-
Notifications
You must be signed in to change notification settings - Fork 2
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
add CreateFullSericeDataframe.py and update README
- Loading branch information
Showing
3 changed files
with
109 additions
and
14 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,95 @@ | ||
| import pandas as pd | ||
| import numpy as np | ||
|
|
||
| dist_matrix_path = "../data/generated_distance_matrices/distance_matrix_new.npy" | ||
| dist_matrix_df_path = "../data/indoor_outdoor_distances_galv.csv" | ||
| indoor_pts_path = "../data/galveston_indoor_pts.csv" | ||
| outdoor_pts_path = "../data/galveston_outdoor_pts.csv" | ||
| old_pts_path = "../data/FUE_Galveston.csv" | ||
| all_pts_path = "../data/indoor_outdoor_pts_galv.csv" | ||
|
|
||
| def create_full_service_location_df(): | ||
| ''' | ||
| 1. Loads the npy distance matrix file | ||
| (made for the galveston moody gardens + indoor + outdoor points) | ||
| and converts it into a csv file --> saves the csv file in the data folder | ||
| 2. Concatonates the galveston_indoor_pts and galveston_outdoor_pts dataframes | ||
| into one full dataframe will all service locations | ||
| 3. Performs data cleaning on the merged service locations df --> | ||
| saves the csv in the data folder | ||
| :params: None | ||
| :return: None | ||
| ''' | ||
| #load the distance matrix file, it is a numpy array | ||
| dist_matrix_array = np.load(dist_matrix_path) | ||
|
|
||
| #convert the distance matrix to a dataframe | ||
| dist_matrix_df=pd.DataFrame(dist_matrix_array) | ||
| #name columns | ||
| dist_matrix_df.columns = [str(i) for i in range(dist_matrix_array.shape[1])] | ||
|
|
||
| #save to csv | ||
| dist_matrix_df.to_csv(dist_matrix_df_path, index=False) | ||
|
|
||
| # Load the indoor pts df and the original FUE df | ||
| indoor = pd.read_csv(indoor_pts_path) | ||
| old_galveston = pd.read_csv(old_pts_path) | ||
|
|
||
| #1. merge the indoor pts df with the columns "Weekly_Dropoff_Totes" | ||
| #and "Daily_Pickup_Totes" from the original df | ||
| galveston_sub = old_galveston.loc[:, ["Name", "Weekly_Dropoff_Totes", "Daily_Pickup_Totes"]] | ||
| indoor = pd.merge(indoor, | ||
| galveston_sub, | ||
| on = "Name", | ||
| how = 'left') | ||
|
|
||
| # 2. add the "Moody Gardens" location to the top of the indoor dataframe | ||
| #extract the Moody Gardens columns from the old dataframe | ||
| moody_gardens = old_galveston.loc[(old_galveston.loc[:, "Name"]== | ||
| "Moody Gardens"), ( | ||
| ["Name", "Longitude", "Latitude", "Daily_Pickup_Totes", "Weekly_Dropoff_Totes"])] | ||
|
|
||
| #concatenate the column on top of the indoor points df | ||
| moody_plus_indoor = pd.concat([moody_gardens, indoor]) | ||
|
|
||
| # 3. add a column "location_type" = "indoor" to every point in the indoor dataframe | ||
| moody_plus_indoor.loc[:, "location_type"] = "indoor" | ||
|
|
||
| # 4. add a column "pickup_type" = "Truck" to every point in the indoor dataframe | ||
| moody_plus_indoor.loc[:, "pickup_type"] = "Truck" | ||
|
|
||
| #Load outdoor pts df | ||
| outdoor = pd.read_csv(outdoor_pts_path) | ||
|
|
||
| # 5. add a daily pickup value of 1.0 and weekly dropoff value to 0.0 to every outdoor point | ||
| outdoor.loc[:, "Daily_Pickup_Totes"] = 1.0 | ||
| outdoor.loc[:, "Weekly_Dropoff_Totes"] = 0.0 | ||
|
|
||
| # 6. add a column "location_type" = "outdoor" to every point in the outdoor dataframe | ||
| outdoor.loc[:, "location_type"] = "outdoor" | ||
|
|
||
| # 7. capitalize the "longitude" and "latitude" columns in the outdoor df | ||
| outdoor = outdoor.rename(columns={"longitude": "Longitude", "latitude": "Latitude"}) | ||
|
|
||
| # 8. concatonate the indoor and outdoor points into a single dataframe | ||
| full_service_locations = pd.concat([moody_plus_indoor, outdoor]) | ||
|
|
||
| # 9. sets "location_type" = "depot" for "Moody Garden" | ||
| full_service_locations.loc[(full_service_locations.loc[:, "Name"]== "Moody Gardens"), | ||
| "location_type"] = "depot" | ||
|
|
||
| # 10. fill in missing values for pickup and dropoff totes | ||
| full_service_locations.loc[full_service_locations.loc[:, "Daily_Pickup_Totes"].isna(), "Daily_Pickup_Totes"] = 1.0 | ||
| full_service_locations.loc[full_service_locations.loc[:, "Weekly_Dropoff_Totes"].isna(), "Weekly_Dropoff_Totes"] = 1.0 | ||
|
|
||
| columns_to_convert = ["Daily_Pickup_Totes", "Weekly_Dropoff_Totes"] | ||
| full_service_locations[columns_to_convert] = full_service_locations[columns_to_convert].astype(int) | ||
| #reset index and check | ||
| full_service_locations.reset_index(inplace=True, drop=True) | ||
|
|
||
| #save to csv file | ||
| full_service_locations.to_csv(all_pts_path, index=False) | ||
|
|
||
| if __name__ == "__main__": | ||
| create_full_service_location_df() |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters