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:

• int8, int16, int32, int64: signed integer types, occupying 1, 2, 4, and 8 bytes respectively;
• uint8 , uint16, uint32, uint64: unsigned integer type, occupying 1, 2, 4, and 8 bytes respectively;
• float16, float32, float64, float128: floating point type, occupying 2, 4, 8, respectively 16 bytes;
• bool: Boolean type, occupies 1 byte;
• char: character type, occupies 1 byte;
• complex64, complex128: complex type, respectively Occupies 8 or 16 bytes.

3. Methods and techniques for numpy data type conversion

1. astype() method: All elements of the array can be converted to the specified type through the astype() method. The following is a sample code:

import numpy as np

Create an array of floating point numbers

arr_float = np.array([1.2, 2.3, 3.4, 4.5 ])

Convert array elements to integer type

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]

2. view() method: The view() method can create a new array object, but shares the memory space of the original data. Here is a sample code:

import numpy as np

Create an integer array

arr_int = np.array([1, 2, 3, 4] )

Convert integer array view to floating point array view

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]

3. asarray() function: The asarray() function can convert the input data into an ndarray array and try not to copy the data. If the input data is already an ndarray array, no copying is done. Here is a sample code:

import numpy as np

Create a list of integers

lst_int = [1, 2, 3, 4, 5]

Convert list of integers to array

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:

1. Data cleaning and preprocessing: During the data cleaning and preprocessing process, according to specific needs, it may be necessary to convert string data into numeric data, or to convert specific data types. Convert to facilitate subsequent calculations and analysis.
2. Feature Engineering: In the process of machine learning and model training, feature engineering is an important step. Depending on different feature meanings and model requirements, type conversion of features may be required to ensure smooth model training.
3. Data visualization: When visualizing data, in order to use appropriate drawing methods and techniques, corresponding conversions need to be performed according to the type and distribution characteristics of the data.

The following is a case analysis about data type conversion in feature engineering:

import numpy as np

Create a data set containing age and income features

dataset = np.array([[25, 50000],

                [30, 60000],
                [35, 70000],
                [40, 80000]])

Convert the age feature to a floating point number type

dataset_float = dataset.astype(np.float)

Convert income characteristics to logarithmic type

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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