SimPy with pandas: Powerful Data Analysis for Simulation Results

Published on February 23, 2026

SimPy generates data. pandas makes sense of it. Together, they're unstoppable. <h2 id="why-pandas">Why pandas?</h2> SimPy gives you raw data—lists, dictionaries, timestamps. pandas gives you: - DataFrames for structured analysis - Aggregations, groupings, pivots - Time series analysis - Easy export to CSV, Excel, databases <h2 id="collecting-data-for-pandas">Collecting Data for pandas</h2> Structure your data from the start: <div class="code-block"><pre><code>import simpy import random import pandas as pd class Simulation: def __init__(self, env): self.env = env self.records = [] # Will become DataFrame def customer(self, customer_id, server): arrival = self.env.now with server.request() as req: yield req wait = self.env.now - arrival service = random.expovariate(1/5) yield self.env.timeout(service) self.records.append({ 'customer_id': customer_id, 'arrival': arrival, 'wait': wait, 'service': service, 'departure': self.env.now }) def to_dataframe(self): return pd.DataFrame(self.records) </code></pre></div> <h2 id="basic-analysis">Basic Analysis</h2> <div class="code-block"><pre><code># Run simulation sim = Simulation(env) # ... run ... df = sim.to_dataframe() # Summary statistics print(df.describe()) # Specific metrics print(f"Mean wait: {df['wait'].mean():.2f}") print(f"Median wait: {df['wait'].median():.2f}") print(f"90th percentile: {df['wait'].quantile(0.9):.2f}") print(f"Max wait: {df['wait'].max():.2f}") </code></pre></div> <h2 id="time-based-analysis">Time-Based Analysis</h2> <div class="code-block"><pre><code># Add time bins df['hour'] = (df['arrival'] / 60).astype(int) # Hourly statistics hourly = df.groupby('hour').agg({ 'customer_id': 'count', 'wait': ['mean', 'max'], 'service': 'mean' }).round(2) print(hourly) # Rolling average df['rolling_wait'] = df['wait'].rolling(window=50).mean() </code></pre></div> <h2 id="segmentation-analysis">Segmentation Analysis</h2> <div class="code-block"><pre><code># If you have customer types df['customer_type'] = ['VIP' if x % 10 == 0 else 'Regular' for x in df['customer_id']] # Compare segments segment_stats = df.groupby('customer_type').agg({ 'wait': ['count', 'mean', 'std', 'max'], 'service': 'mean' }) print(segment_stats) </code></pre></div> <h2 id="time-series-from-monitor">Time Series from Monitor</h2> <div class="code-block"><pre><code>class TimeSeriesCollector: def __init__(self, env, resources): self.env = env self.resources = resources self.records = [] def monitor(self, interval=1): while True: record = {'time': self.env.now} for name, resource in self.resources.items(): record[f'{name}_queue'] = len(resource.queue) record[f'{name}_busy'] = resource.count self.records.append(record) yield self.env.timeout(interval) def to_dataframe(self): return pd.DataFrame(self.records).set_index('time') # Usage collector = TimeSeriesCollector(env, {'server': server}) env.process(collector.monitor()) # ... run ... ts_df = collector.to_dataframe() print(ts_df.head()) </code></pre></div> <h2 id="resampling-and-aggregation">Resampling and Aggregation</h2> <div class="code-block"><pre><code># Time series resampling (assuming time in minutes) ts_df.index = pd.to_timedelta(ts_df.index, unit='m') # 10-minute averages resampled = ts_df.resample('10T').mean() # Or custom aggregation hourly = ts_df.resample('60T').agg({ 'server_queue': ['mean', 'max'], 'server_busy': 'mean' }) </code></pre></div> <h2 id="exporting-results">Exporting Results</h2> <div class="code-block"><pre><code># To CSV df.to_csv('simulation_results.csv', index=False) # To Excel with multiple sheets with pd.ExcelWriter('results.xlsx') as writer: df.to_excel(writer, sheet_name='Raw Data', index=False) summary.to_excel(writer, sheet_name='Summary') hourly.to_excel(writer, sheet_name='Hourly') # To SQL database from sqlalchemy import create_engine engine = create_engine('sqlite:///simulation.db') df.to_sql('runs', engine, if_exists='replace', index=False) </code></pre></div> <h2 