Wrap-up

Streamlined Data Ingestion with pandas

Amany Mahfouz

Instructor

Recap

Chapters 1 and 2

read_csv() and read_excel()
Setting data types, choosing data to load, handling missing data and errors

Clip art of a csv file

Microsoft Excel logo

Recap

Chapter 3

read_sql() and sqlalchemy
SQL SELECT, WHERE, aggregate functions and joins

Clip art of a database

Recap

Chapter 4

read_json(), json_normalize(), and requests
Working with APIs and nested JSONs
Concatenating and merging datasets

Where to next?

Learn more about data wrangling in pandas
- Working with indexes, transforming values, dropping rows and columns
- Reshaping data by merging, melting, pivoting
- Data Manipulation with Python Skill Track

Where to next?

Explore a variety of analysis topics
- Descriptive statistics (e.g. medians, means, standard deviation)
- Inferential statistics (hypothesis testing, correlation, regression)
- Exploratory Data Analysis in Python
- Introduction to Linear Modeling in Python

Where to next?

Learn data visualization techniques
- seaborn and matplotlib libraries
- Introduction to Data Visualization in Python
- Introduction to Data Visualization with Matplotlib

Where to next?

Wrangle data as part of a fuller data science workflow

Congrats!

Streamlined Data Ingestion with pandas

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