Practical skills and case analysis of numpy data type conversion
Introduction:
In the process of data analysis and scientific calculation, it is often necessary to type conversion of data to adapt to different computing needs. As a commonly used scientific computing library in Python, numpy provides a wealth of data type conversion functions and methods. This article will introduce the practical skills of data type conversion in numpy and demonstrate its specific application through case analysis.
1. Background and significance of data type conversion
When performing data analysis and scientific calculations, different types of data may require different processing methods and calculation methods. For example, when calculating averages or sums, if the data type is an integer, the precision of the decimal part may be lost; and if the data type is a floating point number, computing resources may be wasted. Therefore, selecting and converting appropriate data types is very important to improve calculation efficiency, reduce memory overhead, and improve calculation accuracy.
2. Data types in numpy
In numpy, data types are represented by dtype objects, which define the storage method and calculation rules of data. Numpy provides the following commonly used data types:
3. Methods and techniques for numpy data type conversion
import numpy as np
arr_float = np.array([1.2, 2.3, 3.4, 4.5 ])
arr_int = arr_float.astype(np.int32)
print(arr_float) # [1.2 2.3 3.4 4.5]
print(arr_int) # [1 2 3 4]
import numpy as np
arr_int = np.array([1, 2, 3, 4] )
arr_float = arr_int.view(np.float32)
print(arr_int) # [1 2 3 4]
print(arr_float) # [1.0 2.0 3.0 4.0]
import numpy as np
lst_int = [1, 2, 3, 4, 5]
arr_int = np.asarray(lst_int)
print(lst_int) # [1, 2, 3, 4, 5]
print (arr_int) # [1 2 3 4 5]
IV. Case Analysis: Application of Data Type Conversion in Practical Applications
In practical applications of scientific computing and data analysis, data type conversion is often used In the following situations:
The following is a case analysis about data type conversion in feature engineering:
import numpy as np
dataset = np.array([[25, 50000],
[30, 60000], [35, 70000], [40, 80000]])
dataset_float = dataset.astype(np.float)
dataset_log = np.log(dataset_float)
print(dataset) # [[25 50000]
# [30 60000] # [35 70000] # [40 80000]]
print(dataset_float) # [[2.5e 01 5.0e 04]
# [3.0e+01 6.0e+04] # [3.5e+01 7.0e+04] # [4.0e+01 8.0e+04]]
print(dataset_log) # [[ 3.21887582 10.81977828]
# [ 3.40119738 11.0020999 ] # [ 3.55534806 11.15625156] # [ 3.68887945 11.28978294]]
Through the above cases, we can see that through the appropriate With data type conversion, we can better adapt to different model training needs, improve the efficiency of data analysis and calculation, while maintaining the accuracy and precision of the data.
Conclusion:
As a commonly used scientific computing library in Python, numpy provides a wealth of data type conversion methods. In the process of data analysis and scientific computing, selecting and converting appropriate data types is very important to improve calculation efficiency, reduce memory overhead, and maintain calculation accuracy. This article introduces the practical skills and methods of data type conversion in numpy, and demonstrates its specific application scenarios in practical applications through case analysis. We hope that readers can make full use of the data type conversion function to improve calculation efficiency and accuracy when using numpy for data analysis and scientific calculations.
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