# Query Options

The following options are available:

- **max_columns**: the maximum number of columns to return, top N columns is computed from count of non-null values.

- **global_min_total_count**: require that at least this many rows contain a non-null value, or drop the column.

- **apply_feature_filters**: flag on/off applying feature filtering (default true)

- **apply_charset_filter**: flag on/off cleaning the values of numerical and categorical feature columns (default true)
  
- **drop_empty_rows**: if on, remove rows" that have no non-null values. (default false)

- **expand_numerical_features**: expand numerical feature arrays into multiple columns.  (default false)

- **drop_numerical_zero_features**: drop numerical feature columns that contain all zeros. (default false)

- **drop_constant_feature_columns**: drop numerical feature columns that are constant (default false)

- **throw_expression_errors**: use "fail fast" behavior with invalid expressions (default false)

- **debug_expressions**: return extended debugging information about TQL expression evaluation with the result set.

- **fill_na**: replace all numerical features with non-numeric values with 0.0.

- **numerical_feature_precision**: how many decimal places to return.

- **numerical_feature_epsilon**: abs(val) < eps will be rounded down to zero.

- **fix_column_names**: specify if backend should rename duplicate column names. (default true)



```python
from zeenk.tql import *
```

## The drop_constant_feature_columns Option
Constant feature columns will be dropped 


```python
# constant feature columns will be dropped 
select(
  numerical(1.01), #constant numerical feature columns will be dropped
  categorical('"blue"'), #constant categorical feature columns will be dropped 
  categorical('if(random() < .5, "cat", "dog")'), # non-constant columns will NOT be dropped
)\
.from_events('lethe4')\
.options(drop_constant_feature_columns=1)\
.limit(3)
```




<h4><em>Query results:</em></h4>partition "_default"
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
    </tr>
    <tr>
      <th>1</th>
    </tr>
    <tr>
      <th>2</th>
    </tr>
  </tbody>
</table>
</div><i>query produced 3 rows x 0 columns in 0.46 seconds</i><br><br>



## The numerical_feature_epsilon Option
Numerical features, labels, and weights with a value < epsilon will be rounded down to zero.


```python
select(
  label(.01), # epsilon will be applied to label columns
  weight(.01), # epsilon will be applied to weight columns
  numerical(.01), # epsilon will be applied to numerical feature columns
  numerical('"foo:.01"'), # epsilon will be to mixed numerical feature columns 
  categorical(.01), # epsilon will NOT be applied here since the column type is CATEGORICAL
  .01 # epsilon will NOT be applied here since the column type is METADATA
)\
.from_events('lethe4')\
.options(numerical_feature_epsilon=1)\
.limit(3)
```




<h4><em>Query results:</em></h4>partition "_default"
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>_label</th>
      <th>_weight</th>
      <th>_c2</th>
      <th>_c3</th>
      <th>_c4</th>
      <th>_c5</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0</td>
      <td>foo:0</td>
      <td>0.01</td>
      <td>0.01</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0</td>
      <td>foo:0</td>
      <td>0.01</td>
      <td>0.01</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.0</td>
      <td>0.0</td>
      <td>0</td>
      <td>foo:0</td>
      <td>0.01</td>
      <td>0.01</td>
    </tr>
  </tbody>
</table>
</div><i>query produced 3 rows x 6 columns in 0.48 seconds</i><br><br>




```python

```