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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -324,7 +324,7 @@ H NaN
dtype: float64
```
#### Example: Applying numpy functions ([pandas.Series.apply](https://pandas.pydata.org/docs/reference/api/pandas.Series.apply.html))
#### Example: Applying functions ([pandas.Series.apply](https://pandas.pydata.org/docs/reference/api/pandas.Series.apply.html))
```python
Series.apply(func, convert_dtype=_NoDefault.no_default, args=(), *, by_row='compat', **kwargs)
@ -385,6 +385,89 @@ E 0.000000
dtype: float64
```
## [pandas.Series.isnull](https://pandas.pydata.org/docs/reference/api/pandas.Series.isnull.html)
**Note: A value set to NONE will lead to a NaN.​**
```python
Series.isnull()
```
> Series.isnull is an alias for Series.isna.
>
> Detect missing values.
>
> Return a boolean same-sized object indicating if the values are NA. NA values, such as None or numpy.NaN, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True).
```python
Series.notna()
```
> Detect existing (non-missing) values.
>
> Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values (unless you set pandas.options.mode.use_inf_as_na = True). NA values, such as None or numpy.NaN, get mapped to False values.
```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.isnull())
print()
print(data_3[data_3.isnull()])
print()
print(data_3[data_3.notna()])
```
Output
```python
A NaN
B NaN
C NaN
D 1.142000
E 1.620137
F NaN
G NaN
H NaN
dtype: float64
A True
B True
C True
D False
E False
F True
G True
H True
dtype: bool
A NaN
B NaN
C NaN
F NaN
G NaN
H NaN
dtype: float64
D 1.142000
E 1.620137
dtype: float64
```
## [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame)
```python