To check the distribution of a dataset’s values, start by using summary statistics and visualization tools to analyze how data points are spread across different ranges. Summary statistics like mean, median, standard deviation, and quartiles provide a numerical overview. For example, the mean and median can reveal skewness: if they differ significantly, the data might be skewed left or right. The interquartile range (IQR) highlights the middle 50% of values, which helps identify outliers. In Python, libraries like pandas make this straightforward with methods like df.describe(), which outputs count, mean, standard deviation, min, max, and quartiles in one step.
Next, visualize the distribution using plots. Histograms are the most common tool, grouping data into bins to show frequency. For instance, using matplotlib in Python, plt.hist(df['column'], bins=20) creates a histogram with 20 bins. Box plots complement this by showing quartiles, median, and outliers. Density plots (e.g., seaborn’s kdeplot) smooth the histogram to estimate probability density, useful for comparing distributions. For categorical data, bar charts (df['category'].value_counts().plot(kind='bar')) show frequency per category. These visuals help spot patterns like bimodal distributions or heavy tails that summary stats alone might miss.
Finally, use statistical tests for formal analysis. Tests like Shapiro-Wilk (for normality) or Kolmogorov-Smirnov (comparing to a known distribution) quantify how well the data fits a theoretical model. For example, scipy.stats.shapiro(df['column']) returns a test statistic and p-value to assess normality. Combining these methods ensures a robust analysis: stats for quick insights, visuals for intuitive understanding, and tests for validation. Always consider context—for skewed data, log transformations might be needed before modeling. For developers, automating these checks with scripts (e.g., generating summary reports and plots during data preprocessing) streamlines the workflow.