This IPython notebook illustrates how to read a CSV file from disk as a table and set its metadata.
First, we need to import py_entitymatching package and other libraries as follows:
In [2]:
import py_entitymatching as em
import pandas as pd
import os, sys
First, we need to get the path of the CSV file in disk. For the convenience of the user, we have included some sample files in the package. The path of a sample CSV file can be obtained like this:
In [3]:
# Get the datasets directory
datasets_dir = em.get_install_path() + os.sep + 'datasets'
# Get the path of the input table
path_A = datasets_dir + os.sep + 'person_table_A.csv'
In [4]:
# Display the contents of the file in path_A
!cat $path_A | head -3
Once we get the CSV file path, we can use it read the contents and set metadata.
There are three different ways to read a CSV file and set metadata:
First, read the CSV files as follows:
In [5]:
A = em.read_csv_metadata(path_A)
In [6]:
A.head()
Out[6]:
In [7]:
# Display the 'type' of A
type(A)
Out[7]:
Then set the metadata for the table. We see ID is the key attribute (since it contains unique values and no value is missing) for the table. We can set this metadata as follows:
In [8]:
em.set_key(A, 'ID')
Out[8]:
In [9]:
# Get the metadata that were set for table A
em.get_key(A)
Out[9]:
Now the CSV file is read into the memory and the metadata (i.e. key) is set for the table.
In the above, we saw that we first read in the CSV file and then set the metadata. These two steps can be combined into a single step like this:
In [10]:
A = em.read_csv_metadata(path_A, key='ID')
In [11]:
# Display the 'type' of A
type(A)
Out[11]:
In [12]:
# Get the metadata that were set for the table A
em.get_key(A)
Out[12]:
The user can specify the metadata in a file.
This file MUST be in the same directory as the CSV file and the file name should be same, except the extension is set to '.metadata'.
In [13]:
# Specify the metadata for table A (stored in person_table_A.csv).
# Get the file name (with full path) where the metadata file must be stored
metadata_fname = 'person_table_A.metadata'
metadata_file = datasets_dir + os.sep + metadata_fname
# Specify the metadata for table A . Here we specify that 'ID' is the key attribute for the table.
# Note that this step requires write permission to the datasets directory.
with open(metadata_file, 'w') as the_file:
the_file.write('#key=ID')
Note: In the above, we used Unix shell command echo to write the metadata contents. If you are on Windows, you can use echo|set /p instead of echo to acheive the same effect.
In [14]:
# If you donot have write permissions to the datasets directory, first copy the file to the local directory and
# then create a metadata file like this (you need to uncomment the following lines and then execute):
# import shutil
# shutil.copy2('path_A', './person_table_A.metadata')
# metadata_local_file = 'person_table_A.metadata'
# with open(metadata_local_file, 'w') as the_file:
# the_file.write('#key=ID'))
In [15]:
# Read the CSV file for table A
A = em.read_csv_metadata(path_A)
In [16]:
# Get the key for table A
em.get_key(A)
Out[16]:
In [17]:
# Remove the metadata file
os.remove(metadata_file) if os.path.exists(metadata_file) else None
os.remove('person_table_A.csv') if os.path.exists('person_table_A.csv') else None
os.remove(metadata_fname) if os.path.exists(metadata_fname) else None