Python Google Map Introduction With Gmaps – In this article we are going to talk about creating Google Map in Python using Gmaps.
gmaps is a Jupyter plugin for embedding Google maps in Jupyter notebooks. It is designed to help visualize and interact with geographical data.
Also you can check the documentation of gmaps – Read Docs
1: The easiest way to install gmaps is with conda:
$ conda install -c conda-forge gmaps
2: Installing jupyter-gmaps with pip
Make sure that you have enabled ipywidgets widgets extensions:
$ jupyter nbextension enable –py –sys-prefix widgetsnbextension
You can then install gmaps with:
$ pip install gmaps
Then tell Jupyter to load the extension with:
$ jupyter nbextension enable –py –sys-prefix gmaps
Most operations on Google Maps require that you tell Google who you are. To authenticate with Google Maps, you need to go Google Cloud Console
and create a new project and after that create your API Key from credentials. The API key is a string that starts with the letters AI.
So now lets get started
gmaps is a plugin for Jupyter for embedding Google Maps in your notebooks. It is designed as a data visualization tool. To demonstrate gmaps, let’s plot the earthquake dataset, included in the package:
Run your jupyter notebook and add this code
import gmaps import gmaps.datasets gmaps.configure(api_key='AI...') earthquake_df = gmaps.datasets.load_dataset_as_df('earthquakes') earthquake_df.head()
After run you will see this result
The earthquake data has three columns: a latitude and longitude indicating the earthquake’s epicentre and a weight denoting the magnitude of the earthquake at that point. Let’s plot the earthquakes on a Google map:
locations = earthquake_df[['latitude', 'longitude']] weights = earthquake_df['magnitude'] fig = gmaps.figure() fig.add_layer(gmaps.heatmap_layer(locations, weights=weights)) fig
This will be the result
This gives you a fully-ﬂedged Google map. You can zoom in and out, switch to satellite view and even to street view if you really want. The heatmap adjusts as you zoom in and out.
gmaps is built around the idea of adding layers to a base map. After you’ve authenticated with Google maps, you start by creating a ﬁgure, which contains a base map:
import gmaps gmaps.configure(api_key='AI...') fig = gmaps.figure() fig
Run this code this will be the result
You then add layers on top of the base map. For instance, to add a heatmap layer:
import gmaps import gmaps.datasets gmaps.configure(api_key='AIz') fig = gmaps.figure(map_type='SATELLITE') # generate some (latitude, longitude) pairs locations = [(51.5, 0.1), (51.7, 0.2), (51.4, -0.2), (51.49, 0.1)] heatmap_layer = gmaps.heatmap_layer(locations) fig.add_layer(heatmap_layer) fig
And this will be the result
The locations array can either be a list of tuples, as in the example above, a numpy array of shape $N times 2$ or a dataframe with two columns.Most attributes on the base map and the layers can be set through named arguments in the constructor or as instance attributes once the instance is created.
Your ﬁrst action with gmaps will usually be to build a base map:
import gmaps gmaps.configure(api_key='AIz') gmaps.figure(center=new_york_coordinates, zoom_level=12)
Run the code this will be the result
If you do not set the map zoom and center, the viewport will automatically focus on the data as you add it to the map. Google maps offers three different base map types. Choose the base map type by setting the map_type parameter:
import gmaps gmaps.configure(api_key='AIz') new_york_coordinates = (40.75, -74.00) gmaps.figure(center=new_york_coordinates, zoom_level=12, map_type='HYBRID')
Run the code this will be the result
Also this is another map type
import gmaps gmaps.configure(api_key='AIz') new_york_coordinates = (40.75, -74.00) gmaps.figure(center=new_york_coordinates, zoom_level=12, map_type='TERRAIN')
So this will be the result
There are four map types available:
Also you can watch the complete video for this article
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners
In this post, we will show how we can use Python to get data from Google Trends. Let’s have a look at the top trending searches for today in the US (14th of March, 2020). As we can see, the top search is about Coronavirus tips with more than 2M searches, and at the 7th position is Rick Pitino with around 100K searches.
We will use the pytrends package which is an unofficial API for Google Trends which allows a simple interface for automating downloading of reports from Google Trends. The main feature is to allow the script to login to Google on your behalf to enable a higher rate limit. At this point, I want to mention that I couldn’t use this package and I created a new anaconda environment installing the pandas 0.25 version.
You can install the pytrends package with pip:
pip install pytrends
#google-trends #how-to-use-google-trend #google #google-api #python
Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc…
You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like init, call, str etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).
Now there are a number of such special methods, which you might have come across too, in Python. We will just be taking an example of a few of them to understand how they work and how we can use them.
class AnyClass: def __init__(): print("Init called on its own") obj = AnyClass()
The first example is _init, _and as the name suggests, it is used for initializing objects. Init method is called on its own, ie. whenever an object is created for the class, the init method is called on its own.
The output of the above code will be given below. Note how we did not call the init method and it got invoked as we created an object for class AnyClass.
Init called on its own
Let’s move to some other example, add gives us the ability to access the built in syntax feature of the character +. Let’s see how,
class AnyClass: def __init__(self, var): self.some_var = var def __add__(self, other_obj): print("Calling the add method") return self.some_var + other_obj.some_var obj1 = AnyClass(5) obj2 = AnyClass(6) obj1 + obj2
#python3 #python #python-programming #python-web-development #python-tutorials #python-top-story #python-tips #learn-python
Streamlit is a great library that helps us create python apps with minimum effort. Not only it’s easy but its UI is beautiful and seems very professional. Our Idea for this post is to create an Instagram Dashboard having some descriptive statistics about a user’s profile like most frequent hashtags, top liked posts, engagement rate, etc. Having said that, we need an application that takes as input a user name and will scrape its information from Instagram to return the final Dashboard.
#python #instagram #python #python app #streamlit #create an instagram profile analyzer app using python