Agile Data Science Workflows made easy

Prepare, explore, visualize and create Machine Learning models for Big Data with the fastest open source library on the planet.

Get it

Get started fast:

pip install pyoptimus

Features

What can Optimus do for you

Simple and Robust

Prepare, explore, visualize your data in few lines of code.
Flexible

Easy, fast, parallelized and scalable data cleansing, exploration and Machine Learning Models creation.
Local or Cloud

In your laptop, local cluster or in the cloud.

Connect to files and databases

Load and save locally (or remotely) Excel, CSV, JSON, parquet, Avro. Get and insert data from Mysql, Redshift, SQL Server, Postgres, Oracle, Casandra, and Presto.

# Put your db credentials here
db =  op.connect(
    driver="mysql",
    host="165.227.196.70", 
    database= "optimus", 
    user= "test", 
    password = "test")

# Convert a table a dataframe
df = db.table_to_df("test_data")

Easy API

In a little more than 10 lines you can, remove white spaces, accents in all columns, lowercase all columns data, drop a "dummyCol", transform date format, sort a column, convert integers to a "string", replace "taco" per "taaaccoo" and "pizza" per "pizzza"

# This is a custom function
def func(value, arg):
    return "this was a number"
    
new_df = df\
    .rows.sort("rank", "desc")\
    .cols.copy("age", "new_age")\
    .cols.lower(["names","function"])\
    .cols.date_transform("date arrival", "yyyy/MM/dd", "dd-MM-YYYY")\
    .cols.years_between("date arrival", "dd-MM-YYYY", output_cols = "from arrival")\
    .cols.remove_accents("names")\
    .cols.remove_special_chars("names")\
    .rows.drop(df["rank"]>8)\
    .cols.rename(str.lower)\
    .cols.trim("*")\
    .cols.unnest("japanese name", output_cols="other names")\
    .cols.unnest("last position seen",separator=",", output_cols="pos")\
    .cols.drop(["last position seen", "japanese name","date arrival", "cybertronian", "nulltype"])

Try it in Google Colab

Powerful features

All you need to handle your data in one place.

Data Enrichment

You can connect to any external API to enrich your data using Optimus.
Machine Learning

To apply random forest just need to import the ML Library and one line of code.
String Clustering

Cluster similar strings and change it for single value.

Used by Forward thinking companies

Here are a few of our favorites!

What People Say

“The group of BBVA Data & Analytics in Mexico has been using Optimus for the past months, and we have boosted our performance for cleansing, exploring and analyzing our data by 10x factor.”