I have real estate properties and their details (17 columns) in a CSV file (nearly half a million entries). One of the columns provides a location but is actually somewhat a bit too detailed. I want to categorize my entries so I want to simplify the location to give me more generic areas. I would have the areas I want to categorize the entries into in a list such as:
keywords = ['Downtown','Park View','Industrial District', ... ]
So ideally I would like to take an entry that has for example Sky Tower Downtown Los Angeles and then classify it as Downtown.
So the task is to first detect the keyword in the location column and then append it to a new column (right beside it if possible). If no keyword is found in the entry, I would to classify it as Other.
It would look something like this:
Date
Record_Type
Location
Proterty_Type
...
Price
19-Mar-21
Active Listing
Sky Tower Downtown Los Angeles
Apartment
...
15000
19-Mar-21
Active Listing
Central Park Residential Tower, 5th Avenue
Apartment
...
17000
20-Mar-21
Active Listing
Meadow Gardens, Park View
Villa
...
125000
To something like:
Date
Record_Type
Location
Area
Proterty_Type
...
Price
19-Mar-21
Active Listing
Sky Tower Downtown Los Angeles
Downtown
Apartment
...
15000
19-Mar-21
Active Listing
Central Park Residential Tower, 5th Avenue
Other
Apartment
...
17000
20-Mar-21
Active Listing
Meadow Gardens, Park View
Park View
Villa
...
125000
Finally it saves it all to a new csv file. I would also ideally like yo use pandas to read/write on the csv.
Thanks in advance!
Edit:
I have tried methods such as the following threads, but I get errors and I don't know whats wrong, so Im open to fresh ideas.
How to append a new column to a CSV file using Python?
Adding new column to CSV in Python
If you have this datafame:
Date Record_Type Location Proterty_Type Price
0 19-Mar-21 Active Listing Sky Tower Downtown Los Angeles Apartment 15000
1 19-Mar-21 Active Listing Central Park Residential Tower, 5th Avenue Apartment 17000
2 20-Mar-21 Active Listing Meadow Gardens, Park View Villa 125000
Then:
keywords = ["Downtown", "Park View", "Industrial District"]
df.insert(
loc=3,
column="Area",
value=df["Location"].apply(
lambda x: next((kw for kw in keywords if kw in x), "Other")
),
)
print(df)
Creates Area column next to Location and prints:
Date Record_Type Location Area Proterty_Type Price
0 19-Mar-21 Active Listing Sky Tower Downtown Los Angeles Downtown Apartment 15000
1 19-Mar-21 Active Listing Central Park Residential Tower, 5th Avenue Other Apartment 17000
2 20-Mar-21 Active Listing Meadow Gardens, Park View Park View Villa 125000
Related
I have a dataframe with 6 columns, the first two are an id and a name column, the remaining 4 are potential matches for the name column.
id name match1 match2 match3 match4
id name match1 match2 match3 match4
1 NXP Semiconductors NaN NaN NaN NaN
2 Cincinnati Children's Hospital Medical Center Montefiore Medical center Children's Hospital Los Angeles Cincinnati Children's Hospital Medical Center SSM Health SLU Hospital
3 Seminole Tribe of Florida The State Board of Administration of Florida NaN NaN NaN
4 Miami-Dade County County of Will County of Orange NaN NaN
5 University of California California Teacher's Association Yale University University of Toronto University System of Georgia
6 Bon Appetit Management Waste Management Sculptor Capital NaN NaN
I'd like to use SequenceMatcher to compare the name column with each match column if there is a value and return the match value with the highest ratio, or closest match, in a new column at the end of the dataframe.
So the output would be something like this:
id name match1 match2 match3 match4 best match
1 NXP Semiconductors NaN NaN NaN NaN NaN
2 Cincinnati Children's Hospital Medical Center Montefiore Medical center Children's Hospital Los Angeles Cincinnati Children's Hospital Medical Center SSM Health SLU Hospital Cincinnati Children's Hospital Medical Center
3 Seminole Tribe of Florida The State Board of Administration of Florida NaN NaN NaN The State Board of Administration of Florida
4 Miami-Dade County County of Will County of Orange NaN NaN County of Orange
5 University of California California Teacher's Association Yale University University of Toronto University System of Georgia California Teacher's Association
6 Bon Appetit Management Waste Management Sculptor Capital NaN NaN Waste Management
I've gotten the data into the dataframe and have been able to compare one column to a single other column using the apply method:
df['diff'] = df.apply(lambda x: diff.SequenceMatcher(None, x[0].strip(), x[1].strip()).ratio(), axis=1)
However, I'm not sure how to loop over multiple columns in the same row. I also thought about trying to reformat my data so it that the method above would work, something like this:
name match
name1 match1
name1 match2
name1 match3
However, I was running into issues dealing with the NaN values. Open to suggestions on the best route to accomplish this.
