Pandas VS blaze

Compare Pandas vs blaze and see what are their differences.


Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more (by pandas-dev)


NumPy and Pandas interface to Big Data (by blaze)
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Pandas blaze
144 0
32,341 3,015
1.6% 0.7%
10.0 0.0
2 days ago almost 2 years ago
Python Python
BSD 3-clause "New" or "Revised" License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of Pandas. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-14.
  • Best Data Structure for this?
    1 project | | 17 Jan 2022
    If you really want to store it all (labels included) in one data structure, you should look up pandas.
  • SEC Speed is a myth.
    1 project | | 15 Jan 2022
    Another question you may be asking is: "What about skill players?" Well, what about them? Skill players are defined as players that consistently tote the rock. I was able to filter out skill player's performance in different combine events using pandas. For our purposes, the following positions (as listed on PRF) were considered 'skill players': WR, RB, QB, TE, DB, LB. In included linebackers but if you want to not include them, knock yourself out. It kind of only helps my case that the likes of Roquan Smith and Nakobe Dean don't count for the SEC. When only considering skill players, the SEC ranks 2nd to the Big 12 in 40-yard dash times. In the other combine events for which there is data, the SEC ranks first in none of them.
  • Open source projects that are good to read to learn best practices?
    2 projects | | 14 Jan 2022
  • 5 Useful Pandas Methods You May Not Know Existed (Part 2)
    1 project | | 9 Jan 2022
    You glossed over the fact that `.pct_change` isn't actually "percent change" as documented. More fun reading:
  • Career change - data analysis
    1 project | | 9 Jan 2022
    I suggest pandas might be a great tool for you as you will be able to read write excel / csv files and process them and see how you get on.
  • Trading Algos - 5 Key Metrics and How to Implement Them in Python
    4 projects | | 8 Jan 2022
    Now to implement this one, we'll have to do some manipulation to our account values. Let's use the power of numpy to help us out here (oh and it's also the same in pandas too. We'll be using np.diff to take the returns of our account values and resampling them.
  • What does it mean to scale your python powered pipeline?
    4 projects | | 3 Jan 2022
    Increase code efficiency: Python is designed for ease of use and easy extension, but not performance. As a developer, the onus is on you to do more work so that the application executes less code. Whenever possible use vectorized library functions instead of loops. Python is successful in data science because of the pre-compiled code offered by data-appropriate libraries in the pydata stack such as pandas and numpy.
  • How do I combine two lists together to form a x y coordinate reference point?
    3 projects | | 2 Jan 2022
  • Top 7 Dev Tools for AI Startups
    4 projects | | 30 Dec 2021
    Built on top of Python, pandas is an open source data analysis and manipulation tool, similar to NumPy. While it relies on NumPy arrays for much of its manipulation and computation, pandas makes it easier to visualize and explore data, helping our team make better sense of the large amounts of data we work with on a daily basis.
  • Appending Data to DataFrames
    1 project | | 24 Dec 2021
    Dataframes are not meant to be as flexible as lists in terms of extending the data they hold, dataframes are much more "deliberate". Ideally if you're trying to dynamically add data to a dataframe you should first collect all the data and then initialize the dataframe once. Or collect separate dataframes and concat them once. The pandas developers are even thinking about deprecating append (see here)


Posts with mentions or reviews of blaze. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning blaze yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing Pandas and blaze you can also consider the following projects:

Cubes - Light-weight Python OLAP framework for multi-dimensional data analysis

orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis

Dask - Parallel computing with task scheduling

Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

NumPy - The fundamental package for scientific computing with Python.

SymPy - A computer algebra system written in pure Python

pyexcel - Single API for reading, manipulating and writing data in csv, ods, xls, xlsx and xlsm files

SciPy - SciPy library main repository

astropy - Repository for the Astropy core package

Bubbles - [NOT MAINTAINED] Bubbles – Python ETL framework