We Keep Coding

How would I be able to remove this part of the variable?

So I am making a code like a guessing game. The data for the guessing game is in the CSV file so I decided to use pandas. I have tried to use pandas to import my csv file, pick a random row and put the data into variables so I can use it in the rest of the code but, I can't figure out how to format the data in the variable correctly.
I've tried to split the string with split() but I am quite lost.
ar = pandas.read_csv('names.csv')
ar.columns = ["Song Name","Artist","Intials"]
randomsong = ar.sample(1)
songartist = randomsong["Artist"]
songname = (randomsong["Song Name"])
songintials = randomsong["Intials"]
print(songname)
My CSV file looks like this.
Song Name,Artist,Intials
Someone you loved,Lewis Capaldi,SYL
Bad Guy,Billie Eilish,BG
Ransom,Lil Tecca,R
Wow,Post Malone, W
I expect the output to be the name of the song from the csv file. For Example
Bad Guy
Instead the output is
1 Bad Guy
Name: Song Name, dtype:object
If anyone knows the solution please let me know. Thanks

You're getting a series object as output. You can try
randomsong["Song Name"].to_string()

Use df['column].values to get values of the column.
In your case, songartist = randomsong["Artist"].values[0] because you want only the first element of the returned list.

Is there an R or Python function for separating information in non-delimited strings, where the information varies?

I am currently cleaning up a messy data sheet in which information is given in one excel cell where the different characteristics are not delimited (no comma, spaces are random).
Thus, my problem is to separate the different information without a delimitation I could use in my code (can't use a split command)
I assume that I need to include some characteristics of each part of information, such that the corresponding characteristic is recognized. However, I don't have a clue how to do that since I am quite new to Python and I only worked with R in the framework of regression models and other statistical analysis.
Short data example:
INPUT:
"WMIN CBOND12/05/2022 23554132121"
or
"WalMaInCBND 12/05/2022-23554132121"
or
"WalmartI CorpBond12/05/2022|23554132121"
EXPECTED OUTPUT:
"Walmart Inc.", "Corporate Bond", "12/05/2022", "23554132121"
So each of the "x" should be classified in a new column with the corresponding header (Company, Security, Maturity, Account Number)
As you can see the input varies randomly but I want to have the same output for each of the three inputs given above (I have over 200k data points with different companies, securities etc.)
First Problem is how to separate the information effectively without being able to use a systematic pattern.
Second Problem (lower priority) is how to identify the company without setting up a dictionary with 50 different inputs for 50k companies.
Thanks for your help!

I recommend to first introduce useful seperators where possible and construct a dictionary of replacements for processing with regular expressions.
import re
s = 'WMIN CBOND12/05/2022 23554132121'
# CAREFUL this not a real date regex, this should just
# illustrate the principle of regex
# see https://stackoverflow.com/a/15504877/5665958 for
# a good US date regex
date_re = re.compile('([0-9]{2}/[0-9]{2}/[0-9]{4})')
# prepend a whitespace before the date
# this is achieved by searching the date within the string
# and replacing it with itself with a prepended whitespace
# /1 means "insert the first capture group", which in our
# case is the date
s = re.sub(date_re, r' \1', s)
# split by one or more whitespaces and insert
# a seperator (';') to make working with the string
# easier
s = ';'.join(s.split())
# build a dictionary of replacements
replacements = {
'WMIN': 'Walmart Inc.',
'CBOND': 'Corporate Bond',
}
# for each replacement apply subsitution
# a better, but more replicated solution for
# this is given here:
# https://stackoverflow.com/a/15175239/5665958
for pattern, r in replacements.items():
s = re.sub(pattern, r, s)
# use our custom separator to split the parts
out = s.split(';')
print(out)

Using python and regular expressions:
import re
def make_filter(pattern):
pattern = re.compile(pattern)
def filter(s):
filtered = pattern.match(s)
return filtered.group(1), filtered.group(2), filtered.group(3), filtered.group(4)
return filter
filter = make_filter("^([a-zA-Z]+)\s([a-zA-Z]+)(\d+/\d+/\d+)\s(\d+)$")
filter("WMIN CBOND12/05/2022 23554132121")
The make_filter function is just an utility to allow you to modify the pattern. It returns a function that will filter the output according to that pattern. I use it with the "^([a-zA-Z]+)\s([a-zA-Z]+)(\d+/\d+/\d+)\s(\d+)$" pattern that considers some text, an space, some text, a date, an space, and a number. If you want to kodify this pattern provide more info about it. The output will be ("WMIN", "CBOND", "12/05/2022", "23554132121").

