moj-analytical-services / splink_demos

Preparation - moj-analytical-services/splink_demos

Preparation for record linkage analysis involves cleaning, transforming, and preparing data. Here are the possible options and examples for each step, using the provided documentation and code snippets.

Cleaning Data

Cleaning data involves removing inconsistencies, duplicates, and errors in the data. Here are some examples:

Removing Duplicates

To remove duplicates in a CSV file, you can use the pandas library in Python. Here’s an example:

import pandas as pd

df = pd.read_csv('data.csv')
df.drop_duplicates(inplace=True)
df.to_csv('cleaned_data.csv', index=False)

This code reads the data from data.csv, removes duplicates, and saves the cleaned data to cleaned_data.csv.

Handling Missing Values

To handle missing values, you can use the pandas library in Python. Here’s an example:

import pandas as pd

df = pd.read_csv('data.csv')

# Replace missing values with the mean of the column
df.fillna(df.mean(), inplace=True)

# Alternatively, you can drop rows with missing values
df.dropna(inplace=True)

df.to_csv('cleaned_data.csv', index=False)

This code reads the data from data.csv, replaces missing values with the mean of the column, and saves the cleaned data to cleaned_data.csv. Alternatively, you can drop rows with missing values using the dropna method.

Transforming Data

Transforming data involves converting data types, encoding categorical variables, and scaling numerical variables. Here are some examples:

Converting Data Types

To convert data types in a CSV file, you can use the pandas library in Python. Here’s an example:

import pandas as pd

df = pd.read_csv('data.csv')

# Convert a column to a specific data type
df['column_name'] = df['column_name'].astype('data_type')

df.to_csv('transformed_data.csv', index=False)

This code reads the data from data.csv, converts a column to a specific data type, and saves the transformed data to transformed_data.csv.

Encoding Categorical Variables

To encode categorical variables, you can use the pandas library in Python. Here’s an example:

import pandas as pd

df = pd.read_csv('data.csv')

# One-hot encoding
df = pd.get_dummies(df)

df.to_csv('transformed_data.csv', index=False)

This code reads the data from data.csv, performs one-hot encoding on the categorical variables, and saves the transformed data to transformed_data.csv.

Scaling Numerical Variables

To scale numerical variables, you can use the sklearn library in Python. Here’s an example:

from sklearn.preprocessing import StandardScaler
import pandas as pd

df = pd.read_csv('data.csv')

# Scale numerical variables
scaler = StandardScaler()
df[['numerical_column_1', 'numerical_column_2']] = scaler.fit_transform(df[['numerical_column_1', 'numerical_column_2']])

df.to_csv('transformed_data.csv', index=False)

This code reads the data from data.csv, scales the numerical variables using the StandardScaler from sklearn, and saves the transformed data to transformed_data.csv.

Preparing Data

Preparing data involves splitting the data into training and testing sets, and saving the data in a format that can be used for record linkage analysis. Here are some examples:

Splitting Data

To split the data into training and testing sets, you can use the sklearn library in Python. Here’s an example:

from sklearn.model_selection import train_test_split
import pandas as pd

df = pd.read_csv('transformed_data.csv')

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df.drop('target_column', axis=1), df['target_column'], test_size=0.2)

X_train.to_csv('training_data.csv', index=False)
X_test.to_csv('testing_data.csv', index=False)

This code reads the transformed data from transformed_data.csv, splits the data into training and testing sets using the train_test_split method from sklearn, and saves the training and testing data to training_data.csv and testing_data.csv, respectively.

Saving Data

To save the data in a format that can be used for record linkage analysis, you can use the csv library in Python. Here’s an example:

import csv

# Write data to a CSV file
with open('data.csv', mode='w') as f:
writer = csv.writer(f)
writer.writerow(['column_1', 'column_2', 'column_3'])
writer.writerows(data)

This code writes the data to a CSV file, where data is a list of lists containing the data to be written.

Sources: