MMTU-benchmark/MMTU huggingface dataset:
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:
|   Purchase ID | SN            |   Age | Gender   |   Item ID | Item Name                   |   Price |
|--------------:|:--------------|------:|:---------|----------:|:----------------------------|--------:|
|           390 | Asur53        |    26 | Male     |        75 | Brutality Ivory Warmace     |    2.42 |
|           391 | Alarap40      |    18 | Male     |        20 | Netherbane                  |    3.27 |
|           392 | Sida61        |    20 | Female   |       181 | Reaper's Toll               |    1.66 |
|           393 | Hiarideu73    |    20 | Male     |        54 | Eternal Cleaver             |    2.5  |
|           394 | Lassassasda30 |    12 | Male     |        60 | Wolf                        |    3.54 |
|           395 | Ennoncil86    |    17 | Male     |       152 | Darkheart                   |    1.74 |
|           396 | Siralsudeu54  |    20 | Male     |       131 | Fury                        |    1.44 |
|           397 | Eusri26       |    25 | Male     |       100 | Blindscythe                 |    3.27 |
|           398 | Ardcil81      |    34 | Male     |        96 | Blood-Forged Skeletal Spine |    3.09 |
|           399 | Marilsasya33  |    19 | Male     |       143 | Frenzied Scimitar           |    1.56 |


Target Schema:
| SN           |   Price |
|:-------------|--------:|
| Adastirin33  |    4.48 |
| Aeda94       |    4.91 |
| Aela59       |    4.32 |
| Aelastirin39 |    3.15 |
| Aelin32      |    6.94 |
| Aelly27      |    4.18 |
| Aellynun67   |    3.74 |
| Aelollo59    |    2.49 |
| Aenarap34    |    2.96 |
| Aeral43      |    4.4  |

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
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:
| County      | r1403       |
|:------------|:------------|
| Adams       | NR          |
| Boulder     | $1,396,479  |
| Clear Creek | $524,541    |
| Denver      | $10,746,117 |
| Garfield    | NR          |
| Gilpin      | $131,685    |
| Gunnison    | NR          |
| Jefferson   | $1,056,723  |
| Lake        | NR          |
| Ouray       | NR          |


Table #2
Filename: source_1.csv
Sample Rows in Markdown:
| County      | r1402      |
|:------------|:-----------|
| Adams       | NR         |
| Boulder     | NR         |
| Clear Creek | $445,976   |
| Denver      | $8,349,069 |
| Garfield    | NR         |
| Gilpin      | $113,337   |
| Gunnison    | NR         |
| Jefferson   | $880,367   |
| Lake        | NR         |
| Ouray       | NR         |


Table #3
Filename: source_2.csv
Sample Rows in Markdown:
| County     |
|:-----------|
| Kit Carson |
| La Plata   |
| Lake       |
| Larimer    |
| Las Animas |
| Lincoln    |
| Logan      |
| Mesa       |
| Mineral    |
| Moffat     |


Table #4
Filename: source_3.csv
Sample Rows in Markdown:
| County      | r1401      |
|:------------|:-----------|
| Adams       | NR         |
| Clear Creek | $459,640   |
| Denver      | $8,482,593 |
| Garfield    | NR         |
| Gilpin      | $197,379   |
| Gunnison    | NR         |
| Jefferson   | $853,675   |
| Lake        | NR         |
| Ouray       | NR         |
| Park        | NR         |


Target Schema:
| County      | r1401      | r1403       |
|:------------|:-----------|:------------|
| Adams       | NR         | NR          |
| Clear Creek | $459,640   | $524,541    |
| Denver      | $8,482,593 | $10,746,117 |
| Garfield    | NR         | NR          |
| Gilpin      | $197,379   | $131,685    |
| Gunnison    | NR         | NR          |
| Jefferson   | $853,675   | $1,056,723  |

