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Python in Digital Scholarship

This guide will provide an introduction to using Python in research and instruction and what resources are available in the Freedman Center.

Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is the process of analyzing datasets to summarize their main characteristics, often using statistics and visualizations. In research and data science, EDA is a critical early step to understand the data you are working with, detect patterns or anomalies, and form hypotheses. Python, with its data analysis libraries, is well-equipped for EDA tasks.

Common steps and tools in EDA using Python include:

  • Data Loading: Using Pandas, you can read data from various sources (CSV, Excel, SQL databases, JSON, etc.) into a DataFrame, which is a tabular data structure with labeled axes. For example:

    import pandas as pd
df = pd.read_csv('data.csv')  # Load data into a DataFrame

  • Data Inspection: Pandas provides convenient methods to quickly inspect the dataset. df.head() displays the first few rows, and df.info() shows data types and missing values. df.describe() gives summary statistics (mean, median, quartiles, etc.) for numerical columns. This helps in understanding the scale and distribution of the data.

  • Cleaning and Preprocessing: EDA often involves cleaning data (handling missing values or outliers, correcting data types) and creating new variables if needed. Python’s capabilities (Pandas for data manipulation, or even regular expressions for parsing text) are useful here. For example, one might fill missing values with df.fillna() or filter outliers using boolean indexing.

  • Summary Statistics and Grouping: You can compute aggregates like mean, standard deviation, or counts overall or grouped by categories. Pandas’ groupby and aggregation functionality is frequently used to summarize data across different categories (e.g., average test scores by student group).

  • Visualization for EDA: Plotting is integral to EDA. Quick plots such as histograms, box plots, or scatter plots help reveal distribution shapes, potential correlations, or anomalies. Python’s Matplotlib and Seaborn libraries integrate with Pandas DataFrames to produce visualizations. For instance, df['age'].hist() might show the distribution of an age column, or sns.scatterplot(x='height', y='weight', data=df) could reveal relationships between two variables. (See the next section for more on visualization.)

  • Iterative Exploration: EDA is often done interactively (for example, in Jupyter Notebooks), allowing researchers to iteratively refine their analysis. One can write small code snippets to answer specific questions about the data (e.g., “What is the average value for each category?”, “Are there correlations between variables X and Y?”) and visualize results on the fly.

EDA is not usually about formal hypothesis testing, but about seeing what the data can tell us beyond formal modeling. By the end of a thorough EDA, you should have a better grasp of the data’s quality, potential transformations needed, and initial insights that guide further analysis or modeling. Python’s popularity in data science is in large part due to these powerful EDA capabilities provided by libraries like Pandas and Seaborn, which make exploring data both efficient and intuitive.