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# Plotting dates

Sooner or later, we all have had the need to plot some information over time, be it for the bank account balance each month, the total web site accesses for each day of the year, or one of many other reasons.

Matplotlib has a plotting function ad hoc for dates, *plot_date()* that considers data on X, Y, or both axes, as dates, labeling the axis accordingly.

As usual, we now present an example, and we will discuss it later:

In [1]: import matplotlib as mpl

In [2]: import matplotlib.pyplot as plt

In [3]: import numpy as np

In [4]: import datetime as dt

In [5]: dates = [dt.datetime.today() + dt.timedelta(days=i)

...: for i in range(10)]

In [6]: values = np.random.rand(len(dates))

In [7]: plt.plot_date(mpl.dates.date2num(dates), values, linestyle='-

');

In [8]: plt.show()

First, a note about linestyle keyword argument: without it, there's no line connecting the markers that are displayed alone.

We created the *dates* array using *timedelta()*, a *datetime* function that helps us define a date interval—*10* days in this case. Note how we had to convert our date values using the *date2num()* function. This is because Matplotlib represents dates as float values corresponding to the number of days since 0001-01-01 UTC.

Also note how the X-axis labels, the ones that have data values, are badly rendered.

Matplotlib provides ways to address the previous two points—date formatting and conversion, and axes formatting.

## Date formatting

Commonly, in Python programs, *dates* are represented as *datetime* objects, so we have to first convert other data values into *datetime* objects, sometimes by using the *dateutil* companion module, for example:

import datetime

date = datetime.datetime(2009, 03, 28, 11, 34, 59, 12345)

or

import dateutil.parser

datestrings = ['2008-07-18 14:36:53.494013','2008-07-20

14:37:01.508990', '2008-07-28 14:49:26.183256']

dates = [dateutil.parser.parse(s) for s in datestrings]

Once we have the *datetime* objects, in order to let Matplotlib use them, we have to convert them into floating point numbers that represent the number of days since 0001-01-01 00:00:00 UTC.

To do that, Matplotlib itself provides several helper functions contained in the *matplotlib.dates* module:

*date2num()*: This function converts one or a sequence of datetime objects to float values representing days since 0001-01-01 00:00:00 UTC (the fractional parts represent hours, minutes, and seconds)*num2date()*: This function converts one or a sequence of float values representing days since 0001-01-01 00:00:00 UTC to*datetime*objects (or a sequence, if the input is a sequence)*drange(dstart, dend, delta)*: This function returns a*date*range (a sequence) of float values in Matplotlib date format;*dstart*and*dend*are*datetime*objects while*delta*is a*datetime.timedelta*instance

Usually, what we will end up doing is converting a sequence of *datetime* objects into a Matplotlib representation, such as:

dates = list of datetime objects

mpl_dates = matplotlib.dates.date2num(dates)

*drange()* can be useful in situations like this one:

import matplotlib as mpl

from matplotlib import dates

import datetime as dt

date1 = dt.datetime(2008, 9, 23)

date2 = dt.datetime(2009, 4, 12)

delta = dt.timedelta(days=10)

dates = mpl.dates.drange(date1, date2, delta)

where *dates* will be a sequence of floats starting from *date1* and ending at *date2* with a *delta* timestamp between each item of the list.

## Axes formatting with axes tick locators and formatters

As we have already seen, the X labels on the first image are not that nice looking. We would expect Matplotlib to allow a better way to label the axis, and indeed, there is.

The solution is to change the two parts that form the axis ticks—**locators** and **formatters**. Locators control the tick's position, while formatters control the formatting of labels. Both have a major and minor mode: the major locator and formatter are active by default and are the ones we commonly see, while minor mode can be turned on by passing a relative locator or formatter function (because minors are turned off by default by assigning NullLocator and NullFormatter to them).

