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GrokkingDataScientist.tar.gz

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Master the skills that can get you a $100K+ salary even if you bunked your statistics classes. 

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Grokking Data Science

## General Matplotlib Tips
Before we dive into the details of creating visualizations with Matplotlib, here are a few useful tips about using this package:
<br>
### 1. Importing Matplotlib
Just as we used the *np* shorthand for NumPy, and *pd* for Pandas, ***plt*** is the standard shorthand for Matplotlib:

``` python
import matplotlib.pyplot as plt

``` 

Note that Matplotlib is a huge library, so we are only importing the pyplot part of it. This is useful to save memory and speedup code. Otherwise we’ll be importing gigabytes of libraries even when we are just interested in using it to perform some trivial tasks.

### 2. Setting Styles
`plt.style` directive can be used to choose different *prettyfying* styles for our figures. There a number of pre-defined [styles](https://matplotlib.org/users/style_sheets.html) provided by Matplotlib. For example, there’s a pre-defined style called *ggplot*, which tries to copy the *look and feel* of ggplot, a popular plotting package for R. Below are some examples, both code and visual outputs, of the available styles; you can go through the [official reference sheet](https://matplotlib.org/3.1.1/gallery/style_sheets/style_sheets_reference.html) for a complete overview. 

``` python
# Examples of available styles
plt.style.use('classic')
plt.style.use('ggplot')
plt.style.use('seaborn-whitegrid')
plt.style.use(['dark_background', 'presentation'])
``` 


# General Matplotlib Tips
Before we dive into the details of creating visualizations with Matplotlib, here are a few useful tips about using this package:
<br>
## 1. Importing Matplotlib
Just as we used the *np* shorthand for NumPy, and *pd* for Pandas, ***plt*** is the standard shorthand for Matplotlib:

``` python
import matplotlib.pyplot as plt

``` 

Note that Matplotlib is a huge library, so we are only importing the pyplot part of it. This is useful to save memory and speedup code. Otherwise we’ll be importing gigabytes of libraries even when we are just interested in using it to perform some trivial tasks.

## 2. Setting Styles
`plt.style` directive can be used to choose different *prettyfying* styles for our figures. There a number of pre-defined [styles](https://matplotlib.org/users/style_sheets.html) provided by Matplotlib. For example, there’s a pre-defined style called *ggplot*, which tries to copy the *look and feel* of ggplot, a popular plotting package for R. Below are some examples, both code and visual outputs, of the available styles; you can go through the [official reference sheet](https://matplotlib.org/3.1.1/gallery/style_sheets/style_sheets_reference.html) for a complete overview. 

``` python
# Examples of available styles
plt.style.use('classic')
plt.style.use('ggplot')
plt.style.use('seaborn-whitegrid')
plt.style.use(['dark_background', 'presentation'])
``` 


Python Fundamentals for Data Science

The Fundamentals of Statistics

Machine Learning 101

End-to-End Machine Learning Project

The Real Talk

Data Visualization - Matplotlib Tips

General Matplotlib Tips

1. Importing Matplotlib