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



target: target.csv
train: source_0.csv
source_1.csv
source_2.csv
source_3.csv
test: test_0.csv
test_1.csv
test_2.csv
test_3.csv
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:
| city          |   driver_count | type   |
|:--------------|---------------:|:-------|
| Richardfort   |             38 | Urban  |
| Williamsstad  |             59 | Urban  |
| Port Angela   |             67 | Urban  |
| Rodneyfort    |             34 | Urban  |
| West Robert   |             39 | Urban  |
| West Anthony  |             70 | Urban  |
| West Angela   |             48 | Urban  |
| Martinezhaven |             25 | Urban  |
| Karenberg     |             22 | Urban  |
| Barajasview   |             26 | Urban  |


Table #2
Filename: source_1.csv
Sample Rows in Markdown:
| city                | date                |   fare |       ride_id |
|:--------------------|:--------------------|-------:|--------------:|
| Leahton             | 2018-02-28 20:02:59 |  31.01 | 2116500207936 |
| Johnton             | 2018-04-11 14:40:12 |  16.63 | 7248459450448 |
| Port David          | 2018-01-21 02:29:36 |  32.92 |  857956984195 |
| New Kimberlyborough | 2018-04-01 04:31:44 |  35.11 | 8275820093507 |
| Port Frank          | 2018-02-26 05:03:19 |  25.18 | 7280804922163 |
| Barajasview         | 2018-01-24 05:58:33 |   9.21 | 1841073494391 |
| South Phillip       | 2018-04-21 10:31:27 |  32.85 | 2752328490329 |
| North Jasmine       | 2018-04-03 13:15:16 |  19.66 | 9206122228723 |
| New Kimberlyborough | 2018-03-13 09:48:42 |  14.84 | 1839171122889 |
| North Jasmine       | 2018-01-05 16:39:33 |  26.72 | 3669060555225 |


Target Schema:
| city            | type   |    fare |
|:----------------|:-------|--------:|
| Amandaburgh     | Urban  | 23.072  |
| Barajasview     | Urban  | 28.7544 |
| Carriemouth     | Urban  | 27.2505 |
| Christopherfurt | Urban  | 22.5857 |
| Deanville       | Urban  | 24.868  |
| East Kaylahaven | Urban  | 22.62   |
| Erikaland       | Urban  | 22.5211 |
| Grahamburgh     | Urban  | 22.9356 |
| Huntermouth     | Urban  | 28.8445 |
| Hurleymouth     | Urban  | 26.3852 |

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



target: target.csv
train: source_0.csv
source_1.csv
test: test_0.csv
test_1.csv
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:
| city                | date                |   fare |       ride_id |
|:--------------------|:--------------------|-------:|--------------:|
| Leahton             | 2018-02-28 20:02:59 |  31.01 | 2116500207936 |
| Johnton             | 2018-04-11 14:40:12 |  16.63 | 7248459450448 |
| Port David          | 2018-01-21 02:29:36 |  32.92 |  857956984195 |
| New Kimberlyborough | 2018-04-01 04:31:44 |  35.11 | 8275820093507 |
| Port Frank          | 2018-02-26 05:03:19 |  25.18 | 7280804922163 |
| Barajasview         | 2018-01-24 05:58:33 |   9.21 | 1841073494391 |
| South Phillip       | 2018-04-21 10:31:27 |  32.85 | 2752328490329 |
| North Jasmine       | 2018-04-03 13:15:16 |  19.66 | 9206122228723 |
| New Kimberlyborough | 2018-03-13 09:48:42 |  14.84 | 1839171122889 |
| North Jasmine       | 2018-01-05 16:39:33 |  26.72 | 3669060555225 |


Table #2
Filename: source_1.csv
Sample Rows in Markdown:
| city          |   driver_count | type   |
|:--------------|---------------:|:-------|
| Richardfort   |             38 | Urban  |
| Williamsstad  |             59 | Urban  |
| Port Angela   |             67 | Urban  |
| Rodneyfort    |             34 | Urban  |
| West Robert   |             39 | Urban  |
| West Anthony  |             70 | Urban  |
| West Angela   |             48 | Urban  |
| Martinezhaven |             25 | Urban  |
| Karenberg     |             22 | Urban  |
| Barajasview   |             26 | Urban  |


