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 - group by a single column

import pandas as pd

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

male_total = df.groupby(["Gender"]).get_group("Male").sum()["Price"]
female_mean = df.groupby(["Gender"]).get_group("Female").mean()["Price"]
print("The total price of male group is {}.".format(male_total))
print("The mean price of female group is {}".format(female_mean))
print(male_total, female_mean)

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

```python
df.groupby(["Gender"]).get_group("Male").sum()["Price"]
```
At `line 7`, the code above returns the total price of `Male` group.

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

The mean price of `Female` group uses the same process at the `line 8`. 

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

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Challenge Solution Review

Solution review - group by a single column