welcome! Yeah, we would definitely need to see more examples and regex seems to be the way to go... but since there seems to be no structure, I think it's better to think of this as seperate steps.
We KNOW there's a date which is (X)X/(X)X/XXXX (ie. one or two digit day, one or two digit month, four digit year, maybe with or without the slashes, right?) and after that there's numbers. So solve that part first, leaving only the first two categories. That's actually the easy part :) but don't lose heart!
if these two categories might not have ANY delimiter (for example WMINCBOND 12/05/202223554132121, or delimiters are not always delimiters for example IMAGINARY COMPANY X CBOND, then you're in deep trouble. :) BUT this is what we can do:
Gather a list of all the codes (hopefully you have that).
use str_detect() on each code and see if you can recognize the exact string in any of the dataset (if you do have the codes lemme know I'll write the code to do this part).
What's left after identifying the code will be the CBOND, whatever that is... so do that part last... what's left of the string will be that. Alternatively, you can use the same str_detect() if you have a list of whatever CBOND stuff is.
ONLY AFTER YOU'VE IDENTIFIED EVERYTHING, you can then replace the codes for what they stand for.
If you have the code-list let me know and I'll post the code.

edit
s = c("WMIN CBOND12/05/2022 23554132121",
"WalMaInCBND 12/05/2022-23554132121",
"WalmartI CorpBond12/05/2022|23554132121")
ID = gsub("([a-zA-Z]+).*","\\1",s)
ID2 = gsub(".* ([a-zA-Z]+).*","\\1",s)
date = gsub("[a-zA-Z ]+(\\d+\\/\\d+\\/\\d+).*","\\1",s)
num = gsub("^.*[^0-9](.*$)","\\1",s)
data.frame(ID=ID,ID2=ID2,date=date,num=num,stringsAsFactors=FALSE)
ID ID2 date num
1 WMIN CBOND 12/05/2022 23554132121
2 WalMaInCBND WalMaInCBND 12/05/2022-23554132121 12/05/2022 23554132121
3 WalmartI CorpBond 12/05/2022 23554132121
Works for cases 1 and 3 but I haven't figured out a logic for the second case, how can we know where to split the string containing the company and security if they are not separated?

Python- Insert new values into 'nested' list?

