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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Introduction
This IPython notebook illustrates how to remove features from feature table.
First, we need to import py_entitymatching package and other libraries as follows
Step1:  Then, read the (sample) input tables for blocking purposes
Step2:  Removing Features from Feature Table
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Python Code:
# Import py_entitymatching package
import py_entitymatching as em
import os
import pandas as pd
Explanation: Introduction
This IPython notebook illustrates how to remove features from feature table.
First, we need to import py_entitymatching package and other libraries as follows:
End of explanation
# Get the datasets directory
datasets_dir = em.get_install_path() + os.sep + 'datasets'
# Get the paths of the input tables
path_A = datasets_dir + os.sep + 'person_table_A.csv'
path_B = datasets_dir + os.sep + 'person_table_B.csv'
# Read the CSV files and set 'ID' as the key attribute
A = em.read_csv_metadata(path_A, key='ID')
B = em.read_csv_metadata(path_B, key='ID')
# Get features (for blocking)
feature_table = em.get_features_for_blocking(A, B, validate_inferred_attr_types=False)
# Get features (for matching)
# feature_table = em.get_features_for_matching(A, B)
Explanation: Then, read the (sample) input tables for blocking purposes
End of explanation
type(feature_table)
feature_table.head()
# Drop first row
feature_table = feature_table.drop(0)
feature_table.head()
#Remove all the features except involving name (Include only the features where the left attribute is name)
feature_table = feature_table[feature_table.left_attribute=='name']
feature_table
#Remove all the features except involving jaccard (Include only the features where the sim function is jaccard)
feature_table = feature_table[feature_table.simfunction=='jaccard']
feature_table
Explanation: Removing Features from Feature Table
End of explanation
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