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

# Solution review - Drop duplicated rows on SN column and return the total price of male group

import pandas as pd

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

df.drop_duplicates(subset=["SN"], keep="last", inplace=True)
male_total = df.groupby(["Gender"]).get_group("Male").sum()["Price"]
print(male_total)

From `line 3` to `line 5`, the CSV file is loaded to the DataFrame object.

`line 7` drops duplicated rows based on the `SN` column. In this example, we use the `last` strategy.

At line 7, the code returns the total price of `Male` group.

As you can see, at first we group data from the `Gender` column by `groupby()`. Then, get the Male group by `get_group()`. `sum()` is performed in this group. Finally, the Price column is returned.

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

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Challenge Solution Review

Solution review - Drop duplicated rows on SN column and return the total price of male group