Expert level Python, Pandas Data Analyst needed to Transform the data
Budget: $30 – $250 USD
Need to populate missing fields in a dataframe based on certain columns.
For example: In Tickets df, We have the following columns:
prices, no.of passengers, operators, routes ( LA-Las Vegas), etc
In Buses df we have the following columns:
Date, distance, occupancy, Bus_number, Operator, routes(LA - Las Vegas), etc.
Now we are merging these two tables to do further data manipulation.
We want to pull in prices, no.of passengers from Tickets df into Buses df.
Primary keys will be Routes, Operators
The problem here is, for all “operators” in Buses df, we don’t have a matching “operator” in Tickets df to pull.
For those Operators with no match in Tickets df, we need to fill in the “average price” for a ticket & “no.of passengers” based on available operators data for the same “route”.
For example: In Tickets df, We have the following columns:
prices, no.of passengers, operators, routes ( LA-Las Vegas), etc
In Buses df we have the following columns:
Date, distance, occupancy, Bus_number, Operator, routes(LA - Las Vegas), etc.
Now we are merging these two tables to do further data manipulation.
We want to pull in prices, no.of passengers from Tickets df into Buses df.
Primary keys will be Routes, Operators
The problem here is, for all “operators” in Buses df, we don’t have a matching “operator” in Tickets df to pull.
For those Operators with no match in Tickets df, we need to fill in the “average price” for a ticket & “no.of passengers” based on available operators data for the same “route”.