Pandas dataframe - remove outliers [duplicate]

Question

Given a pandas dataframe, I want to exclude rows corresponding to outliers (Z-value = 3) based on one of the columns.

The dataframe looks like this:

df.dtypes
_id                   object
_index                object
_score                object
_source.address       object
_source.district      object
_source.price        float64
_source.roomCount    float64
_source.size         float64
_type                 object
sort                  object
priceSquareMeter     float64
dtype: object

For the line:

dff=df[(np.abs(stats.zscore(df)) < 3).all(axis='_source.price')]

The following exception is raised:

-------------------------------------------------------------------------    
TypeError                                 Traceback (most recent call last)
<ipython-input-68-02fb15620e33> in <module>()
----> 1 dff=df[(np.abs(stats.zscore(df)) < 3).all(axis='_source.price')]

/opt/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py in zscore(a, axis, ddof)
   2239     """
   2240     a = np.asanyarray(a)
-> 2241     mns = a.mean(axis=axis)
   2242     sstd = a.std(axis=axis, ddof=ddof)
   2243     if axis and mns.ndim < a.ndim:

/opt/anaconda3/lib/python3.6/site-packages/numpy/core/_methods.py in _mean(a, axis, dtype, out, keepdims)
     68             is_float16_result = True
     69 
---> 70     ret = umr_sum(arr, axis, dtype, out, keepdims)
     71     if isinstance(ret, mu.ndarray):
     72         ret = um.true_divide(

TypeError: unsupported operand type(s) for +: 'NoneType' and 'NoneType'

And the return value of

np.isreal(df['_source.price']).all()

is

True

Why do I get the above exception, and how can I exclude the outliers?

Answer 1

If one wants to use the Interquartile Range of a given dataset (i.e. IQR, as shown by a Wikipedia image below) (Ref):

def Remove_Outlier_Indices(df):
    Q1 = df.quantile(0.25)
    Q3 = df.quantile(0.75)
    IQR = Q3 - Q1
    trueList = ~((df < (Q1 - 1.5 * IQR)) |(df > (Q3 + 1.5 * IQR)))
    return trueList

Based on the above eliminator function, the subset of outliers according to the dataset' statistical content can be obtained:

# Arbitrary Dataset for the Example
df = pd.DataFrame({'Data':np.random.normal(size=200)})

# Index List of Non-Outliers
nonOutlierList = Remove_Outlier_Indices(df)

# Non-Outlier Subset of the Given Dataset
dfSubset = df[nonOutlierList]