Given a domain name like "Google.com", the python script below will return its server IP address like this "216.58.223.238".
import socket
import pandas as pd
websites_df = pd.read_html('https://en.wikipedia.org/wiki/List_of_most_visited_websites')
for d in websites_df[0]['Domain Name']:
IP_addres = socket.gethostbyname(d)
print(d, ' - ', IP_addres)
That is it!
In programming there is always more than one way to solve the same problem. This variations depends on individual skills and way of thinking.
In this article I will demonstrate different ways to solve the same problem using python scripting.
The problem:
Python program to map two lists into a dictionary.
countries = ['Nigeria', 'Germany', 'Italy', 'USA', 'Japan', 'Ghana']
score = [39, 23, 12, 67, 45, 11]The Solution:
1) Using zip() function
# Using zip() function
countries = ['Nigeria', 'Germany', 'Italy', 'USA', 'Japan', 'Ghana']
score = [39, 23, 12, 67, 45, 11]
data = dict(zip(countries, score))
print(data)2) Using Dictionary Comprehension
# Using Dictionary Comprehension
countries = ['Nigeria', 'Germany', 'Italy', 'USA', 'Japan', 'Ghana']
score = [39, 23, 12, 67, 45, 11]
data = {key:value for key, value in zip(countries, score)}
print(data)3) Using For loop
# Using For loop
countries = ['Nigeria', 'Germany', 'Italy', 'USA', 'Japan', 'Ghana']
score = [39, 23, 12, 67, 45, 11]
countries_score = zip(countries, score)
data_dict = {}
for key, value in countries_score:
if key in data_dict:
# handling duplicate keys
pass
else:
data_dict[key] = value
print(data_dict)4) Using For and Range
# Using For and Range
countries = ['Nigeria', 'Germany', 'Italy', 'USA', 'Japan', 'Ghana']
score = [39, 23, 12, 67, 45, 11]
data = {countries[i]: score[i] for i in range(len(countries))}
print(data)All the four solutions above will give same output result as seen below:-
{'Nigeria': 39, 'Germany': 23, 'Italy': 12, 'USA': 67, 'Japan': 45, 'Ghana': 11}That is it!
QuickGrid is a free software for making contour maps or 3D mesh using XYZ dataset. It is a good free alternative to Surfer.
Download and in the QuickGrid lets see how quick it is to generate a contour map.
First we have to prepare our dataset like this:-
The first column is X (Easting or Longitude), second column is Y (Northing or Latitude) and the last column is Z (Height or Altitude). Note that there is not column name for the data and is saved as a .CSV file.
To load in the dataset, go to: File >> Input scattered data points >> Input metric data points
There is a lot more you can do with QuickGrid, however this is a good start for you to explore more on the software.
Thank you for following.
In this post, I will show how to overlay an image map on the leafletjs web map interface.
The image map is a seen below:-
We need to get the coordinates of the opposite corners in this format "[[y1, x1], [y2, x2]]".
To overlay rhe image, the code is thus:-
<!DOCTYPE html>
<html>
<head>
<title>Leaflet Map...</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<link rel="stylesheet" href="https://unpkg.com/leaflet@1.7.1/dist/leaflet.css" integrity="sha512-xodZBNTC5n17Xt2atTPuE1HxjVMSvLVW9ocqUKLsCC5CXdbqCmblAshOMAS6/keqq/sMZMZ19scR4PsZChSR7A==" crossorigin=""/>
<script src="https://unpkg.com/leaflet@1.7.1/dist/leaflet.js" integrity="sha512-XQoYMqMTK8LvdxXYG3nZ448hOEQiglfqkJs1NOQV44cWnUrBc8PkAOcXy20w0vlaXaVUearIOBhiXZ5V3ynxwA==" crossorigin=""></script>
<!-- JQuery -->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<style type="text/css">
#slider{
position: fixed;
z-index: 900;
border: 2px solid gray;
top: 100px;
left: 20px;
}
</style>
</head>
<body>
<div id="slider">
<h4>Image Opacity: <span id="image-opacity">0.5<span/> </h4>
<input type='range' id='sldOpacity' min='0' max="1" step='0.1' value='0.5' >
</div>
<div id="mapid" style="width: 100%; height: 600px;"></div>
<script>
// Create the map obj...
var mymap = L.map('mapid', {minZoom: 2, maxZoom: 20})
.setView([8.54090, 7.71428], 13);
// Set a default base map to...
