Python is free, it is open source, and it has a huge community.
Python is one of the most popular and loved programming languages in the world!
The CodeEval blog published its data on the "Most Popular Coding Languages" on February 2016. It shows Python in the first place in popularity, based on usage in the CodeEval community. Meanwhile, the StackOverflow trends graph shows increasing interest in Python during the last 5 years.
Python can be used for many things: managing data bases, creating graphical user interfaces, making websites, and much more… including science. Because of the many uses, the world of Python includes many, many Libraries (you load the parts that you need).
NumPy is for working with data in the form of arrays (vectors, matrices). It has a myriad built-in functions or methods that work on arrays directly. To load the library into your current session of interactive Python, into a saved Python script, or into a Jupyter notebook, you use:
[1.0, 0.5, 2.5]
[[ 1.0, 0.5, 2.5], [ 0.5, 1.1, 2.0]]
:means "all elements in this dimension"
numpyis loaded, its built-in functions are called like this:
argis the function argument: arrays to operats on, and parameters)
# By the way: comments in code cells start with a hash. # here are two arrays, saved as variables x and y: x = numpy.array([1.0, 0.5, 2.5]) y = numpy.array([[ 1.0, 0.5, 2.5], [ 0.5, 1.1, 2.0]])
# The print function works on arrays: print(x)
[ 1. 0.5 2.5]
[[ 1. 0.5 2.5] [ 0.5 1.1 2. ]]
Let's review what happened there. We first loaded
numpy, giving us the full power to use arrays. We created two arrays:
y… then we print
x and we print
y. They look nice.
Numpy has a built-in function to find out the "shape" of an array, which means: how many elements does this array have in each dimension? We find that
y is a two-by-three array (it has two dimensions).
What is the first element of
x? We can use square brackets and the zero-index to find out:
Exercise: Now, try it yourself. What is the first element of
Right. The first element of
y is a 3-wide array of numbers. If we want to access the first element of this now, we use:
Exercise: Try picking out different elements of the array
This is super powerful!
Matplotlib is for making all kinds of plots. To get an idea of the great variety of plots possibe, have a look at the online Gallery. You can see that Matplotlib itself is a pretty big library. We can load a portion of the library (called a module) that has the basic plotting funtions with:
from matplotlib import pyplot
pyplot module is loaded, its built-in functions are called like this:
pyplot.function(arg) (where arg is the function argument).
Did you know that the size of households—that is, the number of people living in each household—has been steadily decreasing in the US and many other countries? This has perhaps surprising consequences. Even if population growth slows down, or stops altogether, the number of households keeps increasing at a fast rate.
More households means more $CO_2$ emissions! This is bad for the planet.
#Load the data from local disk year, av_size = numpy.loadtxt(fname='data/statistic_id183648.csv', delimiter=',', skiprows=1, unpack=True)
[ 2016. 2015. 2014. 2013. 2012. 2011. 2010. 2009. 2008. 2007. 2006. 2005. 2004. 2003. 2002. 2001. 2000. 1999. 1998. 1997. 1996. 1995. 1994. 1993. 1992. 1991. 1990. 1989. 1988. 1987. 1986. 1985. 1984. 1983. 1982. 1981. 1980. 1979. 1978. 1977. 1976. 1975. 1974. 1973. 1972. 1971. 1970. 1960.]
Exercise: Now print the variable
av_size, correspondig to the average size of households (in numbers of people) for each year:
Great! The next thing we want to do is make a plot of the changing size of households over the years. To do that, we need to load the
Matplotlib module called
from matplotlib import pyplot %matplotlib inline
from business about?
matplotlib is a pretty big (and awesome!) library. All that we need is a subset of the library for creating 2D plots, so we ask for the
pyplot module of the
Plotting the data is as easy as calling the function
plot() from the module
[<matplotlib.lines.Line2D at 0x10ff57978>]
But what if we'd like to get a title on this plot, or add labels to the axes? (We should always have labelled axes!). Also, we notice a long jump from the year 1960 to 1970: let's add markers to the plot and change the line style to a dotted line.
pyplot.plot(year, av_size, linestyle=':', marker='o') pyplot.title("Household size in the US, 1960–2016 \n", fontsize=16) pyplot.ylabel("Average number of people per household")
<matplotlib.text.Text at 0x110238fd0>
Exercise: In the same cell above, now add a label on the x-axis, using the
pyplot.xlabel() function, and re-execute it.
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