While this is a general tuning operation and can be applied to all Matplotlib plots, there are some specific locators and formatters for date plotting, provided by *matplotlib.dates*:

*MinuteLocator*,*HourLocator*,*DayLocator*,*WeekdayLocator*,*MonthLocator*,*YearLocator*are all the locators available that place a tick at the time specified by the name, for example,*DayLocator*will draw a tick at each day. Of course, a minimum knowledge of the date interval that we are about to draw is needed to select the best locator.*DateFormatter*is the tick formatter that uses*strftime()*to format strings.

The default locator and formatter are *matplotlib.ticker*.*AutoDateLocator* and *matplotlib.ticker.AutoDateFormatter*, respectively. Both are set by the *plot_date()* function when called. So, if you wish to set a different locator and/or formatter, then we suggest to do that after the *plot_date()* call in order to avoid the *plot_date()* function resetting them to the default values.

Let's group all this up in an example:

In [1]: import matplotlib as mpl

In [2]: import matplotlib.pyplot as plt

In [3]: import numpy as np

In [4]: import datetime as dt

In [5]: fig = plt.figure()

In [6]: ax2 = fig.add_subplot(212)

In [7]: date2_1 = dt.datetime(2008, 9, 23)

In [8]: date2_2 = dt.datetime(2008, 10, 3)

In [9]: delta2 = dt.timedelta(days=1)

In [10]: dates2 = mpl.dates.drange(date2_1, date2_2, delta2)

In [11]: y2 = np.random.rand(len(dates2))

In [12]: ax2.plot_date(dates2, y2, linestyle='-');

In [13]: dateFmt = mpl.dates.DateFormatter('%Y-%m-%d')

In [14]: ax2.xaxis.set_major_formatter(dateFmt)

In [15]: daysLoc = mpl.dates.DayLocator()

In [16]: hoursLoc = mpl.dates.HourLocator(interval=6)

In [17]: ax2.xaxis.set_major_locator(daysLoc)

In [18]: ax2.xaxis.set_minor_locator(hoursLoc)

In [19]: fig.autofmt_xdate(bottom=0.18) # adjust for date labels

display

In [20]: fig.subplots_adjust(left=0.18)

In [21]: ax1 = fig.add_subplot(211)

In [22]: date1_1 = dt.datetime(2008, 9, 23)

In [23]: date1_2 = dt.datetime(2009, 2, 16)

In [24]: delta1 = dt.timedelta(days=10)

In [25]: dates1 = mpl.dates.drange(date1_1, date1_2, delta1)

In [26]: y1 = np.random.rand(len(dates1))

In [27]: ax1.plot_date(dates1, y1, linestyle='-');

In [28]: monthsLoc = mpl.dates.MonthLocator()

In [29]: weeksLoc = mpl.dates.WeekdayLocator()

In [30]: ax1.xaxis.set_major_locator(monthsLoc)

In [31]: ax1.xaxis.set_minor_locator(weeksLoc)

In [32]: monthsFmt = mpl.dates.DateFormatter('%b')

In [33]: ax1.xaxis.set_major_formatter(monthsFmt)

In [34]: plt.show()

The result of executing the previous code snippet is as shown:

We drew the subplots in reverse order to avoid some minor overlapping problems.

*fig.autofmt_xdate()* is used to nicely format date tick labels. In particular, this function rotates the labels (by using *rotation* keyword argument, with a default value of 30°) and gives them more room (by using *bottom* keyword argument, with a default value of 0.2).

We can achieve the same result, at least for the additional spacing, with:

fig = plt.figure()

fig.subplots_adjust(bottom=0.2)

ax = fig.add_subplot(111)

This can also be done by creating the *Axes* instance directly with:

ax = fig.add_axes([left, bottom, width, height])

while specifying the explicit dimensions.

