How do I mask two different ranges of values in Seaborn-CodePudding

So I wish to red color the values in heatmap that are between the 2.3e-6-0.05. And I wanted to do that with plotting one heatmap on another. But I can't seem to find a way to mask numbers of different values. Here is my try.

from scipy.stats import pearsonr

N = 10
data = np.random.uniform(0, 45, size=(N, N))
for x, y in np.random.randint(0, N, 50).reshape(-1, 2):
    data[x, y] = np.nan  # fill in some nans at random places
df = pd.DataFrame(data)

def pearsonr_pval(x,y):
    return pearsonr(x,y)[1]


data = df.loc[:, (df != 0).any(axis=0)]
data = data.iloc[:,3:50]
to_log = data.columns
df_log = data[to_log].applymap(lambda x: np.log(x 1))
X = df_log.corr(method = pearsonr_pval)

sns.set_style("darkgrid")
mask = np.zeros_like(X)
mask[np.triu_indices_from(mask)] = True 
with sns.axes_style("white"):
    f, ax = plt.subplots(figsize=(20, 20))
    ax = sns.heatmap(X,
                             mask=mask,
                             vmax=1,
                             vmin=0,
                             square=True, 
                             cmap="YlGnBu",
                             annot_kws={"size": 1})
    ax = sns.heatmap(X,
                            mask=(X.values<2.3e-6) & (0.05<X.values) & mask.astype(bool),
                            vmax=1,
                            vmin=0,
                            square=True, 
                            cmap="rocket",
                            annot_kws={"size": 1})

But I get an error: TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'

Edit with the code above I get :

CodePudding user response：

As explained in this answer, for element-wise Boolean comparisons in Pandas you need to use & and |, and to enclose each condition in parentheses. So to combine your three conditions, you would need

mask=(X<2.3e-6) | (0.05<X) | mask.astype(bool),