L.tileLayer('http://{s}.google.com/vt/lyrs=m&x={x}&y={y}&z={z}',{
// minZoom: 0,
maxZoom: 18,
subdomains:['mt0','mt1','mt2','mt3']
}).addTo(mymap);
var imageUrl = "Group_Assignment.jpg",
imageBounds = [ [8.58061, 7.68495], [8.50206, 7.75181] ];
var Old_Imge = L.imageOverlay(imageUrl, imageBounds, {
opacity:0.4,
}).addTo(mymap);
Old_Imge.bringToFront();
var overlayMaps = {
'Old Image' : Old_Imge
};
var baseLayers = {
// Basemaps go here...
};
// Adding baseMaps and overlayMaps
L.control.layers(baseLayers, overlayMaps, {collapsed: false}).addTo(mymap);
$(document).ready(function(){
// jQuery methods go here...
$('#sldOpacity').on('change', function () {
$('#image-opacity').html(this.value);
Old_Imge.setOpacity(this.value);
});
}); // end Jquery doc ready.
</script>
</body>
</html>Note that the code above include adding the image to layer control and a slider to give some controls on the overlaid image. See live demo below:-
That is it!
The Nigerian senior national football team (super eagle) has had several coaches from 1949 till date. Lets prepare a data I found online about these coaches for use in any GIS platform.
The dataset for this exercise was collected from this hash tag: #Born2RichSports #Deliveringthebestinsports.
We will use python to wrangle this data into a GIS friendly format. Lets get started...
See all Super Eagles coach list from 1949 till date
------------------------------------
England: Jack Finch (1949)
Nigeria: Daniel Anyiam (1954–1956)
England: Les Courtier (1956–1960)
Israel: Moshe “Jerry” Beit haLevi (1960–1961)
Hungary: George Vardar (1961–1963)
England: Joey Blackwell (1963–1964)
Nigeria: Daniel Anyiam (1964–1965)
Hungary: József Ember (1965–1968)
Spain: Sabino Barinaga (1968–1969)
Nigeria: Peter ‘Eto’ Amaechina (1969–1970)
West Germany: Karl-Heinz Marotzke (1970–1971)
Brazil: Jorge Penna (1972–1973)
West Germany: Karl-Heinz Marotzke (1974)
Socialist Federal Republic of Yugoslavia: Tihomir Jelisavčić (1974–1978)
Brazil: Otto Glória (1979–1982)
West Germany: Gottlieb Göller (1981)
Nigeria: Festus Onigbinde (1983–1984)
Nigeria: Chris Udemezue (1984–1986)
Nigeria: Patrick Ekeji (1985)
Nigeria: Paul Hamilton (1987–1989)
West Germany: Manfred Höner (fr) (1988–1989)
Netherlands: Clemens Westerhof (1989–1994) as Technical Adviser
Nigeria: Shaibu Amodu (1994–1995)
Netherlands: Jo Bonfrere (1995–1996)
Nigeria: Shaibu Amodu (1996–1997)
France: Philippe Troussier (1997)
Nigeria: Monday Sinclair (1997–1998)
Federal Republic of Yugoslavia: Bora Milutinović (1998)
Netherlands: Thijs Libregts (1999)
Netherlands: Jo Bonfrere (1999–2001)
Nigeria: Shaibu Amodu (2001–2002)
Nigeria: Festus Onigbinde (2002)
Nigeria: Christian Chukwu (2002–2005)
Nigeria: Augustine Eguavoen (2005–2007)
Germany: Berti Vogts (2007–2008)
Nigeria: James Peters (2008)
Nigeria: Shaibu Amodu (2008–2010)
Sweden: Lars Lagerbäck (2010)
Nigeria: Augustine Eguavoen (2010)
Nigeria: Samson Siasia (2010–2011)
Nigeria: Stephen Keshi (2011–2014)
Nigeria: Shaibu Amodu (2014)
Nigeria: Stephen Keshi (2014)
Nigeria: Daniel Amokachi (2014–2015)
Nigeria: Stephen Keshi (2015)
Nigeria: Sunday Oliseh (2015-2016)
Germany: Gernot Rohr (2016–present)
#Born2RichSports #Deliveringthebestinsports
COPIEDEach row consist of the coach's country, coach's name and the year/period he severed. We need to separate each detail into its own column (that is three columns in this case).
