# Analyzing Stock Data with Numpy – The Reshape and Average Functions

### Daily Data Science Puzzle

```import numpy as np

# apple stock prices (May 2018)
prices = [ 189, 186, 186, 188,
187, 188, 188, 186,
188, 188, 187, 186 ]
prices = np.array(prices)

data_3day = prices.reshape(4,3)

print(int(np.average(data_3day)))
print(int(np.average(data_3day[-1])))
```

What is the output of this puzzle?

Numpy is a popular Python library for data science focusing on linear algebra. This puzzle performs a miniature stock analysis of the Apple stock.

First, we create a numpy array from the raw price data.

Second, we create a new array `data_3day` for more convenient analysis. This array bundles the price data from three days into each row. We examine some rows in more detail later.

Third, we average the 3-day price data of the first and last row using the numpy `average()` function. Doing this results in data points that are more robust against outliers. Comparing the first and the last 3-day price period reveals that the Apple stock price remains stable in our mini data set.

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