The release of Pandas 2.0 and its subsequent versions introduced various features and enhancements, marking a significant evolution in using Pandas for data manipulation and analysis. Here's a closer look at some of the new features:
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Installation of optional dependencies:
In Pandas 2.0, when installing pandas through pip, you can install a set of optional dependencies by specifying extras, for example: pip install " pandas[performance, aws]>=2.0.0". Available extras include options for performance, computing, file system support, cloud providers, data formats, and more. -
Enhanced numeric data type support in indexes:
Indexes can now accommodate any numpy numeric data type, overcoming the previous limitation of only supporting int64, uint64 and float64 data types. -
PyArrow integration:
One of the defining features of Pandas 2.0 is its integration with PyArrow, making operations more memory efficient. Users can now use PyArrow as their memory format instead of the NumPy data structures originally used, which solves the problem of inefficient memory usage. -
Nullable data types:
Support for nullable data types makes it easier to handle missing values. This feature allows for more straightforward handling of null values, especially in integer columns, by specifying the use of nullable data types when reading data into a DataFrame, for example: pd.read_csv(my_file, use_nullable_dtypes=True). -
Copy-on-write performance enhancement:
To minimize memory usage and improve performance when processing large data sets, a memory optimization technique called copy-on-write has been implemented. -
Enhanced extended array support and non-nanosecond datetime resolution:
This release also brings enhanced extended array support and non-nanosecond datetime resolution. -
Performance improvements:
Continuous performance improvements have been made in different versions, improving the overall efficiency of the entire library.
These updates are the result of more than three years of continuous development efforts and mark an important step in making Pandas more robust and user-friendly for data manipulation and analysis tasks.
Example: Using nullable data types
import pandas as pd
# 假设'my_file.csv'有一些列有缺失值
data = pd.read_csv('my_file.csv', use_nullable_dtypes=True)
# 这将确保有缺失值的整数数据列将使用支持空值的Int64数据类型，而不是转换为浮点数。
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