There are several ways to prepare this data, here I saved the text above in a text file to read it into python object like this...
Then read each row/line into a list item for pandas dataframe as seen below...
with open(r"C:\Users\Yusuf_08039508010\Desktop\SuperEagle Coaches.txt", encoding='utf-8') as f:
data = f.read()
coaches_list = data.split('\n')
print(coaches_list)
Now read the list into a dataframe. Next we can split the entries into separate columns for use in a GIS software.
coaches_df = pd.DataFrame(coaches_list, columns=['Coaches'])
coaches_df
coaches_df['Country'] = coaches_df['Coaches'].apply( lambda x: x.split(': ')[0] )
coaches_df['Coach Name'] = coaches_df['Coaches'].apply( lambda x: x.split(': ')[1].split(' (')[0] )
coaches_df['Period'] = coaches_df['Coaches'].apply( lambda x: x.split(': ')[1].split(' (')[1].replace(')', '') )
coaches_df
For example, a quick look at the country column we see that the coaches came from 13 unique countries.
That is it!
LeafletJS web map supports not just open source basemaps but also other preparatory basemaps such as Google maps and ESRI maps.
In this post, we shall see how to add varying flavors of Google basemaps.
The code below was inspired by this stackoverflow question.
<!DOCTYPE html>
<html>
<head>
<title>Leaflet Map...</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<!-- <link rel="shortcut icon" type="image/x-icon" href="docs/images/favicon.ico" /> -->
<link rel="stylesheet" href="https://unpkg.com/leaflet@1.7.1/dist/leaflet.css" integrity="sha512-xodZBNTC5n17Xt2atTPuE1HxjVMSvLVW9ocqUKLsCC5CXdbqCmblAshOMAS6/keqq/sMZMZ19scR4PsZChSR7A==" crossorigin=""/>
<script src="https://unpkg.com/leaflet@1.7.1/dist/leaflet.js" integrity="sha512-XQoYMqMTK8LvdxXYG3nZ448hOEQiglfqkJs1NOQV44cWnUrBc8PkAOcXy20w0vlaXaVUearIOBhiXZ5V3ynxwA==" crossorigin=""></script>
</head>
<body>
<div id="mapid" style="width: 100%; height: 600px;"></div>
<script>
// Create the map obj...
var mymap = L.map('mapid', {minZoom: 2, maxZoom: 20})
.setView([0, 0], 2);
// Restrict panning to this bounds
var southWest = L.latLng(-90, -180),
northEast = L.latLng(90, 180);
var bounds = L.latLngBounds(southWest, northEast);
mymap.setMaxBounds(bounds);
// Set a default base map to...
L.tileLayer('http://{s}.google.com/vt/lyrs=m&x={x}&y={y}&z={z}',{
// minZoom: 0,
maxZoom: 18,
subdomains:['mt0','mt1','mt2','mt3']
}).addTo(mymap);
// CREATE GOOGLE MAP LAYER
// 1- Streets...
googleStreets = L.tileLayer('http://{s}.google.com/vt/lyrs=m&x={x}&y={y}&z={z}',{
maxZoom: 20,
subdomains:['mt0','mt1','mt2','mt3']
});
// 2- Hybrid...
googleHybrid = L.tileLayer('http://{s}.google.com/vt/lyrs=s,h&x={x}&y={y}&z={z}',{
maxZoom: 20,
subdomains:['mt0','mt1','mt2','mt3']
});
// 3- Satellite...
googleSat = L.tileLayer('http://{s}.google.com/vt/lyrs=s&x={x}&y={y}&z={z}',{
maxZoom: 20,
subdomains:['mt0','mt1','mt2','mt3']
});
// 4- Terrain...
googleTerrain = L.tileLayer('http://{s}.google.com/vt/lyrs=p&x={x}&y={y}&z={z}',{
maxZoom: 20,
subdomains:['mt0','mt1','mt2','mt3']
});
var overlayMaps = {
// Other layers will go here....
};
var baseLayers = {
'Google Street Map':googleStreets,
'Google Hybrid Map':googleHybrid,
'Google Satellite Map':googleSat,
'Google Terrain Map':googleTerrain,
};
// Adding baseMaps and overlayMaps
L.control.layers(baseLayers, overlayMaps, {collapsed: false}).addTo(mymap);
</script>
</body>
</html>That is it!
