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

import pandas as pd 

df = pd.read_csv("raw_data.csv", 
                 sep=",", 
                 header=0)

item_uniq = len(df["Item ID"].unique())
total_price = df["Price"].sum()
mean_price = df["Price"].mean()
purchase_cnt = df["Price"].count()

print(item_uniq, total_price, mean_price, purchase_cnt)

First, you need to load the file `read_csv` at `line 3`. 

If you want to get all unique values from one column, `unique()` is the function you need at `line 7`.

In this challenge, you need to return three values about `price`. So, you should get the `price` column by `[]` operation. Then perform `sum`, `mean`, and `count` on this column respectively.

In this lesson, we explain the solution to the last challenge lesson.

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Challenge Solution Review