> ## Documentation Index
> Fetch the complete documentation index at: https://anaconda.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Load CSV data in Metaflow steps

When your flow needs data from a CSV file, you can bundle the file with the flow using `IncludeFile`. The file's contents become available to every step, locally and on remote compute. This page shows how to read a CSV into a flow, transform the data, and store the result as a flow artifact you can access later from any script or notebook.

**1. Acquire the CSV**

This example uses a CSV from the [Metaflow tutorials](https://docs.metaflow.org/getting-started/tutorials/season-1-the-local-experience/episode00), downloaded by the `save_data_locally` function defined outside of the flow.

**2. Run the flow**

This flow shows how to:

* Include a CSV saved locally for all steps in the flow.
* Add a feature to each data point.
* Save the new data as a flow artifact.

```py title="load_csv_data.py" expandable theme={null}
from metaflow import FlowSpec, step, IncludeFile
import pandas as pd

def save_data_locally():
    url = "https://raw.githubusercontent.com/" + \
          "Netflix/metaflow/master/metaflow"
    data_path = "/tutorials/02-statistics/movies.csv"
    local_path = "./movies.csv"
    df = pd.read_csv(url+data_path)
    df.to_csv(local_path)

class CSVFlow(FlowSpec):
    
    data = IncludeFile("data", default="./movies.csv")
    
    @step
    def start(self):
        self.next(self.use_csv)
        
    @step
    def use_csv(self):
        import pandas as pd 
        from io import StringIO
        df = pd.read_csv(StringIO(self.data),
                         index_col=0)
        f = lambda x: x < 2000
        df["is_before_2000"] = df["title_year"].apply(f)
        self.new_df = df
        self.next(self.end)
        
    @step
    def end(self):
        result = self.new_df.is_before_2000.sum() 
        print(f"Number of pre-2000 movies is {result}.")
        
if __name__ == "__main__":
    save_data_locally()
    CSVFlow()
```

```bash theme={null}
python load_csv_data.py run
```

```text theme={null}
    ...
     [1654221300950244/end/3 (pid 71595)] Task is starting.
     [1654221300950244/end/3 (pid 71595)] Number of pre-2000 movies is 1023.
     [1654221300950244/end/3 (pid 71595)] Task finished successfully.
    ...
```

**3. Access artifacts outside of the flow**

Run the following in any script or notebook to access the contents of the dataframe that was stored as a flow artifact with `self.new_df`:

```python theme={null}
from metaflow import Flow 
run = Flow("CSVFlow").latest_run
assert run.successful
run.data.new_df.is_before_2000.sum()
```

```text theme={null}
    1023
```