Aggregation

A. Summation

As we know how to calculate the sum of individual values between multiple arrays. To sum the values within a single array, we use the np.sum function.

The function takes in a NumPy array as its required argument, and uses the axis keyword argument. If the axis keyword argument is not specified, np.sum returns the overall sum of the array. The code below shows how to use np.sum

In addition to regular sums, NumPy can perform cumulative sums using np.cumsum. Like np.sum, np.cumsum also takes in a NumPy array as a required argument and uses the axis argument. If the axis keyword argument is not specified, np.cumsum will return the cumulative sums for the flattened array. The code below shows how to use np.cumsum. For a 2-D NumPy array, setting axis=0 returns an array with cumulative sums across each column, while axis=1 returns the array with cumulative sums across each row. Not setting axis returns a cumulative sum across all the values of the flattened array.

B. Concatenation

An important part of aggregation is combining multiple datasets. In NumPy, this equates to combining multiple arrays into one. The function we use to do this is np.concatenate. Like the summation functions, np.concatenate uses the axis keyword argument. However, the default value for axis is 0 (i.e. dimension 0).

Furthermore, the required argument for np.concatenate is a list of arrays, which the function combines into a single array.

The code below shows how to use np.concatenate, which aggregates arrays by joining them along a specific dimension. For 2-D arrays, not setting the axis argument (defaults to axis=0) concatenates the arrays vertically. When we set axis=1, the arrays are concatenated horizontally.

Time to Code!

Each coding exercise in this chapter will be to complete a small function that takes in 2-D NumPy matrices as input. The first function to complete is get_sums, which returns the overall sum and column sums of data.

Set total_sum equal to np.sum applied to data.

Set col_sum equal to np.sum applied to >data, with axis set to 0. Return a tuple of total_sum and col_sum, in that order.

The next function to complete is get_cumsum, which returns the cumulative sums for each row of data.

Set row_cumsum equal to np.cumsum applied to data with axis set to 1.

Then return row_cumsum.

The final function, concat_arrays, takes in two 2-D NumPy arrays as input. It returns the column-wise and row-wise concatenations of the input arrays.

Set col_concat equal to np.concatenate applied to a list of data1, data2, in that order.

Set row_concat equal to np.concatenate applied to a list of data1, data2, in that order. The axis keyword argument should be set to 1.

Return a tuple containing col_concat and row_concat, in that order.