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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -482,6 +482,86 @@ E 0.425544
dtype: float64
```
### [pandas.Series.dropna](https://pandas.pydata.org/docs/reference/api/pandas.Series.dropna.html) or [pandas.Series.fillna](https://pandas.pydata.org/docs/reference/api/pandas.Series.fillna.html)
```python
Series.dropna(*, axis=0, inplace=False, how=None, ignore_index=False)
```
> Return a new Series with missing values removed.
```python
Series.fillna(value=None, *, method=None, axis=None, inplace=False, limit=None, downcast=_NoDefault.no_default)
```
> Fill NA/NaN values using the specified method.
```python
import pandas as pd
import numpy as np
index_1 = pd.Series(["A", "B", "C", "D", "E"])
rng = np.random.default_rng()
np_data_1 = rng.random((5))
data_1 = pd.Series(np_data_1, index=index_1)
index_2 = pd.Series(["D", "E", "F", "G", "H"])
rng = np.random.default_rng()
np_data_2 = rng.random((5))
data_2 = pd.Series(np_data_2, index=index_2)
data_3 = data_1 + data_2
print(data_3)
print()
print(data_3.dropna())
print()
print(data_3.fillna(0.0)) # 0.0 => Value for filling
print()
print()
print(data_3.fillna(3.3)) # 3.3 => Value for filling
print()
```
Output:
```python
A NaN
B NaN
C NaN
D 0.660655
E 0.256244
F NaN
G NaN
H NaN
dtype: float64
D 0.660655
E 0.256244
dtype: float64
A 0.000000
B 0.000000
C 0.000000
D 0.660655
E 0.256244
F 0.000000
G 0.000000
H 0.000000
dtype: float64
A 3.300000
B 3.300000
C 3.300000
D 0.660655
E 0.256244
F 3.300000
G 3.300000
H 3.300000
dtype: float64
```
## [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame)
```python