1.
Preface
2.
IDEs and you
2.1.
The tools of the trade
2.2.
Introducing PyCharm
2.3.
What they can do for you
2.3.1.
Static analysis
2.3.1.1.
syntax highlighting and code completion
2.3.1.2.
Programming error detection
2.3.1.3.
Call graphs
2.3.1.4.
Provide rich refactoring services
2.3.2.
Integrate with various development tools
2.3.2.1.
Run configurations
2.3.2.2.
Debuggers
2.3.2.3.
Provide rich version control integration
3.
Python
3.1.
History
3.2.
Applications in Data Science
3.3.
Noteworthy distributions
3.3.1.
CPython
3.3.2.
Anaconda
3.4.
A primer on syntax
3.4.1.
"Hello world"
3.4.2.
Comments and string literals
3.4.3.
Basic math operators
3.4.4.
Variables
3.4.5.
Boolean operations
3.4.6.
Types
3.4.7.
Builtin containers
3.4.7.1.
list
3.4.7.2.
dict
3.4.7.3.
set
3.4.8.
For loops
3.4.9.
While loops
3.4.10.
Functions
3.4.11.
Introduction to modules
3.4.12.
Introduction to packages
3.4.13.
import this
3.4.13.1.
pathlib and cross-platform file paths
3.5.
Intermission
3.5.1.
Code Style
3.5.2.
Project Structure
3.6.
Doing data science using SciPy and other third-party libraries
3.6.1.
IPython
3.6.2.
Numpy
3.6.3.
Jupyter Notebook
3.6.3.1.
Common pitfalls and their mitigations
3.6.4.
Pandas and the DataFrame
3.6.4.1.
Adding calculated data to dataframes
3.6.4.2.
Basic manipulations
3.6.4.3.
Writing datasets
3.6.5.
SciPy
3.6.6.
matplotlib
3.6.6.1.
Plotting data, the manual way
3.6.6.2.
Plotting data, via Pandas wrappers
3.6.7.
Seaborn
3.6.7.1.
Plotting data, via Seaborn wrappers
3.6.8.
Poetry
3.6.8.1.
A Primer on dependency management
4.
Copyright Notice
Light (default)
Rust
Coal
Navy
Ayu
UCI Bootcamp 2021: Python for Data Science
Modules
A module is a
.py
source file.
all modules are importable.
all modules have a name.
all modules exist within the project's hierarchy.
modules may exist within
packages
, or be stand-alone.