I ended up solving this using the second idea of reformatting the table. Using the melt function I was able to get a two column table of the name field with each possible match. From there I used the original lambda function to compare the two columns and output a ratio. From there it was relatively easy to go through and see the most likely matches, although it did require some manual effort.
df = pd.read_csv('output.csv')
df1 = df.melt(id_vars = ['id', 'name'], var_name = 'match').dropna().drop('match',1).sort_values('name')
df1['diff'] = df1.apply(lambda x: diff.SequenceMatcher(None, x[1].strip(), x[2].strip()).ratio(), axis=1)
df1.to_csv('comparison-output.csv', encoding='utf-8')
Write code to get a list of tickers for all S&P 500 stocks from Wikipedia. As of 2/24/2021, there are 505 tickers in that list. You can use any method you want as long as the code actually queries the following website to get the list:
https://en.wikipedia.org/wiki/List_of_S%26P_500_companies
One way would be to use the requests module to get the HTML code and then use the re module to extract the tickers. Another option would be the .read_html function in pandas and then export the tickers column to a list.
You should save the tickers in a list with the name sp500_tickers
This will grab the data in the table named 'constituents'.
# find a specific table by table count
import pandas as pd
import requests
from bs4 import BeautifulSoup
res = requests.get("https://en.wikipedia.org/wiki/List_of_S%26P_500_companies")
soup = BeautifulSoup(res.content,'lxml')
table = soup.find_all('table')[0]
df = pd.read_html(str(table))
print(df[0].to_json(orient='records'))
Result:
[{"Symbol":"MMM","Security":"3M Company","SEC filings":"reports","GICS Sector":"Industrials","GICS Sub-Industry":"Industrial Conglomerates","Headquarters Location":"St. Paul, Minnesota","Date first added":"1976-08-09","CIK":66740,"Founded":"1902"},{"Symbol":"ABT","Security":"Abbott Laboratories","SEC filings":"reports","GICS Sector":"Health Care","GICS Sub-Industry":"Health Care Equipment","Headquarters Location":"North Chicago, Illinois","Date first added":"1964-03-31","CIK":1800,"Founded":"1888"},{"Symbol":"ABBV","Security":"AbbVie Inc.","SEC filings":"reports","GICS Sector":"Health Care","GICS Sub-Industry":"Pharmaceuticals","Headquarters Location":"North Chicago, Illinois","Date first added":"2012-12-31","CIK":1551152,"Founded":"2013 (1888)"},{"Symbol":"ABMD","Security":"Abiomed","SEC filings":"reports","GICS Sector":"Health Care","GICS Sub-Industry":"Health Care Equipment","Headquarters Location":"Danvers, Massachusetts","Date first added":"2018-05-31","CIK":815094,"Founded":"1981"},{"Symbol":"ACN","Security":"Accenture","SEC filings":"reports","GICS Sector":"Information Technology","GICS Sub-Industry":"IT Consulting & Other Services","Headquarters Location":"Dublin, Ireland","Date first added":"2011-07-06","CIK":1467373,"Founded":"1989"},{"Symbol":"ATVI","Security":"Activision Blizzard","SEC filings":"reports","GICS Sector":"Communication Services","GICS Sub-Industry":"Interactive Home Entertainment","Headquarters Location":"Santa Monica, California","Date first added":"2015-08-31","CIK":718877,"Founded":"2008"},{"Symbol":"ADBE","Security":"Adobe Inc.","SEC filings":"reports","GICS Sector":"Information Technology","GICS Sub-Industry":"Application Software","Headquarters Location":"San Jose, California","Date first added":"1997-05-05","CIK":796343,"Founded":"1982"},
Etc., etc., etc.
