Alright, let's break down how to tackle this mixed-type dataset—you’ve got binary, ordered/unordered categorical, and percentage data here, so we need a targeted, step-by-step approach. Here’s what I’d recommend based on your dataset structure:

Before diving into analysis, get your data in shape to avoid headaches later:

• Handle missing values: Start by checking for gaps with df.isnull().sum() (using pandas). For categorical columns (like sample source or sequencing tech), fill missing values with the mode (most common category). For the abundance percentage column, use the median or mean—better yet, group by related categories (e.g., fill missing abundance for freshwater samples with the median abundance of all freshwater samples) to preserve context.
• Fix data types:
  • Mark binary/unordered categorical columns (sample source, sequencing tech) as category type in pandas to save memory and enable proper statistical tests.
  • Critical: For ordered categorical columns (salinity: freshwater → brackish → salt; depth: surface → abyssopelagic), define them as ordered categories so tools respect their hierarchy. Example:

    df['salinity'] = pd.Categorical(
        df['salinity'],
        categories=['freshwater', 'brackish', 'salt'],
        ordered=True
    )
    

• Validate percentage data: Confirm your abundance column stays within 0-100 (or 0-1 if using proportions). Trim or flag any out-of-range values—these are likely typos or measurement errors.

EDA is key to understanding what your data is telling you. Focus on both single-variable and multi-variable relationships:

• Single-variable checks:
  • Binary (sample source): Use a bar chart to compare counts of SEDIMENT vs WATER samples. Calculate their relative frequencies to see if your dataset is balanced.
  • Categorical (sequencing tech, salinity, depth): Bar charts show category distributions—watch for underrepresented groups (e.g., if only 5 samples use tech D, you might need to merge it with another category for modeling).
  • Percentage (abundance): Histograms or boxplots reveal the overall distribution. Note outliers, median, and mean to gauge central tendency.
• Multi-variable relationships:
  • Test associations between categorical variables: Use cross-tabulations (pd.crosstab(df['sample_source'], df['salinity'])) paired with a chi-squared test to see if sample source and salinity are statistically linked.
  • Compare abundance across categories: Use boxplots or violin plots to visualize how abundance changes with depth (ordered) or sample source. Run ANOVA or Kruskal-Wallis tests to confirm if differences between groups are significant.

Your approach depends on whether you’re predicting abundance (regression) or a categorical outcome (e.g., sample source):

• Regression (predict abundance):
  • Avoid one-hot encoding ordered categories—it throws away their hierarchical meaning. Instead, use ordinal encoding (assign 0,1,2 to freshwater, brackish, salt) or target encoding (replace each category with the mean abundance of samples in that group).
  • For binary variables (sample source), use simple label encoding (SEDIMENT=1, WATER=0) or one-hot encoding.
  • Models like random forests or gradient boosting trees work great here—they handle mixed data types natively and don’t require extensive preprocessing. Linear regression is an option too, if your data meets linearity assumptions.
• Classification (predict a categorical variable):
  • Treat abundance as a feature, along with your other categorical variables. Use models like logistic regression, random forests, or XGBoost. Again, prioritize ordinal encoding for ordered categories to retain their value.

Here’s some Python code (pandas + seaborn) to kick off your analysis:

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import chi2_contingency

# Load your dataset
df = pd.read_csv('your_dataset.csv')

# Set ordered categories
df['salinity'] = pd.Categorical(df['salinity'], categories=['freshwater', 'brackish', 'salt'], ordered=True)
df['depth'] = pd.Categorical(df['depth'], categories=['surface', 'epi', 'meso', 'bathy', 'abyssopelagic'], ordered=True)

# Plot sample source distribution
sns.countplot(data=df, x='sample_source')
plt.title('Distribution of Sample Sources')
plt.show()

# Compare abundance across depth categories
sns.boxplot(data=df, x='depth', y='abundance')
plt.title('Abundance by Water Depth')
plt.xticks(rotation=45)
plt.show()

# Test association between sample source and salinity
cross_tab = pd.crosstab(df['sample_source'], df['salinity'])
chi2_stat, p_val, dof, expected = chi2_contingency(cross_tab)
print(f"Chi-squared statistic: {chi2_stat:.2f}, p-value: {p_val:.4f}")

内容的提问来源于stack exchange，提问作者Plumeria