QGIS Attribute table Conditional formatting

Not in all instances that we want to run query to select items on attribute table. Sometimes we just want to highlight of format the attribute table so that certain item that met a specified characteristics are noticeable on the table.

For example, we may want to see rows that have duplicate records or rows whose name ends with a certain alphabet. Whatever the case maybe, QGIS has a handy tool called "Conditional formatting" that allow you format your attribute table as you wanted. Let see some few examples:-

Conditional formatting base on duplicate records

We can use the count() function which returns the count of matching features to check for duplicte records. To do this, we check if the count is greater than one, if so than the record appeared more than once and hence it is duplicated.

The expression is: count("Name" , "Name") > 1 where "Name" is the attribute field name. We can select the duplicated attributes as seen below. However, what we wanted here is to conditionally format the table base on duplicate records.

To achieve conditional formatting, click on the "conditional formatting" icon as seen below. Then add a new rule. Note, you can format just the field or the full row.

Add a name, enter the conditional expression (in this case it is: count("Name" , "Name") > 1). Then set the background and text colors. After which you will click on 'Done' button to apply the formatting.

Conditional formatting base on name ending with 'a'

Here we will use the regexp_match() function to find names that end with alphabet "a". The correct expression is: regexp_match("Name", 'a$'), this has to do with regular expressions.

We will add this formatting to the one above. So, we just need to add another "New Rule", to format items under 'Name' field/column that ends with 'a'.

We now have colors separating all the criteria we wanted in a nice color table.

That is it!

Download image files using python requests module

 Here I have list of data with links to their corresponding images. Specifically, this is a list of presidents in the USA and Nigeria.

I have to download the images/photos. So, I wrote a python script that uses the requests library to download the images as seen below:-

There are two approaches when saving an image file (or any file) on the hard disk. One is using the open with close methods or using the with statement.

import requests
import pandas as pd

image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/7/70/John_Adams%2C_Gilbert_Stuart%2C_c1800_1815.jpg/842px-John_Adams%2C_Gilbert_Stuart%2C_c1800_1815.jpg"

# -------- 1st approach ------------

response = requests.get(image_url)

img_file = open("downloaded_img/sample_image.png", "wb")
img_file.write(response.content)
img_file.close()

# -------- 2nd approach ------------

img_file = requests.get(image_url).content
with open('downloaded_img/image_name.jpg', 'wb') as f:
    f.write(img_file)

Downloading the US presidents photos:-

us_presidents_df = pd.read_csv(r"C:\Users\Yusuf_08039508010\Desktop\US Presidents\USA List.csv")

# Bulk processing....
for name, img in zip(us_presidents_df['Name'], us_presidents_df['Image']):
    print('Processing....', name, img)
    
    img_file = requests.get(img).content
    with open('downloaded_img/US Presidents/'+name+'.jpg', 'wb') as f:
        f.write(img_file)

Downloading Nigerian presidents photos:-

nigeria_presidents_df = pd.read_csv(r"C:\Users\Yusuf_08039508010\Desktop\NG List.csv")

for name, img in zip(nigeria_presidents_df['Name'], nigeria_presidents_df['Image']):
    print('Processing....', name, img)
    
    response = requests.get(img)

    img_file = open('downloaded_img/NG Presidents/'+name+'.png', "wb")
    img_file.write(response.content)
    img_file.close()
    
print('Completed....')

That is it!

Extracting Hyperlink 'href' attribute from excel

On this page I will show you how to extract the link/url from the text in each cell of the excel sheet. The excel document (.xlsx file) is as seen below, contains hyperlink text. We are interested in the actual url embedded in the href link anchor.

We can access each link be right clicking on each cell and edit hyperlink as seen below;-

Doing this manually is tedious especially if we have to do it for many cells. So, here I will use a python module for manipulating excel files. This module is called: openpyxl.