That's JSON. If you want a table, kind of like what you would use in Excel, simply print the df.
Result:
[ Symbol Security SEC filings GICS Sector \
0 MMM 3M Company reports Industrials
1 ABT Abbott Laboratories reports Health Care
2 ABBV AbbVie Inc. reports Health Care
3 ABMD Abiomed reports Health Care
4 ACN Accenture reports Information Technology
.. ... ... ... ...
500 YUM Yum! Brands Inc reports Consumer Discretionary
501 ZBRA Zebra Technologies reports Information Technology
502 ZBH Zimmer Biomet reports Health Care
503 ZION Zions Bancorp reports Financials
504 ZTS Zoetis reports Health Care
GICS Sub-Industry Headquarters Location \
0 Industrial Conglomerates St. Paul, Minnesota
1 Health Care Equipment North Chicago, Illinois
2 Pharmaceuticals North Chicago, Illinois
3 Health Care Equipment Danvers, Massachusetts
4 IT Consulting & Other Services Dublin, Ireland
.. ... ...
500 Restaurants Louisville, Kentucky
501 Electronic Equipment & Instruments Lincolnshire, Illinois
502 Health Care Equipment Warsaw, Indiana
503 Regional Banks Salt Lake City, Utah
504 Pharmaceuticals Parsippany, New Jersey
Date first added CIK Founded
0 1976-08-09 66740 1902
1 1964-03-31 1800 1888
2 2012-12-31 1551152 2013 (1888)
3 2018-05-31 815094 1981
4 2011-07-06 1467373 1989
.. ... ... ...
500 1997-10-06 1041061 1997
501 2019-12-23 877212 1969
502 2001-08-07 1136869 1927
503 2001-06-22 109380 1873
504 2013-06-21 1555280 1952
[505 rows x 9 columns]]
Alternatively, you can export the df to a CSV file.
df.to_csv('constituents.csv')
I'm learning how to scrape using Beautiful soup with selenium and I found a website that has multiple tables and found table tags (first time dealing with them). I'm learning how to try to scrape those texts from each table and append each element to respected list. First im trying to scrape the first table, and the rest I want to do on my own. But I cannot access the tag for some reason.
I also incorporated selenium to access the sites, because when I copy the link to the site onto another tab, the list of tables disappears, for some reason.
My code so far:
import requests
from bs4 import BeautifulSoup
import pandas as pd
import re
from selenium import webdriver
from selenium.webdriver.support.ui import Select
PATH = "C:\Program Files (x86)\chromedriver.exe"
driver = webdriver.Chrome(PATH)
targetSite = "https://www.sdvisualarts.net/sdvan_new/events.php"
driver.get(targetSite)
select_event = Select(driver.find_element_by_name('subs'))
select_event.select_by_value('All')
select_loc = Select(driver.find_element_by_name('loc'))
select_loc.select_by_value("All")
driver.find_element_by_name("submit").click()
targetSite = "https://www.sdvisualarts.net/sdvan_new/viewevents.php"
event_title = []
name = []
address = []
city = []
state = []
zipCode = []
location = []
webSite = []
fee = []
event_dates = []
opening_dates = []
description = []
try:
page = requests.get(targetSite )
soup = BeautifulSoup(page.text, 'html.parser')
items = soup.find_all('table', {"class":"popdetail"})
for i in items:
event_title.append(item.find('b', {'class': "text"})).text.strip()
name.append(item.find('td', {'class': "text"})).text.strip()
address.append(item.find('td', {'class': "text"})).text.strip()
city.append(item.find('td', {'class': "text"})).text.strip()
state.append(item.find('td', {'class': "text"})).text.strip()
zipCode.append(item.find('td', {'class': "text"})).text.strip()
Can someone let me know if I am doing this correctly, This is my first time dealing with site's urls elements disappear when copied onto a new tab and/or window
So far, I am unable to append any information to each list.
One issue is with the for loop.
you have for i in items:, but then you are calling item instead of i.
