In this blog post I will outline the the performance of Pandas to Julia. For those of you who may not know Julia is a dynamically-typed high-level programming language specifically designed for high performance. The offical site provides us with this speed benchmark as seen below:

Although you may not know the speciics of the nechmakrs shown in the chart, it is clear that Julia is sginficantly more performant than Python. Let’s put it to the test!
To make this presentation as practical as possible I will cinduct this speed test using a real world example. “Crypto Currency Price Corrleations” and to do this I will leverage pannel data tehcniques, Pandas DataFrames in Python and DataFrames in Julia.
Let’s get into the code.
DataFrame constructor in Julia
using DataFrames
df = DataFrame(
A = 1:4,
B = ["M", "F", "F", "M"]
)
println(df)
DataFrame constructor in Pandas
import pandas as pd
df = DataFrame(
{
"A": [1, 2, 3, 4],
"B": ["M", "F", "F", "M"]
}
)
print(df)
MathJax
You can enable MathJax by setting mathjax: true
on a page or globally in the _config.yml
. Some examples:
Euler’s formula relates the complex exponential function to the trigonometric functions.
The Euler-Lagrange differential equation is the fundamental equation of calculus of variations.
The Schrödinger equation describes how the quantum state of a quantum system changes with time.
Images
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The .large
wrapper can be used to increase the width of an image or iframe.

Flower by Tj Holowaychuk
Swiss Alps by René Reichelt
Embedded content
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