code: def corr_heatmap(
      x,
      mask_half=True,
      cmap="RdYlGn_r",
      vmin=-1,
      vmax=1,
      linewidths=0.5,
      square=True,
      figsize=(10, 10),
      **kwargs
  ):
      
  
      if mask_half:
          mask = np.zeros_like(x.corr().values)
          mask[np.triu_indices_from(mask)] = True
      else:
          mask = None
      with sns.axes_style("white"):
          return sns.heatmap(
              x.corr(),
              cmap=cmap,
              vmin=vmin,
              vmax=vmax,
              linewidths=linewidths,
              square=square,
              mask=mask,
              **kwargs
          )
docs: Wrapper around seaborn.heatmap for visualizing correlation matrix.
  
      Parameters
      ----------
      x : DataFrame
          Underlying data (not a correlation matrix)
      mask_half : bool, default True
          If True, mask (whiteout) the upper right triangle of the matrix
      All other parameters passed to seaborn.heatmap:
      https://seaborn.pydata.org/generated/seaborn.heatmap.html
  
      Example
      -------
      # Generate some correlated data
      >>> import numpy as np
      >>> import pandas as pd
      >>> k = 10
      >>> size = 400
      >>> mu = np.random.randint(0, 10, k).astype(float)
      >>> r = np.random.ranf(k ** 2).reshape((k, k)) * 5
      >>> df = pd.DataFrame(np.random.multivariate_normal(mu, r, size=size))
      >>> corr_heatmap(df, figsize=(6, 6))