The *subplots_adjust()* function allows us to control the spacing around the subplots by using the following keyword arguments:

*bottom, top, left, right*: Controls the spacing at the bottom, top, left, and right of the subplot(s)*wspace, hspace*: Controls the horizontal and vertical spacing between subplots

We can also control the spacing by using these parameters in the Matplotlib configuration file:

figure.subplot.<position> = <value>

## Custom formatters and locators

Even if it's not strictly related to date plotting, tick formatters allow for custom formatters too:

...

import matplotlib.ticker as ticker

...

def format_func(x, pos):

return <a transformation on x>

...

formatter = ticker.FuncFormatter(format_func)

ax.xaxis.set_major_formatter(formatter)

...

The function *format_func* will be called for each label to draw, passing its value and position on the axis. With those two arguments, we can apply a transformation (for example, divide x by 10) and then return a value that will be used to actually draw the tick label.

Here's a general note on *NullLocator*: it can be used to remove axis ticks by simply issuing:

ax.xaxis.set_major_locator(matplotlib.ticker.NullLocator())

# Text properties, fonts, and LaTeX

Matplotlib has excellent text support, including mathematical expressions, TrueType font support for raster and vector outputs, newline separated text with arbitrary rotations, and Unicode.

We have total control over every text property (font size, font weight, text location, color, and so on) with sensible defaults set in the *rc* configuration file. Specifically for those interested in mathematical or scientific figures, Matplotlib implements a large number of TeX math symbols and commands to support mathematical expressions anywhere in the figure.

We already saw some text functions, but the following list contains all the functions which can be used to insert text with the *pyplot* interface, presented along with the corresponding API method and a description:

Pyplot function |
API method |
Description |

text() |
mpl.axes.Axes.text() |
Adds text at an arbitrary location to the Axes |

xlabel() |
mpl.axes.Axes.set_xlabel() |
Adds an axis label to the X-axis |

ylabel() |
mpl.axes.Axes.set_ylabel() |
Adds an axis label to the Y-axis |

title() |
mpl.axes.Axes.set_title() |
Adds a title to the Axes |

figtext() |
mpl.figure.Figure.text() |
Adds text at an arbitrary location to the Figure |

suptitle() |
mpl.figure.Figure.suptitle() |
Adds a centered title to the Figure |

annotate() |
mpl.axes.Axes.annotate() |
Adds an annotation with an optional arrow to the Axes |

All of these commands return a *matplotlib.text.Text* instance. We can customize the text properties by passing keyword arguments to the functions or by using *matplotlib.artist.setp()*:

t = plt.xlabel('some text', fontsize=16, color='green')

We can do it as:

t = plt.xlabel('some text')

plt.setp(t, fontsize=16, color='green')

Handling objects allows for several new possibilities; such as setting the same property to all the objects in a specific group. Matplotlib has several convenience functions to return the objects of a plot. Let's take the example of the tick labels:

ax.get_xticklabels()

This line of code returns a sequence of object instances (the labels for the X-axis ticks) that we can tune:

for t in ax.get_xticklabels():

t.set_fontsize(5.)

or else, still using *setp()*:

setp(ax.get_xticklabels(), fontsize=5.)

It can take a sequence of objects, and apply the same property to all of them.

To recap, all of the properties such as *color, fontsize, position, rotation,* and so on, can be set either:

- At function call using keyword arguments
- Using
*setp()*referencing the*Text*instance - Using the modification function

## Fonts

Where there is text, there are also fonts to draw it. Matplotlib allows for several font customizations.

The most complete documentation on this is currently available in the Matplotlib configuration file, */etc/matplotlibrc*. We are now reporting that information here.

There are six font properties available for modification.

