The easiest way is to just set:

reader.fieldnames = "email", "name", "id",  "phone"

You can save the old fieldnames if you want too.

Though you already have an accepted answer, I figured I'd add this for anyone else interested in a different solution-

• The csv module's DictReader object has a public attribute called fieldnames (as of Python 2.6 and above). https://docs.python.org/3.4/library/csv.html#csv.csvreader.fieldnames

An implementation could be as follows:

import csv

with open('C:/mypath/to/csvfile.csv', 'r') as f:
    dict_reader = csv.DictReader(f)

    #get header fieldnames from DictReader and store in list
    headers = dict_reader.fieldnames

    #sample file reading logic
    for line in dict_reader:
        print(line[headers[0]])
        

In the above, dict_reader.fieldnames returns a list of your headers (assuming the headers are in the top row). Which allows...

>>> print(headers)
['MyColumn1', 'MyColumn2', 'MyColumn3']

If your headers are in, say the 2nd row (with the very top row being row 1), you could do as follows:

import csv

with open('C:/mypath/to/csvfile.csv', 'r') as f:
    #you can eat the first line before creating DictReader.
    #if no "fieldnames" param is passed into
    #DictReader object upon creation, DictReader
    #will read the upper-most line as the headers
    f.readline()
    
    dict_reader = csv.DictReader(f)
    headers = dict_reader.fieldnames

    #sample file reading logic
    for line in dict_reader:
        print(line[headers[0]])

You are looking for DictReader

with open('info.csv') as f:
    reader = csv.DictReader(f, delimiter=';')
    for row in reader:
        name = row['name']
        blah = row['blah']

to quote from the link:

Create an object which operates like a regular reader but maps the information read into a dict whose keys are given by the optional fieldnames parameter. ... If the fieldnames parameter is omitted, the values in the first row of the csvfile will be used as the fieldnames.

I'd say your best bet is to do a simple assert that heading is not None:

try:
    with open("inpu.csv", newline="") as f:
        reader = csv.DictReader(f, skipinitialspace=True)
        headings = reader.fieldnames
        assert headings is not None
        assert len(headings) == 3

        menu: list[list[str]] = []
        for row in reader:
            menu.append([row[headings[0]], row[headings[1]], row[headings[2]]])
except IOError as e:
    print(f"couldn't open CSV: {e}")
except AssertionError:
    print(
        f"couldn't get fieldnames, check CSV and ensure first line is a properly encoded row"
    )
else:
    print(menu)

If it fails, you'll know to look at the CSV file for any kind of issues.

• Before the assert, VSCode shows that headings could be Sequence[str] | None.
• After the assert, the type checker realizes that it cannot have been None, so headings must be Sequence[str].

Also, look at Barmar's and Mark Tolonen's comments, you can do what you much more simply by just reading the CSV as a list of strings:

reader = csv.reader(f)
menu = list(reader)[1:]

print(menu)

[
    ["foo", "1", "a"],
    ["bar", "2", "b"],
    ["baz", "3", "c"],
]

You can simply re-use the same key for fields you want to ignore; you'll end up with one such entry in the row dictionary with one of the column values. You do have to provide enough fields to cover all columns up to the last you do want to include.

I'd provide the sequence as a keyword argument, and you could build the tuple through multiplication:

my_tot_reader = csv.DictReader(
    tot_file, delimiter=';', restkey='ignored',
    fieldnames=("Date", "Pivot ID") + ('ignored',) * 5 + ('status',)) 

I used the field name 'ignored' instead.

Under the hood dict(zip(fields, columns)) is used, which will only produce as many (column, field) combinations as there are matching columns and fields; if fields is shorter than the number of columns the remaining columns are ignored. Duplicate field names result in just one dictionary key surviving:

>>> columns = ('some_date', 'some_id', 'foo', 'bar', 'baz', 'spam', 'eggs', 'some_status', 'qux', 'flub')
>>> fields = ("Date", "Pivot ID") + ('ignored',) * 5 + ('status',)
>>> dict(zip(fields, columns))
{'Date': 'some_date', 'Pivot ID': 'some_id', 'ignored': 'eggs', 'status': 'some_status'}

The extreneous columns are then assigned to the restkey key as a list. Simply not use the 'ignored' key in the resulting rows, or delete it with del row['ignored'] if you want to use the row dictionary elsewhere.