Introduction to pandarallel
pandarallel is a simple and efficient tool to parallelize Pandas operations on all available CPUs. It helps users to process and analyze data faster and improve data processing efficiency.
Functional Features
1. Easy to use: pandarallel is simple to use, easy to get started, just a few lines of code can easily parallelize Pandas operations.
2. Efficient parallelism: pandarallel can parallelize Pandas operations on all available CPUs, thus speeding up data processing and improving processing efficiency.
3. Strong compatibility: pandarallel compatible with all Pandas operations, whether aggregation, conversion, screening or other operations, you can use pandarallel parallelization.
4. Highly configurable: pandarallel provides a number of configuration options that can be customized according to the user's needs for parallelized processing.
mounting
pandarallel can be installed using pip with the following command:
pip install pandarallel
usage example
Here is a simple example of parallelizing Pandas data using pandarallel.
First, import the necessary libraries and data:
import pandas as pd
from pandarallel import pandarallel
df = pd.read_csv('')Then, initialize pandarallel:
(progress_bar=True)
Next, parallelization is performed:
df['new_column'] = df['old_column'].parallel_apply(lambda x: x*2)
Finally, save the results:
df.to_csv('', index=False)This example uses the parallel_apply method to apply a function in parallel to a column in the Pandas data and uses the to_csv method to save the result to a file.
Usage Scenarios
1. Big Data Processing: For big data processing, pandarallel can parallelize Pandas operations on all available CPUs to improve data processing efficiency.
2. Data analysis: pandarallel can accelerate data processing, thus speeding up data analysis, enabling users to more quickly analyze data.
3. Machine Learning: For machine learning tasks, pandarallel can accelerate the process of data preprocessing, making model training more efficient.
summarize
pandarallel is a simple and efficient tool to parallelize Pandas operations on all available CPUs. It helps users to process and analyze data faster and more efficiently. pandarallel is simple to use, easy to get started with, and compatible with all Pandas operations. It also provides many configuration options that can be customized according to the user's needs. If you are looking for an efficient data processing tool, then pandarallel is the best choice for you.
Project Address:
/nalepae/pandarallel
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