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The data summarised in Table 1 have been provided in the le, road_network_data.csv. Write a function, network_import(), to import these data into a dataframe, network_df, and add columns for the calculated (peak hour) capacity and conductance of each segment. Note that a NumPy array can be added as a column to a dataframe with the following type of command: »> df[‘Column_name’]=array_name Assign a useful index to the dataframe and then export it to a .csv le. The function should return the dataframe, network_df, and a NumPy array, locations, containing the names of all the nodes (eg. ‘Darra’, ‘Logan’, etc). All location names should appear in the array only once. network_import() => network_df, locations 
2. Write a function, create_adj(*) to manipulate the imported data and create an adjacency matrix for the road network. Remember that an adjacency matrix would have the unique locations (i.e. nodes) as both row indexes and column indexes. The function should return the adjacency matrix in the form of an array with dimensions, (num_nodes x num_nodes). create_adj(*) => adj_arr
3. Write a function, create_inc(*), to manipulate the imported data and create an incidence matrix for the road network. Remember that an incidence matrix would have the road segments as row indexes and the nodes as column indexes. i.e. it should be an array with dimensions, (num_segments x num_nodes). create_inc(*) => inc_arr
4. Write a function, create_con(*), to manipulate the imported data and create a conductance matrix for the road network. This matrix should be called cond_arr and it should have dimensions, (num_segments x num_segments). create_cond(*) => cond_arr

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