Target Schema:
| city            | type   |    fare |
|:----------------|:-------|--------:|
| Amandaburgh     | Urban  | 23.072  |
| Barajasview     | Urban  | 28.7544 |
| Carriemouth     | Urban  | 27.2505 |
| Christopherfurt | Urban  | 22.5857 |
| Deanville       | Urban  | 24.868  |
| East Kaylahaven | Urban  | 22.62   |
| Erikaland       | Urban  | 22.5211 |
| Grahamburgh     | Urban  | 22.9356 |
| Huntermouth     | Urban  | 28.8445 |
| Hurleymouth     | Urban  | 26.3852 |

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



target: target.csv
train: source_0.csv
source_1.csv
test: test_0.csv
test_1.csv
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:
|   customer_id |   amount | date       |
|--------------:|---------:|:-----------|
|         95260 |       25 | 2009-04-15 |
|         57890 |       20 | 2012-03-08 |
|        185580 |       30 | 2012-11-30 |
|        252010 |       60 | 2015-10-14 |
|        186910 |       30 | 2013-10-08 |
|         77950 |       30 | 2008-04-12 |
|         24980 |       30 | 2008-11-08 |
|        261690 |      500 | 2015-12-22 |
|        198000 |       25 | 2015-11-03 |
|        227600 |       30 | 2015-02-11 |


Target Schema:
|   customer_id |   amount_x |   amount_y |   avg_amount_spent |
|--------------:|-----------:|-----------:|-------------------:|
|            80 |          4 |    75      |            75      |
|            90 |          6 |   121.333  |           121.333  |
|           130 |          1 |    60      |            60      |
|           190 |          2 |    65      |            65      |
|           220 |          1 |    30      |            30      |
|           240 |          1 |    10      |            10      |
|           290 |          3 |    58.3333 |            58.3333 |
|           320 |          1 |    15      |            15      |
|           330 |          3 |    36.6667 |            36.6667 |
|           400 |          1 |    25      |            25      |

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
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:
|       x |       y | label   |
|--------:|--------:|:--------|
| 1.98886 | 3.10233 | g       |
| 1.74817 | 3.41406 | g       |
| 1.28044 | 3.6944  | g       |
| 1.78928 | 3.41418 | g       |
| 1.10323 | 3.04995 | g       |
| 1.44789 | 3.5359  | g       |
| 1.9086  | 3.66379 | g       |
| 1.29361 | 3.51489 | g       |
| 1.28778 | 3.94459 | g       |
| 1.13003 | 3.58656 | g       |


Table #2
Filename: source_1.csv
Sample Rows in Markdown:
|       x |       y | label   |
|--------:|--------:|:--------|
| 3.01988 | 3.55624 | purple  |
| 3.02621 | 3.13646 | purple  |
| 3.02831 | 3.05992 | purple  |
| 3.24621 | 3.12134 | purple  |
| 3.86003 | 3.04455 | purple  |
| 3.53883 | 3.10749 | purple  |
| 3.55282 | 3.22571 | purple  |
| 3.84203 | 3.71299 | purple  |
| 3.12417 | 3.55972 | purple  |
| 3.27918 | 3.01256 | purple  |


Table #3
Filename: source_2.csv
Sample Rows in Markdown:
|       x |       y | label   |
|--------:|--------:|:--------|
| 3.88331 | 1.32664 | b       |
| 3.62367 | 1.52706 | b       |
| 3.75094 | 1.88594 | b       |
| 3.3489  | 1.35727 | b       |
| 3.26993 | 1.90854 | b       |
| 3.89589 | 1.62336 | b       |
| 3.42809 | 1.01582 | b       |
| 3.96484 | 1.92944 | b       |
| 3.66344 | 1.6909  | b       |
| 3.6217  | 1.99732 | b       |


Table #4
Filename: source_3.csv
Sample Rows in Markdown:
|       x |       y | label   |
|--------:|--------:|:--------|
| 1.41919 | 1.09835 | r       |
| 1.68522 | 1.42111 | r       |
| 1.20445 | 1.95789 | r       |
| 1.87812 | 1.53317 | r       |
| 1.02739 | 1.69188 | r       |
| 1.67047 | 1.31552 | r       |
| 1.4173  | 1.6865  | r       |
| 1.55869 | 1.83463 | r       |
| 1.14039 | 1.01829 | r       |
| 1.1981  | 1.75014 | r       |


