Numeric Data

Numerical data represents measurable quantities and consists of numbers that can be used for arithmetic operations. It is typically divided into two types: discrete and continuous. Discrete numerical data includes countable values, such as the number of customers visiting a store, while continuous numerical data can take any value within a range, such as height, weight, or temperature. Numerical data is the backbone of quantitative analysis, enabling statistical computations, modeling, and visualization. Common techniques for analyzing numerical data include calculating summary statistics (mean, median, standard deviation), visualizing distributions with histograms or box plots, and identifying relationships using scatter plots. In Python, libraries like pandas and NumPy handle numerical data efficiently, while tools such as Matplotlib, Seaborn, and Plotly help create insightful visualizations. Properly understanding, cleaning, and processing numerical data is crucial for extracting patterns, identifying trends, and making data-driven decisions.

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Code Example

How it appears in Pandas:

data = {
    'Values': [100, 200, 300]
}

df = pd.DataFrame(data)
print(df)

Output:

   Values
0     100
1     200
2     300