id="scenario-comparison">Scenario Comparison</h2> <div class="code-block"><pre><code>def run_scenario(config, seed): random.seed(seed) env = simpy.Environment() sim = Simulation(env, config) env.run(until=1000) df = sim.to_dataframe() df['scenario'] = config['name'] df['seed'] = seed return df # Run multiple scenarios scenarios = [ {'name': '2_servers', 'servers': 2}, {'name': '3_servers', 'servers': 3}, {'name': '4_servers', 'servers': 4} ] all_results = [] for config in scenarios: for seed in range(10): # 10 replications df = run_scenario(config, seed) all_results.append(df) combined = pd.concat(all_results, ignore_index=True) # Compare scenarios comparison = combined.groupby('scenario')['wait'].agg(['mean', 'std', 'max']) print(comparison) </code></pre></div> <h2 id="pivot-tables">Pivot Tables</h2> <div class="code-block"><pre><code># Customer type by hour pivot = df.pivot_table( values='wait', index='hour', columns='customer_type', aggfunc='mean' ) print(pivot) </code></pre></div> <h2 id="statistical-tests">Statistical Tests</h2> <div class="code-block"><pre><code>from scipy import stats # Compare two scenarios scenario_a = combined[combined['scenario'] == '2_servers']['wait'] scenario_b = combined[combined['scenario'] == '3_servers']['wait'] t_stat, p_value = stats.ttest_ind(scenario_a, scenario_b) print(f"t-statistic: {t_stat:.4f}, p-value: {p_value:.4f}") </code></pre></div> <h2 id="complete-pandas-integration">Complete pandas Integration</h2> <div class="code-block"><pre><code>import simpy import random import pandas as pd import numpy as np class PandasSimulation: def __init__(self, env, config): self.env = env self.config = config self.server = simpy.Resource(env, capacity=config['servers']) # Data collectors self.entity_data = [] self.time_series = [] def customer(self, cid): arrival = self.env.now with self.server.request() as req: yield req wait = self.env.now - arrival service = random.expovariate(1/self.config['service_rate']) yield self.env.timeout(service) self.entity_data.append({ 'id': cid, 'arrival': arrival, 'wait': wait, 'service': service, 'departure': self.env.now, 'system_time': self.env.now - arrival }) def arrivals(self): cid = 0 while True: yield self.env.timeout(random.expovariate(self.config['arrival_rate'])) self.env.process(self.customer(cid)) cid += 1 def monitor(self, interval=1): while True: self.time_series.append({ 'time': self.env.now, 'queue': len(self.server.queue), 'busy': self.server.count, 'utilisation': self.server.count / self.server.capacity }) yield self.env.timeout(interval) def run(self, duration): self.env.process(self.arrivals()) self.env.process(self.monitor()) self.env.run(until=duration) def get_entity_df(self): return pd.DataFrame(self.entity_data) def get_timeseries_df(self): return pd.DataFrame(self.time_series) def summary(self): df = self.get_entity_df() return { 'customers': len(df), 'mean_wait': df['wait'].mean(), 'median_wait': df['wait'].median(), 'p90_wait': df['wait'].quantile(0.9), 'max_wait': df['wait'].max(), 'mean_system_time': df['system_time'].mean(), 'throughput': len(df) / self.env.now } # Run random.seed(42) env = simpy.Environment() sim = PandasSimulation(env, { 'servers': 2, 'arrival_rate': 0.5, 'service_rate': 0.3 }) sim.run(1000) # Analyse entity_df = sim.get_entity_df() ts_df = sim.get_timeseries_df() print(sim.summary()) </code></pre></div> <h2 id="summary">Summary</h2> SimPy + pandas: - Structure data collection from the start - Use DataFrames for analysis - Group, aggregate, pivot - Compare scenarios easily - Export to any format Collect smart. Analyse smarter. <h2 id="next-steps">Next Steps</h2> <ul> <li><a href="/blog_posts/simpy-with-matplotlib.html">SimPy with matplotlib</a></li> <li><a href="/blog_posts/simpy-collect-statistics.html">Collecting Statistics</a></li> <li><a href="/blog_posts/simpy-visualize-results.html">Visualising Results</a></li> </ul>

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