# feature.get\_stats()

Rasgo makes it easy for you to get the stats for any feature stored within Rasgo from Python without needing to use the GUI. Once you have a feature, you can get its stats by calling `get_stats`.

**`feature = rasgo.get.feature(id)`**

**`fstats = feature.get_stats()`**

You can access the individual stats either directly as

**`fstats.meanVal`**

Or create a dictionary and work with the stats in the dictionary

**`fstats_dict = fstats.dict()`**\
\&#xNAN;**`fstats_dict['meanVal']`**

The available statistics are:

| **Field Name**   | **Statistic**                                |
| ---------------- | -------------------------------------------- |
| **recCt**        | **Record Count**                             |
| **distinctCt**   | **Number of Distinct Values**                |
| **nullRecCt**    | **Number Null**                              |
| **zeroValRecCt** | **Number that is zero value**                |
| **meanVal**      | **Mean**                                     |
| **medianVal**    | **Median**                                   |
| **maxVal**       | **Maximum**                                  |
| **minVal**       | **Minimum**                                  |
| **sumVal**       | **Sum**                                      |
| **stdDevVal**    | **Standard Deviation**                       |
| **varianceVal**  | **Variance**                                 |
| **rangeVal**     | **Range**                                    |
| **skewVal**      | **Skewness**                                 |
| **kurtosisVal**  | **Kurtosis**                                 |
| **q1Val**        | **25th Percentile**                          |
| **q3Val**        | **75th Percentile**                          |
| **IQRVal**       | **IQR**                                      |
| **pct5Val**      | **5th Percentile**                           |
| **pct95Val**     | **95th Percentile**                          |
| **outlierCt**    | **Total number of outliers**                 |
| **lowOutlier**   | **Value below which a record is an outlier** |
| **highOutlier**  | **Value above which a record is an outlier** |

If you have recently uploaded a dataframe with `publish.features_from_df`, you can easily get these statistics for each column in a pandas dataframe. `publish.features_from_df` Returns a featureset

**`featureset = rasgo.publish.features_from_df(df, dimensions, features, granularity, tags)`**

You can get a list of features contained in this featureset using get.features\_by\_featureset.\
\&#xNAN;**`features = rasgo.get.features_by_featureset(featureset.id)`**

And you can create the stats dataframe for this list of features (or any other list you’ve created) by

**`fstatlist = []`**\
\&#xNAN;**`for f in featurelist:`**\
\&#xNAN;**`statdict = f.get_stats().dict()`**\
\&#xNAN;**`statdict['featureName'] = f.name`**\
\&#xNAN;**`fstatlist.append(statdict)`**\
\&#xNAN;**`df = pd.DataFrame(fstatlist)`**