Gain insights into Pandas' powerful tools for data analysis. Learn about Series, DataFrames, and optimize performance for data operations, filtering, and file uploads in data science and machine learning.

Pandas is a very popular Python library that provides powerful, flexible and high-performance tools to process data.

If you’re looking to get into data science, machine learning, or you just want to brush up on your analytics skills, then look no further. In this course, you’ll learn how to process and analyze data with Pandas. 

You’ll start out by covering the basic data structures in Pandas: Series and dataframes. In the latter half of the course, you’ll learn how to perform operations on different data structures, and how to optimize performance for file uploads, data filtering, and more.

Data Analysis & Processing with Pandas

You may have already noticed that when you load a large file into a DataFrame object, it consumes more memory than you thought. This is a weakness of pandas. But, there are still some methods to reduce memory usage. In this lesson, we'll you some of those methods.

# Type conversion

As we mentioned in the lesson [Type conversion](https://www.educative.io/courses/data-analysis-processing-with-pandas/type-conversion), `int64` takes much more memory than `int8`. When we load data from a CSV file, if we don't specify the `dtype` for each column, the default `dtype` would be `int64`, `float64`, or `object`. These are all largest data types.  If one column only stores numbers from 0 to 20, `int8` is enough. For `float64`, you can use `float32` as a replacement.

Let's see how much memory we can save. 

Here is a conclusion, the optimization effect is very obvious. The total memory usage has dropped from `1.5MB` to `195KB`, very impressive. Memory usage was reduced by nearly `90 percent`. The only thing we did was change the data type from `int64` to `int8`.

Here is the complete code so you can have a try.

In this lesson, we show you how to reduce memory usage in pandas.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Reduce Memory Usage

Type conversion