Property name |
Values and description |

font.family |
It has five values: - serif (example, Times)
- sans-serif (example, Helvetica)
- cursive (example, Zapf-Chancery)
- fantasy (example, Western)
- monospace (example, Courier)
Each of these font families has a default list of font names in decreasing order of priority associated with them (next table). In addition to these generic font names, font.family may also be an explicit name of a font available on the system. |

font.style |
Three values: normal (or roman), italic, or oblique. The oblique style will be used for italic, if it is not present. |

font.variant |
Two values: normal or small-caps. For TrueType fonts, which are scalable fonts, small-caps is equivalent to using a font size of smaller, or about 83% of the current font size. |

font.weight |
Effectively has 13 values-normal, bold, bolder, lighter, 100, 200, 300, ..., 900. normal is the same as 400, and bold is 700. bolder and lighter are relative values with respect to the current weight. |

font.stretch |
11 values-ultra-condensed, extra-condensed, condensed, semi-condensed, normal, semi-expanded, expanded, extra-expanded, ultra-expanded, wider, and narrower. This property is not currently implemented. It works if the font supports it, but only few do. |

font.size |
The default font size for text, given in points. 12pt is the standard value. |

The list of font names, selected by *font.family*, in the priority search order is:

Property name |
Font list |

font.serif |
Bitstream Vera Serif, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif |

font.sans-serif |
Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif |

font.cursive |
Apple Chancery, Textile, Zapf Chancery, Sand, cursive |

font.fantasy |
Comic Sans MS, Chicago, Charcoal, Impact, Western, fantasy |

font.monospace |
Bitstream Vera Sans Mono, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace |

The first valid and available (that is, installed) font in each family is the one that will be loaded. If the fonts are not specified, the Bitstream Vera Sans fonts are used by default.

As usual, we can set these values in the configuration file or in the code accessing the *rcParams* dictionary provided by Matplotlib.

# Using Latex formatting

If you have ever used LaTeX, you know how powerful it can be at rendering mathematical expressions. Given its root in the scientific field, Matplotlib allows us to embed TeX text in its plots. There are two ways available

- Mathtext
- Using an external TeX renderer

## Mathtext

Matplotlib includes an internal engine to render TeX expression, mathtext. The mathtext module provides TeX style mathematical expressions using FreeType 2 and the default font from TeX, Computer Modern.

As Matplotlib ships with everything it needs to make mathtext work, there is no requirement to install a TeX system (or any other external program) on the computer for it to be used.

The markup character used to signal the start and the end of a *mathtext* string is *$*;encapsulating a string inside a pair of *$* characters will trigger the *mathtext* engine to render it as a TeX mathematical expression.

We should use raw strings (preceding the quotes with an r character) and surround the mathtext with dollar signs ($), as in TeX. The use of raw strings is important so that backslashes (used for TeX symbols escaping) are not mangled by the Python interpreter.

Matplotlib accepts TeX equations in any text expressions, so regular text and *mathtext* can be interleaved within the same string.

An example of the kind of text we can generate is:

In [1]: import matplotlib.pyplot as plt

In [2]: fig = plt.figure()

In [3]: ax= fig.add_subplot(111)

In [4]: ax.set_xlim([1, 6]);

In [5]: ax.set_ylim([1, 9]);

In [6]: ax.text(2, 8, r"$ mu alpha tau pi lambda omega tau

lambda iota beta $");

In [7]: ax.text(2, 6, r"$ lim_{x rightarrow 0} frac{1}{x} $");

In [8]: ax.text(2, 4, r"$ a leq b leq c Rightarrow a

leq c$");

In [9]: ax.text(2, 2, r"$ sum_{i=1}^{infty} x_i^2$");

In [10]: ax.text(4, 8, r"$ sin(0) = cos(frac{pi}{2})$");

In [11]: ax.text(4, 6, r"$ sqrt[3]{x} = sqrt{y}$");

In [12]: ax.text(4, 4, r"$ neg (a wedge b) Leftrightarrow neg a

vee neg b$");

In [13]: ax.text(4, 2, r"$ int_a^b f(x)dx$");

In [14]: plt.show()

The preceding code snippet results in the following:

The escape sequence is almost the same as that of LaTeX; consult the Matplotlib and/or LaTeX online documentation to see the full list.

