I need help with doing step 3 part 2 I am not sure how to tackle the problem Thank you Download Pittsburgh Healthy Ride datasets from https drive google com file d 1NH5ZEjWF XAIuOw52ge0aCX10534ipk view usp sharing Unzip the files and make sure that the list of subfolders is in the same folder as your Python script You should see the following list of subfolders 2015 Q2 2015 Q3 2015 Q4 2016 Q1 2016 Q2 2016 Q3 2016 Q4 Each subfolder contains two comma separated values ( csv) files HealthyRide Rentals year quarter (ex HealthyRide Rentals 2015 Q2) HealthyRideStations year (ex HealthyRideStations2015) Each HealthyRide Rentals year quarter file contains the following fields (columns) Tripid, Starttime, Stoptime, Bikeid, Tripduration, From station id, From station name, To station id, To station name, Usertype Each HealthyRideStations year file contains the following fields StationNum, StationName, RackQnty, Latitude, Longitude Note that From station id and To station id map to StationNum you will need to join the two datasets on those identifiers Steps to complete the program Create two Pandas dataframes one to store ride data and another to store station data For loop to iterate through subfolders and files Append each row from HealthyRide Rentals year quarter to the dataframe of ride data An easy way to do this would be to create a temporary dataframe inside the loop, read in the data, and append the temporary dataframe to the master dataframe Hint you will need to use the concat() or append() function https pandas pydata org pandas docs stable merging html For the HealthyRideStations year file append only unique rows In other words, you need to make sure that the dataset that contains stations data does not have duplicates

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I need help with doing step 3 part 2...I am not sure how to tackle the problem. Thank you! Download Pittsburgh Healthy Ride datasets from

I need help with doing step 3 part 2...I am not sure how to tackle the problem. Thank you!

Download Pittsburgh Healthy Ride datasets from https://drive.google.com/file/d/1NH5ZEjWF-XAIuOw52ge0aCX10534ipk-/view?usp=sharing
Unzip the files and make sure that the list of subfolders is in the same folder as your Python script
1. You should see the following list of subfolders:
  1. 2015-Q2
  2. 2015-Q3
  3. 2015-Q4
  4. 2016-Q1
  5. 2016-Q2
  6. 2016-Q3
  7. 2016-Q4
2. Each subfolder contains two comma-separated values (.csv) files:
  1. HealthyRide Rentals [year] [quarter] (ex: HealthyRide Rentals 2015 Q2)
  2. HealthyRideStations[year] (ex: HealthyRideStations2015)
3. Each HealthyRide Rentals [year] [quarter] file contains the following fields (columns): Tripid, Starttime, Stoptime, Bikeid, Tripduration, From station id, From station name, To station id, To station name, Usertype
4. Each HealthyRideStations[year] file contains the following fields: StationNum, StationName, RackQnty, Latitude, Longitude
5. Note that From station id and To station id map to StationNum - you will need to join the two datasets on those identifiers
Steps to complete the program
1. Create two Pandas dataframes - one to store ride data and another to store station data
2. For loop to iterate through subfolders and files
  1. Append each row from HealthyRide Rentals [year] [quarter] to the dataframe of ride data. An easy way to do this would be to create a temporary dataframe inside the loop, read in the data, and append the temporary dataframe to the master dataframe. Hint: you will need to use the .concat() or .append() function: https://pandas.pydata.org/pandas-docs/stable/merging.html
  2. For the HealthyRideStations[year] file append only unique rows. In other words, you need to make sure that the dataset that contains stations data does not have duplicates.