What I'm trying to do isn't a huge problem in php, but I can't find much assistance for Python.
In simple terms, from a list which produces output as follows:
{"marketId":"1.130856098","totalAvailable":null,"isMarketDataDelayed":null,"lastMatchTime":null,"betDelay":0,"version":2576584033,"complete":true,"runnersVoidable":false,"totalMatched":null,"status":"OPEN","bspReconciled":false,"crossMatching":false,"inplay":false,"numberOfWinners":1,"numberOfRunners":10,"numberOfActiveRunners":8,"runners":[{"status":"ACTIVE","ex":{"tradedVolume":[],"availableToBack":[{"price":2.8,"size":34.16},{"price":2.76,"size":200},{"price":2.5,"size":237.85}],"availableToLay":[{"price":2.94,"size":6.03},{"price":2.96,"size":10.82},{"price":3,"size":33.45}]},"sp":{"nearPrice":null,"farPrice":null,"backStakeTaken":[],"layLiabilityTaken":[],"actualSP":null},"adjustmentFactor":null,"removalDate":null,"lastPriceTraded":null,"handicap":0,"totalMatched":null,"selectionId":12832765}...
All I want to do is add in an extra field, containing the 'runner name' in the data set below, into each of the 'runners' sub lists from the initial data set, based on selection_id=selectionId.
So initially I iterate through the full dataset, and then create a separate list to get the runner name from the runner id (I should point out that runnerId===selectionId===selection_id, no idea why there are multiple names are used), this works fine and the code is shown below:
for market_book in market_books:
market_catalogues = trading.betting.list_market_catalogue(
market_projection=["RUNNER_DESCRIPTION", "RUNNER_METADATA", "COMPETITION", "EVENT", "EVENT_TYPE", "MARKET_DESCRIPTION", "MARKET_START_TIME"],
filter=betfairlightweight.filters.market_filter(
market_ids=[market_book.market_id],
),
max_results=100)
data = []
for market_catalogue in market_catalogues:
for runner in market_catalogue.runners:
data.append(
(runner.selection_id, runner.runner_name)
)
So as you can see I have the data in data[], but what I need to do is add it to the initial data set, based on the selection_id.
I'm more comfortable with Php or Javascript, so apologies if this seems a bit simplistic, but the code snippets I've found on-line only seem to assist with very simple Python lists and nothing 'nested' (to me the structure seems similar to a nested array).
As per the request below, here is the full list:
{"marketId":"1.130856098","totalAvailable":null,"isMarketDataDelayed":null,"lastMatchTime":null,"betDelay":0,"version":2576584033,"complete":true,"runnersVoidable":false,"totalMatched":null,"status":"OPEN","bspReconciled":false,"crossMatching":false,"inplay":false,"numberOfWinners":1,"numberOfRunners":10,"numberOfActiveRunners":8,"runners":[{"status":"ACTIVE","ex":{"tradedVolume":[],"availableToBack":[{"price":2.8,"size":34.16},{"price":2.76,"size":200},{"price":2.5,"size":237.85}],"availableToLay":[{"price":2.94,"size":6.03},{"price":2.96,"size":10.82},{"price":3,"size":33.45}]},"sp":{"nearPrice":null,"farPrice":null,"backStakeTaken":[],"layLiabilityTaken":[],"actualSP":null},"adjustmentFactor":null,"removalDate":null,"lastPriceTraded":null,"handicap":0,"totalMatched":null,"selectionId":12832765},{"status":"ACTIVE","ex":{"tradedVolume":[],"availableToBack":[{"price":20,"size":3},{"price":19.5,"size":26.36},{"price":19,"size":2}],"availableToLay":[{"price":21,"size":13},{"price":22,"size":2},{"price":23,"size":2}]},"sp":{"nearPrice":null,"farPrice":null,"backStakeTaken":[],"layLiabilityTaken":[],"actualSP":null},"adjustmentFactor":null,"removalDate":null,"lastPriceTraded":null,"handicap":0,"totalMatched":null,"selectionId":12832767},{"status":"ACTIVE","ex":{"tradedVolume":[],"availableToBack":[{"price":11,"size":9.75},{"price":10.5,"size":3},{"price":10,"size":28.18}],"availableToLay":[{"price":11.