Error in sc.tl.dendrogram: The truth value of a Index is ambiguous.

I think with a recent numpy or Pandas update, an if clause in sc.tl.dendrogram no longer works properly.

    import numpy as np
    import pandas as pd
    import scanpy as sc

    # Use pbmc3k dataset
    adata = sc.datasets.pbmc3k()
    sc.pp.filter_genes(adata, min_counts=1)
    sc.pp.log1p(adata)
    sc.pp.normalize_total(adata)
    sc.pp.highly_variable_genes(adata)
    sc.tl.pca(adata)
    sc.pp.neighbors(adata)
    sc.tl.leiden(adata)
    sc.tl.rank_genes_groups(adata, groupby='leiden')

    # Save the ranks.
    results_dict = dict()
    for cluster_i in adata.uns['rank_genes_groups']['names'].dtype.names:
        # print(cluster_i)
        # Get keys that we want from the dataframe.
        data_keys = list(
            set(['names', 'scores', 'logfoldchanges', 'pvals', 'pvals_adj']) &
            set(adata.uns['rank_genes_groups'].keys())
        )
        # Build a table using these keys.
        key_i = data_keys.pop()
        results_dict[cluster_i] = pd.DataFrame(
            row[cluster_i] for row in adata.uns['rank_genes_groups'][key_i]
        )
        results_dict[cluster_i].columns = [key_i]
        for key_i in data_keys:
            results_dict[cluster_i][key_i] = [
                row[cluster_i] for row in adata.uns['rank_genes_groups'][key_i]
            ]
        results_dict[cluster_i]['cluster'] = cluster_i
    marker_df = pd.concat(results_dict, ignore_index=True)

    marker_df = marker_df.sort_values(by=['scores'], ascending=False)
    # Make dataframe of the top 3 markers per cluster
    marker_df_plt = marker_df.groupby('cluster').head(3)
    
    # here sc.tl.dendrogram will fail
    _ = sc.pl.dotplot(
        adata,
        var_names=marker_df_plt['names'],
        groupby='leiden',
        dendrogram=True,
        use_raw=False,
        show=False,
        color_map='Blues'
        save='{}.png'.format('test')
    )

/lib/python3.6/site-packages/scanpy/tools/_dendrogram.py in dendrogram(adata, groupby, n_pcs, use_rep, var_names, use_raw, cor_method, linkage_method, optimal_ordering, key_added, inplace)
    130         corr_matrix, method=linkage_method, optimal_ordering=optimal_ordering
    131     )
--> 132     dendro_info = sch.dendrogram(z_var, labels=categories, no_plot=True)
    133
    134     # order of groupby categories

/lib/python3.6/site-packages/scipy/cluster/hierarchy.py in dendrogram(Z, p, truncate_mode, color_threshold, get_leaves, orientation, labels, count_sort, distance_sort, show_leaf_counts, no_plot, no_labels, leaf_font_size, leaf_rotation, leaf_label_func, show_contracted, link_color_func, ax, above_threshold_color)
   3275                          "'bottom', or 'right'")
   3276
-> 3277     if labels and Z.shape[0] + 1 != len(labels):
   3278         raise ValueError("Dimensions of Z and labels must be consistent.")
   3279

/lib/python3.6/site-packages/pandas/core/indexes/base.py in __nonzero__(self)
   2148     def __nonzero__(self):
   2149         raise ValueError(
-> 2150             f"The truth value of a {type(self).__name__} is ambiguous. "
   2151             "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
   2152         )

ValueError: The truth value of a Index is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

Versions:

scanpy==1.5.1 anndata==0.7.3 umap==0.4.4 numpy==1.17.5 scipy==1.5.0 pandas==1.0.5 scikit-learn==0.23.1 statsmodels==0.11.1 python-igraph==0.8.2 leidenalg==0.8.1

Conda environment is attached. environment.txt

[letaylor]

A temporary fix is to downgrade scipy to 1.4.1.

[kbreau92]

If you go into the hierarchy.py file and change: if labels and Z.shape[0] + 1 != len(labels):

to: if (labels is not None) and (Z.shape[0] + 1 != len(labels)):

It fixes the issue.