piRSquared - 10 months ago 39

Python Question

consider the

`pd.DataFrame`

`df`

`df = pd.DataFrame([`

[np.nan, 1, np.nan],

[2, np.nan, np.nan],

[np.nan, np.nan, 3 ],

], list('abc'), list('xyz'))

df

and the

`pd.Series`

`s`

`s = pd.Series([10, 20, 30], list('abc'))`

How do I fill in missing values of

`df`

`s`

`s`

`df`

For example:

- is
`df.loc['c', 'x']`

`NaN`

- is
`s.loc['c']`

`30`

Answer Source

pandas handles this on a column basis with no issues. Suppose we had a different `s`

```
s = pd.Series([10, 20, 30], ['x', 'y', 'z'])
```

then we could

```
df.fillna(s)
x y z
a 10.0 1.0 30.0
b 2.0 20.0 30.0
c 10.0 20.0 3.0
```

But that's not what you want. Using your `s`

```
s = pd.Series([10, 20, 30], ['a', 'b', 'c'])
```

then `df.fillna(s)`

does nothing. But we know that it works for columns, so:

```
df.T.fillna(s).T
x y z
a 10.0 1.0 10.0
b 2.0 20.0 20.0
c 30.0 30.0 3.0
```