Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames

Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames

Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level

Overview/Description
Explore advanced data manipulation and analysis with Pandas DataFrames, a Python library that shares similarities with relational databases. To take this course, prior basic experience is needed with Pandas DataFrames, data loading, and Jupyter Notebook data manipulation. You will learn to iterate data in your DataFrame. See how to export data to Excel files, JSON (JavaScript Object Notation) files, and CSV (comma separated values) files. Sort the contents of a DataFrame and manage missing data. Group data with a multi-index. Merge disparate data into a single DataFrame through join and concatenate operations. Finally, you will determine when and where to integrate data with structured queries, similar to SQL.

Expected Duration (hours)
0.8

Lesson Objectives

Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames

Course Overview

learn how to iterate over a DataFrame's rows and columns

export the contents of a DataFrame into files of various formats

describe and apply the different techniques involved in handling datasets where some information is missing

implement a hierarchical index and access the DataFrame's contents based on that index

combine two similar DataFrames using the concat operation

apply a join operation on two related but dissimilar DataFrames using the merge function

load data into a Pandas DataFrame from a table in a relational database

use Pandas for advanced tabular data manipulation

Course Number:
it_dspydsdj_04_enus

Expertise Level
Intermediate