Seaborn distplot and KDE data confusion
The difference is caused by the fact that seaborn.distplot and matplotlib.pyplot.hist use different defaults for the number of bins. The bins are ranges of values for which the number of observations are counted before being plotted. For more information on what bins are check the Wikipedia page for histograms.

In your example, the standard matplotlib plot has bigger bins than the seaborn plot since it uses bins=10, whereas seaborn seems uses the Freedman-Diaconis rule to determine the number of bins, which in this case would give a bin width of about 0.5 and bins=18.

Setting the number of bins used equal for both the seaborn and matplotlib.pyplot plots gives the following histograms:


As you can see, using the same value for the number of bins gives the exact same plots. I used the following code to produce this plot, in which you can also change the number of bins used by both plots to compare them.

import seaborn as sns
import matplotlib.pyplot as plt

# Set number of bins
nbins = 10

# Load dataset 
x = sns.load_dataset("tips")

# Set up subplots
fig, axs = plt.subplots(1, 2, figsize=(12, 5))

# Seaborn plot
sns.distplot(x.tip, ax=axs[0], bins=nbins, kde=False)
axs[0].set_title("Seaborn plot")

# Matplotlib.pyplot plot
axs[1].hist(x.tip, bins=nbins)
axs[1].set_title("Matplotlib.pyplot plot")

# Set title
fig.suptitle(f"Histograms using $bins=${nbins}")

fig.show()