Intermediate Python for Data Science

The most basic plot is the line plot. A general recipe is given here. import matplotlib.pyplot as plt plt.plot(x,y) plt.show()

plt.xscale(‘log’)

Filip has demonstrated how you could control the y-ticks by specifying two arguments: plt.yticks([0,1,2], [“one”,”two”,”three”]) In this example, the ticks corresponding to the numbers 0, 1 and 2 will be replaced by one, two and three, respectively.

Remember lists? They could contain anything, even other lists. Well, for dictionaries the same holds. Dictionaries can contain key:value pairs where the values are again dictionaries.

In the sample code on the right, the same cars data is imported from a CSV files as a Pandas DataFrame. To select only the cars_per_cap column from cars, you can use: cars[‘cars_per_cap’] cars[[‘cars_per_cap’]] The single bracket version gives a Pandas Series, the double bracket version gives a Pandas DataFrame.

Square brackets can do more than just selecting columns. You can also use them to get rows, or observations, from a DataFrame. The following call selects the first five rows from the cars DataFrame: cars[0:5]

Each pair of commands here gives the same result. cars. loc[‘RU’] cars. iloc[4] cars. loc[[‘RU’]] cars. iloc[[4]] cars. loc[[‘RU’, ‘AUS’]] cars. iloc[[4, 1]]

fam = [1.73, 1.68, 1.71, 1.89] for height in fam : print(height)

fam = [1.73, 1.68, 1.71, 1.89] for index, height in enumerate(fam) : print(“index “ + str(index) + “: “ + str(height))

world = { “afghanistan”:30.55, “albania”:2.77, “algeria”:39.21 } for key, value in world.items() : print(key + “ – “ + str(value))

for x in np.nditer(my_array) : …

Remember how you could use matplotlib to build a line plot? import matplotlib.pyplot as plt plt.plot(x, y) plt.show() The first list you pass is mapped onto the x axis and the second list is mapped onto the y axis. If you pass only one argument, Python will know what to do and will use the index of the list to map onto the x axis, and the values in the list onto the y axis.