Develop Reference

Creating unique dataframes based on one with adding a value to the column

I have a dataset with statistics by region. I would like to build several other city datasets based on this dataset. At the same time, when creating in each such set, I would like to add a column with the name of the city.
That is, from one data set, I would like to receive three.
I'll give you an example. Initial dataset:
df
date name_region
2022-01-01 California
2022-01-02 California
2022-01-03 California
Next, I have a list with cities: city_list = ['Los Angeles', 'San Diego', 'San Jose']
As an output, I want to have 3 datasets (or more, depending on the number of items in the list):
df_city_1
date name_region city
2022-01-01 California Los Angeles
2022-01-02 California Los Angeles
2022-01-03 California Los Angeles
df_city_2
date name_region city
2022-01-01 California San Diego
2022-01-02 California San Diego
2022-01-03 California San Diego
df_city_3
date name_region city
2022-01-01 California San Jose
2022-01-02 California San Jose
2022-01-03 California San Jose
It would be ideal if, at the same time, the data set could be accessed by a key determined by an element in the list:
df_city['Los Angeles']
date name_region city
2022-01-01 California Los Angeles
2022-01-02 California Los Angeles
2022-01-02 California Los Angeles
How can I do that? I found only a way of this division into several data sets, when the original set already has information on the unique values of the column (in this case, the same cities), , but this does not suit me very well.

Another possible solution:
dfs = []
for city in city_list:
dfs.append(df.assign(city = city))
cities = dict(zip(city_list, dfs))
cities['Los Angeles']
Output:
date name_region city
0 2022-01-01 California Los Angeles
1 2022-01-02 California Los Angeles
2 2022-01-02 California Los Angeles
#ouroboros1, to whom I thank, suggests a very nice way of shortening my code:
cities = dict(zip(city_list, [df.assign(city = city) for city in city_list]))

You can use a dictionary comprehension, and add the column city each time using df.assign.
import pandas as pd
data = {'date': {0: '2022-01-01', 1: '2022-01-02', 2: '2022-01-02'},
'name_region': {0: 'California', 1: 'California', 2: 'California'}}
df = pd.DataFrame(data)
city_list = ['Los Angeles', 'San Diego', 'San Jose']
# "df_city" as a `dict`
df_city = {city: df.assign(city=city) for city in city_list}
# accessing each `df` by key (i.e. a `list` element)
print(df_city['Los Angeles'])
date name_region city
0 2022-01-01 California Los Angeles
1 2022-01-02 California Los Angeles
2 2022-01-02 California Los Angeles

After updating a df from filtred df old values reappear

i have a df where i have a requirement to filter it into new df and work on it and after working i wanted to update it to the original df like.
Street
City
State
Zip
4210 Nw Lake Dr
Lees Summit
Mo
64064
9810 Scripps Lake Dr. Suite A San Diego
Ca - 92131
1124 Ethel St
Glendale
Ca
91207
4000 E Bristol St Ste 3 Elkhart
In-46514
my intened output is
Street
City
State
Zip
4210 Nw Lake Dr
Lees Summit
Mo
64064
9810 Scripps Lake Dr. Suite A San Diego
Ca
92131
1124 Ethel St
Glendale
Ca
91207
4000 E Bristol St Ste 3 Elkhart
In
46514
So firstly i filtered the original dataframe into a new df and worked on it.
with following code
Filter3_df= Final[Final['State'].isnull()]
Filter3_df['temp'] = Filter3_df['City'].str.extract('([A-Za-z]+)')
mask2= Filter3_df['temp'].notnull()
Filter3_df.loc[mask2, 'Zip'] = Filter3_df.loc[mask2, 'City'].str[-5:]
Filter3_df.loc[mask2, 'State'] = Filter3_df.loc[mask2, 'temp']
del Filter3_df['temp']
Filter3_df['City']= float('NaN')
after this the table for Filter3_df looks like this
Street
City
State
Zip
9810 Scripps Lake Dr. Suite A San Diego
Ca
92131
4000 E Bristol St Ste 3 Elkhart
In
46514
but when i update this filtered_df back to the original df using
Final.update(Filter3_df)
I am not getting the intended output instead I am getting the output as
Street
City
State
Zip
4210 Nw Lake Dr
Lees Summit
Mo
64064
9810 Scripps Lake Dr. Suite A San Diego
Ca - 92131
Ca
92131
1124 Ethel St
Glendale
Ca
91207
4000 E Bristol St Ste 3 Elkhart
In-46514
In
46514
kindly let me know where am i going wrong.

From the docs, pandas.DataFrame.update:
Modify in place using non-NA values from another DataFrame.
Replace Filter3_df['City']= float('NaN'), which is NA for floats, with the value you really want:
Filter3_df['City'] = ""

Cannot turn string into an integer python3

I'm attempting to convert the following into integers. I have literally tried everything and keep getting errors.
For instance:
pop2007 = pop2007.astype('int32')
ValueError: invalid literal for int() with base 10: '4,779,736'
Below is the DF I'm trying to convert. I've even attempted the .values method with no success.
pop2007
Alabama 4,779,736
Alaska 710,231
Arizona 6,392,017
Arkansas 2,915,918
California 37,253,956
Colorado 5,029,196
Connecticut 3,574,097
Delaware 897,934
Florida 18,801,310
Georgia 9,687,653
Idaho 1,567,582
Illinois 12,830,632
Indiana 6,483,802
Iowa 3,046,355
Kansas 2,853,118
Kentucky 4,339,367
Louisiana 4,533,372
Maine 1,328,361
Maryland 5,773,552
Massachusetts 6,547,629
Michigan 9,883,640
Minnesota 5,303,925
Mississippi 2,967,297
Missouri 5,988,927
Montana 989,415
Nebraska 1,826,341
Nevada 2,700,551
New Hampshire 1,316,470
New Jersey 8,791,894
New Mexico 2059179
New York 19378102
North Carolina 9535483
North Dakota 672591
Ohio 11536504
Oklahoma 3751351
Oregon 3831074
Pennsylvania 12702379
Rhode Island 1052567
South Carolina 4625364
South Dakota 814180
Tennessee 6346105
Texas 25,145,561
Utah 2,763,885
Vermont 625,741
Virginia 8,001,024
Washington 6,724,540
West Virginia 1,852,994
Wisconsin 5,686,986
Wyoming 563,626
Name: 3, dtype: object

You can't turn a string with commas into an integer. Try this.
my_int = '1,000,000'
my_int = int(my_int.replace(',', ''))
print(my_int)

Have you tried pop2007.replace(',','') to remove the commas from your string values before converting to integers?

Beautiful soup scraping with selenium

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...

Python3 Pandas Dataframe Split Columns

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