Data Transformation

Data transformation refers to the process of converting data from one format or structure into another format. It is an important concept in computer science and has many applications in fields like data processing, algorithms, and machine learning. Some common types of data transformations include:

  • Filtering - Selecting a subset of data based on some criteria.
  • Sorting - Arranging data in a particular order, often for searching or prioritizing.
  • Normalization - Scaling data to fit in a specified range. Useful for machine learning algorithms.
  • Aggregation - Combining multiple data items into summary statistics like sum, count, average etc.
  • Encoding - Converting data into alternate representations like vectors and matrices to expose patterns.

Example in Java:

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int[] data = {3, 5, 2, 4, 7};

// Filtering 
int[] filtered = Arrays.stream(data)
                       .filter(x -> x > 3)
                       .toArray();
                       
// Sorting
Arrays.sort(data); 

// Normalization 
double[] normalized = Arrays.stream(data)
                            .map(x -> (x - min) / (max - min))
                            .toArray();
                            
// Aggregation
int sum = Arrays.stream(data).sum();                          

// Encoding
double[][] encoded = new double[data.length][];
for(int i = 0; i < data.length; i++){
  encoded[i] = featureVector(data[i]); 
}

Example in C++:

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vector<int> data {3, 5, 1, 4, 2};

// Filtering
vector<int> filtered;
for(int x : data) {
  if (x > 3) filtered.push_back(x);
}

// Sorting
sort(data.begin(), data.end());

// Normalization
auto minmax = minmax_element(data.begin(), data.end());
for(int &x : data) {
  x = (x - minmax.first) / (minmax.second - minmax.first); 
}

// Aggregation
int sum = accumulate(data.begin(), data.end(), 0); 

// Encoding
vector<vector<double>> encoded(data.size());
for(int i = 0; i < data.size(); i++) {
  encoded[i] = encode(data[i]);
}

Example in Python:

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data = [3, 1, 5, 2, 4]

# Filtering  
filtered = [x for x in data if x > 3]

# Sorting
data.sort()

# Normalization
maxval = max(data)
minval = min(data)
data = [(x - minval)/(maxval - minval) for x in data]

# Aggregation
total = sum(data)

# Encoding
encoded = [encode(x) for x in data]

In summary, data transformation involves converting data from one format to another to prepare it for analysis and algorithms. Common transformations include filtering, sorting, normalization, aggregation and encoding.