Target Schema:
| label   |   x |   y |
|:--------|----:|----:|
| b       |  20 |  20 |
| g       |  20 |  20 |
| purple  |  20 |  20 |
| r       |  10 |  10 |

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



target: target.csv
train: source_0.csv
source_1.csv
source_2.csv
source_3.csv
test: test_0.csv
test_1.csv
test_2.csv
test_3.csv
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:
|   WarNum | TransTo   |
|---------:|:----------|
|        1 |           |
|        1 |           |
|        4 |           |
|        4 |           |
|        7 |           |
|        7 |           |
|       10 |           |
|       10 |           |
|       10 |           |
|       10 |           |


Table #2
Filename: source_1.csv
Sample Rows in Markdown:
|   WarNum | TransTo   |
|---------:|:----------|
|      751 |           |
|      751 |           |
|      752 |           |
|      753 |           |
|      755 |           |
|      755 |           |
|      756 | 170.0     |
|      756 | 170.0     |
|      756 | 170.0     |
|      757 |           |


Table #3
Filename: source_2.csv
Sample Rows in Markdown:
|   WarNum | TransTo   |
|---------:|:----------|
|      300 |           |
|      300 |           |
|      301 |           |
|      302 |           |
|      303 |           |
|      304 |           |
|      305 |           |
|      306 |           |
|      307 |           |
|      308 |           |


Table #4
Filename: source_3.csv
Sample Rows in Markdown:
|   WarNum | TransTo   |
|---------:|:----------|
|     1500 |           |
|     1501 |           |
|     1502 |           |
|     1503 |           |
|     1505 |           |
|     1506 |           |
|     1508 |           |
|     1509 |           |
|     1510 |           |
|     1511 |           |


Target Schema:
|   TransTo |   WarNum |
|----------:|---------:|
|         1 |        1 |
|         4 |        4 |
|        10 |        1 |
|        19 |        3 |
|        37 |        1 |
|        61 |        1 |
|        67 |        2 |
|        76 |        1 |
|        79 |        2 |
|       100 |        1 |

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



target: target.csv
train: source_0.csv
source_1.csv
source_2.csv
source_3.csv
test: test_0.csv
test_1.csv
test_2.csv
test_3.csv
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:
|   WarNum | TransTo   |
|---------:|:----------|
|      300 |           |
|      300 |           |
|      301 |           |
|      302 |           |
|      303 |           |
|      304 |           |
|      305 |           |
|      306 |           |
|      307 |           |
|      308 |           |


Table #2
Filename: source_1.csv
Sample Rows in Markdown:
|   WarNum | TransTo   |
|---------:|:----------|
|        1 |           |
|        1 |           |
|        4 |           |
|        4 |           |
|        7 |           |
|        7 |           |
|       10 |           |
|       10 |           |
|       10 |           |
|       10 |           |


Table #3
Filename: source_2.csv
Sample Rows in Markdown:
|   WarNum | TransTo   |
|---------:|:----------|
|      751 |           |
|      751 |           |
|      752 |           |
|      753 |           |
|      755 |           |
|      755 |           |
|      756 | 170.0     |
|      756 | 170.0     |
|      756 | 170.0     |
|      757 |           |


Table #4
Filename: source_3.csv
Sample Rows in Markdown:
|   WarNum | TransTo   |
|---------:|:----------|
|     1500 |           |
|     1501 |           |
|     1502 |           |
|     1503 |           |
|     1505 |           |
|     1506 |           |
|     1508 |           |
|     1509 |           |
|     1510 |           |
|     1511 |           |


Target Schema:
|   WarNum |   TransTo |
|---------:|----------:|
|        1 |         0 |
|        4 |         0 |
|        7 |         0 |
|       10 |         0 |
|       13 |         0 |
|       16 |         0 |
|       19 |         0 |
|       22 |         0 |
|       25 |         0 |
|       28 |         0 |

