Learn to structure your data more effectively and do some basic analysis: group, apply functions, and more.

Python Data Analysis and Visualization

Data Analysis & Processing with Pandas

## Create a DataFrame from a Numpy ndarray

Since a DataFrame is similar to a 2D Numpy array, we can create one from a Numpy `ndarray`. 
>You should remember that the input Numpy array must be 2D, otherwise you will get a ValueError.

If you pass a raw Numpy `ndarray`, the index and column names start at 0 by default.  You can also assign different column names to your data which will be discussed in a later [lesson](https://www.educative.io/courses/data-analysis-processing-with-pandas/rename-columns/).

# Create a DataFrame from a Numpy ndarray

Since a DataFrame is similar to a 2D Numpy array, we can create one from a Numpy `ndarray`. 
>You should remember that the input Numpy array must be 2D, otherwise you will get a ValueError.

If you pass a raw Numpy `ndarray`, the index and column names start at 0 by default.  You can also assign different column names to your data which will be discussed in a later [lesson](https://www.educative.io/courses/data-analysis-processing-with-pandas/rename-columns/).

A pandas DataFrame can be created in a number of ways, let's see how we can do it.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Creating a DataFrame From Arrays and Lists

Create a DataFrame from a Numpy ndarray