# 04-03 Subplots text and annotations

### 04 - 03 Subplots, Text and Annotations¶

As seen from the previous example of plotting histogram of an RGB image, sometimes it is helpful to visualize different data/ views of same data together. Matplotlib has the concept of subplots that we have been using (without even knowing about it) since Intro to Matplotlib to create figure and axes object.

The concept of subplots revolve around the argument that, while an Axes object can only belong to one Figure (and it MUST), A Figure can have many Axes objects. These axes are typically called subplots. They act just like regular Axes.

Lets look at a simple example

In [1]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
plt.style.use('seaborn-darkgrid')
import pandas as pd

In [26]:
# 1 row and 2 column figure
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15,7))
# when subplot has more than one row or column, the second argument
# returned by plt.subplots is a tuple.

# two axes objects
ax1 = axes[0]
ax2 = axes[1]

# plot on first axes object (aka first subplot at posn row=1, column=1)
ax1.plot(np.arange(10), np.random.randint(1, 99, size=10), label='Boston')
ax1.set_title('A tale of two cities')
ax1.grid(which='major')
ax1.legend()

# plot on second axes object (aka subplot at posn row=1, column=2)
theta = np.linspace(-np.pi, np.pi, 100)
ax2.plot(theta, np.sin(theta), ls='-.', label='Sine')
ax2.set_xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
ax2.set_xticklabels(['$-\pi$', '$-\pi/2$', 0, '$\pi/2$', '$\pi$'])
ax2.set_title("Sine Wave")
ax2.grid(which='major')
ax2.legend()

# Add a centered title for the whole figure
fig.suptitle('My Fancy Subplot')
plt.show()


#### 04 - 03.01 Spacing¶

The spacing between the subplots can be adjusted using plt.subplots_adjust().

Play around with the example below to see how the different arguments affect the spacing.

In [27]:
fig, axes = plt.subplots(2, 2, figsize=(10, 5))
left=0.125, right=0.9,
top=0.9,    bottom=0.1)


A common complaint with matplotlib users is that the labels do not fit with the subplots, or the label of one subplot spills onto another subplot's area. Matplotlib does not currently have any sort of robust layout engine, as it is a design decision to minimize the amount of magic that matplotlib performs. They intend to let users have complete control over their plots. $LaTeX$ users would be quite familiar with the amount of frustration that can occur with placement of figures in their documents.

That said, there have been some efforts to develop tools that users can use to help address the most common compaints. The Tight Layout feature, when invoked, will attempt to resize margins, and subplots so that nothing overlaps.

In [64]:
def example_plot(ax):
ax.plot(np.random.randn(100), color=np.random.rand(3,1))
ax.set_xlabel('X points', fontsize=10)
ax.set_ylabel('Y points', fontsize=10)

fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2, figsize=(10,5))
example_plot(ax1)
example_plot(ax2)
example_plot(ax3)
example_plot(ax4)
# try commenting the below line and see the difference
plt.tight_layout()


Notice how the colors are randomly generated. It is not always the best practice, but good enough for now.

For a future practice, create your own list of colors and cycle through them in your plots. You can find one such example in Miscellaneous plots module

#### 04 - 03.02 Sharing axes¶

There will be times when you want to have the x axis and/or the y axis of your subplots to be "shared". Sharing an axis means that the axis in one or more subplots will be tied together such that any change in one of the axis changes all of the other shared axes. This works very nicely with autoscaling arbitrary datasets that may have overlapping domains. Furthermore, when interacting with the plots (panning and zooming), all of the shared axes will pan and zoom automatically.

In [31]:
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(10, 5))
theta = np.linspace(-np.pi, np.pi, 100)

# Sine plot
ax1.plot(theta, np.sin(theta), ls='-.', label='Sine', color='darkorange')
ax1.set_xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
ax1.set_xticklabels(['$-\pi$', '$-\pi/2$', 0, '$\pi/2$', '$\pi$'])
ax1.legend()

ax1.set_xlim(-np.pi, np.pi)

# Cosine plot
ax2.plot(theta, np.cos(theta), ls='-.', label='Cosine', color='darkolivegreen')
ax2.set_xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
ax2.set_xticklabels(['$-\pi$', '$-\pi/2$', 0, '$\pi/2$', '$\pi$'])
ax2.legend()

Out[31]:
<matplotlib.legend.Legend at 0x1066089e8>

#### 04 - 03.03 GridSpec¶

Under the hood, matplotlib utilizes GridSpec to lay out the subplots. While plt.subplots() is fine for simple cases, sometimes you will need more advanced subplot layouts. In such cases, you should use GridSpec directly. GridSpec is outside the scope of this tutorial, but it is handy to know that it exists. GridSpec Guide is a guide on how to use it.

