| id | name | host_id | host_name | neighbourhood_group | neighbourhood | latitude | longitude | |
| 0 | 2539 | Clean & quiet apt home by the park | 2787 | John | Brooklyn | Kensignton | 40.64749 | -73.97237 |
| 1 | 2595 | Skylit Midtown Castle | 2845 | Jennifer | Manhattan | Midtown | 40.75362 | -73.98377 |
| 2 | 3647 | The Village Of Harlem ... New York ! | 4632 | Elisabeth | Manhattan | Harlem | 40.80902 | -73.94190 |
| 3 | 3831 | Cozy Entire Floor of Brownstone | 4869 | LisaRoxanne | Brooklyn | Clinton Hill | 40.68514 | -73,95976 |
| 4 | 5022 | Entire Apt: Spacious Studio/ Loft by central park | 7192 | Laura | Manhattan | East Harlem | 40.79851 | -73.94399 |
| 5 | 5099 | Large Cozy 1BR Apartment in Midtown East | 7322 | Chris | Manhattan | Murray Hill | 40.74767 | -73.97500 |
| 6 | 5121 | BlissArtsSpace! | 7356 | Garon | Brooklyn | Bedford-Stuyvesant | 40.68688 | -7395596 |
| 7 | 5178 | Large Furnished Room Near Bway | 8967 | Shunichi | Manhattan | Hell’s Kitchen | 40.76489 | -73.98493 |
Assuming that other file is stored in CSV format named “nysc.csv” Import pandas as pd
(i) To create the Data frame of the above data.
(ii) To print the first 10 records
(iii) To display the name and Id.