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



target: target.csv
train: source_0.csv
source_1.csv
source_2.csv
source_3.csv
test: test_0.csv
test_1.csv
test_2.csv
test_3.csv
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:
|   Purchase ID | SN            |   Age | Gender   |   Item ID | Item Name                   |   Price |
|--------------:|:--------------|------:|:---------|----------:|:----------------------------|--------:|
|           390 | Asur53        |    26 | Male     |        75 | Brutality Ivory Warmace     |    2.42 |
|           391 | Alarap40      |    18 | Male     |        20 | Netherbane                  |    3.27 |
|           392 | Sida61        |    20 | Female   |       181 | Reaper's Toll               |    1.66 |
|           393 | Hiarideu73    |    20 | Male     |        54 | Eternal Cleaver             |    2.5  |
|           394 | Lassassasda30 |    12 | Male     |        60 | Wolf                        |    3.54 |
|           395 | Ennoncil86    |    17 | Male     |       152 | Darkheart                   |    1.74 |
|           396 | Siralsudeu54  |    20 | Male     |       131 | Fury                        |    1.44 |
|           397 | Eusri26       |    25 | Male     |       100 | Blindscythe                 |    3.27 |
|           398 | Ardcil81      |    34 | Male     |        96 | Blood-Forged Skeletal Spine |    3.09 |
|           399 | Marilsasya33  |    19 | Male     |       143 | Frenzied Scimitar           |    1.56 |


Target Schema:
| Gender                |   Purchase ID |   SN |   Age |   Item ID |   Item Name |   Price |
|:----------------------|--------------:|-----:|------:|----------:|------------:|--------:|
| Female                |            56 |   56 |    56 |        56 |          56 |      56 |
| Male                  |           325 |  325 |   325 |       325 |         325 |     325 |
| Other / Non-Disclosed |             9 |    9 |     9 |         9 |           9 |       9 |

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
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:
| city                | date                |   fare |       ride_id |
|:--------------------|:--------------------|-------:|--------------:|
| Leahton             | 2018-02-28 20:02:59 |  31.01 | 2116500207936 |
| Johnton             | 2018-04-11 14:40:12 |  16.63 | 7248459450448 |
| Port David          | 2018-01-21 02:29:36 |  32.92 |  857956984195 |
| New Kimberlyborough | 2018-04-01 04:31:44 |  35.11 | 8275820093507 |
| Port Frank          | 2018-02-26 05:03:19 |  25.18 | 7280804922163 |
| Barajasview         | 2018-01-24 05:58:33 |   9.21 | 1841073494391 |
| South Phillip       | 2018-04-21 10:31:27 |  32.85 | 2752328490329 |
| North Jasmine       | 2018-04-03 13:15:16 |  19.66 | 9206122228723 |
| New Kimberlyborough | 2018-03-13 09:48:42 |  14.84 | 1839171122889 |
| North Jasmine       | 2018-01-05 16:39:33 |  26.72 | 3669060555225 |


Table #2
Filename: source_1.csv
Sample Rows in Markdown:
| city          |   driver_count | type   |
|:--------------|---------------:|:-------|
| Richardfort   |             38 | Urban  |
| Williamsstad  |             59 | Urban  |
| Port Angela   |             67 | Urban  |
| Rodneyfort    |             34 | Urban  |
| West Robert   |             39 | Urban  |
| West Anthony  |             70 | Urban  |
| West Angela   |             48 | Urban  |
| Martinezhaven |             25 | Urban  |
| Karenberg     |             22 | Urban  |
| Barajasview   |             26 | Urban  |


Target Schema:
| city            |       a |    b |
|:----------------|--------:|-----:|
| Amandaburgh     | 23.072  |  180 |
| Barajasview     | 28.7544 |  416 |
| Carriemouth     | 27.2505 | 1092 |
| Christopherfurt | 22.5857 |  861 |
| Deanville       | 24.868  |  735 |
| East Kaylahaven | 22.62   | 1300 |
| Erikaland       | 22.5211 |  333 |
| Grahamburgh     | 22.9356 | 1098 |
| Huntermouth     | 28.8445 |  740 |
| Hurleymouth     | 26.3852 |  828 |