## External TeX renderer

Matplotlib also allows to manage all the text layout using an external LaTeX engine. This is limited to *Agg*, *PS*, and *PDF* backends and is commonly needed when we want to create graphs to be embedded into LaTeX documents, where rendering uniformity is really pleasant.

To activate an external TeX rendering engine for text strings, we need to set this parameter in the configuration file:

text.usetex : True

or use the *rcParams* dictionary:

rcParams['text.usetex'] = True

This mode requires LaTeX, *dvipng*, and *Ghostscript* to be correctly installed and working. Also note that usually external TeX management is slower than Matplotlib's *mathtext* and that all the texts in the figure are drawn using the external renderer, not only the mathematical ones.

There are several optimizations and configurations that you will need to do when dealing with TeX, postscripts and so, we invite you to consult an official documentation for additional information.

When the previous example is executed and rendered using an external LaTeX engine, the result is:

Also, look at how the tick label's text is rendered in the same font as the text in the figure, as in this real world example:

In [1]: import matplotlib as mpl

In [2]: import matplotlib.pyplot as plt

In [3]: mpl.rcParams['text.usetex'] = True

In [4]: import numpy as np

In [5]: x = np.arange(0., 5., .01)

In [6]: y = [np.sin(2*np.pi*xx) * np.exp(-xx) for xx in x]

In [7]: plt.plot(x, y, label=r'$sin(2pi x)exp(-x)$');

In [8]: plt.plot(x, np.exp(-x), label=r'$exp(-x)$');

In [9]: plt.plot(x, -np.exp(-x), label=r'$-exp(-x)$');

In [10]: plt.title(r'$sin(2pi x)exp(-x)$ with the two asymptotes

$pmexp(-x)$');

In [11]: plt.legend();

In [12]: plt.show()

The preceding code snippet results in a sinusoidal line contained in two asymptotes:

# Contour plots and image plotting

We will now discuss the features Matplotlib provides to create contour plots and display images.

## Contour plots

**Contour lines** (also known as level lines or isolines) for a function of two variables are curves where the function has constant values. Mathematically speaking, it's a graph image that shows:

f(x, y) = L

with L constant. Contour lines often have specific names beginning with iso- (from Greek, meaning equal) according to the nature of the variables being mapped.

There are a lot of applications of contour lines in several fields such as meteorology (for temperature, pressure, rain precipitation, wind speed), geography, oceanography, cartography (elevation and depth), magnetism, engineering, social sciences, and so on.

The absolutely most common examples of contour lines are those seen in weather forecasts, where lines of isobars (where the atmospheric pressure is constant) are drawn over the terrain maps. In particular, those are contour maps because contour lines are drawn above a map in order to add specific information to it.

The density of the lines indicates the slope of the function. The gradient of the function is always perpendicular to the contour lines, and when the lines are close together, the length of the gradient is large and the variation is steep.

Here is a contour plot from a random number matrix:

In [1]: import matplotlib.pyplot as plt

In [2]: import numpy as np

In [3]: matr = np.random.rand(21, 31)

In [4]: cs = plt.contour(matr)

In [5]: plt.show()

where the contour lines are colored from blue to red in a scale from the lower to the higher values.

The *contour()* function draws contour lines, taking a 2D array as input (a list of list notations). In this case, it's a matrix of 21x31 random elements. The number of level lines to draw is chosen automatically, but we can also specify it as an additional parameter, *N*:

contour(matrix, N)

The previous line of code tells us to draw N automatically chosen level lines.

There is also a similar function that draws a filled contours plot, *contourf()*

In [6]: csf = plt.contourf(matr)

In [7]: plt.colorbar();

In [8]: plt.show()

*contourf()* fills the spaces between the contours lines with the same color progression used in the contour() plot: dark blue is used for low value areas, while red is used for high value areas, fading in between for the intermediate values.

Contour colors can be changed using a **colormap**, a set of colors used as a lookup table by Matplotlib when it needs to select more colors, specified using the *cmap* keyword argument.

