I am trying to plot Dendrogram to cluster data but this error is stopping me.
my date is here "https://assets.datacamp.com/production/repositories/655/datasets/2a1f3ab7bcc76eef1b8e1eb29afbd54c4ebf86f2/eurovision-2016.csv"
I first chose columns to work with
target_col = df_euro["To country"]
feat = df_euro[["Jury A","Jury B","Jury C","Jury D","Jury E"]]
#Convert them into ndarrays
x = feat.to_numpy(dtype ='float32')
y = target_col.to_numpy()
# Calculate the linkage: mergings
mergings = linkage(x, method = 'complete')
# Plot the dendrogram
dendrogram(
mergings,
labels = y,
leaf_rotation = 90,
leaf_font_size = 6
)
plt.show()
But I'm getting this error which I can't understand. I googled it and checked that both has same shape (1066,5) and (1066,) There is No NA in both features and target_col
I know the issue is with labels but i couldn't find away to solve it. find Any help will be really appreciated :)
Edit: Here is entire traceback
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-113-7fffdc847e5e> in <module>
4 mergings = linkage(feat, method = 'complete')
5 # Plot the dendrogram
----> 6 dendrogram(
7 mergings,
8 labels = target_col,
C:ProgramDataAnaconda3libsite-packagesscipyclusterhierarchy.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
C:ProgramDataAnaconda3libsite-packagespandascoregeneric.py in __nonzero__(self)
1476
1477 def __nonzero__(self):
-> 1478 raise ValueError(
1479 f"The truth value of a {type(self).__name__} is ambiguous. "
1480 "Use a.empty, a.bool(), a.item(), a.any() or a.all()."
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().