This chapter informed us about a great number of common NumPy functions. We read a file with loadtxt
and wrote to a file with savetxt
. We made an identity matrix with the eye
function. We read a CSV file containing stock quotes with the loadtxt
function. The NumPy average
and mean
functions allow one to calculate the weighted average and arithmetic mean of a data set.
A few common statistics functions were also mentioned – first, the min
and max
functions that we used to determine the range of the stock prices; second, the median
function that gives the median of a data set; and finally, the std
and var
functions that return the standard deviation and variance of a set of numbers.
We calculated the simple stock returns with the diff
function that returns back the differences between sequential elements. The log
function computes the natural logarithms of array elements.
By default, loadtxt
tries to convert all data into floats. The loadtxt
function has a special parameter for this purpose. The parameter is called converters
and is a dictionary that links columns with the so-called converter functions.
We defined a function and passed it as an argument to the apply_along_axis
function. We implemented an algorithm with the requirement to find the maximum value across arrays.
We learned that the ones
function can create an array with ones and the convolve
function calculates the convolution of a data set with the specified weights.
We computed exponentially decreasing weights with the exp
and linspace
functions. linspace
gave us an array with evenly spaced elements, and then we calculated the exponential for these numbers. We called the ndarray
sum
method in order to normalize the weights.
We got acquainted with the NumPy fill
function. This function fills an array with a scalar value, the only parameter of the fill
function.
After this tour through the common NumPy functions, we will continue covering convenience NumPy functions such as polyfit
, sign
, and piecewise
in the next chapter.