# Data Wrangling with Python Datatable - Conditional Statements

## Transformation based on a Single Condition

[Source data](https://stackoverflow.com/questions/65728964/how-to-apply-a-function-to-a-dataframe-row-based-on-a-condition-and-values-of-an/65729035?noredirect=1#comment116213987_65729035
)

Task: if `c` is positive, then value should be `a - b`, else `b - a`.


```python
from datatable import dt, f, update, ifelse

df = dt.Frame({"a": [1, 1, 2, 4], 
               "b": [2, 2, 3, 2], 
               "c": [3, -3, 2, -1]})
```

```
df

   |     a      b      c
   | int32  int32  int32
-- + -----  -----  -----
 0 |     1      2      3
 1 |     1      2     -3
 2 |     2      3      2
 3 |     4      2     -1
[4 rows x 3 columns]
```

The [ifelse](https://datatable.readthedocs.io/en/latest/api/dt/ifelse.html) function handles conditional transformations. It is similar to python's [if...else](https://docs.python.org/3/tutorial/controlflow.html#if-statements) statements or SQL's [case_when](https://www.postgresql.org/docs/current/functions-conditional.html) function.

**BREAKDOWN OF SOLUTION**

- Step 1 : Define the condition, with the `True` and `False` values.


```python
condition = f.c >= 0 # positive values
true = f.a - f.b
false = f.b - f.a
```

- Step 2 : Create the [ifelse](https://datatable.readthedocs.io/en/latest/api/dt/ifelse.html) expression.


```python
if_statement = ifelse(condition, true, false)
```

- Step 3: Apply the `if_statement` to the datatable frame to get the results


```python
df[:, update(b = if_statement)]
```

```
df

   |     a      b      c
   | int32  int32  int32
-- + -----  -----  -----
 0 |     1     -1      3
 1 |     1      1     -3
 2 |     2     -1      2
 3 |     4     -2     -1
[4 rows x 3 columns]
```
## Transformation based on Multiple Conditions

[Source data](https://stackoverflow.com/a/19913845/7175713)


```python
df = dt.Frame({"Type": ["A", "B", "B", "C"], 
               "Set": ["Z", "Z", "X", "Y"]})
```

```
df

   | Type   Set  
   | str32  str32
-- + -----  -----
 0 | A      Z    
 1 | B      Z    
 2 | B      X    
 3 | C      Y    
[4 rows x 2 columns]
```

Task: 

1. if `Set` is equal to 'Z'  and `Type` is equal to 'A' then assign 'yellow' to `color`.
2. If `Set` is equal to 'Z' and `Type` is equal to 'B' then assign 'blue' to `color`.
3. If `Type` is equal to 'B' then assign 'purple' to `color`.
4. Otherwise, assign 'black' to `color`.

**BREAKDOWN OF SOLUTION**

- Step 1 : Define the conditions, with the `True` and `False` values.


```python
condition1 = (f.Set == "Z") & (f.Type == "A")
true1 = "yellow"
condition2 = (f.Set == "Z") & (f.Type == "B")
true2 = "blue"
condition3 = f.Type == "B"
true3 = "purple"
false = "black"
```

- Step 2 : Create the [ifelse](https://datatable.readthedocs.io/en/latest/api/dt/ifelse.html) expression.


```python
if_statements = ifelse(condition1, true1, 
                       condition2, true2, 
                       condition3, true3, 
                       false)
```

- Step 3: Apply the `if_statements` to the datatable frame to get the results


```python
df[:, update(color = if_statements)]
df

   | Type   Set    color 
   | str32  str32  str32 
-- + -----  -----  ------
 0 | A      Z      yellow
 1 | B      Z      blue  
 2 | B      X      purple
 3 | C      Y      black 
[4 rows x 3 columns]
```

Resources: 

- [ifelse](https://datatable.readthedocs.io/en/latest/api/dt/ifelse.html)