> For the complete documentation index, see [llms.txt](https://water-strategy.gitbook.io/waterstrategy/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://water-strategy.gitbook.io/waterstrategy/portugues/tutoriais/pywr-scenarios-reading-external-dataframe-and-adding-custom-rules/reading-csv-dataframe.md).
# Lendo CSV DataFrame
Vá para **Lançamento de novo reservatório** nodo e mudança **Fluxo máximo** para **-400**, isso permitirá o fluxo de água através desse nó
O arquivo baixado, **Tâmisa\_Lee\_Vale\_demand.csv**, contém um conjunto de dados de séries temporais estruturado com duas colunas principais:
**etapa de tempo:** Essa coluna servirá como índice para a série temporal. Ela representa a progressão do tempo no conjunto de dados, permitindo a organização cronológica.
**Demanda de Lee Valley:** Essa coluna contém os dados que representam os valores da demanda no Vale Lee.
Tâmisa_Lee_Valley_Demand
Selecione o nó de saída **Demanda de Lee Valley** e edite o **Fluxo máximo**
Em Opções, selecione **PYWR\_PARAMETER**
Cole a seguinte configuração:
```json
{
“tipo”: “DataFrameParameter”,
“url”: “Tâmisa_Lee_Valley_demand.csv “,
“coluna”: “Demanda de Lee Valley”,
“index_col”: “etapa de tempo”,
“parse_dates”: verdadeiro
}
```
Agora, ela deve ter a seguinte aparência:
### Resultados
Clique no nó Lee Valley Demand > Outputs > simulated\ \_flow para ver os resultados da simulação
simulated_flow (Demanda de Lee Valley)
Agora, verifique o **simulado\_volume** pelo **Novo reservatório**
volume simulado (Novo reservatório)
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