Pandas Method Chaining: getting KeyError on calculated column

I’m scraping web data to get US college football poll top 25 information that I store in a Pandas dataframe. The data has multiple years of poll information, with preseason and final polls for each year. Each poll ranks teams from 1 to 25. Team ranks are determined by the voting points each team received; the team with most points is ranked 1, etc. Both rank and points are included in the dataset. Here’s the head of the raw data df:

cols = ['Year','Type', 'Team (FPV)', 'Rank', 'Pts']
all_wks_raw[cols].head()

The dataframe has columns for Rank and Pts (Points). The Rank column (dytpe object) contains numeric ranks of 1-25 plus “RV” for teams that received points but did not rank in the top 25. The Pts column is dtype int64. Since Pts for teams that did not make the top 25 are included in the data, I’m able to re-rank the teams based on Pts and thus extend rankings beyond the top 25. The resulting revrank column ranks teams from 1 to between 37 and 61, depending how many teams received points in that poll. Revrank is the first new column I create.

The revrank column should equal the Rank column for the first 25 teams, but before I can test it I need to create a new column that converts Rank to numeric. The result is rank_int, which is my second created column. Then I try to create a third column that calculates the difference between the two created columns, and this is where I get the KeyError. Here’s the chain:

all_wks_clean = (all_wks_raw
    #create new column that converts Rank to numeric-this works
    .assign(rank_int = pd.to_numeric(all_wks_raw['Rank'], errors='coerce').fillna(0))
                 
    #create new column that re-ranks teams based on Points: extends rankings beyond original 25-this works
    .assign(gprank = all_wks_raw.reset_index(drop=True).groupby(['Year','Type'])['Pts'].rank(ascending=0,method='min'))
                 
    #create new column that takes the difference between gprank and rank_int columns created above-this fails with KeyError: 'gprank'
    .assign(ck_rank = all_wks_raw['gprank'] - all_wks_raw['rank_int'])
)

Are the results of the first two assignments not being passed to the third? Am I missing something in the syntax? Thanks for the help.

Edited 7/20/2022 to add complete code; note that this code scrapes data from the College Poll Archive web site:

dict = {1119: [2016, '2016 Final AP Football Poll', 'Final'], 1120: [2017, '2017 Preseason AP Football Poll', 'Preseason'],
        1135: [2017, '2017 Final AP Football Poll', 'Final'], 1136: [2018, '2018 Preseason AP Football Poll', 'Preseason'],
        1151: [2018, '2018 Final AP Football Poll', 'Final'], 1152: [2019, '2019 Preseason AP Football Poll', 'Preseason']}

#get one week of poll data from College Poll Archive ID parameter
def getdata(id):
    coldefs = {'ID':key, 'Year': value[0], 'Title': value[1], 'Type':value[2]} #define dictionary of scalar columns to add to dataframe
    urlseg = 'https://www.collegepollarchive.com/football/ap/seasons.cfm?appollid='
    url = urlseg + str(id)
    dfs = pd.read_html(url)
    df = dfs[0].assign(**coldefs)
    return df

all_wks_raw = pd.DataFrame()

for key, value in dict.items():
    print(key, value[0], value[2])
    onewk = getdata(key)
    all_wks_raw = all_wks_raw.append(onewk)
   
all_wks_clean = (all_wks_raw
    #create new column that converts Rank to numeric-this works
    .assign(rank_int = pd.to_numeric(all_wks_raw['Rank'], errors='coerce').fillna(0))
                 
    #create new column that re-ranks teams based on Points: extends rankings beyond original 25-this works
    .assign(gprank = all_wks_raw.reset_index(drop=True).groupby(['Year','Type'])['Pts'].rank(ascending=0,method='min'))
                 
    #create new column that takes the difference between gprank and rank_int columns created above-this fails with KeyError: 'gprank'
    .assign(ck_rank = all_wks_raw['gprank'] - all_wks_raw['rank_int'])
)