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May 10, 2022
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docstring update #1100

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f49a255
docs update
samukweku May 8, 2022
4a0ba7e
docs update
samukweku May 8, 2022
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133 changes: 56 additions & 77 deletions janitor/functions/fill.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,50 +20,36 @@ def fill_direction(df: pd.DataFrame, **kwargs) -> pd.DataFrame:
and `downup`.


Functional usage syntax:

```python
import pandas as pd
import janitor as jn

df = pd.DataFrame(...)
df = jn.fill_direction(
df = df,
column_1 = direction_1,
column_2 = direction_2,
)
```

Method-chaining usage syntax:

>>> import pandas as pd
>>> import janitor as jn
>>> df = pd.DataFrame(
... {
... 'col1': [1, 2, 3, 4],
... 'col2': [None, 5, 6, 7],
... 'col3': [8, 9, 10, None],
... 'col4': [None, None, 11, None],
... 'col5': [None, 12, 13, None]
... }
... )
>>> df
col1 col2 col3 col4 col5
0 1 NaN 8.0 NaN NaN
1 2 5.0 9.0 NaN 12.0
2 3 6.0 10.0 11.0 13.0
3 4 7.0 NaN NaN NaN
>>> df.fill_direction(
... col2 = 'up',
... col3 = 'down',
... col4 = 'downup',
... col5 = 'updown'
... )
col1 col2 col3 col4 col5
0 1 5.0 8.0 11.0 12.0
1 2 5.0 9.0 11.0 12.0
2 3 6.0 10.0 11.0 13.0
3 4 7.0 10.0 11.0 13.0
Example:

>>> import pandas as pd
>>> import janitor as jn
>>> df = pd.DataFrame(
... {
... 'col1': [1, 2, 3, 4],
... 'col2': [None, 5, 6, 7],
... 'col3': [8, 9, 10, None],
... 'col4': [None, None, 11, None],
... 'col5': [None, 12, 13, None]
... }
... )
>>> df
col1 col2 col3 col4 col5
0 1 NaN 8.0 NaN NaN
1 2 5.0 9.0 NaN 12.0
2 3 6.0 10.0 11.0 13.0
3 4 7.0 NaN NaN NaN
>>> df.fill_direction(
... col2 = 'up',
... col3 = 'down',
... col4 = 'downup',
... col5 = 'updown'
... )
col1 col2 col3 col4 col5
0 1 5.0 8.0 11.0 12.0
1 2 5.0 9.0 11.0 12.0
2 3 6.0 10.0 11.0 13.0
3 4 7.0 10.0 11.0 13.0

:param df: A pandas DataFrame.
:param kwargs: Key - value pairs of columns and directions.
Expand Down Expand Up @@ -138,39 +124,32 @@ def fill_empty(

This method mutates the original DataFrame.

Functional usage syntax:

```python
df = fill_empty(df, column_names=[col1, col2], value=0)
```

Method chaining syntax:


>>> import pandas as pd
>>> import janitor
>>> df = pd.DataFrame(
... {
... 'col1': [1, 2, 3],
... 'col2': [None, 4, None ],
... 'col3': [None, 5, 6]
... }
... )
>>> df
col1 col2 col3
0 1 NaN NaN
1 2 4.0 5.0
2 3 NaN 6.0
>>> df.fill_empty(column_names = 'col2', value = 0)
col1 col2 col3
0 1 0.0 NaN
1 2 4.0 5.0
2 3 0.0 6.0
>>> df.fill_empty(column_names = ['col2', 'col3'], value = 0)
col1 col2 col3
0 1 0.0 0.0
1 2 4.0 5.0
2 3 0.0 6.0
Example:

>>> import pandas as pd
>>> import janitor
>>> df = pd.DataFrame(
... {
... 'col1': [1, 2, 3],
... 'col2': [None, 4, None ],
... 'col3': [None, 5, 6]
... }
... )
>>> df
col1 col2 col3
0 1 NaN NaN
1 2 4.0 5.0
2 3 NaN 6.0
>>> df.fill_empty(column_names = 'col2', value = 0)
col1 col2 col3
0 1 0.0 NaN
1 2 4.0 5.0
2 3 0.0 6.0
>>> df.fill_empty(column_names = ['col2', 'col3'], value = 0)
col1 col2 col3
0 1 0.0 0.0
1 2 4.0 5.0
2 3 0.0 6.0


:param df: A pandas DataFrame.
Expand Down
64 changes: 32 additions & 32 deletions janitor/functions/update_where.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,38 +24,38 @@ def update_where(

Example usage:

>>> data = {
... "a": [1, 2, 3, 4],
... "b": [5, 6, 7, 8],
... "c": [0, 0, 0, 0],
... }
>>> df = pd.DataFrame(data)
>>> df
a b c
0 1 5 0
1 2 6 0
2 3 7 0
3 4 8 0
>>> df.update_where(
... conditions = (df.a > 2) & (df.b < 8),
... target_column_name = 'c',
... target_val = 10
... )
a b c
0 1 5 0
1 2 6 0
2 3 7 10
3 4 8 0
>>> df.update_where( # supports pandas *query* style string expressions
... conditions = "a > 2 and b < 8",
... target_column_name = 'c',
... target_val = 10
... )
a b c
0 1 5 0
1 2 6 0
2 3 7 10
3 4 8 0
>>> data = {
... "a": [1, 2, 3, 4],
... "b": [5, 6, 7, 8],
... "c": [0, 0, 0, 0],
... }
>>> df = pd.DataFrame(data)
>>> df
a b c
0 1 5 0
1 2 6 0
2 3 7 0
3 4 8 0
>>> df.update_where(
... conditions = (df.a > 2) & (df.b < 8),
... target_column_name = 'c',
... target_val = 10
... )
a b c
0 1 5 0
1 2 6 0
2 3 7 10
3 4 8 0
>>> df.update_where( # supports pandas *query* style string expressions
... conditions = "a > 2 and b < 8",
... target_column_name = 'c',
... target_val = 10
... )
a b c
0 1 5 0
1 2 6 0
2 3 7 10
3 4 8 0

:param df: The pandas DataFrame object.
:param conditions: Conditions used to update a target column
Expand Down