Pandas for Data-Driven Engineering
Data-driven engineering is the process of reading, cleansing, calculating, rearranging, and exporting data. Pandas is a library for working with data with common high-level functions that simplify the processing steps of analytics and informatics.
7️⃣.1️⃣ Pandas Install and Import
7️⃣.2️⃣ Pandas Series
7️⃣.3️⃣ Pandas DataFrame
7️⃣.4️⃣ DataFrame Analytics
7️⃣.5️⃣ DataFrame Visualization
7️⃣.6️⃣ DataFrame Export
This video is an introduction to the Python Pandas library and functions.
Data-Driven Engineering: https://apmonitor.com/dde
DDE Python Overview: https://apmonitor.com/dde/index.php/M...
DDE Python Basics: https://apmonitor.com/dde/index.php/M...
1️⃣ Python Basics
Data-driven engineering relies on information, often stored in the form of characters (strings) and numbers (integers and floating point numbers). It is essential to import, export, and get data into the correct form so that information can be extracted. This series includes an introduction to Python Basics as foundational elements.
2️⃣ Python Tuple
Tuple (e.g. (i,x,e)) is immutable (does not change) as an efficient storage mechanism for constant sets of values.
3️⃣ Python List
List (e.g. [i,x,e]) is a mutable set of values where it is possible to add elements, remove elements, sort.
4️⃣ Python Set
Set (e.g. {i,x,e}) is a data structure that is similar to list but not sorted and has no duplicate values.
5️⃣ Python Dictionary
Dictionary (e.g. {'i':i,'x':x,'e':e}) is a data structure with a reference value based on key.
6️⃣ NumPy
NumPy expands upon the basic Python functions to create an array. Matrix and vector operations are designed as a foundation for numerical calculations.
7️⃣ Pandas
Pandas reads, cleanses, calculates, rearranges, and exports data. It is a library for working with data with common high-level functions that simplify the processing steps of analytics and informatics.
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