In this post, I will share three ways to plot lines and add it to layer control. The general concept is that you need to have at least two pairs of coordinates (line starting point and line ending point) to draw a line with the L.polyline() function.
Lets get started...
1) Plot line from given point coordinates
Here we will plot line between two point by simply providing the starting and ending point coordinates of the line.
<!DOCTYPE html>
<html>
<head>
<title>Leaflet Map...</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<!-- <link rel="shortcut icon" type="image/x-icon" href="docs/images/favicon.ico" /> -->
<link rel="stylesheet" href="https://unpkg.com/leaflet@1.7.1/dist/leaflet.css" integrity="sha512-xodZBNTC5n17Xt2atTPuE1HxjVMSvLVW9ocqUKLsCC5CXdbqCmblAshOMAS6/keqq/sMZMZ19scR4PsZChSR7A==" crossorigin=""/>
<script src="https://unpkg.com/leaflet@1.7.1/dist/leaflet.js" integrity="sha512-XQoYMqMTK8LvdxXYG3nZ448hOEQiglfqkJs1NOQV44cWnUrBc8PkAOcXy20w0vlaXaVUearIOBhiXZ5V3ynxwA==" crossorigin=""></script>
</head>
<body>
<!-- Several ways to plot lines and add to layer control: From set of corrdinates and from geoJSON file -->
<div id="mapid" style="width: 100%; height: 600px;"></div>
<script>
// Create the map obj...
var mymap = L.map('mapid', {minZoom: 2, maxZoom: 20})
.setView([0, 0], 2);
// Restrict panning to this bounds
var southWest = L.latLng(-90, -180),
northEast = L.latLng(90, 180);
var bounds = L.latLngBounds(southWest, northEast);
mymap.setMaxBounds(bounds);
// Google Street Map - Set basemap...
L.tileLayer('http://{s}.google.com/vt/lyrs=m&x={x}&y={y}&z={z}',{
// minZoom: 0,
maxZoom: 18,
subdomains:['mt0','mt1','mt2','mt3']
}).addTo(mymap);
// ------------------------------
// 1) Plot line from given point coordinates...
var pointA = new L.LatLng(40.71560515,-74.03714387);
var pointB = new L.LatLng(9.72406588,10.99793816);
var pointC = new L.LatLng(51.51778519,-0.09678893);
var pointList1 = [pointA, pointC];
var pointList2 = [pointB, pointC];
var line_network = L.polyline([pointList1, pointList2], {
color: 'red',
weight: 3,
opacity: 0.5,
smoothFactor: 1
}); //.addTo(mymap)
line_network.bindPopup('This line was drawn from given point coordinates')
// Add to layerGroup and layer control...
var lines = L.layerGroup([line_network]);
var overlayMaps = {
'Line Method 1': lines,
};
var baseLayers = {
// basemaps goes here...
};
L.control.layers(baseLayers, overlayMaps, {collapsed: false}).addTo(mymap);
</script>
</body>
</html>Here you need to prepare the line GeoJSON file, then use L.geoJSON() function to draw it on the map. So, create a JS file and save the GeoJSON code in it with a variable name you will refer to in the main HTML/JS code (depending on how you structure you code files).
In this case I had a Lines.js file as seen below which holds the GeoJSON code in a variable named "line_data", then I referenced it in index.html file as seen below.
Here we got two shapefile layers, one is a line representing roads and the other is polygon representing grid mesh labeled as seen below.
A road could extend multiple grids and we want to get all the names of the grid in which a road is found.
As an example, the selected road in yellow color below extends from grid A1, B1, C1, D1, E1, F1, G1 and H1.
We now need to join the attributes of the two layers and export to CSV file as seen below like so: Vector >> Data Management Tools >> Join Attribute by Location..
The output should be a roads layer with attribute from the grids layer. Export the attribute to CSV for wrangling in python.
As you can see from the CSV file above, there multiple road names with different grid labels. As an example, we have three rows for '1st Avenue' as highlighted above. We just want it on on row with all the grid labels separated by comma like this: 1st Avenue - F3, E4, F4.
