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 - Find the most popular item

import pandas as pd

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

df = df.groupby(["Item ID"]).count()
df.rename(columns={"Price": "Item Count"}, inplace=True)
ic = df.sort_values("Item Count", ascending=False).head(1)["Item Count"]
print(ic.index[0])
print(ic.values[0])

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

`line 7` groups the data by the `Item ID` column, meanwhile, `count()` is performed on each group.

`line 8` renames the original column, `Price`, to `Item Count` by using `rename()`. 

Then the data is sorted by `Item Count`, columns are sorted by `sort_values`, and returns the column `Item Count` of the first row by `head(1)`.

Finally, you can get the `Item ID` from the index since the data is grouped by `Item ID`, making the index of the group data `Item ID`.

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

Introduction

Basic Operations on DataFrames

Other topics

Improve performance

Challenge Solution Review

Solution review - Find the most popular item