Before we start: This Python tutorial is a part of our series of Python Package tutorials. The steps explained ahead are related to the sample project introduced here.

Saving a DataFrame

In our DataFrame examples, we’ve been using a Grades.CSV file that contains information about students and their grades for each lecture they’ve taken:

Importing a Data Set in to Python

When we are done dealing with our data we might want to save it as a CSV file so that it can be shared with a coworker or stored as a record.

This can be simple done by:

Report_Card.to_csv("Report_Card.csv")

Next steps

You know how to save your DataFrame using Python’s Pandas library, but there’s lots of other things you can do with Pandas:

Get The Machine Learning Packages You Need – No Configuration Required

We’ve built the hard-to-build packages so you don’t have to waste time on configuration…get started right away!

Some Popular ML Packages You Get Pre-compiled – With ActivePython

Machine Learning:

  • TensorFlow (deep learning with neural networks)*
  • scikit-learn (machine learning algorithms)
  • keras (high-level neural networks API)

Data Science:

  • pandas (data analysis)
  • NumPy (multidimensional arrays)
  • SciPy (algorithms to use with numpy)
  • HDF5 (store & manipulate data)
  • matplotlib (data visualization)

Security:

  • cryptography (recipes and primitives)
  • pyOpenSSL (python interface to OpenSSL)
  • passlib and bcrypt (password hashing)
  • requests-oauthlib (Oauth support)
  • ecdsa (cryptographic signature)
  • PyCryptodome (PyCrypto replacement)
  • service_identity (prevents pyOpenSSL man-in-the-middle attacks)

With deep roots in open source, and as a founding member of the Python Foundation, ActiveState actively contributes to the Python community. We offer the convenience, security and support that your enterprise needs while being compatible with the open source distribution of Python.

Download ActiveState Python to get started or contact us to learn more about using ActiveState Python in your organization.

You can also start by trying our mini ML runtime for Linux or Windows that includes most of the popular packages for Machine Learning and Data Science, pre-compiled and ready to for use in projects ranging from recommendation engines to dashboards.

 

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