## How to normalize data using NumPy or Pandas

When it comes to machine learning, working with normalized numbers may lead to faster convergence while training the models. Here we will show how you can normalize your dataset in Python using either NumPy or Pandas.

### NumPy

To normalize a NumPy array, you can use:

```import numpy as np

for col in range(data.shape):
data[:,col] -= np.average(data[:,col])
data[:,col] /= np.std(data[:,col])```

Here data.shape is the number of columns in the dataset, and we are using NumPy to normalize the average and standard deviation of each column to 0 and 1 respectively.

### Pandas

Normalizing a Pandas dataframe is even easier:

```import pandas as pd

df = (df-df.mean())/df.std()```

This will normalize each column of the dataframe.

## Symbolic Regression in Python Tutorial

This step-by-step video shows how you can easily run a symbolic regression optimization in Python using TuringBot.

## Symbolic Regression in Python with TuringBot Library: An Easy Guide

TuringBot offers a great library for running Symbolic Regression in Python. The API allows the search to be fully customized. Here we will show how to install, load, and use this library to turn your data into formulas.

## Python: Symbolic Regression in 3 Easy Steps

Looking for a Symbolic Regression library for Python that will allow you to turn your data into nice mathematical formulas? TuringBot is by far the easiest to use. Here we will show how to use it.

## Symbolic Regression in Python with TuringBot

In this tutorial, we are going to show a very easy way to do symbolic regression in Python.

## August 4, 2020 turing 3 Comments

Finding mathematical formulas from data is an extremely useful machine learning task. A formula is the most compressed representation of a table, allowing large amounts of data to be compressed into something simple, while also making explicit the relationship that exists between the different variables.