We also can **change colors** for the 3rd variable manually. To do that, we need to create a kind of a **palette**. 

It is important to create a new **color palette** for the hue variable and add this **palette** as an argument in the plot function.

The short list of the `matplotlib` colors: blue, green, red, green/blue, yellow, purple, white, black, etc.

**Let's change** ``gender`` **colors in the Tips problem!**

# Importing libraries needed
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Reading the file 
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c5b4ea8f-8a30-439f-9625-ddf2effbd9ac/bilibili.csv')

# Setting colors for the hue variable
hue_colors = {'woman':'red', 'man':'black'}

# Creating the 3-variable scatterplot 
sns.scatterplot(x = 'bill', y = 'tips', hue = 'gender', data=df, palette = hue_colors)

# Showing the plot 
plt.show()

Together we will make the first dive into visualization based on a seaborn library. We will learn how to build simple plots and how to customize them. You will cope faster with tasks if you know something about pandas. Do not hesitate, and let's go!

In this section, we will learn about the seaborn library and build our first plot! We will also learn about the friendship between the pandas and the Seaborn.

We continue learning plots with Seaborn.\n\nYou will learn some more new plots and even build 3-variable plot!

Plots seem so boring without customization...\nIn this section, we will learn how to make our plots perfectly styled!!

Colors in a 3-variable Plot

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First Dive into seaborn Visualization

Colors in a 3-variable Plot

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