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In this tutorial, we will make an interactive Matplotlib Line Graph showing a city's temperatures for the next seven days. So we will learn aboutmatplotlib.widgets, and aboutrequests because we use theopen-meteo.com API for our data. We're also using thejson moduleso we can read the response data.

To get started, let's import the necessary libraries:

$ pip install requests matplotlib seaborn

We'll be usingseaborn for automatic styling. Open up a new Python file to follow along, and import the following:

import matplotlib.pyplot as pltfrom matplotlib.widgets import RadioButtonsimport seabornseaborn.set_style("darkgrid")import requestsimport json

Locations

After the imports, we define some locations from which the user can choose. The API we will use only acceptsWG84 coordinates and not place names.

So we look up some places and their coordinates and insert them into a dictionary where the key is the place's name, and the value is a two-item list with the latitude and longitude. You can extend this Python dictionary if you want:

# Define some Locations to choose from.# Latitude and Longitudelocations = {    'Schaffhausen': ['47.7', '8.6'],    'Sydney': ['-33.86', '151.20'],    'Kyiv': ['50.4422', '30.5367'],    'Constantine': ['36.368258', '6.560254'],    'Yakutsk': ['62.0', '129.7'],}

You can alsouse geocoding to automatically get the latitude and longitude from a city name;this tutorial should help.

Matplotlib Setup

Now let's set up matplotlib. For this, we call thesubplots() function and save the two items it returns in two variables. We usually use this function if we want multiple axes. Thefig variable holds info about the whole thing, and theax contains information about one plot. Then we define a variablep that will store the plot info of our single plot:

# Setting Up Matplotlib, using the OOP Approachfig, ax = plt.subplots()# the plot is created with the first locationp = None

Get Temperatures Function

In the below function, we use the requests library to get the data from open-meteo.com free API, and then parse it using the json module and return the times and temperatures:

# make a function to get the temperatures of a given locationdef getTemperatures(location):    # get the lat and long of the location    lat, lon = locations[location]    req = requests.get(f'https://api.open-meteo.com/v1/forecast?latitude={lat}&longitude={lon}&hourly=temperature_2m')    req = json.loads(req.text)    # get the tempratures    temperatures = req['hourly']['temperature_2m']    # get the times    times = req['hourly']['time']    return times, temperatures

We can request that URL with theget() method from requests. We parse the JSON data from there to a valid Python dictionary with the json.loads() function. Here you can see what such a request could look like.

Downloading & Storing the Data

Let's make a Python dictionary that stores the location and its corresponding times and temperatures:

# Make a dictionary with the locations as keys and the getTemperatures() function as valueslocation2data = {}for location in locations:    location2data[location] = getTemperatures(location)

Change Location Function

Now let's get to the function that will change the location we are currently showing. The buttons will call it, and this connection will pass the name of the button to the function, and this name will be the new desired location. We also get the globalp variable we defined earlier:

def changeLocation(newLocation):    global p

Next, we simply uselocation2data to get the times and temperatures of the passed location:

    # get the data of the location from the dictionary    times, temperatures = location2data[newLocation]

After getting the data, we check whetherp is notNone and if that's the case, we set the data to our newly found temperatures value and refresh the plot:

    if p:        p.set_ydata(temperatures)        # reflect changes in the plot        plt.draw()

Ifp is not defined, we have to make it first. To do this, we use theplot() method onax. We give it thetimes list as an x-axis, and we make a list comprehension for the y-axis. We also set a line style withls, a linewidth withlw.

This function will make the plot and return a list of each line. We get the first and only item from there and save it top:

    else:        # Make a Plot and save the first object to a variable        # p will be a Line2D object which can be changed at a later time        p = ax.plot(times, temperatures, ls=':', lw=3)[0]

Now we also edit the sticks because they won't be nice with 168 data points. For this, we start by making a list where seven numbers of 24 (hours) apart from each other are chosen:

        # set the x-axis to the times        xRange = list(range(0, 168, 24)) + [168]        ax.set_xticks(xRange)

Setting the y axis for the temperatures as well:

        # set the y-axis to the temperatures        yRange = list(range(-20, 55, 5))        ax.set_yticks(yRange)

Now last but not least, we also set the label color and rotation for these tick labels with thetick_params method.

        plt.tick_params(axis="both", which='both', labelrotation=-10) # rotate the labels

In all cases, we update the title to reflect the current location. We can do this on the fly. And after the function is defined, we call it with a starting location, so the plot is not empty:

    # set the title    ax.set_title('Temperatures in ' + newLocation)# Call the change Location function for the first timechangeLocation('Schaffhausen')

Setup Radio Buttons

Now we make some radio buttons. To do this, we simply call theRadioButtons() class, and we provide it with a position that is represented by the axes, and we give the location names as a labels argument.

# Making the Radio Buttonsbuttons = RadioButtons(    ax=plt.axes([0.1, 0.1, 0.2, 0.2]),    labels=locations.keys())

Then we need to also connect the clicked event of these buttons to our function.

# Connect clicked event to function.buttons.on_clicked(changeLocation)

We also adjust the position of the plot, so there is room for the radio button panel.

# adjust the plot sizeplt.subplots_adjust(left=0.1, bottom=0.40)

Labels and showing the plot

In the end, before we show the plot, we also give the x and y axis a label and color them.

# Label the Plotax.set_xlabel('Times [Next Seven Days]')ax.xaxis.label.set_color(labelColor)ax.set_ylabel('Temperatures [Celcius]')ax.yaxis.label.set_color(labelColor)plt.show()

Showcase

Below you see our little program in action. It's getting hot in Constantine!

Conclusion

Excellent! You have successfully learned to:

• Create an interactive plot using matplotlib.
• Make simple HTTP GET requests to open-meteo.com API.

See how you can add more features to this program, such as an Entry field for latitude and longitude or automatically getting the coordinates from the city name;this tutorial should help you.

You can always get the complete code for this tutorialhere.

Learn also:Zipf's Word Frequency Plot with Python.

Happy coding ♥

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