Is there a better way to transform a data frame into a "truth table" than looping through it?

I have a view in Redshift that I'm reading from to create a data frame. The table is structured like the following with about 49k records:

My goal was to create a data frame structured like this:

I know this structure as a "truth table" but not sure what other people call it.

I found a way to do this in Python by looping through the query results, like this:

import pandas as pd
import pandas.io.sql as sqlio

# Redshift query
df = sqlio.read_sql_query(master_order_event_view, conn) 

events = df.event_text.unique()
unique_sessions = df.session_id.unique()

# Creating Dataframe with session IDs as index and event_text values as columns
truth_df = pd.DataFrame(0, index=unique_sessions, columns=events) 

for session_id, event_text in zip(df["session_id"], df["event_text"]):
    truth_df.at[session_id, event_text] = 1
    
return truth_df

My question is: is there a built-in Pandas command/library to do this? I looked around the docs a lot but I couldn't find anything that satisfied it. Perhaps calling it a "truth table" is my problem.

Whether or not there's a built-in way to do this, does anyone have any suggestions of how to make this more efficient? Restructuring the view, using a different library, etc.

Thanks!

Big shoutout to @Dani Mesejo and @sammywemmy who gave me some suggestions.

for session_id, event_text in zip(df["session_id"], df["event_text"]):
    truth_df.at[session_id, event_text] = 1

can be replaced by either of the following:

Using crosstab:

truth_df = pd.crosstab(df.session_id, df.event_text)

or groupby:

truth_df = df.groupby(["session_id", "event_text"]).size().unstack(fill_value=0)

Both work great but according to @sammywemmy , groupby is a bit speedier.