1. Understand the two different Matplotlib interfaces (It has caused a lot of confusion!) .
2. Understand the elements in a figure, so that you can easily look up the APIs to solve your problem.
3. Take a glance of a few common types of plots so the readers would have a better idea about when / how to use them.
4. Learn how to increase the ‘dimension’ of your plots.
5. Learn how to partition the figure using GridSpec.

1. Set up a goal.
2. Prepare the variables.
3. Prepare the visualization.

import matplotlib.pyplot as plt
plt.figure()
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.title('Test figure')        
plt.show()

import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(3,3))
ax.bar(x=['A','B','C'], height=[3.1,7,4.2], color='r')
ax.set_xlabel(xlabel='X title', size=20)
ax.set_ylabel(ylabel='Y title' , color='b', size=20)
plt.show()

fig, ax = plt.subplots(figsize=(3,3))
ax.plot(['Alice','Bob','Catherine'], [4,6,3], color='r')
ax.set_xlabel('TITLE 1')
for tick in ax.get_xticklabels():
    tick.set_rotation(45)
plt.show()

# 2D kde plots
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
np.random.seed(1)
numerical_1 = np.random.randn(100)
np.random.seed(2)
numerical_2 = np.random.randn(100)
fig, ax = plt.subplots(figsize=(3,3))
sns.kdeplot(data=numerical_1,
            data2= numerical_2,
            ax=ax,
            shade=True, 
            color="blue",  
            bw=1)
plt.show()

# heat map
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame(dict(categorical_1=['apple', 'banana', 'grapes',
                                      'apple', 'banana', 'grapes',
                                      'apple', 'banana', 'grapes'], 
                  categorical_2=['A','A','A','B','B','B','C','C','C'], 
                  value=[10,2,5,7,3,15,1,6,8]))
pivot_table = df.pivot("categorical_1", "categorical_2", "value")
# try printing out pivot_table to see what it looks like!
fig, ax = plt.subplots(figsize=(5,5))
sns.heatmap(data=pivot_table, 
            cmap=sns.color_palette("Blues"),
            ax=ax)

plt.show()

1. Overlay plots
   If several line charts share the same x and y variables, you can call Seaborn plots multiple times and plot all of them on the same figure. In the example below, we added one more categorical variable [value = alpha, beta] in the plot with overlaying plots.

fig, ax = plt.subplots(figsize=(4,4))
sns.lineplot(x=['A','B','C','D'], 
             y=[4,2,5,3],
             color='r',
             ax=ax)
sns.lineplot(x=['A','B','C','D'], 
             y=[1,6,2,4], 
             color='b',
             ax=ax)    
ax.legend(['alpha', 'beta'], facecolor='w')
plt.show()

sns.set(style="white", rc={"lines.linewidth": 3})
fig, ax1 = plt.subplots(figsize=(4,4))
ax2 = ax1.twinx()
sns.barplot(x=['A','B','C','D'],
            y=[100,200,135,98], 
            color='#004488',
            ax=ax1)
sns.lineplot(x=['A','B','C','D'], 
             y=[4,2,5,3],
             color='r',
             marker="o",
             ax=ax2)
plt.show()
sns.set()

import matplotlib.pyplot as plt
categorical_1 = ['A', 'B', 'C', 'D']
colors        = ['green', 'red', 'blue', 'orange']
numerical = [[6, 9, 2, 7],
             [6, 7, 3, 8],
             [9, 11, 13, 15],
             [3, 5, 9, 6]]
number_groups = len(categorical_1) 
bin_width = 1.0/(number_groups+1)
fig, ax = plt.subplots(figsize=(6,6))
for i in range(number_groups):
    ax.bar(x=np.arange(len(categorical_1)) + i*bin_width, 
           height=numerical[i],
           width=bin_width,
           color=colors[i],
           align='center')
ax.set_xticks(np.arange(len(categorical_1)) + number_groups/(2*(number_groups+1)))
# number_groups/(2*(number_groups+1)): offset of xticklabel
ax.set_xticklabels(categorical_1)
ax.legend(categorical_1, facecolor='w')
plt.show()

import seaborn as sns
tips = sns.load_dataset("tips")
ax = sns.scatterplot(x="total_bill", y="tip",                      
                     hue="size", size="size",
                     sizes=(20, 200), hue_norm=(0, 7),
                     legend="full", data=tips)
plt.show()