And secondly, if you are using selenium to render the page, then you should probably use selenium to get the html. They also have some embedded tables within tables, so it's not as straight forward as iterating through the <table> tags. What I ended up doing was having pandas read in the tables (returns a list of dataframes), then iterating through those as there is a pattern of how the dataframes are constructed.
import pandas as pd
from selenium import webdriver
from selenium.webdriver.support.ui import Select
PATH = "C:\Program Files (x86)\chromedriver.exe"
driver = webdriver.Chrome(PATH)
targetSite = "https://www.sdvisualarts.net/sdvan_new/events.php"
driver.get(targetSite)
select_event = Select(driver.find_element_by_name('subs'))
select_event.select_by_value('All')
select_loc = Select(driver.find_element_by_name('loc'))
select_loc.select_by_value("All")
driver.find_element_by_name("submit").click()
targetSite = "https://www.sdvisualarts.net/sdvan_new/viewevents.php"
event_title = []
name = []
address = []
city = []
state = []
zipCode = []
location = []
webSite = []
fee = []
event_dates = []
opening_dates = []
description = []
dfs = pd.read_html(driver.page_source)
driver.close
for idx, table in enumerate(dfs):
if table.iloc[0,0] == 'Event Title':
event_title.append(table.iloc[-1,0])
tempA = dfs[idx+1]
tempA.index = tempA[0]
tempB = dfs[idx+4]
tempB.index = tempB[0]
tempC = dfs[idx+5]
tempC.index = tempC[0]
name.append(tempA.loc['Name',1])
address.append(tempA.loc['Address',1])
city.append(tempA.loc['City',1])
state.append(tempA.loc['State',1])
zipCode.append(tempA.loc['Zip',1])
location.append(tempA.loc['Location',1])
webSite.append(tempA.loc['Web Site',1])
fee.append(tempB.loc['Fee',1])
event_dates.append(tempB.loc['Dates',1])
opening_dates.append(tempB.loc['Opening Days',1])
description.append(tempC.loc['Event Description',1])
df = pd.DataFrame({'event_title':event_title,
'name':name,
'address':address,
'city':city,
'state':state,
'zipCode':zipCode,
'location':location,
'webSite':webSite,
'fee':fee,
'event_dates':event_dates,
'opening_dates':opening_dates,
'description':description})
Output:
print (df.to_string())
event_title name address city state zipCode location webSite fee event_dates opening_dates description
0 The San Diego Museum of Art Welcomes a Special... San Diego Museum of Art 1450 El Prado, Balboa Park San Diego CA 92101 Central San Diego https://www.sdmart.org/ NaN Starts On 6-18-2020 Ends On 1-10-2021 Opens virtually on June 18. The work will beco... The San Diego Museum of Art is launching its f...
1 New Exhibit: Miller Dairy Remembered Lemon Grove Historical Society 3185 Olive Street, Treganza Heritage Park Lemon Grove CA 91945 Central San Diego http://www.lghistorical.org Children 12 and under free and must be accompa... Starts On 6-27-2020 Ends On 12-4-2020 Exhibit on view Saturdays 11 am to 2 pm; close... From 1926 there were cows smack in the midst o...
2 Gizmos and Shivelight Distinction Gallery 317 E. Grand Ave Escondido CA 92025 North County Inland http://www.distinctionart.com NaN Starts On 7-14-2020 Ends On 9-5-2020 08/08/20 - 09/05/20 Distinction Gallery is proud to present our so...
3 Virtual Opening - July Exhibitions Vision Art Museum 2825 Dewey Rd. Suite 100 San Diego CA 92106 Central San Diego http://www.visionsartmuseum.org Free Starts On 7-18-2020 Ends On 10-4-2020 NaN Join Visions Art Museum for a virtual exhibiti...
4 Laying it Bare: The Art of Walter Redondo and ... Fresh Paint Gallery 1020-B Prospect Street La Jolla CA 92037 Central San Diego http://freshpaintgallery.com/ NaN Starts On 8-1-2020 Ends On 9-27-2020 Tuesday through Sunday. Mondays closed. A two-person exhibit of new abstract expressio...
5 Online oil painting lessons with Concetta Antico NaN NaN NaN NaN NaN Virtual http://concettaantico.com/live-online-oil-pain... NaN Starts On 8-10-2020 Ends On 8-31-2020 NaN Anyone can learn to paint like the masters! Ov...
