pyoptimus 21.8.0b3

Optimus is the missing framework for cleaning and pre-processing data in a distributed fashion.

To launch a live notebook server to test optimus using binder or Colab, click on one of the following badges:

Installation (pip):

In your terminal just type pip install pyoptimus

Requirements

• Python>=3.7

Examples

You can go to the 10 minutes to Optimus notebook where you can find the basic to start working.

Also you can go to Examples and found specific notebooks about data cleaning, data munging, profiling, data enrichment and how to create ML and DL models.

Besides check the Cheat Sheet

Feedback

Feedback is what drive Optimus future, so please take a couple of minutes to help shape the Optimus' Roadmap: http://bit.ly/optimus_survey

Also if you want to a suggestion or feature request use https://github.com/hi-primus/optimus/issues

Start Optimus

Start Optimus using "pandas", "dask", "cudf" or "dask_cudf".

from optimus import Optimus
op = Optimus("pandas")

Loading data

Now Optimus can load data in csv, json, parquet, avro, excel from a local file or URL.

#csv
df = op.load.csv("../examples/data/foo.csv")

#json
df = op.load.json("../examples/data/foo.json")

# using a url
df = op.load.json("https://raw.githubusercontent.com/hi-primus/optimus/develop-21.8/examples/data/foo.json")

# parquet
df = op.load.parquet("../examples/data/foo.parquet")

# ...or anything else
df = op.load.file("../examples/data/titanic3.xls")

Also, you can load data from oracle, redshift, mysql and postgres.

Saving Data

#csv
df.save.csv("data/foo.csv")

# json
df.save.json("data/foo.json")

# parquet
df.save.parquet("data/foo.parquet")

You can also save data to oracle, redshift, mysql and postgres.

Create dataframes

Also, you can create a dataframe from scratch

df = op.create.dataframe({
    'A': ['a', 'b', 'c', 'd'],
    'B': [1, 3, 5, 7],
    'C': [2, 4, 6, None],
    'D': ['1980/04/10', '1980/04/10', '1980/04/10', '1980/04/10']
})

Using display you have a beautiful way to show your data with extra information like column number, column data type and marked white spaces.

display(df)

Cleaning and Processing

Optimus was created to make data cleaning a breeze. The API was designed to be super easy to newcomers and very familiar for people that comes from Pandas. Optimus expands the standard DataFrame functionality adding .rows and .cols accessors.

For example you can load data from a url, transform and apply some predefined cleaning functions:

new_df = df\
    .rows.sort("rank", "desc")\
    .cols.lower(["names", "function"])\
    .cols.date_format("date arrival", "yyyy/MM/dd", "dd-MM-YYYY")\
    .cols.years_between("date arrival", "dd-MM-YYYY", output_cols="from arrival")\
    .cols.normalize_chars("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"])

Troubleshooting

If you have issues, see our Troubleshooting Guide

Contributing to Optimus

Contributions go far beyond pull requests and commits. We are very happy to receive any kind of contributions
including:

• Documentation updates, enhancements, designs, or bugfixes.
• Spelling or grammar fixes.
• README.md corrections or redesigns.
• Adding unit, or functional tests
• Triaging GitHub issues -- especially determining whether an issue still persists or is reproducible.
• Blogging, speaking about, or creating tutorials about Optimus and its many features.
• Helping others on our official chats

Backers and Sponsors

Become a backer or a sponsor and get your image on our README on Github with a link to your site.