fig = plt.figure(figsize=(7,7))
gs = gridspec.GridSpec(nrows=3, 
                       ncols=3, 
                       figure=fig, 
                       width_ratios= [1, 1, 1],
                       height_ratios=[1, 1, 1],
                       wspace=0.3,
                       hspace=0.3)
ax1 = fig.add_subplot(gs[0, 0])
ax1.text(0.5, 0.5, 'ax1: gs[0, 0]', fontsize=12, fontweight="bold", va="center", ha="center")  # adding text to ax1
ax2 = fig.add_subplot(gs[0, 1:3])
ax2.text(0.5, 0.5, 'ax2: gs[0, 1:3]', fontsize=12, fontweight="bold", va="center", ha="center")
ax3 = fig.add_subplot(gs[1:3, 0:2])
ax3.text(0.5, 0.5, 'ax3: gs[1:3, 0:2]', fontsize=12, fontweight="bold", va="center", ha="center")
ax4 = fig.add_subplot(gs[1:3, 2])
ax4.text(0.5, 0.5, 'ax4: gs[1:3, 2]', fontsize=12, fontweight="bold", va="center", ha="center")
plt.show()

df = pd.read_csv('BlackFriday.csv', usecols = ['User_ID', 'Gender', 'Age', 'Purchase'])
df_gp_1 = df[['User_ID', 'Purchase']].groupby('User_ID').agg(np.mean).reset_index()
df_gp_2 = df[['User_ID', 'Gender', 'Age']].groupby('User_ID').agg(max).reset_index()
df_gp = pd.merge(df_gp_1, df_gp_2, on = ['User_ID'])

freq = ((df_gp.Age.value_counts(normalize = True).reset_index().sort_values(by = 'index').Age)*100).tolist()
number_gp = 7
# freq = the percentage for each age group, and there’re 7 age groups.
def ax_settings(ax, var_name, x_min, x_max):
    ax.set_xlim(x_min,x_max)
    ax.set_yticks([])
    
    ax.spines['left'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    
    ax.spines['bottom'].set_edgecolor('#444444')
    ax.spines['bottom'].set_linewidth(2)
    
    ax.text(0.02, 0.05, var_name, fontsize=17, fontweight="bold", transform = ax.transAxes) 
    return None
# Manipulate each axes object in the left. Try to tune some parameters and you'll know how each command works.
fig = plt.figure(figsize=(12,7))
gs = gridspec.GridSpec(nrows=number_gp, 
                       ncols=2, 
                       figure=fig, 
                       width_ratios= [3, 1],
                       height_ratios= [1]*number_gp,
                       wspace=0.2, hspace=0.05
                      )
ax = [None]*(number_gp + 1)
features = ['0-17', '18-25', '26-35', '36-45', '46-50', '51-55', '55+']
# Create a figure, partition the figure into 7*2 boxes, set up an ax array to store axes objects, and create a list of age group names.  
for i in range(number_gp):
    ax[i] = fig.add_subplot(gs[i, 0])
    
    ax_settings(ax[i], 'Age: ' + str(features[i]), -1000, 20000)    
    
    sns.kdeplot(data=df_gp[(df_gp.Gender == 'M') & (df_gp.Age == features[i])].Purchase, 
            ax=ax[i], shade=True, color="blue",  bw=300, legend=False)
    sns.kdeplot(data=df_gp[(df_gp.Gender == 'F') & (df_gp.Age == features[i])].Purchase, 
            ax=ax[i], shade=True, color="red",  bw=300, legend=False)
    
    if i < (number_gp - 1): 
        ax[i].set_xticks([])
# this 'for loop' is to create a bunch of axes objects, and link them to GridSpec boxes. Then, we manipulate them with sns.kdeplot() and ax_settings() we just defined.
ax[0].legend(['Male', 'Female'], facecolor='w')
# adding legends on the top axes object     
ax[number_gp] = fig.add_subplot(gs[:, 1])
ax[number_gp].spines['right'].set_visible(False)
ax[number_gp].spines['top'].set_visible(False)
ax[number_gp].barh(features, freq, color='#004c99', height=0.4)
ax[number_gp].set_xlim(0,100)
ax[number_gp].invert_yaxis()
ax[number_gp].text(1.09, -0.04, '(%)', fontsize=10, transform = ax[number_gp].transAxes)   
ax[number_gp].tick_params(axis='y', labelsize = 14)
# manipulate the bar plot on the right. Try to comment out some of the commands to see what they actually do to the bar plot.


plt.show()