6 MOMENTUM: A Creative Industry Symposium Vanguard Culture Via Zoom San Diego California 92101 Virtual https://www.eventbrite.com/e/momentum-a-creati... $10 suggested donation Starts On 8-17-2020 Ends On 9-7-2020 NaN MOMENTUM: A Creative Industry Symposium Monday...
7 Virtual Locals Invitational Show Art & Frames of Coronado 936 ORANGE AVE Coronado CA 92118 0 https://www.artsteps.com/view/5eed0ad62cd0d65b... free Starts On 8-21-2020 Ends On 8-1-2021 NaN Art and Frames of Coronado invites you to our ...
8 HERE & Now R.B. Stevenson Gallery 7661 Girard Avenue, Suite 101 La Jolla California 92037 Central San Diego http://www.rbstevensongallery.com Free Starts On 8-22-2020 Ends On 9-25-2020 Tuesday through Saturday R.B.Stevenson Gallery is pleased to announce t...
9 Art Unites Learning: Normal 2.0 Art Unites NaN San Diego NaN 92116 Central San Diego https://www.facebook.com/events/956878098104971 Free Starts On 8-25-2020 Ends On 8-25-2020 NaN Please join us on Tuesday, August 25th as we: ...
10 Image Quest Sojourn; Visual Journaling for Per... Pamela Underwood Studios Virtual NaN NaN NaN Virtual http://www.pamelaunderwood.com/event/new-onlin... $595.00 Starts On 8-26-2020 Ends On 11-11-2020 NaN Create a personal Image Quest resource journal...
11 Behind The Exhibition: Southern California Con... Oceanside Museum of Art 704 Pier View Way Oceanside California 92054 Virtual https://oma-online.org/events/behind-the-exhib... No fee required. Donations recommended. Starts On 8-27-2020 Ends On 8-27-2020 NaN Join curator Beth Smith and exhibitions manage...
12 Lay it on Thick, a Virtual Art Exhibition San Diego Watercolor Society 2825 Dewey Rd Bldg #202 San Diego California 92106 0 https://www.sdws.org NaN Starts On 8-30-2020 Ends On 9-26-2020 NaN The San Diego Watercolor Society proudly prese...
13 The Forum: Marketing & Branding for Creatives Vanguard Culture Via Zoom San Diego CA 92101 South San Diego http://vanguardculture.com/ $5 suggested donation Starts On 9-1-2020 Ends On 9-1-2020 NaN Attention creative industry professionals! Joi...
14 Write or Die Solo Exhibition You Belong Here 3619 EL CAJON BLVD San Diego CA 92104 Central San Diego http://www.youbelongsd.com/upcoming-events/wri... $10 donation to benefit You Belong Here Starts On 9-4-2020 Ends On 9-6-2020 NaN Write or Die is an immersive installation and ...
15 SDVAN presents Art San Diego at Bread and Salt San Diego Visual Arts Network 1955 Julian Avenue San Digo CA 92113 Central San Diego http://www.sdvisualarts.net and https://www.br... Free Starts On 9-5-2020 Ends On 10-24-2020 NaN We are pleased to announce the four artist rec...
16 The Coming of Treganza Heritage Park Lemon Grove Historical Society 3185 Olive Street Lemon Grove CA 91945 Central San Diego http://www.lghistorical.org Free for all ages Starts On 9-10-2020 Ends On 9-10-2020 The park is open daily, 8 am to 8 pm. Covid 19... Lemon Grove\'s central city park will be renam...
17 Online oil painting course | 4 weeks NaN NaN NaN NaN NaN Virtual http://concettaantico.com/live-online-oil-pain... NaN Starts On 9-14-2020 Ends On 10-5-2020 NaN Over 4 weekly Zoom lessons, learn the techniqu...
18 Online oil painting course | 4 weeks NaN NaN NaN NaN NaN Virtual http://concettaantico.com/live-online-oil-pain... NaN Starts On 10-12-2020 Ends On 11-2-2020 NaN Over 4 weekly Zoom lessons, learn the techniqu...
19 36th Annual Mission Fed ArtWalk Mission Fed ArtWalk Ash Street San Diego California 92101 Central San Diego www.missionfedartwalk.org Free Starts On 11-7-2020 Ends On 11-8-2020 Sat and Sun Nov 7 and 8 Mission Fed ArtWalk returns to San Diego’s Lit...
