# 04 - Work Process

## Professional Work Process

But first, let's get the work process straight.

We'll manage data using [baserow.io](https://baserow.io/) (get an account or run your own instance), and create an API token.

So, let's say we have a view for data, like this:

Use the [baserow-client](https://github.com/NiklasRosenstein/python-baserow-client) library to read it:

```
In [1]:
from fxy.io.baserow import BaserowIO
API = 'https://data.mindey.com/'
API_TOKEN = 'C9gZFb15B8KAvWGihyr8YOGm62SSDlTD'
io = BaserowIO(API, token=API_TOKEN)
df = io.get_table(232)
df
1it [00:00,  4.64it/s]

Out[1]:
   id                   order uid                  date       Variable    Unit    value
0   1  1.00000000000000000000   1  2022-04-04T11:00:00Z  blood:glucose  mmol/l  5.34000
1   2  2.00000000000000000000   2  2022-05-20T12:00:00Z  blood:glucose  mmol/l  4.74000
2   3  3.00000000000000000000   3  2022-04-04T11:00:00Z  urine:glucose  mmol/l  4.12000
3   4  4.00000000000000000000   4  2022-05-20T12:00:00Z  urine:glucose  mmol/l  4.11000
```

Now, we can do the same mean computation, like:

```
In [2]: df.value = pandas.to_numeric(df.value)
        df.value.mean()
Out[2]: 4.5775
```

**Tip:** To apply "to\_numeric" wherever possible, use something like:

```
df_numeric = df.apply(pandas.to_numeric, errors='coerce').fillna(df)
```

Read more about type conversions [here](https://towardsdatascience.com/how-to-change-datatypes-in-pandas-in-4-minutes-677addf9a409).


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