PyQGIS Count Features in Layers

 

# Get path to layer location on disc...
layers_on_panel = QgsProject.instance().mapLayers()
layer_paths = [l.source() for l in layers_on_panel.values()]

# Count Features in layer...
for lpath in layer_paths:
    if 'power_substation_point' in lpath:
        # Read the vector layer...
        layer = QgsVectorLayer(lpath, '', 'ogr')
        # Count the features...
        print( layer.featureCount() )

That is it!

Legal news scrapping

 The code snippet below helps in scrapping data from the legal news website.

Some steps require manual interaction, so the snippet is separated into notebook cells. The # ******** indicates beginning and end of a cell.

url = 'https://www.legalnews.com/Home/Login'
pw = ''
un = ''

path = r"chromedriver.exe"

service = Service(executable_path=path)
driver = webdriver.Chrome(service=service)

driver.get(url)
time.sleep(2)

# Login...
driver.find_element(By.XPATH, '//*[@id="email"]').send_keys(un)
driver.find_element(By.XPATH, '//*[@id="password"]').send_keys(pw)
driver.find_element(By.XPATH, '//*[@id="btnlogin"]').click()

time.sleep(3)
driver.find_element(By.XPATH, '//*[@id="top-right"]/div/a[1]').click()
# **********************************************
# -------------------
# Do some manual click to change the table view to table... then run the next cell
# -------------------
# **********************************************

dfList = []
# **********************************************


html_data = driver.page_source
soup = BeautifulSoup(html_data, 'html.parser')

# Read table to df...
tb = pd.read_html(html_data)

# Extract URL of rows...
prod_title = soup.find_all('tr') # ['data-href']

noticeURL_list = []
for t in prod_title:
    try:
        noticeURL_list.append(f"https://www.legalnews.com{t['data-href']}")
    except Exception:
        pass
    
tb[1]['URL'] = noticeURL_list

# Make df a farmiliar dataframe... :)
df = tb[1]

dfList.append(df)
# **********************************************


# Click 'Next Page' btn...  *** CLICK ON PAGE 2 MANUALLY TO AVOID ERROR ***
i = 2
p = 2
for x in range(10):
    print(f'Clicking on page... {i}')
    
    driver.find_element(By.XPATH, f'//*[@id="divListView"]/div[1]/div[1]/a[{p}]').click()
    time.sleep(2)

    html_data = driver.page_source
    soup = BeautifulSoup(html_data, 'html.parser')

    # Read table to df...
    tb = pd.read_html(html_data)

    # Extract URL of rows...
    prod_title = soup.find_all('tr') # ['data-href']

    noticeURL_list = []
    for t in prod_title:
        try:
            noticeURL_list.append(f"https://www.legalnews.com{t['data-href']}")
        except Exception:
            pass

    tb[1]['URL'] = noticeURL_list

    # Make df a farmiliar dataframe... :)
    df = tb[1]
    dfList.append(df)
    i = i+1
    
print('Done...')

# **********************************************

df2 = pd.concat(dfList).drop_duplicates()
df2.to_excel(f'LegalNews__April2024_Table1.xlsx', index=False)
df2

That is it!

Downloading US Census Tracts

 According to Wikipedia, A census tract aka census area or census district or meshblock is a geographic region defined for the purpose of taking a census. Sometimes these coincide with the limits of cities, towns or other administrative areas and several tracts commonly exist within a county. 

The US Census Tracts can be downloaded from the United States Census Bureau. It is provided in TIGER/Line format. TIGER stands for "Topologically Integrated Geographic Encoding and Referencing", or TIGER, or TIGER/Line is a format used by the United States Census Bureau to describe land attributes such as roads, buildings, rivers, and lakes, as well as areas such as census tracts.

The state of Alabama will look like this:-

This map layer can then be joined with any tabular record for further analysis.