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



target: target.csv
train: source_0.csv
source_1.csv
test: test_0.csv
test_1.csv
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:
| movie                      | director             |   m_year |   crit_rank | critic           | crit_org             | crit_cn   |
|:---------------------------|:---------------------|---------:|------------:|:-----------------|:---------------------|:----------|
| Grizzly Man                | Werner Herzog        |     2005 |           6 | Arturo Aguilar   | Rolling Stone Mexico | Mexico    |
| 4 Months, 3 Weeks & 2 Days | Cristian Mungiu      |     2007 |           7 | Arturo Aguilar   | Rolling Stone Mexico | Mexico    |
| Holy Motors                | Leos Carax           |     2012 |           8 | Arturo Aguilar   | Rolling Stone Mexico | Mexico    |
| The Last of the Unjust     | Claude Lanzmann      |     2013 |           9 | Arturo Aguilar   | Rolling Stone Mexico | Mexico    |
| There Will Be Blood        | Paul Thomas Anderson |     2007 |          10 | Arturo Aguilar   | Rolling Stone Mexico | Mexico    |
| The Piano Teacher          | Michael Haneke       |     2001 |           1 | Matthew Anderson | BBC Culture          | UK        |
| Margaret                   | Kenneth Lonergan     |     2011 |           2 | Matthew Anderson | BBC Culture          | UK        |
| American Psycho            | Mary Harron          |     2000 |           3 | Matthew Anderson | BBC Culture          | UK        |
| 4 Months, 3 Weeks & 2 Days | Cristian Mungiu      |     2007 |           4 | Matthew Anderson | BBC Culture          | UK        |
| Caché                      | Michael Haneke       |     2005 |           5 | Matthew Anderson | BBC Culture          | UK        |


Target Schema:
| crit_cn   |   critic |
|:----------|---------:|
| Mexico    |        5 |
| US        |       30 |

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
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
|   a |   b |
|----:|----:|
|   5 |  10 |
|   6 |  11 |
|   7 |  12 |
|   8 |  13 |
|   9 |  14 |


Output Table:
|   a |   b |
|----:|----:|
|   5 |  10 |
|   7 |  12 |
|   9 |  14 |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
|   a |   b |
|----:|----:|
|   5 |   7 |
|   6 |   8 |
|  -1 |   9 |
|  -2 |  10 |


Output Table:
|   a |   b |
|----:|----:|
|   5 |   7 |
|   6 |   8 |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
|   value1 |   value2 |
|---------:|---------:|
|      1.1 |      7.1 |
|      2   |      8   |
|      3   |      9   |
|      4   |     10   |
|      5   |     11   |
|      6   |     12   |


Output Table:
|   value1 |   value2 |
|---------:|---------:|
|      1.1 |      7.1 |
|      2   |      8   |
|      3   |      9   |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
| COL   |
|:------|
| 23.0  |
|       |
|       |
|       |
|       |
| 21.0  |
|       |
|       |
|       |
| 25.0  |


Output Table:
|   COL |
|------:|
|    23 |
|    23 |
|    23 |
|    23 |
|    23 |
|    21 |
|    21 |
|    21 |
|    21 |
|    25 |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
|   one |   two |   three |   four |   five |
|------:|------:|--------:|-------:|-------:|
|     1 |     2 |       3 |      4 |      5 |
|     1 |     1 |       1 |      1 |      1 |


Output Table:
| index   |   0 |   1 |
|:--------|----:|----:|
| one     |   1 |   1 |
| two     |   2 |   1 |
| three   |   3 |   1 |
| four    |   4 |   1 |
| five    |   5 |   1 |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
| X   | Y   | Z   |
|:----|:----|:----|
| X1  | Y2  | Z3  |
| X1  | Y1  | Z1  |
| X1  | Y1  | Z1  |
| X1  | Y1  | Z2  |


Output Table:
| Z1   | Z2   | Z3   |
|:-----|:-----|:-----|
| 1.0  | 1.0  |      |
|      |      | 1.0  |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
| ip          | useragent   |
|:------------|:------------|
| 192.168.0.1 | a           |
| 192.168.0.1 | a           |
| 192.168.0.1 | b           |
| 192.168.0.2 | b           |


Output Table:
|   0 |
|----:|
|   2 |
|   1 |
|   1 |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
| name   |   type | date       |   value |
|:-------|-------:|:-----------|--------:|
| A      |     11 | 2012-01-01 |       4 |
| B      |     11 | 2012-01-01 |       5 |
| A      |     12 | 2012-02-01 |       6 |
| B      |     12 | 2012-02-01 |       7 |