We also added a *colorbar()* call to draw a color bar next to the plot to identify the ranges the colors are assigned to. In this case, there are a few bins where the values can fall because *rand()* NumPy function returns values between 0 and 1.

Labeling the level lines is important in order to provide information about what levels were chosen for display; *clabel()* does this by taking as input a contour instance, as returned by a previous *contour()* call:

In [1]: import matplotlib.pyplot as plt

In [2]: import numpy as np

In [3]: x = np.arange(-2, 2, 0.01)

In [4]: y = np.arange(-2, 2, 0.01)

In [5]: X, Y = np.meshgrid(x, y)

In [6]: ellipses = X*X/9 + Y*Y/4 - 1

In [7]: cs = plt.contour(ellipses)

In [8]: plt.clabel(cs);

In [9]: plt.show()

Here, we draw several ellipses and then call *clabel()* to display the selected levels. We used the NumPy *meshgrid()* function to get the coordinate matrices, X and Y, from the two coordinate vectors, x and y. The output of this code is shown in the following image:

# Image plotting

Matplotlib also has basic image plotting capabilities provided by the functions: *imread()* and *imshow()*.

*imread()* reads an image from a file and converts it into a NumPy array; *imshow()* takes an array as input and displays it on the screen:

import matplotlib.pyplot as plt

f = plt.imread('/path/to/image/file.ext')

plt.imshow(f)

Matplotlib can only read PNG files natively, but if the **Python Imaging Library** (usually known as **PIL**) is installed, then this library will be used to read the image and return an array (if possible).

Note that when working with images, the origin is in the upper-left corner. This can be changed using the *origin* keyword argument, *origin='lower'* (which is the only other acceptable value, in addition to the default *'upper'*), which will set the origin on the lower-left corner. We can also set it as a configuration parameter, and the key name is *image.origin*.

Just note that once the image is an array, we can do all the transformations we like. *imshow()* can plot any 2D sets of data and not just the ones read from image files. For example, let's take the ellipses code we used for contour plot and see what *imshow()* draws.

In [1]: import matplotlib.pyplot as plt

In [2]: import numpy as np

In [3]: x = np.arange(-2, 2, 0.01)

In [4]: y = np.arange(-2, 2, 0.01)

In [5]: X, Y = np.meshgrid(x, y)

In [6]: ellipses = X*X/9 + Y*Y/4 - 1

In [7]: plt.imshow(ellipses);

In [8]: plt.colorbar();

In [9]: plt.show()

This example creates a full spectrum of colors starting from deep blue of the image center, slightly turning into green, yellow, and red near the image corners:

# Summary

We've come a long way even in this article, so let's recap the arguments we touched:

- Object-oriented interfaces and the relationship with
*pyplot*and*pylab* - How to draw subplots and multiple figures
- How to manipulate axes so that they can be shared between subplots or can be shared between two plots
- Logarithmic scaled axes
- How to plot dates, and tune tick formatters and locators
- Text properties, fonts, and LaTeX typewriting both with the internal engine
*mathtext*and with an external renderer - Contour plots and image plotting

With the information that we have gathered so far, we are ready to extract Matplotlib from a pure script or interactive usage inside the Python interpreter and learn how we can embed this library in a GUI Python application.

[ 1 | **2** ]

**If you have read this article you may be interested to view :**

- Advanced Matplotlib: Part 1
- Plotting data using Matplotlib: Part 1
- Plotting data using Matplotlib: Part 2
- Plotting Geographical Data using Basemap

## About the Author :

## Sandro Tosi

Sandro Tosi is a Debian Developer, Open Source evangelist, and Python enthusiast. After completing a B.Sc. in Computer Science from the University of Firenze, he worked as a consultant for an energy multinational as System Analyst and EAI Architect, and now works as System Engineer for one of the biggest and most innovative Italian Internet companies.

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