5,"size":12},{"price":13.5,"size":2},{"price":14,"size":7.75}]},"sp":{"nearPrice":null,"farPrice":null,"backStakeTaken":[],"layLiabilityTaken":[],"actualSP":null},"adjustmentFactor":null,"removalDate":null,"lastPriceTraded":null,"handicap":0,"totalMatched":null,"selectionId":12832766},{"status":"ACTIVE","ex":{"tradedVolume":[],"availableToBack":[{"price":48,"size":2},{"price":46,"size":5},{"price":42,"size":5}],"availableToLay":[{"price":60,"size":7},{"price":70,"size":5},{"price":75,"size":10}]},"sp":{"nearPrice":null,"farPrice":null,"backStakeTaken":[],"layLiabilityTaken":[],"actualSP":null},"adjustmentFactor":null,"removalDate":null,"lastPriceTraded":null,"handicap":0,"totalMatched":null,"selectionId":12832769},{"status":"ACTIVE","ex":{"tradedVolume":[],"availableToBack":[{"price":18.5,"size":28.94},{"price":18,"size":5},{"price":17.5,"size":3}],"availableToLay":[{"price":21,"size":20},{"price":23,"size":2},{"price":24,"size":2}]},"sp":{"nearPrice":null,"farPrice":null,"backStakeTaken":[],"layLiabilityTaken":[],"actualSP":null},"adjustmentFactor":null,"removalDate":null,"lastPriceTraded":null,"handicap":0,"totalMatched":null,"selectionId":12832768},{"status":"ACTIVE","ex":{"tradedVolume":[],"availableToBack":[{"price":4.3,"size":9},{"price":4.2,"size":257.98},{"price":4.1,"size":51.1}],"availableToLay":[{"price":4.4,"size":20.97},{"price":4.5,"size":30},{"price":4.6,"size":16}]},"sp":{"nearPrice":null,"farPrice":null,"backStakeTaken":[],"layLiabilityTaken":[],"actualSP":null},"adjustmentFactor":null,"removalDate":null,"lastPriceTraded":null,"handicap":0,"totalMatched":null,"selectionId":12832771},{"status":"ACTIVE","ex":{"tradedVolume":[],"availableToBack":[{"price":24,"size":6.75},{"price":23,"size":2},{"price":22,"size":2}],"availableToLay":[{"price":26,"size":2},{"price":27,"size":2},{"price":28,"size":2}]},"sp":{"nearPrice":null,"farPrice":null,"backStakeTaken":[],"layLiabilityTaken":[],"actualSP":null},"adjustmentFactor":null,"removalDate":null,"lastPriceTraded":null,"handicap":0,"totalMatched":null,"selectionId":12832770},{"status":"ACTIVE","ex":{"tradedVolume":[],"availableToBack":[{"price":5.7,"size":149.33},{"price":5.5,"size":29.41},{"price":5.4,"size":5}],"availableToLay":[{"price":6,"size":85},{"price":6.6,"size":5},{"price":6.8,"size":5}]},"sp":{"nearPrice":null,"farPrice":null,"backStakeTaken":[],"layLiabilityTaken":[],"actualSP":null},"adjustmentFactor":null,"removalDate":null,"lastPriceTraded":null,"handicap":0,"totalMatched":null,"selectionId":10064909}],"publishTime":1551612312125,"priceLadderDefinition":{"type":"CLASSIC"},"keyLineDescription":null,"marketDefinition":{"bspMarket":false,"turnInPlayEnabled":false,"persistenceEnabled":false,"marketBaseRate":5,"eventId":"28180290","eventTypeId":"2378961","numberOfWinners":1,"bettingType":"ODDS","marketType":"NONSPORT","marketTime":"2019-03-29T00:00:00.000Z","suspendTime":"2019-03-29T00:00:00.000Z","bspReconciled":false,"complete":true,"inPlay":false,"crossMatching":false,"runnersVoidable":false,"numberOfActiveRunners":8,"betDelay":0,"status":"OPEN","runners":[{"status":"ACTIVE","sortPriority":1,"id":10064909},{"status":"ACTIVE","sortPriority":2,"id":12832765},{"status":"ACTIVE","sortPriority":3,"id":12832766},{"status":"ACTIVE","sortPriority":4,"id":12832767},{"status":"ACTIVE","sortPriority":5,"id":12832768},{"status":"ACTIVE","sortPriority":6,"id":12832770},{"status":"ACTIVE","sortPriority":7,"id":12832769},{"status":"ACTIVE","sortPriority":8,"id":12832771},{"status":"LOSER","sortPriority":9,"id":10317013},{"status":"LOSER","sortPriority":10,"id":10317010}],"regulators":["MR_INT"],"countryCode":"GB","discountAllowed":true,"timezone":"Europe\/London","openDate":"2019-03-29T00:00:00.000Z","version":2576584033,"priceLadderDefinition":{"type":"CLASSIC"}}}