Output Table:
| 11         | 12         |
|:-----------|:-----------|
| 2012-01-01 | 2012-02-01 |
| 4.0        | 6.0        |
| 5.0        | 7.0        |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
|   Distribuzione Ponderata |   Rotazioni a volume |
|--------------------------:|---------------------:|
|                    74.04  |     140250           |
|                    90.085 |     321529           |
|                    88.65  |     120711           |
|                    49.79  |     162259           |
|                    52.125 |          1.25217e+06 |
|                    54.23  |     213258           |
|                    79.28  |     520454           |
|                    35.29  |     554927           |
|                    15.04  |     466233           |
|                    79.17  |     118629           |


Output Table:
|   Distribuzione Ponderata |   Rotazioni a volume |
|--------------------------:|---------------------:|
|                    90.085 |     321529           |
|                    74.04  |     140250           |
|                    88.65  |     120711           |
|                    79.28  |     520454           |
|                    79.17  |     118629           |
|                    54.23  |     213258           |
|                    52.125 |          1.25217e+06 |
|                    49.79  |     162259           |
|                    35.29  |     554927           |
|                    15.04  |     466233           |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
| series   |   step |   value |
|:---------|-------:|--------:|
| A        |    100 |    1000 |
| B        |    100 |    1001 |
| C        |    100 |    1002 |
| A        |    101 |    1003 |
| B        |    101 |    1004 |
| C        |    101 |    1005 |
| A        |    102 |    1006 |
| B        |    102 |    1007 |
| C        |    102 |    1008 |
| A        |    103 |    1009 |


Output Table:
|    A |    B |    C |
|-----:|-----:|-----:|
| 1000 | 1001 | 1002 |
| 1003 | 1004 | 1005 |
| 1006 | 1007 | 1008 |
| 1009 | 1010 | 1011 |
| 1012 | 1013 | 1014 |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
| RPT_Date   |   STK_ID | STK_Name   |   sales |
|:-----------|---------:|:-----------|--------:|
| 1980-01-01 |        0 | Arthur     |       0 |
| 1980-01-02 |        1 | Beate      |       4 |
| 1980-01-03 |        2 | Cecil      |       2 |
| 1980-01-04 |        3 | Dana       |       8 |
| 1980-01-05 |        4 | Eric       |       4 |
| 1980-01-06 |        5 | Fidel      |       5 |
| 1980-01-07 |        6 | George     |       4 |
| 1980-01-08 |        7 | Hans       |       7 |
| 1980-01-09 |        8 | Ingrid     |       7 |
| 1980-01-10 |        9 | Jones      |       4 |


Output Table:
| RPT_Date   |   STK_ID | STK_Name   |   sales |
|:-----------|---------:|:-----------|--------:|
| 1980-01-03 |        2 | Cecil      |       2 |
| 1980-01-05 |        4 | Eric       |       4 |
| 1980-01-07 |        6 | George     |       4 |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
|   doc_created_month |   doc_created_year | speciality    |   doc_id_count |
|--------------------:|-------------------:|:--------------|---------------:|
|                   8 |               2016 | Acupuncturist |              1 |
|                   2 |               2017 | Acupuncturist |              1 |
|                   4 |               2017 | Acupuncturist |              1 |
|                   4 |               2017 | Allergist     |              1 |
|                   5 |               2018 | Allergist     |              1 |
|                  10 |               2018 | Allergist     |              2 |


Output Table:
|   doc_id_count |
|---------------:|
|              1 |
|              2 |
|              3 |
|              4 |
|              5 |
|              7 |

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



inputs: input_0.csv
Task: Transform-by-input-output-table (source dataset: AutoPandas)

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

Given input table(s) and a corresponding output table below, please write Python pandas code that can transform the input table into the given output table. 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.

Input Table:


Table #0
Filename: input_0.csv
Sample Rows in Markdown:
| 0    |
|:-----|
|      |
| 11.0 |
| 12.0 |
|      |
| 16.0 |
| 18.0 |


Output Table:
|   0 |
|----:|
|  11 |
|  12 |
|  16 |
|  18 |

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



inputs: input_0.csv