correlation plot python seaborn
import matplotlib.pyplot as plt import seaborn as sns figure = plt.figure(figsize=(12, 6)) sns.heatmap(train_data.corr(), annot=True,cmap=plt.cm.cool) plt.tight_layout() plt.xlabel('Corr') plt.show()
correlation plot python seaborn
import matplotlib.pyplot as plt import seaborn as sns figure = plt.figure(figsize=(12, 6)) sns.heatmap(train_data.corr(), annot=True,cmap=plt.cm.cool) plt.tight_layout() plt.xlabel('Corr') plt.show()
seaborn create a correlation matrix
import seaborn as sns %matplotlib inline # calculate the correlation matrix corr = auto_df.corr() # plot the heatmap sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns)
how to plot a correlation matrix seaborn
from string import ascii_letters import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt sns.set_theme(style="white") d = pd.read_csv("../path_to/csv_name.csv") # Compute the correlation matrix corr = d.corr() # Generate a mask for the upper triangle: triu means upper triangle mask = np.triu(np.ones_like(corr, dtype=bool)) # Set up the matplotlib figure f, ax = plt.subplots(figsize=(11, 9)) # Generate a custom diverging colormap cmap = sns.diverging_palette(230, 20, as_cmap=True) # Draw the heatmap with the mask and correct aspect ratio sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0, square=True, linewidths=.5, cbar_kws={"shrink": .5})
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