<｜begin▁of▁sentence｜>### Instruction:
Given a numeric dataset with 50 samples and 10 features, how can you create a scree plot to visually assess the explained variance by each principal component? Use graspologic's screeplot function to make the scree plot with the first 10 components and an elbow indicator.
### Response:
import matplotlib.pyplot as plt
from graspologic.plot import screeplot
from sklearn.decomposition import PCA
import numpy as np

# Generate some data and perform PCA
X = np.random.randn(50, 10)
pca = PCA().fit(X)

# Plot the scree plot
fig, ax = plt.subplots()
screeplot(X=pca.singular_values_, title='Scree Plot', context='talk', font_scale=1.5, figsize=(8, 6), ax=ax, cumulative=False, show_first=10, show_elbow=True)
plt.show()
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