Task: Transform-by-output-target-schema (source dataset: github-pipelines)

Your task is to create a data transformation pipeline in Python using pandas libraries.

Given Source Tables below, and a Target Schema provided by users that demonstrates how the desired output table should look like schematically, please write Python pandas code that can transform the Source Tables into an output table, that matches the schema/structure given by the Target Schema.  Please note that the data values in the Target Schema are intended to represent the format and layout of the output table, but may not exactly match the actual data generated from the Source Tables when the python code is run on the Source Tables.

Use operations such as merge, groupby, pivot, apply, or any appropriate python or pandas steps. Your code should save the resulting DataFrame to a CSV file named 'output.csv'. Ensure that the code is executable and can be run directly as is, in a Python environment with pandas installed.

Source Tables:


Table #1
Filename: source_0.csv
Sample Rows in Markdown:
|       id | name                                             |   host_id | host_name        | neighbourhood_group   | neighbourhood                         |   latitude |   longitude | room_type       |   price |   minimum_nights |   number_of_reviews | last_review   |   reviews_per_month |   calculated_host_listings_count |   availability_365 |
|---------:|:-------------------------------------------------|----------:|:-----------------|:----------------------|:--------------------------------------|-----------:|------------:|:----------------|--------:|-----------------:|--------------------:|:--------------|--------------------:|---------------------------------:|-------------------:|
| 16496029 | The Central Place VI                             |   2320210 | Kim              | Eixample              | la Dreta de l'Eixample                |    41.3901 |     2.16365 | Entire home/apt |     218 |                3 |                   7 | 2018-03-16    |                0.54 |                               35 |                199 |
| 16496295 | Lovely Room in Barcelona center                  | 102876877 | Dana             | Eixample              | la Nova Esquerra de l'Eixample        |    41.3821 |     2.15701 | Private room    |      31 |                2 |                  47 | 2018-05-11    |                2.65 |                                2 |                 84 |
| 16497459 | Lovely room in Barcelona center                  | 102876877 | Dana             | Eixample              | l'Antiga Esquerra de l'Eixample       |    41.3833 |     2.16    | Private room    |      28 |                2 |                   6 | 2017-02-07    |                0.34 |                                2 |                 18 |
| 16501827 | Big bright rooms in BCN center.                  | 108415963 | Marcos           | Sants-Montjuïc        | Hostafrancs                           |    41.3728 |     2.14288 | Private room    |      18 |                1 |                  31 | 2018-05-20    |                1.77 |                                2 |                341 |
| 16503468 | Habitación individual cerca a la Sagrada Familia |  83729645 | Mariajose        | Sant Martí            | el Camp de l'Arpa del Clot            |    41.4117 |     2.17874 | Private room    |      50 |                4 |                  12 | 2018-05-22    |                0.69 |                                1 |                365 |
| 16503717 | Big and comfortable room in BCN center           | 108415963 | Marcos           | Sants-Montjuïc        | Sants                                 |    41.3739 |     2.14085 | Private room    |      19 |                1 |                  28 | 2018-05-11    |                1.6  |                                2 |                350 |
| 16503978 | Ático Grenier Flat terrace  Sagrada Familia      | 108441327 | Ivana            | Eixample              | la Sagrada Família                    |    41.4015 |     2.1734  | Entire home/apt |      98 |                2 |                  29 | 2018-05-11    |                1.8  |                                1 |                261 |
| 16506242 | ROOM + BREAKFAST- SUPER CENTRIC with balcony     |  29648516 | Loving Barcelona | Ciutat Vella          | Sant Pere, Santa Caterina i la Ribera |    41.3868 |     2.17732 | Private room    |      40 |                1 |                  10 | 2018-05-15    |                1.05 |                                8 |                148 |
| 16506765 | Fabulous design apartment close to beach         |   7281883 | Michael          | Ciutat Vella          | la Barceloneta                        |    41.3773 |     2.19148 | Entire home/apt |     220 |               32 |                   4 | 2018-05-13    |                0.23 |                                4 |                228 |
| 16507693 | Triple room with balcony in the city center      | 104173257 | Carlos           | Ciutat Vella          | el Barri Gòtic                        |    41.3795 |     2.17961 | Private room    |      89 |                2 |                  82 | 2018-06-06    |                4.69 |                                3 |                136 |


Target Schema:
| neighbourhood                                |   price_24 |
|:---------------------------------------------|-----------:|
| Can Baró                                     |         23 |
| Canyelles                                    |          2 |
| Ciutat Meridiana                             |          8 |
| Diagonal Mar i el Front Marítim del Poblenou |         72 |
| Horta                                        |         29 |
| Hostafrancs                                  |        108 |
| Montbau                                      |          4 |
| Navas                                        |         34 |
| Pedralbes                                    |         18 |
| Porta                                        |         16 |

Return only the final code in markdown codeblock format, without any additional text or explanation. 



target: target.csv
train: source_0.csv
test: test_0.csv