A detailed introduction to pkl format files in Python

1. Introduction to pkl format file

pkl format file is a file format used in Python for serializing objects.Full name is pickle. It can convert any object in Python into a format that can be saved to disk or transmitted over the network, and then read out from disk or received from the network and restore it to the original Python object. This capability makes pkl format files very useful in Python programming, especially when complex data structures or custom objects need to be saved and loaded.

The use of pkl format files depends on Python's pickle module. The pickle module provides two main functions:

()Used to serialize and save Python objects to a file;
()Used to read the serialized object from the file and restore it to the original Python object.

2. How to save pkl format file

Saving pkl format files is very simple, we only need to use the pickle moduledump()Just function.

Here is a simple example:
```
import pickle

# Create a dictionary objectdata = {
    'name': 'Alice',
    'age': 30,
    'city': 'New York'
}

# Open a file for writingwith open('', 'wb') as f:
    # Use() to serialize and save dictionary objects to a file    (data, f)
```
In the above code, we first create a dictionary object containing name, age, and citydata. Then, we open a name calledand open it in binary write mode ('wb'). Finally, we use()The function willdataThe object is serialized and saved to a file.

It should be noted that since the pkl format file is a binary file, we need to specify the binary write mode ('wb') when opening the file.

3. How to load pkl format files

Loading pkl format files is also very simple, we only need to use the pickle moduleload()Just function.

Here is an example of loading a pkl format file:
```
import pickle

# Open a file for readingwith open('', 'rb') as f:
    # Use() to read the serialized object from the file and restore it to the original Python object    loaded_data = (f)

# Print loaded dataprint(loaded_data)
```
In the above code, we first open the saved previously in binary read mode ('rb')document. Then, we use()Functions read the serialized object from the file and restore it to the original Python objectloaded_data. Finally, we print out the loaded data to verify the loaded results.

It is also important to note that since the pkl format file is a binary file, we need to specify the binary reading mode (‘rb’) when opening the file.

4. Use scenarios of pkl file

PKL format files have a wide range of application scenarios in Python programming. Here are some common usage examples:

Object persistence: The pkl format file can save Python objects to disk to realize persistent storage of objects. This is very useful for situations where complex data structures or custom objects need to be saved for a long time.
Data exchange: The pkl format file can be used to exchange data between different Python programs or different machines. By serializing the data into a pkl format file, it is easy to transfer and share data between different programs or machines.
Cache mechanism: When processing complex calculations or large amounts of data, you can use pkl format files as the cache mechanism. Saving intermediate results or calculation results as pkl files can quickly load when needed, avoiding the overhead of repeated calculations or data loading.

5. Things to note about pkl files

When using pkl format files, you need to pay attention to the following points:

Security: Since pkl format files can serialize any Python objects, you need to be extra careful when loading pkl files. Avoid loading of pkl files from untrusted sources to prevent potential security risks.
Version compatibility: Different versions of Python or pickle modules may differ when serializing and loading objects. Therefore, when saving and loading pkl files, it is best to ensure that the versions of Python and pickle modules used are consistent to avoid compatibility issues.
File size: For pkl files containing large amounts of data or complex objects, their file size may be large. When saving and transferring pkl files, you need to pay attention to file size issues to avoid occupying too much storage space or transmission bandwidth.

6. Expansion application of pkl file

In addition to basic serialization and deserialization functions, pkl format files can also be expanded and applied in combination with other Python libraries and tools. Here are some examples:

Combined with pandas:pandas is a powerful data processing library that can save DataFrame objects as pkl format files for subsequent loading and analysis. By combining pandas and pickle, we can easily persist the data frame (DataFrame) to disk and quickly load it back if needed.
```
import pandas as pd
import pickle

# Create a pandas DataFramedf = ({
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'city': ['New York', 'Los Angeles', 'Chicago']
})

# Save DataFrame as a pkl filewith open('', 'wb') as f:
    (df, f)

# Load DataFrame from pkl filewith open('', 'rb') as f:
    loaded_df = (f)

# Show loaded DataFrameprint(loaded_df)
```
In the above code, we first create a pandas DataFrame with name, age, and city. Then, we use pickledump()The function saves the DataFrame object as a pkl file. Next, we useload()The function loads the DataFrame from the pkl file and prints it out to verify the loaded result.
Deep Learning Model Saving: In deep learning, we often need to save and load trained models. Many deep learning frameworks such as TensorFlow and PyTorch support saving models as pkl format files or other dedicated formats for subsequent use. By saving the model as a pkl file, we can easily share the model, deploy the model in different environments, or perform model versioning.

Take PyTorch as an example, although PyTorch usually uses its own.pthor.ptFormat to save the model, but you can also combine pickle to save some auxiliary information or custom objects of the model.
```
import torch
import  as nn
import pickle

# Define a simple neural network modelclass SimpleModel():
    def __init__(self):
        super(SimpleModel, self).__init__()
         = (10, 1)

    def forward(self, x):
        return (x)

# Instantiate the model and train it (the training process is omitted here)model = SimpleModel()
# Assuming the model is already trained...
# Save model parameters to pkl filewith open('model_params.pkl', 'wb') as f:
    (model.state_dict(), f)

# Load model parameters from pkl filewith open('model_params.pkl', 'rb') as f:
    loaded_params = (f)

# Instantiate a new model and load parametersnew_model = SimpleModel()
new_model.load_state_dict(loaded_params)
```
In the above code, we define a simple neural network model and save its parameters as a pkl file. We then load the parameters from the pkl file and apply them to a newly instantiated model. In this way, we canUse loaded model parameters for prediction or further analysis without retraining。

7. Summary

As a powerful serialization tool in Python, pkl format files provide convenient ways to persist objects, exchange data and caching mechanisms.By mastering the methods of saving and loading pkl files, we can effectively process complex objects and large amounts of data in Python programs. However, when using pkl files, we also need to pay attention to issues such as security, version compatibility and file size to ensure that they are used correctly and efficiently. By combining other Python libraries and tools, we can further expand the application scenarios of pkl files and realize more advanced data processing and model saving functions.

Attachment: python3 pkl conversion json

Python2 may encounter encoding problems when converting pkl to json, requiring various settings. However, I used some methods provided by bloggers and did not work. After many experiments, I found a slightly simpler method:

'''
Convert a pkl file into json file
'''
import sys
import os
import pickle
import json
import numpy

class NumpyEncoder():
    """ Special json encoder for numpy types """
    def default(self, obj):
        if isinstance(obj, (numpy.int_, , , numpy.int8,
                            numpy.int16, numpy.int32, numpy.int64, numpy.uint8,
                            numpy.uint16, numpy.uint32, numpy.uint64)):
            return int(obj)
        elif isinstance(obj, (numpy.float_, numpy.float16, numpy.float32,
                              numpy.float64)):
            return float(obj)
        elif isinstance(obj, (,)):
            return ()
        return (self, obj)

def convert_dict_to_json(file_path):
    with open(file_path, 'rb') as fpkl, open('%' % file_path, 'w') as fjson:
        data = (fpkl,encoding='latin1')
        (data, fjson, ensure_ascii=False, sort_keys=True, indent=4,cls=NumpyEncoder)

def main():
    # if [1] and ([1]):
        file_path = 'your file path'
        print("Processing %s ..." % file_path)
        convert_dict_to_json(file_path)
    # else:
    #     print("Usage: %s abs_file_path" % (__file__))

if __name__ == '__main__':
    main()

This code segment has made a choice for json and pkl encoding in the NumpyEncoder() class, and there is no need to set the format separately when converting.

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