List of dataframe values beginning with x,y or z

[glidecode]

I have found a way to return all rows of a specified column in a data frame beginning with letter x.
The following will return a new dataframe reduced to the specified column and for row where the expression evaluates to True:

sorted = rawdata[rawdata['Ticker'].astype(str).str[0]=="A"]

However, when I need to check that the first letter is either of [x,y or z] I get stuck.

I have tried:

sorted = rawdata[rawdata['Ticker'].astype(str).str[0]=="A" | "B"]
sorted = rawdata[rawdata['Ticker'].astype(str).str[0]==["A" | "B"]]
sorted = rawdata[rawdata['Ticker'].astype(str).str[0]=="A" | rawdata['Ticker'].astype(str).str[0]=="B"]


As I have spent hours on this problem, I'd be very interested to know what the logic is behind the way Python/Pandas works here.
I'm surprised that there isn't a simple pandas function for this, similar to SQL query style.

[jefsummers]

Did you try using "in", as in ..... in ['x','y','z']
?

[glidecode]

It then gives me this error (which I have run into many times today):
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

[glidecode]

I figured out the problem!

It is described here: https://cumsum.wordpress.com/2021/05/05/...type-bool/

Simply, the expression is interpreted with priority to the "|" - so in my example, it will look to see if the first letter is equal to A or equal to the rest of the expression. Hope this makes sense to anyone looking for the same.

I fixed it like this - simply adding two sets of parentheses:

sorted = rawdata[(rawdata['Ticker'].astype(str).str[0] =="A") | (rawdata['Ticker'].astype(str).str[0] =="B")]