Learn to perform data analysis and processing using DataFrames in pandas.

Pandas: Python for Data Analysis

In this module, we’ll learn how to process and analyze data with pandas. We’ll start by covering the basic data structures in pandas such as series and DataFrames. We’ll learn how to perform operations on different data structures as well as how to optimize performance for file uploads, data filtering, and more.

Data Analysis and 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

Conclusion

Creating a DataFrame From Arrays and Lists

Create a DataFrame from a Numpy ndarray