Python Fundamentals

Figure 1

Value of 65.0 with weight_kg label stuck on it

Figure 2

Value of 65.0 with label weight_kg stuck on it, and value of 143.0 with label weight_lb stuck on it

Figure 3

Value of 100.0 with label weight_kg stuck on it, and value of 143.0 with label weight_lb stuck on it

Analyzing some wave-height data

Figure 1

'data' is a 3 by 3 numpy array containing row 0: ['A', 'B', 'C'], row 1: ['D', 'E', 'F'], and row 2: ['G', 'H', 'I']. Starting in the upper left hand corner, data[0, 0] = 'A', data[0, 1] = 'B', data[0, 2] = 'C', data[1, 0] = 'D', data[1, 1] = 'E', data[1, 2] = 'F', data[2, 0] = 'G', data[2, 1] = 'H', and data[2, 2] = 'I', in the bottom right hand corner.

Figure 2

Per-year maximum height is computed row-wise across all columns using numpy.max(data, axis=1). Per-year average wave height is computed column-wise across all rows using numpy.mean(data, axis=0).

Visualizing Tabular Data

Figure 1

Heat map representing the wave height from the first 50 days. Each cell is colored by value along a color gradient from blue to yellow.

Figure 2

A line graph showing the monthly average wave height over a 37 year period.

Figure 3

A line graph showing the maximum wave height per month over a 37 year period.

Figure 4

A line graph showing the minimum wave height per month over a 37 year period.

Figure 5

Three line graphs showing the daily average, maximum and minimum wave-heights over a 446-day period.

Figure 6

Three plots showing the average, maximum and minimum waveheights plotted on a single pair of axes.

Figure 7

Global surface waveheight

Figure 8

Global surface waveheight with a colourbar

Storing Multiple Values in Lists

Figure 1

veg is represented as a shelf full of produce. There are three rows of vegetables on the shelf, and each row contains three baskets of vegetables. We can label each basket according to the type of vegetable it contains, so the top row contains (from left to right) lettuce, lettuce, and peppers.

Figure 2

veg is now shown as a list of three rows, with veg[0] representing the top row of three baskets, veg[1] representing the second row, and veg[2] representing the bottom row.

Figure 3

To reference a specific basket on a specific shelf, you use two indexes. The first index represents the row (from top to bottom) and the second index represents the specific basket (from left to right). veg is now shown as a two-dimensional grid, with each basket labeled according to its index in the nested list. The first index is the row number and the second index is the basket number, so veg[1][3] represents the basket on the far right side of the second row (basket 4 on row 2): zucchini

Repeating Actions with Loops

Figure 1

Loop variable 'num' being assigned the value of each element in the list `odds` in turn and then being printed

Analyzing Data from Multiple Files

Figure 1

Output from the first iteration of the for loop. Three line graphs showing the average, maximum and minimum waveheight in the 2000s.

Figure 2

Output from the second iteration of the for loop. Three line graphs showing the average, maximum and minimum waveheight in the 2010s.

Figure 3

Output from the third iteration of the for loop. Three line graphs showing the average, maximum and minimum waveheight in the 1980s.

Figure 4

1990s data highlighting NaNs

Making Choices

Figure 1

A flowchart diagram of the if-else construct that tests if variable num is greater than 100

Creating Functions

Figure 1

Labeled parts of a Python function definition