i think i understand what you are trying to do now
first hold your data as a python object (you gave us a json object)
import json
my_data = json.loads(my_json_string)
for item in my_data['runners']:
item['selectionId'] = [item['selectionId'], my_name_here]
the thing is that my_data['runners'][i]['selectionId'] is a string, unless you want to concat the name and the id together, you should turn it into a list or even a dictionary
each item is a dicitonary so you can always also a new keys to it
item['new_key'] = my_value

So, essentially this works...with one exception...I can see from the print(...) in the loop that the attribute is updated, however what I can't seem to do is then see this update outside the loop.
mkt_runners = []
for market_catalogue in market_catalogues:
for r in market_catalogue.runners:
mkt_runners.append((r.selection_id, r.runner_name))
for market_book in market_books:
for runner in market_book.runners:
for x in mkt_runners:
if runner.selection_id in x:
setattr(runner, 'x', x[1])
print(market_book.market_id, runner.x, runner.selection_id)
print(market_book.json())
So the print(market_book.market_id.... displays as expected, but when I print the whole list it shows the un-updated version. I can't seem to find an obvious solution, which is odd, as it seems like a really simple thing (I tried messing around with indents, in case that was the problem, but it doesn't seem to be, its like its not refreshing the market_book list post update of the runners sub list)!

How to make namedtuple when column headers have spaces

I'm trying to make a namedtuple from a DictReader object. My code looks like this. The problem I'm struggling with is I have some really long and ugly column headers in the csv file I'm working with. For the sake of this example, one of the column headers I am working with is:
"What is typically the main dish at your Thanksgiving dinner?".
What is throwing me off is there are a bunch of spaces in this title, so if I understand correctly, the namedtuple thinks these are all arguments. What way would you recommend to solve this? I have referenced several threads and feel like I almost got there through this one: What is the pythonic way to read CSV file data as rows of namedtuples?
I am just using one column header as an example. Here is some code I have so far:
import csv
import collections
filename = 'thanksgiving2015.csv'
with open(filename, 'r', encoding = 'utf-8') as f:
reader = csv.DictReader(f)
columns = collections.namedtuple('columns',
'What is typically the main dish at your
Thanksgiving dinner?')
Should I strip all these column headers of their spaces before making the namedtuple? I could do this before I even import the csv in excel, but I assume there is a nice solution in python.

namedtuple treats a single string as a white-space-delimited list of field names. You need to pass an explicit list of column names instead.
namedtuple('columns', ['What is...', 'some other absurd column name'])
I would rethink using the header values directly as field names, though. Ignore the header, and pass a list of shorter names that you can use as attributes later.

As chepner pointed out, the second argument of nametuple() can either be a space-separated string or a list of strings like:
columns = collections.namedtuple('columns',
['What is typically the main dish at your Thanksgiving dinner?', 'other column'])
However, doing so will fail with:
ValueError: Type names and field names must be valid identifiers
This is because columns (which you should capitalize as Columns) will be an object with 'What is typically...' as an identifier and identifiers can't have spaces. To be clear, you would use it as:
Columns = namedtuple('columns', ['what is', 'this'])
columns = Columns('foo', 'bar')
print(columns.this) # Works fine
print(columns.what is) # Not going to work
If you were using a simple dict(), you would write:
print(columns['what is'])
You can however ask namedtuple to rename invalid identifiers:
Columns = namedtuple('columns', ['what is', 'this'], rename=True)
print(columns._0) # ugly but valid
print(columns.this)

Naming DataFrames iteritively using items from a List + a string

I have a list of names of countries.. and I have a large dataframe where one of the columns is ' COUNTRY ' (yes it has a space before and after the word country) I want to be create smaller DataFrames based on country names
cleaned_df[cleaned_df[' COUNTRY ']==asia_country_list[1]]
seems too long a command to achieve this? It does work though.
Now,
str("%s_data" % (asia_country_list[1]))
gives
'Taiwan_data'
but when I combine the above two:
str("%s_data" % (asia_country_list[1])) = cleaned_df[cleaned_df[' COUNTRY ']==asia_country_list[1]]
I get:
SyntaxError: can't assign to function call
happy to learn other ways as well to achieve this pls.. Thanks vm

I don't think you should do this, but if you really need it :
exec(str("%s_data" % (asia_country_list[1])) +"= cleaned_df[cleaned_df[' COUNTRY ']==asia_country_list[1]]")
should work.
Using a dictionary is likely to solve your problem
D={}
D["%s_data" % (asia_country_list[1]))]=cleaned_df[cleaned_df[' COUNTRY ']==asia_country_list[1]]]
EDIT : the first solution is a bad idea : exec is a dangerous command, if one column is named "del cleaned_df" you will actually execute it, it can get destructive. Typically I am guessing spaces are a problem in your case. It's a bit like SQL injections...