remove outliers in dataframe

#------------------------------------------------------------------------------
# accept a dataframe, remove outliers, return cleaned data in a new dataframe
# see http://www.itl.nist.gov/div898/handbook/prc/section1/prc16.htm
#------------------------------------------------------------------------------
def remove_outlier(df_in, col_name):
    q1 = df_in[col_name].quantile(0.25)
    q3 = df_in[col_name].quantile(0.75)
    iqr = q3-q1 #Interquartile range
    fence_low  = q1-1.5*iqr
    fence_high = q3+1.5*iqr
    df_out = df_in.loc[(df_in[col_name] > fence_low) & (df_in[col_name] < fence_high)]
    return df_out

df = pd.DataFrame(np.random.randn(100, 3))

from scipy import stats
df[(np.abs(stats.zscore(df)) < 3).all(axis=1)]

df[(df["col"] >= x ) & (df["col"] <= y )]

but it's more readable to use:

df[df["col"].between(x,y)]