Hits: 38

# How to Load Data From a csv using Numpy

Numpy is a powerful library in Python for working with numerical data. One of the ways to load data into a Numpy array is by reading it from a CSV (Comma Separated Values) file.

To load data from a CSV file using Numpy, you can use the `numpy.genfromtxt()`

function. This function allows you to specify the file path and the delimiter used in the CSV file. You can also specify the data type of the columns and any missing values.

Once the data is loaded, it will be stored in a Numpy array, which is a powerful data structure that allows you to perform various operations on the data, such as mathematical calculations, sorting, and indexing.

It’s important to note that, before loading the data, you need to make sure that the csv file is in the correct format, including the correct delimiter, correct data type and correct missing values.

In summary, Numpy is a powerful library in Python for working with numerical data, and one of the ways to load data into a Numpy array is by reading it from a CSV (Comma Separated Values) file. You can use the `numpy.genfromtxt()`

function to load the data from the file, specifying the file path and the delimiter used in the CSV file, and also the data type of the columns and any missing values. Once the data is loaded, it will be stored in a Numpy array, which allows you to perform various operations on the data such as mathematical calculations, sorting, and indexing.

In this Applied Machine Learning Recipe, the reader will learn: How to Load Data From a csv using Numpy.

## How to Load Data From a csv using Numpy

#### Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

# Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

**Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!**

Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:

**Applied Statistics with R for Beginners and Business Professionals**

**Data Science and Machine Learning Projects in Python: Tabular Data Analytics**

**Data Science and Machine Learning Projects in R: Tabular Data Analytics**

**Python Machine Learning & Data Science Recipes: Learn by Coding**