20 Mingei Pop Up Workshop: My Daruma Doll New Childrens Museum 200 West Island Avenue San Diego California 92101 Central San Diego http://thinkplaycreate.org/ Free with admission Starts On 11-13-2020 Ends On 11-13-2020 NaN Join Mingei International Museum at The New Ch...
I have a column on my dataframe that contains the following
Wal-Mart Stores, Inc., Clinton, IA 52732
Benton Packing, LLC, Clearfield, UT 84016
North Coast Iron Corp, Seattle, WA 98109
Messer Construction Co. Inc., Amarillo, TX 79109
Ocean Spray Cranberries, Inc., Henderson, NV 89011
W R Derrick & Co. Lexington, SC 29072
I am having problem to capture it using regex so far my regex works for first 2 lines:
[A-Z][A-za-z-\s]+,\s{1}(Inc.|LLC)
How do I split the column to 4 additional columns? i.e. Column1 = Company Name, Column 2 = City, Column 3 = State, Column 4 = Zipcode.
Example of the output is shown below:
Company_Name City State ZipCode
Wal-Mart Stores, Inc. Clinton IA 52732
The names are probably the trickiest part, but if you know that the structure of city, state, zip will always be the same (i.e. no extra commas) you could use rsplit to split the strings. Similarly pandas has a str.rsplit method as well.
df
Address
0 Wal-Mart Stores, Inc., Clinton, IA 52732
1 Benton Packing, LLC, Clearfield, UT 84016
2 North Coast Iron Corp, Seattle, WA 98109
3 Messer Construction Co. Inc., Amarillo, TX 79109
df['Zip'] = df.Address.map(lambda x: x.rsplit(' ', 1)[-1])
df['Name'], df['City'], df['State']= zip(*df.Address.map(lambda x: x.rsplit(' ', 1)[0].rsplit(',', 2)))
df
Address Zip \
0 Wal-Mart Stores, Inc., Clinton, IA 5273 5273
1 Benton Packing, LLC, Clearfield, UT 84016 84016
2 North Coast Iron Corp, Seattle, WA 98109 98109
3 Messer Construction Co. Inc., Amarillo, TX 79109 79109
Name City State
0 Wal-Mart Stores, Inc. Clinton IA
1 Benton Packing, LLC Clearfield UT
2 North Coast Iron Corp Seattle WA
3 Messer Construction Co. Inc. Amarillo TX
I am trying to compare two identical lists in Robot Framework . The code I am using is :
List Test
Lists Should Be Equal #{List_Of_States_USA} #{List_Of_States_USA-Temp}
and the lists are identical with the following values :
#{List_Of_States_USA} Alabama Alaska American Samoa Arizona Arkansas California Colorado
... Connecticut Delaware District of Columbia Florida Georgia Guam Hawaii
... Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana
... Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri
... Montana National Nebraska Nevada New Hampshire New Jersey New Mexico
... New York North Carolina North Dakota Northern Mariana Islands Ohio Oklahoma Oregon
... Pennsylvania Puerto Rico Rhode Island South Carolina South Dakota Tennessee Texas
... Utah Vermont Virgin Islands Virginia Washington West Virginia Wisconsin
... Wyoming
This test fails with the following error:
FAIL Keyword 'Collections.Lists Should Be Equal' expected 2 to 5 arguments, got 114.
I have searched SO and other sites for a solution, but could not figure out why this happened. Thanks in advance for support
You need to use a $ not #. When you use #, robot expands the lists into multiple arguments.
From the robot framework user's guide:
When a variable is used as a scalar like ${EXAMPLE}, its value will be used as-is. If a variable value is a list or list-like, it is also possible to use as a list variable like #{EXAMPLE}. In this case individual list items are passed in as arguments separately.
Consider the case of #{foo} being a list with the values "one", "two" and "three". In such as case the following two are identical:
some keyword #{foo}
some keyword one two three
You need to change your statement to this:
Lists Should Be Equal ${List_Of_States_USA} ${List_Of_States_USA-Temp}
So, As suggested by Bryan-Oakley above, I modified the test as follows:
${L1} Create List #{List_Of_States_USA}
${L2} Create List #{List_Of_States_USA-Temp}
Lists Should Be Equal ${L1} ${L2}
Now the test passed. Thanks Again # Brian