Data Transformation Techniques:

Data transformation is a crucial step in data processing pipelines, where data is manipulated or modified to meet specific requirements. It involves applying various techniques and using libraries to convert data from one format to another, filter or aggregate data, handle missing values, and perform other operations that enhance the quality and usability of the data.

Techniques and Libraries for Data Transformation:

1. Data Cleaning: Data often contains inconsistencies, missing values, or outliers that need to be addressed before further processing. Libraries like pandas in Python provide functions and methods to clean and preprocess data. For example, you can handle missing values using fillna() or remove duplicates using drop_duplicates().

2. Data Filtering and Selection: Sometimes, you need to filter and select a subset of data based on specific criteria. Pandas provides powerful filtering and selection capabilities through methods like loc[] and iloc[]. For example, you can filter rows based on conditionals or select specific columns by name.

3. Data Aggregation: Aggregating data involves combining multiple rows or groups of data into a single representation. This can be useful for generating summary statistics or creating aggregated reports. Pandas offers functions like groupby() and agg() for performing data aggregation operations.

4. Data Transformation with Functions: Pandas allows you to apply custom functions to transform data. You can use the apply() function along with lambda functions or user-defined functions to perform complex data transformations. For instance, you can apply mathematical operations or string manipulations to columns.

5. Data Reshaping: Reshaping data involves transforming its structure, such as converting data from wide to long format or vice versa. Pandas provides functions like melt() and pivot() for reshaping data. These functions are particularly useful when dealing with time series data or preparing data for analysis.

Example Data Transformation:

Let's consider a simple example to demonstrate data transformation using pandas in Python. Assume we have a dataset containing information about employees, including their names, ages, and salaries. We can load this data into a pandas DataFrame and perform some common data transformation operations:

1data = {
2    'Name': ['John', 'Emma', 'Peter', 'Sara'],
3    'Age': [25, 28, 31, 24],
4    'Salary': [50000, 60000, 70000, 55000]
5}
6
7df = pd.DataFrame(data)
8print(df)
9
10# Perform data transformation
11# Python logic here
12
13print('Data transformation complete')

In this example, we create a pandas DataFrame using the provided dictionary of data. We then print the initial data frame before performing any transformation. You can replace the '# Python logic here' comment with the actual data transformation steps you want to apply.

In the next screen, we will explore more advanced data transformation techniques and dive deeper into the capabilities of libraries like pandas in data processing pipelines.

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import pandas as pd

​

data = {

    'Name': ['John', 'Emma', 'Peter', 'Sara'],

    'Age': [25, 28, 31, 24],

    'Salary': [50000, 60000, 70000, 55000]

}

​

df = pd.DataFrame(data)

print(df)

​

# Perform data transformation

# Python logic here

​

print('Data transformation complete')