The examples you provided illustrate two different concepts in data manipulation: interpolation in pandas and generating a linear space in numpy, but they serve different purposes and are not equivalent, although they can produce similar results in certain cases.

1. **`numpy.linspace`**: This function is used to generate an array of evenly spaced numbers over a specified interval. In your example, `np.linspace(1, 4, 7)` generates 7 numbers between 1 and 4, inclusive. It does not consider any existing data points within the range; it simply calculates the values needed to create an evenly spaced set of numbers.

2. **`pandas.Series.interpolate`**: This method is used to fill in missing values in a pandas Series using various interpolation techniques. In your example, you start with a Series containing a value at index 0 (1), NaN values at indices 1 through 5, and a value at index 6 (4). When you call `.interpolate()`, pandas fills in the NaN values by estimating the values at those indices based on the values at the surrounding indices. With the default method, 'linear', it performs linear interpolation, which in this case results in the same values as `np.linspace(1, 4, 7)`.

While the results are the same in this specific example, `np.linspace` is not doing interpolation in the sense that it is estimating missing values between known points. Instead, it is creating a new set of evenly spaced values over a specified range. On the other hand, `pandas.Series.interpolate` is estimating missing values within a dataset based on the existing values.

In summary, `np.linspace` is about creating a sequence of numbers, while `pandas.Series.interpolate` is about estimating missing values in a dataset. They can produce the same output in a specific scenario, but they are conceptually quite different.