DC Code

Question 1

What way do we use matplotlib?

Answer 1

Using the main object-oriented interface provided through the pyplot submodule.

Question 2

How do we import the pyplot submodule?

Answer 2

import matplotlib.pyplot as plt

Question 3

What does the plt.subplots() command do?

Answer 3

Creates two different objects - an axes and figure object.

Question 4

What is the figure object?

Answer 4

A container which holds everything you see on the page.

Question 5

What is the axes object?

Answer 5

The part of the page which holds the data.

Question 6

What is the code to create an empty axes?

Answer 6

import matplotlib as plt
fig, ax = plt.subplots()
plt.show()

Question 7

How do we add data to the axes?

Answer 7

ax.plot(data[“COL-1”], data[“COL-2”])

Question 8

How do you add data to a figure?

Answer 8

Using the Axes object

Question 9

What ways can you customise plots?

Answer 9

• Linestyle
• Point style (markers)
• Colours
• Add axes labels
• Add title label

Question 10

Why are markers useful?

Answer 10

If a plot appears continuous, markers show us where the data exists and which parts are just lines connecting the data points.

Question 11

How do you add a marker?

Answer 11

ax.plot(marker = “o”)

Question 12

How do you edit the lifestyle?

Answer 12

ax.plot(linestyle=”–”)

Question 13

How do you remove a line from the plot?

Answer 13

ax.plot(linestyle=”None”)

Question 14

How do you edit the colour of the plotted data?

Answer 14

ax.plot(color=”r”)

Question 15

How do you add axes labels?

Answer 15

ax.set_xlabel(“Text”)
ax.set_ylabel(“Text”)

Question 16

What convention is used for capitalising the title?

Answer 16

Write it as you would a sentence - only first words and proper nouns are capitalised.

Question 17

How do you add a plot title?

Answer 17

ax.set_title()

Question 18

What are small multiples and what do they achieve?

Answer 18

Small multiples are used to plot several datasets side-by-side. They show similar data across different conditions.

They are a way to reduce the clutter/mess associated when too much data is plotted on one graph. This helps to view trends better.

Question 19

What are small multiples called in matplotlib?

Answer 19

Subplots

Question 20

What does calling subplots() with no inputs achieve?

Answer 20

Creates one subplot.

Question 21

What does passing inputs to subplots() achieve?

Answer 21

subplots(X, Y)

Small multiples are arranged on the page as a grid with rows and columns - X rows and Y columns.

Question 22

When creating small multiples, what can be said of the variable ax?

Answer 22

Ax is no longer only one aces object, it is now an array of axes object with shape X by Y.

Question 23

How can you investigate the number of small multiples?

Answer 23

ax.shape

Shows the shape of the ax array.

Question 24

When we have small multiples, how do we add data using ax.plt()?

Answer 24

We now need to index the axes object and call the plot method on an element of the array.

eg ax[0,0].plt()

Question 25

If there is only one row or one column of plots, how do we call ax.plot?

Answer 25

The resulting array is one dimensional, so you only provide one index to access the elements of the array.

eg ax[0].plt and ax[1].plt

Question 26

When adding x and y labels to small multiples, what should we consider?

Answer 26

Eg for 2 x 1, we only need to add one X axes label to the bottom plot.

Question 27

How do we account for different axis ranges of plots?

Answer 27

plt.subplots(2, 1, sharey=True)

Want to make sure that the subplots have the same range of y axis values. We initialise the figure and its subplots with the keyword argument sharey=True

This improves comparison across datasets

Question 28

If we import a pandas dataframe which represents a time series, what do we need to do?

Answer 28

Tell Pandas to Parse the date column as a date.

import pandas as pd
df = pd.read_csv(“file.csv”, parse_dates = [“date”], index_col = “date”)

Where date is the column name.

Question 29

How do we access the index (now a date) of a dataframe?

Answer 29

df.index

Question 30

Once the date is set as the index, how do we plot the time series data?

Answer 30

ax.plot(df.index, df[|”col”])

Question 31

How do we plot a particular time period?

Answer 31

Slice the DataFrame using two strings to denote the start and end date.

sixties = df[“1960-01-01”:”1969-12-31”]

Use this dataframe in the plot.

Matplotlib will automatically change the axis ticks.

Question 32

If we want to plot two variables with different scales on the same plot, what can we do?

Answer 32

Plot on the same subplot using two different y axis plots. Utilising the twins() method to create a twin of the axes. Share the same x axis, but the y axes are separate.

ax2 = ax.twinx()

Question 33

How can we further highlight the different axis?

Answer 33

Giving each variable its own colour, and the y axis, labels and ticks are the same colour. Add colour to ax.plot(), ax.set_ylabel() and ax.tick_params()

ax.tick_params() clarified in next point

Question 34

How do we set the colour of the axis ticks?

Answer 34

ax.tick_params(“y”, colors = “blue”)

• NB: s in colorS
• First argument takes either x or y

Question 35

How can we prevent retyping out the same code to colour the data of a time series?

Answer 35

Make a function we can reuse.

def plot_timeseries(axes, x, y, color, xlabel, ylabel):
axes.plot(x, y, color = color)
axes.set_xlabel(xlabel)
axes.set_ylabel(ylabel, color = color)
axes.tick_params(“y”, colors = color)

Then call this function passing in the relevant variables.

Question 36

How do we add an annotation to a visualisation?

Answer 36

Using a method of the axes object, annotate.

ax.annotate()

Question 37

What are the minimum inputs for annotate()

Answer 37

ax.annotate(“Annotation text”, xy=(pd.Timestamp(“2015-10-06”), 1))

At the very least it takes the annotation text as input (string) and the xy coordinate we want to annotate.

Question 38

If the x position to annotate is a time stamp, how do we define it?

Answer 38

Using the Pandas object

pd.Timestamp(“2015-10-06”)

Question 39

How do we position the text in an appropriate place?

Answer 39

add argument xytext=(pd.Timestamp(“2008-10-06”), -0.2)

May need to experiment to get a good position

Question 40

How do we connect an arrow between the annotation text and the annotated data?

Answer 40

Add the keyword arrowprops

arrowprops={}

Passing in an empty dictionary results in the default arrow.

Question 41

How do we customise the arrow?