# It is highly recommended to use Pandas for such data processing problems
import pandas as pd
import numpy as np

dataset = {'feature1': np.random.rand(5000),
                 'feature2': np.random.rand(5000),
                 'feature3': np.random.rand(5000)
                 }

# Pandas data frame objects more convenient than Python dicts  to perform data preprocessing operations
dataframe = pd.DataFrame(dataset)


# Let's do  simple filtering... 
# if the value in the row is greater 0.99 - leave it, otherwise drop the row
print(dataframe[(dataframe>0.99).any(axis=1)])


# remove all rows of the dataframe if the value 
# in the column <feature1> lies too far (!) from corresponding median (the median computed for the column <feature1>)
filtering_rule_1  = (dataframe.feature1.median() - dataframe.feature1).abs( ) > 0.3 

print(dataframe[~filtering_rule_1])  # ~ -- means <NOT> operation


# Another filtering approach: using quantiles
lower_bound = .25
upper_bound = .75
quant_df = dataframe.quantile([lower_bound, upper_bound]) # auxiliary dataframe, it consist of quantiles computed for each column

# select outliers, i.e. values lie outside corresponding [lower_bound, upper_bound] intervals
filtering_rule_2 = dataframe.apply(lambda x: (x < quant_df.loc[lower_bound, x.name]) |  (x > quant_df.loc[upper_bound, x.name]), axis=0)


# print filtered dataset: if the row includes outlier value, it is dropped; outlier = the value that lies outside [lower_bound, upper_bound]
print(dataframe[~(filtering_rule_2).any(axis=1)])

# or assign/create a new df
filtered_dataframe = dataframe[~(filtering_rule_2).any(axis=1)]