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Update README.md

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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -385,7 +385,7 @@ E 0.000000
dtype: float64 dtype: float64
``` ```
## [pandas.Series.isnull](https://pandas.pydata.org/docs/reference/api/pandas.Series.isnull.html) ## [pandas.Series.isnull](https://pandas.pydata.org/docs/reference/api/pandas.Series.isnull.html) and [pandas.Series.notnull](https://pandas.pydata.org/docs/reference/api/pandas.Series.notnull.html)
**Note: A value set to NONE will lead to a NaN.​** **Note: A value set to NONE will lead to a NaN.​**
@ -400,8 +400,10 @@ Series.isnull()
> 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). > 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 ```python
Series.notna() Series.notnull()
``` ```
> Series.notnull is an alias for Series.notna.
>
> Detect existing (non-missing) values. > 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. > 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.
@ -429,7 +431,9 @@ print(data_3.isnull())
print() print()
print(data_3[data_3.isnull()]) print(data_3[data_3.isnull()])
print() print()
print(data_3[data_3.notna()]) print(data_3.notnull())
print()
print(data_3[data_3.notnull()])
``` ```
Output Output
@ -438,8 +442,8 @@ Output
A NaN A NaN
B NaN B NaN
C NaN C NaN
D 1.142000 D 0.970744
E 1.620137 E 0.425544
F NaN F NaN
G NaN G NaN
H NaN H NaN
@ -463,8 +467,18 @@ G NaN
H NaN H NaN
dtype: float64 dtype: float64
D 1.142000 A False
E 1.620137 B False
C False
D True
E True
F False
G False
H False
dtype: bool
D 0.970744
E 0.425544
dtype: float64 dtype: float64
``` ```