Comparison: pandas vs. vaex

The following two libraries are useful in the domain of data analysis and manipulation:

Pandas is a powerful data analysis and manipulation library in Python, primarily used for working with structured tabular data.

Vaex is a Python library designed for efficient analysis and visualization of large-scale tabular datasets, emphasizing lazy Out-of-CoreLazy Out-of-Core computation is an approach where data processing and analysis are performed in a memory-efficient and scalable manner, particularly suitable for handling large datasets that cannot fit into the available memory. computation and memory efficiency.

In this Answer, we’ll compare pandas and vaex to understand their differences and strengths.

Comparison

These libraries offer effective data handling, analysis, manipulation, and visualization features. Let’s compare them in the table below:

Elements

pandas

vaex

Data handling

Pandas centralizes around DataFrames, enabling intuitive data manipulation, and efficiently operates on datasets that fit into memory.

Vaex emphasizes lazy Out-of-Core DataFrames, leveraging memory mapping and lazy computations for handling massive datasets.

Performance

Pandas is written in Python, which provides ease of use and a rich ecosystem of libraries. However, due to Python’s interpreter overhead, it may face performance bottlenecks with large datasets.

Vaex utilizes Rust’s performance advantages for speed and scalability, leveraging multithreading, and single instruction multiple data (SIMD) parallelism to achieve high processing speeds for extensive datasets.

Memory efficiency

Pandas copies data when performing operations, potentially leading to memory overhead. As a result, it may face limitations with memory-intensive operations on large datasets.

Vaex implements a zero-memory copy policy, ensuring efficient memory usage, and handles filtering, selections, and subsets without unnecessary memory copies.

Visualization

Pandas offers basic visualizations through integration with libraries like Matplotlib and seaborn but requires additional effort to create advanced visualizations.

Vaex provides built-in support for visualizations, including histograms, density plots, and 3D volume rendering, facilitating interactive exploration of large datasets with minimal effort.

API and syntax

Pandas is renowned for its expressive and intuitive API, which resembles SQL-like syntax. It offers a wide range of functions and methods for efficient data manipulation.

Vaex aims for API similarity to pandas, ensuring a familiar user experience, and introduces additional functionalities inspired by other data processing libraries, enhancing its capabilities.

Use case

Pandas is ideal for exploratory data analysis (EDA), data cleaning, and small to moderate-sized datasets, making it well-suited for data manipulation tasks in single-machine environments.

Vaex is suited for handling massive datasets that exceed memory capacity, making it particularly useful for big data scenarios, parallel computation, and efficient memory management.

Example

Press the “Run” button to read data using both of these libraries as follows:

import time as t
import pandas as pd
import vaex as vx
# pandas
pd_start_time = t.time()
pd_df = pd.read_parquet('/educative/yellow_tripdata_2023-01.parquet')
pd_time_taken = t.time()-pd_start_time
# vaex
vx_start_time = t.time()
vx_df = vx.open('/educative/yellow_tripdata_2023-01.parquet')
vx_time_taken = t.time()-vx_start_time
# results
print(f"=== Read file ===")
print(f"pandas: {pd_time_taken:.3f}\nvaex: {vx_time_taken:.3f}")
Time comparison to create DataFrame using pandas and vaex

In the code above:

  • Lines 1–3: Import the time module as t, pandas library as pd, and vaex library as vx.

  • Lines 5–7: Measure the time taken to read a PARQUET file using Pandas’ read_parquet() function and stores the resulting DataFrame in pd_df.

  • Lines 9–11: Similarly, measure the time taken to read the same PARQUET file using vaex’s open() function and stores the resulting DataFrame in vx_df.

  • Lines 13–14: Print the time taken for data reading using both pandas and vaex, rounded to 3 decimal places, with each time value displayed on a separate line under the header "=== Read file ===".

Note: We use the NYC Taxi and Limousine Commission (TLC)https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page dataset in the given example.

List of different functions

When we run the given code example, we observe that vaex has loaded the given dataset with over 3 million rows almost three times faster than pandas. This difference becomes more noticeable with larger datasets. Here is a list of different functions we can explore further:

Operations

pandas

vaex

Read file

pd.read_csv() OR pd.read_parquet()

vx.read_csv() OR vx.open()

Value counts

pd_df.x.value_counts()

vx_df.x.value_counts()

Mean

pd_df.x.mean()

vx_df.x.mean()

Standard deviation

pd_df.x.std()

vx_df.x.std()

Join

pd_df.join(pd_df_, on="key")

vx_df.join(pd_df_, on=“key”)

Group-by

pd_df.groupby(by="z").agg({"x": ["mean", "std"], "y": ["mean", "std"]})

vx_df.groupby(by="z").agg({"x": ["mean", "std"], "y": ["mean", "std"]})

Conclusion

In this pandas vs. vaex Answer, we conclude that these libraries cater to different needs in the data processing landscape. While pandas excel in versatility and ease of use for smaller datasets, vaex shines in performance and scalability, making it a preferred choice for handling massive datasets and big data use cases. Eventually, the choice between pandas and vaex depends on the specific requirements of the tasks at hand and the scale of the dataset.

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