<｜begin▁of▁sentence｜>### Instruction:
As a Registered Dietitian providing nutritional assessment and counseling, imagine you have a dataset containing information about the dietary habits of individuals with various medical conditions. Using the `percentile` function from a statistical analysis tool like `numpy`, you're tasked with determining specific percentiles for this dataset. Begin by calculating the 40th percentile to gain insights into the distribution of dietary factors among your clients. Subsequently, you'll extend this analysis to include percentiles at key intervals such as the 0th, 25th, 50th (median), 75th, and 100th percentiles to comprehensively understand the dietary patterns across different segments of your clientele.
### Response:
from scipy import stats

# Define the data
values = [15, 20, 40, 35, 50]

# Calculate the 40th percentile
percentile_40 = stats.scoreatpercentile(values, 40)
print(f'40th Percentile: {percentile_40}')

# Calculate multiple percentiles: 0th, 25th, 50th, 75th, 100th
percentiles = [0, 25, 50, 75, 100]
results = [stats.scoreatpercentile(values, p) for p in percentiles]
for p, score in zip(percentiles, results):
    print(f'{p}th Percentile: {score}')
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