【2.1.9】成对图（pairwise-plot）

成对图是探索性分析中的最爱，以理解所有可能的数字变量对之间的关系。 它是双变量分析的必备工具。

例一

# Load Dataset
df = sns.load_dataset('iris')

# Plot
plt.figure(figsize=(10,8), dpi= 80)
sns.pairplot(df, kind="scatter", hue="species", plot_kws=dict(s=80, edgecolor="white", linewidth=2.5))
plt.show()

例2

# Load Dataset
df = sns.load_dataset('iris')

# Plot
plt.figure(figsize=(10,8), dpi= 80)
sns.pairplot(df, kind="reg", hue="species")
plt.show()

例3

infile = 'hsc.csv'
df = pd.read_csv(infile)

import numpy as np
import matplotlib.pyplot as plt
# Seaborn visualization library
import seaborn as sns
# Create the default pairplot
#sns.pairplot(df)
# Function to calculate correlation coefficient between two arrays

def corr(x, y, **kwargs):
    # Calculate the value
    
    #remove nan
    dt = {'x':x,'y':y}
    df_1 = pd.DataFrame(dt)
    df_2 = df_1.dropna()
    x = df_2['x']
    y = df_2['y']
    coef = np.corrcoef(x, y)[0][1]
    # Make the label

    label = r'$\rho$ = ' + str(round(coef, 2))
    # Add the label to the plot
    ax = plt.gca()
    ax.annotate(label, xy = (0.2, 0.95), size = 20, xycoords = ax.transAxes)

    
grid = sns.PairGrid(data= df)    
# Map the plots to the locations
grid = grid.map_lower(plt.scatter)
grid = grid.map_lower(corr)
grid = grid.map_upper(sns.kdeplot, cmap = 'Blues')
grid = grid.map_diag(plt.hist, bins = 10, edgecolor =  'k');
plt.savefig(result_pic)

参考资料

• https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/