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

# Read a CSV file

In the real world, most data is stored in a file. The most common file type is a CSV. If you want to analyze the data, the first step is to read the data from the file. pandas supports multiple file types, like CSV, TSV, and Excel.

There are some commonly used parameters in the **read_csv** function.

- `sep`: If `sep` is None, Python will automatically detect the separator. In this case, the separator is `,`.
- `header`:  If no names are passed, the behavior is identical to `header=0` and column names are inferred from the first line of the file. If the column names are passed explicitly, then the behavior is identical to the header=None. 
- `names`: List of column names provided by the caller. If the file contains the header row, you need to specify the `header=0` to override it.
- `nrows`: Specify how many rows you want to read.
- `usecols`: Return a subset of the columns. You can pass the column names such as `["a", "b"]`

The `head()` function is used to show the first `n` rows. If you don't pass the parameters, the default value is `10`.

Let's try to create a DataFrame object from the file.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Creating a DataFrame From a File

Read a CSV file