Exercise

# NumPy and pandas working together

Pandas depends upon and interoperates with NumPy, the Python library for fast numeric array computations. For example, you can use the
DataFrame attribute `.values`

to represent a DataFrame `df`

as a NumPy
array. You can also pass pandas data structures to NumPy methods. In this exercise, we have imported pandas as `pd`

and loaded world population data every 10 years since 1960 into the DataFrame `df`

. This dataset was derived from the one used in the previous exercise.

Your job is to extract the values and store them in an array using the attribute `.values`

. You'll then use those values
as input into the NumPy `np.log10()`

method to compute the base 10 logarithm of the population values.
Finally, you will pass the entire pandas DataFrame into the same NumPy `np.log10()`

method and compare the results.

Instructions

**100 XP**

- Import
`numpy`

using the standard alias`np`

. - Assign the numerical values in the DataFrame
`df`

to an array`np_vals`

using the attribute`values`

. - Pass
`np_vals`

into the NumPy method`log10()`

and store the results in`np_vals_log10`

. - Pass the entire
`df`

DataFrame into the NumPy method`log10()`

and store the results in`df_log10`

. - Inspect the output of the
`print()`

code to see the`type()`

of the variables that you created.