构造 dtype。如果 formats 和 dtype 都为 None,则这将自动检测格式。为了更快地处理,请使用元组列表而不是列表列表。
步骤
首先,导入所需的库:
import numpy as np
使用 numpy.array() 方法创建一个新数组:
arr1 = np.array([[7, 14, 21], [30, 37, 45]]) arr2 = np.array([[11.3, 18.7, 24], [87.5, 65, 23.8]]) arr3 = np.array([['12', 'bbb', 'john'], ['5.6', '29', 'k']])
显示数组:
print("Array1...
",arr1) print("Array2...
",arr2) print("Array3...
",arr3)
获取数组的类型:
print("
Array1 type...
", arr1.dtype) print("
Array2 type...
", arr2.dtype) print("
Array3 type...
", arr3.dtype)
获取数组的维度:
print("
Array1 Dimensions...
", arr1.ndim) print("
Array2 Dimensions...
", arr2.ndim) print("
Array3 Dimensions...
", arr3.ndim)
要从文本形式的记录列表创建 RecArray,请在 Python NumPy 中使用 numpy.core.records.fromrecords() 方法。名称是使用“names”参数设置的:
rec = np.core.records.fromrecords([arr1,arr2,arr3], names = 'col1, col2, col3') print("
Record Array...
",rec)
基于名称获取列:
print("
Fetching the column1...
",rec.col1) print("
Fetching the column2...
",rec.col2) print("
Fetching the column3...
",rec.col3)
示例
import numpy as np # Create a new array using the numpy.array() method arr1 = np.array([[7, 14, 21], [30, 37, 45]]) arr2 = np.array([[11.3, 18.7, 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 recarray from a list of records in text form, use the numpy.core.records.fromrecords() 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.fromrecords([arr1,arr2,arr3], names = 'col1, col2, col3') print("
Record Array...
",rec) # Fetching the columns based on names print("
Fetching the column1...
",rec.col1) print("
Fetching the column2...
",rec.col2) print("
Fetching the column3...
",rec.col3)
输出
Array1... [[ 7 14 21] [30 37 45]] Array2... [[11.3 18.7 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... [[('7', '14', '21') ('30', '37', '45')] [('11.3', '18.7', '24.0') ('87.5', '65.0', '23.8')] [('12', 'bbb', 'john') ('5.6', '29', 'k')]] Fhing the column1... [['7' '30'] ['11.3' '87.5'] ['12' '5.6']] Fhing the column2... [['14' '37'] ['18.7' '65.0'] ['bbb' '29']] Fhing the column3... [['21' '45'] ['24.0' '23.8'] ['john' 'k']]
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