The pseudocode is as follow:-

• Read or Load the excel file
• Read the active sheet or get the sheet by name
• Access the sheet's column or cells

import openpyxl

# Read or Load the excel file...
work_book = openpyxl.load_workbook(r"C:\Users\Yusuf_08039508010\Desktop\Book1_gethref.xlsx")

# Read the sheet to use by name or active
work_sheet = work_book['Sheet1']
# work_sheet = work_book.active

# Access sheet cells...
print(work_sheet.cell(row=2, column=1).hyperlink.target)
print(work_sheet['A1'].value)
print(work_sheet['A2'].value)
print(work_sheet['A3'].value)


# Access sheet column...
print(work_sheet['A'])

# Loop over column and get the text and hyperlink...
for cell in work_sheet['A']:
    print(cell.value)
    try:
        print(cell.hyperlink.target)
    except AttributeError:
        pass

That is it!

Render pandas Dataframe as HTML

 Here is a situation where we go bunch of spreadsheet files and we want all of them converted into beautiful HTML table.

Fortunately, pandas has the 'to_html()' method which converts a dataframe into html table. However, the table will need some CSS applied to make is look beautiful. So, instead of writing the CSS manually, we can use Bootstrap classes to achieve same decoration.

First lets read the CSV files and create a list dataframes using the code below...

import glob
import pandas as pd


csv_files = glob.glob(r'C:\Users\Yusuf_08039508010\Desktop\*.csv')

# create dataframe list...
df_list = [pd.read_csv(f) for f in csv_files]

Now, lets use the 'to_html()' method to convert the first dataframe into HTML.

html_df = df_list[0]

# render dataframe as html
html = html_df.to_html()

# write html to file
text_file = open("index.html", "w")
text_file.write(html)
text_file.close()

Python URL Encode

Introduction
URL encoding, is a way to encode special characters in URL so that it is in safe and secure to transmit over the internet. It is also known as 'percent encoding'.

URLs can only be sent over the Internet using the ASCII (American Standard Code for Information Interchange) character-set, so characters outside ASCII need to be converted into a valid ASCII format.

Let assume we have some set of phrases that we want to search via google and retrieve the search result on the first page. This mean we have to parse those phrases into a google search url that will look like this: 'https://www.google.com/search?q=X' where X stands for the search phrase.

Now we can replace the X with the specific phrases we wanted to search. But the problem is if one o the phrases contains special characters the search url won't work.

For example, this (https://www.google.com/search?q=Advanced-mobile-success) will work fine but this (https://www.google.com/search?q=Advanced mobile success) won't work fine as expected because of the spaces between the word. Those space need to be converted or encoded into internet friendly character that will look like this "https://www.google.com/search?q=Advanced%20mobile%20success".

Some regular modern browsers have the ability to do this encoding on the fly without you knowing. But when you want to construct such query url in a program or to communicate with an API, it is save if you encode them before parsing them. In python, this encoding can be handled using the urllib.parse module.

When you import the module like this import urllib.parse you will access to this functions quote(),

quote_plus() and urlencode(). They slight do different encoding, however urlencode() is more frequently used so I will talk about it in few seconds.

To encode the query string query_string = 'Hellö Wörld@Python' to our base url, the code will look like this:-

import urllib.parse

base_url = 'https://www.google.com/search?q='
query_string = 'Hellö Wörld@Python'

urllib.parse.quote(query_string)

url = base_url + urllib.parse.quote(query_string)
url

Assuming of base url has more than just the query string parameter, then we will use urlencode() function like this;-

import urllib.parse

base_url = 'https://www.google.com/search?q='
query_string = 'local search on '

params = {'name': 'Umar Yusuf', 'school': ['school 1', 'school 2', 'school 3']}

urllib.parse.urlencode(params, doseq=True)

url = base_url + urllib.parse.quote(query_string) + urllib.parse.urlencode(params)
url

As you can see, we are able to encode multiple parameters into the url suing the urlencode() function. This is how powerful the module is and you can construct very complex friendly urls.

It should be noted that for a simple url encoding like this (https://www.google.ng/maps/@9.057809,7.4903376,15z), the route above will be overkilling. A simple string .format() function will be enough.

This simple url link to a google maps location defined by three variables (latitude, longitude and zoom level). So, we can use the string .format() function to handle its string construct as seen below.

latitude = 9.057809
logitude = 7.4903376
zoom = 15

base_url = 'https://www.google.ng/maps/@{},{},{}z'
url = base_url.format(latitude, logitude, zoom)

Happy coding!