Lets take a look at a small example:

In [62]:
fig = plt.figure(figsize=(15, 7))
grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)
ax0 = plt.subplot(grid[0, 0])
ax1 = plt.subplot(grid[0, 1:])
ax2 = plt.subplot(grid[1, :2])
ax3 = plt.subplot(grid[1, 2])

theta = np.linspace(-np.pi, np.pi, 100)

ax0.plot(theta, np.sin(theta), label='Sine', color='#1abc9c')
ax1.plot(theta, np.tan(theta), label='Tan', color='#3498db')
ax2.plot(theta, np.cos(theta), label='Cosine', color='#e74c3c')
ax3.plot(theta, np.log(theta), label='log', color='#9b59b6')

ax0.legend()
ax1.legend()
ax2.legend()
ax3.legend()

/usr/local/lib/python3.6/site-packages/ipykernel/__main__.py:13: RuntimeWarning: invalid value encountered in log

Out[62]:
<matplotlib.legend.Legend at 0x115dd8da0>

### 04 - 03.04 Text and Annotations¶

The plots that we have made till now, they convey the information visually perfectly fine. However lets load a dataset like 311 noise complaints for the month of Nov 2016 through January 2017

In [ ]:
noise = pd.read_csv('sample_datasets/2012_NYC_Noise_Complaints.csv',
parse_dates=['Created Date'])

In [124]:
import matplotlib as mpl
# All the complaints aggregated by day
total_complaints = noise.groupby(by=noise['Created Date'])['Unique Key'].count()
fig, ax = plt.subplots(figsize=(15, 7))
ax.plot(total_complaints, color='#3498db', label='total complaints')
ax.legend()

Out[124]:
<matplotlib.legend.Legend at 0x11287bf28>

For such plots, conveying information like a particular market dip or simply drawing readers attention requires some small textual cues and labels. Matplotlib provides two ways one can place arbitrary text anywhere they want on a plot. The first is a simple text(). Then there is the fancier annotate() function that can help point out what you want to annotate.

Lets take a look at simple text method which takes the text and the x and y position of where you want to put the text. (Also lets clean up our x-axis a bit)

In [125]:
fig, ax = plt.subplots(figsize=(15, 7))
ax.plot(total_complaints, color='#3498db', label='total complaints')

# formatting the xaxis
ax.xaxis.set_major_locator(mpl.dates.MonthLocator())
ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=1))
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter("%B'%y"));
ax.legend()

# Let point out some occassions
fav_dates={"2016-11-23":"Thanks Giving",
"2016-12-25": "Christmas",
"2016-12-31": "New Years",
"2017-1-1":   "First Day after New Year"
}
for date in fav_dates.items():
ax.text(date[0], total_complaints.loc[date[0]], date[1])


Hmm.. looks like people get crabby after the new years

The above plot conveys the information we want it to but lacks flexibility in terms of pointing at things you want to draw the attention to.

Lets take a look at annotate function which offers much more control and additional features. Lets modify the same plot with annotate function this time.

In [129]:
fig, ax = plt.subplots(figsize=(15, 7))
ax.plot(total_complaints, color='#3498db', label='total complaints')

# formatting the xaxis
ax.xaxis.set_major_locator(mpl.dates.MonthLocator())
ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=1))
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter("%B'%y"));
ax.legend()

# Let point out some occassions
fav_dates={"2016-11-23":"Thanks Giving",
"2016-12-25": "Christmas",
"2016-12-31": "New Years",
"2017-1-1":   "First Day after New Year"
}
for date in fav_dates.items():
xposition = date[0]
yposition = total_complaints.loc[date[0]]
ax.annotate(date[1], xy=(xposition, yposition),
xycoords='data', ha='center',
xytext=(0, 20), textcoords='offset points',
arrowprops=dict(arrowstyle="->"))


The arrow style is controlled through the arrowprops dictionary. These options are fairly well-documented in documentation (press ? or Shift+<TAB>) so we will not repeat ourselves here but let's take a look at some of those options.

In [167]:
fig, ax = plt.subplots(figsize=(15, 7))
ax.plot(total_complaints, color='#3498db', label='total complaints')

# formatting the xaxis
ax.xaxis.set_major_locator(mpl.dates.MonthLocator())
ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=1))
ax.xaxis.set_major_formatter(plt.NullFormatter())
ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter("%B'%y"));
ax.legend()

# Let point out some occassions
fav_dates={"2016-11-23":"Thanks Giving",
"2016-12-25": "Christmas",
"2016-12-31": "New Years",
"2017-1-1":   "First Day after New Year"
}
for date in fav_dates.items():
xposition = date[0]
yposition = total_complaints.loc[date[0]]
ax.annotate(date[1], xy=(xposition, yposition),
xycoords='data', ha='center',
xytext=(50, 25), textcoords='offset points',
arrowprops=dict(arrowstyle="->",

<matplotlib.text.Text at 0x114802978>