tutorial5

README.md

@@ -11,3 +11,5 @@ I am planning to share how to make publication-quality figures in python, I will
     * Link: https://frankligy.medium.com/making-publication-quality-figures-in-python-part-iii-box-plot-bar-plot-scatter-plot-407fa457449
 4. Tutorial IV (Violin plot, dendrogram)
     * Link: https://towardsdatascience.com/making-publication-quality-figures-in-python-part-iv-violin-plot-and-dendrogram-ed0bb8b23ddd
+5. Tutorial V (Seaborn):
+    * Link: https://frankligy.medium.com/all-you-need-to-know-about-seaborn-6678a02f31ff

tutorial5_seaborn.py

@@ -0,0 +1,71 @@
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+
+mpl.rcParams['pdf.fonttype'] = 42
+mpl.rcParams['ps.fonttype'] = 42
+mpl.rcParams['font.family'] = 'Arial'
+
+import numpy as np
+
+np.random.seed(42)
+data1 = np.random.randn(100)
+data2 = np.random.randn(100)
+data3 = np.random.randn(100)
+
+import seaborn as sns
+import pandas as pd
+
+# prepare datasets
+
+penguin = sns.load_dataset('penguins')   # long form
+synthesis = pd.DataFrame({'var1':data1,'var2':data2,'var3':data3})   # wideform
+a = synthesis.stack().reset_index(-1)
+
+# distribution plot
+
+fig,ax = plt.subplots()
+sns.histplot(data=penguin,kde=True,stat='frequency',x='bill_length_mm',hue='species',multiple='layer',
+             kde_kws={'bw_adjust':5},line_kws={'linewidth':7},palette='Set2',ax=ax)
+sns.rugplot(data=penguin,x='bill_length_mm',hue='species',ax=ax)
+sns.histplot(data=synthesis,kde=True,stat='density',common_norm=False,hue_order=['var2','var1','var3'],
+             multiple='layer')
+
+
+sns.kdeplot(data=penguin,x='bill_length_mm',hue='species',clip=(35,100))
+sns.rugplot(data=penguin,x='bill_length_mm',hue='species')
+
+
+
+# categorical plot
+sns.violinplot(data=penguin,x='species',y='bill_length_mm',hue='sex',split=True,bw=0.2,inner='quartile',scale_hue=True,
+               scale='count')
+
+
+
+sns.swarmplot(data=penguin,x='species',y='bill_length_mm',hue='sex',dodge=True)
+
+sns.pointplot(data=penguin,x='species',y='bill_length_mm',hue='sex')
+
+# regression plot
+sns.regplot(data=penguin,x='bill_length_mm',y='bill_depth_mm')
+
+# matrix plot
+sns.heatmap(data=synthesis.iloc[0:5,:],annot=True,linewidths=0.5,square=True,yticklabels=False)
+mask = np.array([[0,0,0],
+         [0,0,0],
+         [0,1,0],
+         [0,0,0],
+         [0,0,0]])
+sns.heatmap(data=synthesis.iloc[0:5,:],annot=True,linewidths=0.5,square=True,yticklabels=False,mask=mask)
+
+
+row_cb = pd.DataFrame(data=np.random.choice(['r','g','b','m'],(100,2)),index=np.arange(100),columns=['hey','ha'])
+sns.clustermap(data=synthesis,row_colors=row_cb)
+
+
+# pair plot and joint plot
+sns.pairplot(data=penguin.iloc[:,[2,3,4,5]],dropna=True)
+sns.jointplot(data=penguin,x='bill_length_mm',y='bill_depth_mm',kind='reg')
+
+