Mapping dictionary keys/values to pandas dataframe

 This is a senario where we have a dataframe representing states and their geo-political zones. The geo-political zone column is however abbreviated as seen below, so we want to have a new column that will hold the full meaning of the corresponding abbreviation.

The abbreviated letters are defined in dictionary, which will be used to map the new full meaning column.

look_up_dict = {'NC': 'North Central', 'NE': 'North East', 'NW': 'North West', 'SE': 'South East', 'SW': 'South West', 'SS': 'South South'}

The lookup dictionary could come from a different dataframe, that is to say the dataframe can be converted to dictionary for this lookup purpose.

A solution here was to use the map() function to create a new column by mapping the dictionar keys to the values on geopolitical zone column 'GeoPZ'

df['Geo Political Zones'] = df['GeoPZ'].map(look_up_dict)

How about if we just want to replace the existing 'GeoPZ' column without creating a new one? There are couple of ways to get this done by using replace() or update() methods or simply by overriding the existing column.

df['GeoPZ'] = df['GeoPZ'].map(look_up_dict)

df['GeoPZ'] = df['GeoPZ'].replace(look_up_dict)

To use update() method, your dictionary keys must be numeric indices. Use .keys() to check your dictionary keys.

df.GeoPZ.update(pd.Series(look_up_dict))

Lambda function

You can use a Lamda function to perform operations on the fly while creating a new column. Lets say, we want a new column that will hold character count/lenght of each state's name. Then a Lamda function will come in handy as seen below;-

df['State_LCount'] = df['State'].map(lambda x: len(str(x)))

Thank you for following!

ColorBrewer for map designers

This article is about choosing a friendly colors for your next map design.

Random color ramp

Greens color ramp

Most modern mapping software will give you great color ideas for your map. However, the problem with the software suggested color is that there are only a handful of those colors and in some cases you will like to manually setup your colors since the default color ranges may not fit into your design.

If you find yourself in this situation and you are struggling to combine colors in a color friendly manner, then you are in the right place.

Here I will talk about a tool that help solve this problem and at the end you should be comfortable selecting proper colors for your map.

To tool is called ColorBrewer. It is a web based tool that provides "Color Advice for Maps".

The tool provides different properties to select based on your map. For example number of data class for your map, nature of the data (Sequential, Diverging and Qualitative). And many other properties as seen on the website.

Some other tools to help you combine friendly colors are:-

1) Flat UI Colors

2) Flat Colors UI

3) Flat UI Color Picker

Study the tools and choose the perfect friendly color for your next map.

Happy mapping!

Recreating NCDC Covid-19 GIS Maps

Ever since the first case of Coronavirus disease (COVID-19) was announced in Nigeria on 27th of February 2020, the Nigeria Centre for Disease Control (NCDC) has in its effort to control the pandemic produced maps to document and visualize the status of the disease.

Some of these maps are part of the online 'COVID-19 Cases Tracking Dashboard' while other are included in pdf reports on 'update of COVID-19 outbreak' in Nigeria as seen below.

When you download and open one of the daily reporst, you will see the map below...

The second map is on the 'COVID-19 Cases Tracking Dashboard' web page.

PyQGIS - Get attribute column names and dataType

 Ocationally, I did like to check the attribute fields/columns of a layer I am working on. Sometimes to get their names, types or just to get the count of how many fields/columns are on that layer.

You could access some of these information when you open the layer's property window, under the information tab as seen below.

Pandas Dataframe to Python Dictionary

Assuming we have this table of "Confirmed COVID-19 Cases in Nigeria" as seen below and we want to convert either the whole table or some of its columns to a python dictionary. If you don't know already, a python dictionary is a data structure that consists of a collection of key-value pairs.

The column are as follow:-

1. Column A: States Affected
2. Column B: No. of Cases (Lab Confirmed)
3. Column C: No. of Cases (on admission)
4. Column D: No. Discharged
5. Column E: No. of Deaths