A Rising Library Beating Pandas in Performance

This article compares the performance of the well-known pandas library with pypolars, a rising DataFrame library written in Rust. See how they compare.

pandas was initially released in 2008 written in Python, Cython, and C. Today, we’re comparing the performance of this well-known library with pypolars, a rising DataFrame library written in Rust. We compare the two while sorting and concatenating a 25Mil-record data and also when joining two CSVs.

Downloading Reddit Usernames data

Let’s first download a CSV file that contains ~26 million reddit usernames from Kaggle: https://www.kaggle.com/colinmorris/reddit-usernames

And let’s form another CSV file that we will use, you can create it with your favorite text editor or through the command line:

#data processing #pandas #performance #python

What is GEEK

Buddha Community

Playing with Pandas library

The techniques for Reshaping, Grouping, and Pivoting the data

Python has turned the world just in a decade with its popularity and efficiency. Python has followed offering a reliable trend of Data Science which comprises of:

· Data Gathering

· Data Cleaning

· Machine Learning models

· Visualization of Data

Pandas is a very fundamental inbuilt library in Python uptakes a lot of the area. It is an open-source library that is easy to use, providing high efficiency and many tools used in the analysis of data for Python programming.

Pandas is an in-memory no SQL type database providing a helping hand for basic SQL constructs, statistical methods, and the capability of graphing. As it was built on top of Cython, it runs quicker along with consuming less time to access some memory within a machine.

→Pandas have a very advanced feature of carrying out some operations on the group of data frames.

→Data Frame: A 2D data that is labeled. It contains different columns and rows.

So, in this article, we’re going to have our quick eyes on some methods of grouping, reshaping, and pivoting the data.

#pandas #data-science #python #artificial-intelligence #playing with pandas library #pandas library

A Rising Library Beating Pandas in Performance

This article compares the performance of the well-known pandas library with pypolars, a rising DataFrame library written in Rust. See how they compare.

pandas was initially released in 2008 written in Python, Cython, and C. Today, we’re comparing the performance of this well-known library with pypolars, a rising DataFrame library written in Rust. We compare the two while sorting and concatenating a 25Mil-record data and also when joining two CSVs.

Downloading Reddit Usernames data

Let’s first download a CSV file that contains ~26 million reddit usernames from Kaggle: https://www.kaggle.com/colinmorris/reddit-usernames

And let’s form another CSV file that we will use, you can create it with your favorite text editor or through the command line:

#data processing #pandas #performance #python

Graph-indexed Pandas DataFrames for analyzing Hierarchical Performance Data

Hatchet

Hatchet is a Python-based library that allows Pandas dataframes to be indexed by structured tree and graph data. It is intended for analyzing performance data that has a hierarchy (for example, serial or parallel profiles that represent calling context trees, call graphs, nested regions’ timers, etc.). Hatchet implements various operations to analyze a single hierarchical data set or compare multiple data sets, and its API facilitates analyzing such data programmatically.

To use hatchet, install it with pip:

$ pip install hatchet

Or, if you want to develop with this repo directly, run the install script from

the root directory, which will build the cython modules and add the cloned

directory to your PYTHONPATH:

$ source install.sh

#data analysis #pandas dataframes #pandas #hierarchical performance data #graph-indexed pandas dataframes for analyzing hierarchical performance data

Python Pandas Objects - Pandas Series and Pandas Dataframe

In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:-

Pandas Series

Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float, python object etc. A Pandas Series can hold only one data type at a time. The axis label of the data is called the index of the series. The labels need not to be unique but must be a hashable type. The index of the series can be integer, string and even time-series data. In general, Pandas Series is nothing but a column of an excel sheet with row index being the index of the series.

Pandas Dataframe

Pandas dataframe is a primary data structure of pandas. Pandas dataframe is a two-dimensional size mutable array with both flexible row indices and flexible column names. In general, it is just like an excel sheet or SQL table. It can also be seen as a python’s dict-like container for series objects.

#python #python-pandas #pandas-dataframe #pandas-series #pandas-tutorial

A Rising Library Beating Pandas in Performance

pandas was initially released in 2008 written in Python, Cython, and C. Today, we’re comparing the performance of this well-known library with pypolars, a rising DataFrame library written in Rust. We compare the two while sorting and concatenating a 25Mil-record data and also when joining two CSVs.

Downloading Reddit Usernames data

Let’s first download a CSV file that contains ~26 million reddit usernames from Kaggle: https://www.kaggle.com/colinmorris/reddit-usernames

And let’s form another CSV file that we will use, you can create it with your favorite text editor or through the command line:

$ cat >> fake_users.csv
author,n
x,5
z,7
y,6

#python #data #pandas #rust