Merge pull request #1065 from cal-itp/corridor_study_sb125

Corridor study SB125

Author: edasmalchi

sb125_analyses/corridor_study/_utils.py

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+"""
+Replica and Streetlight Analysis Utils
+"""
+
+import pandas as pd
+from siuba import *
+import ast
+
+from calitp_data_analysis.sql import to_snakecase
+
+import altair as alt
+from calitp_data_analysis import calitp_color_palette as cp
+
+
+"""
+Replica Analysis Utils
+"""
+##function that returns Replica transit data into df we can analyze easier
+def get_tranist_agency_counts(df, primary_mode_col, transit_mode_col, transit_agency_col, activity_id_col):
+    ## return a df with the agency counts
+    agencies = (df
+         >>filter(_[primary_mode_col] =="public_transit")
+         >>group_by(_[primary_mode_col], _[transit_mode_col], _[transit_agency_col])
+         >>summarize(n =_[activity_id_col].nunique())
+         >>arrange(-_.n))
+    
+    agencies[transit_mode_col] = agencies[transit_mode_col].astype(str)
+    agencies[transit_agency_col] = agencies[transit_agency_col].astype(str)
+    
+    agencies['agency_count'] = [len(set(x.split(", "))) for x in
+                        agencies[transit_agency_col].str.lower()]
+    agencies['n_modes_taken'] = agencies[transit_mode_col].apply(lambda x: len(x.split()))
+    
+    ## return a df with the mode counts
+    modes = (df
+             >>filter(_[primary_mode_col] =="public_transit")
+             >>count(_[transit_mode_col])>>arrange(-_.n))
+    
+    modes[transit_mode_col] = modes[transit_mode_col].astype(str)
+    # modes[transit_agency_col] = modes[transit_agency_col].astype(str)
+    
+    modes['n_modes_taken'] = modes[transit_mode_col].apply(lambda x: len(x.split()))
+
+    return agencies, modes
+
+def get_list_of_agencies(df, transit_agency_col):
+    
+    ## Get just one columns
+    column = df[[transit_agency_col]]
+    #remove single-dimensional entries from the shape of an array
+    col_text = column.squeeze()
+    # get list of words
+    text_list = col_text.tolist()
+    # #join list of words 
+    text_list = ', '.join(text_list).title()
+    
+    text_list = text_list.replace(", ", "', '")
+    text_list = "['" + text_list + "']"
+    
+    agency_list = ast.literal_eval(text_list)
+    agency_list = set(agency_list)
+    
+    return agency_list
+
+def get_dummies_by_agency(df, col):
+    transit_agencies = set()
+    for agencies in df[col].str.split(', '):
+        transit_agencies.update(agencies)
+    unique_agencies = []
+    
+    for agency in transit_agencies:
+        df[agency] = df[col].str.count(agency)
+        unique_agencies.append(agency)
+
+    ### adding column for unique agencies list
+    def get_unique_agencies(agency_list):
+        unique_agencies = set()
+        for agencies in agency_list:
+            unique_agencies.update(agencies.split(', '))
+        return ', '.join(sorted(list(unique_agencies)))
+
+    # Applying the function to each row of the dataframe to get unique agencies
+    df['unique_agencies'] = df[col].str.split(', ').apply(lambda x: get_unique_agencies(x))
+    
+    return df
+
+def get_agencies_occurances(df):
+    
+    ##get list of columns that have agency names
+    columns_to_keep = list(df.columns.values)
+    
+    ## remove columns that are not agency names
+    unwanted_cols = {'transit_agency', 'unique_agencies', 'primary_mode', 'transit_submode', 'n', 'agency_count', 'n_modes_taken' }
+    columns_to_keep = [e for e in columns_to_keep if e not in unwanted_cols]
+    
+    df_agencies = df.loc[:, columns_to_keep]
+    
+    ## transpose the df again
+    df_agencies = pd.DataFrame(df_agencies).transpose().reset_index().rename(columns={'index':'agency'})
+    
+    ##sum up the number of trips by agency
+    df_agencies['n_trips'] = df_agencies[list(df_agencies.columns)].sum(axis=1)
+    
+    ## filter df to only have two cols
+    df_agencies = df_agencies>>select(_.agency, _.n_trips)
+    
+    return df_agencies
+
+"""
+Streetlight Analysis Utils
+"""
+
+