# Read the CSV file...
df = pd.read_csv(r"C:\Users\Yusuf_08039508010\Desktop\Gwarinpa St Guide\Street_Finder.csv")
# Group the data by street col...
group = df.groupby('Street')
# Create a series where each group item has unique values from the grid col....
df2 = group.apply(lambda x: x['Grid'].unique())
df2
What the code above does is: for each unique value in 'Street' column, get unique values in 'Grid' column. As seen from the output above, that is what we wanted. If you save the series above to file, you will have the below output which you can further clean to the final format.
That is it!
A shapefile (points, lines, and polygons) can be read into R object using any of the following packages: sf, rgdal, maptools and PBSmapping.
First you need to install them as follow: install.packages(c('sf', 'rgdal', 'maptools', 'PBSmapping'))
library(sf)
library(rgdal)
library(maptools)
library(PBSmapping)
# read in shapefiles using 'sf'
my_map <- st_read("C:/Users/Yusuf_08039508010/Desktop/Working_Files/2021/08-August/R Poor and Vulnerable/SHP/NIG_ADM.shp")
# read in shapefiles using 'rgdal'
my_map <- readOGR("C:/Users/Yusuf_08039508010/Desktop/Working_Files/2021/08-August/R Poor and Vulnerable/SHP", "NIG_ADM")
# read in shapefiles using 'maptools'
# my_map1 <- readShapePoints("...")
# my_map2 <- readShapeLines("...")
my_map3 <- readShapePoly("C:/Users/Yusuf_08039508010/Desktop/Working_Files/2021/08-August/R Poor and Vulnerable/SHP/NIG_ADM")
# read in shapefiles using 'PBSmapping'
my_map <- importShapefile("C:/Users/Yusuf_08039508010/Desktop/Working_Files/2021/08-August/R Poor and Vulnerable/SHP/NIG_ADM")Note that using maptools is deprecated and you will get a warning message that reads: readShapePoly is deprecated; use rgdal::readOGR or sf::st_read
For more, read the web archive on Read and write ESRI Shapefiles with R.
By far, the sf package is commonly used for reading and manipulating shapefiles and other spatial file types such as geojson, geopackage etc. Lets see more on the library(sf)
The package sf tries to fill this gap, and aims at succeeding sp in the long term. Which means sf was developed base on sp which is now deprecated.
In the code snippet below, you will see:-
# Function to create spatial obj. with sp package: st_point(), st_linestring(), and st_polygon()
# However, the function "st_read()" is mostly used to read existing spatial objects
# Read shp...
myMap_1 <- st_read(dsn = "C:/Users/Yusuf_08039508010/Desktop/Working_Files/Fiverr/2021/010-October/NIG Grid/Oyo/NIG.shp")
# Read geoJSON file...
myMap_2 <- st_read("C:/Users/Yusuf_08039508010/Desktop/ng_State.geojson")
# Read GeoPackage (.gpkg) file...
myMap_3 <- st_read("C:/Users/Yusuf_08039508010/Desktop/Working_Files/GIS Data/NGR/GRID3 Data/GPKG files/Fire Stations/fire-stations (5).gpkg")
# What to use sp package...?
# Coverting form "sf data.frame" to sp package i.e: SpatialPointsDataFrame/SpatialLinesDataFrame/SpatialPolygonsDataFrame
class(myMap_1) # This returns sf df...
myMap_1_sp <- as(myMap_1, "Spatial")
class(myMap_1_sp)
# inspect the structure...
str(myMap_1_sp, max.level=2)
# We can see there are five slots, which can be accessed using @ symbol follow by the name
myMap_1_sp@data
myMap_1_sp@polygons
myMap_1_sp@plotOrder
myMap_1_sp@bbox
myMap_1_sp@proj4string
# To convert back to sf, use this function...
myMap_1 <- st_as_sf(myMap_1_sp)
class(myMap_1) # Now we have our sf df, which is the best for working in tydiverse universe.
# Now normal functions in tydiverse will work on the sf df apart from the core spatial functions such as: st_geometry_type(), st_dimension(), st_bbox() and st_crs().
# sf spatial functions....
st_geometry_type(myMap_1)
st_dimension(myMap_1)
st_bbox(myMap_1)
st_crs(myMap_1)
# tidyverse functions....
glimpse(myMap_1$state_name)
View(myMap_1)That is it!