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Coding Guide for Zooming Advanced Panda Workflows with Modin

In this tutorial, we delve into it modina powerful sales replacement for pandas, leveraging parallel computing can greatly speed up the data workflow. By importing modin.pandas as PD, we convert the pandas code to a distributed computing power. Our goal here is to understand how Modin performs in real data operations like Groupby, Joins, Cleaning and Time Seriental, all running on Google Colab. We benchmark each task with a standard panda library to see faster and higher memory efficiency modin.

!pip install "modin[ray]" -q
import warnings
warnings.filterwarnings('ignore')


import numpy as np
import pandas as pd
import time
import os
from typing import Dict, Any


import modin.pandas as mpd
import ray


ray.init(ignore_reinit_error=True, num_cpus=2)  
print(f"Ray initialized with {ray.cluster_resources()}")

We first installed Modin with Ray Backend, which allows seamless parallelized Pandas operations in Google Colab. We suppress unnecessary warnings to keep the output clean and clear. We then import all the necessary libraries and initialize Ray with 2 CPUs, thus preparing our environment for distributed data framework processing.

def benchmark_operation(pandas_func, modin_func, data, operation_name: str) -> Dict[str, Any]:
    """Compare pandas vs modin performance"""
   
    start_time = time.time()
    pandas_result = pandas_func(data['pandas'])
    pandas_time = time.time() - start_time
   
    start_time = time.time()
    modin_result = modin_func(data['modin'])
    modin_time = time.time() - start_time
   
    speedup = pandas_time / modin_time if modin_time > 0 else float('inf')
   
    print(f"n{operation_name}:")
    print(f"  Pandas: {pandas_time:.3f}s")
    print(f"  Modin:  {modin_time:.3f}s")
    print(f"  Speedup: {speedup:.2f}x")
   
    return {
        'operation': operation_name,
        'pandas_time': pandas_time,
        'modin_time': modin_time,
        'speedup': speedup
    }

We define a benchmark function to compare the execution time of a specific task using pandas and modin. By running each operation and recording its duration, we calculated the Speedup provided by Modin. This provides us with a clear and measurable method to evaluate performance improvements for each operation we tested.

def create_large_dataset(rows: int = 1_000_000):
    """Generate synthetic dataset for testing"""
    np.random.seed(42)
   
    data = {
        'customer_id': np.random.randint(1, 50000, rows),
        'transaction_amount': np.random.exponential(50, rows),
        'category': np.random.choice(['Electronics', 'Clothing', 'Food', 'Books', 'Sports'], rows),
        'region': np.random.choice(['North', 'South', 'East', 'West'], rows),
        'date': pd.date_range('2020-01-01', periods=rows, freq='H'),
        'is_weekend': np.random.choice([True, False], rows, p=[0.3, 0.7]),
        'rating': np.random.uniform(1, 5, rows),
        'quantity': np.random.poisson(3, rows) + 1,
        'discount_rate': np.random.beta(2, 5, rows),
        'age_group': np.random.choice(['18-25', '26-35', '36-45', '46-55', '55+'], rows)
    }
   
    pandas_df = pd.DataFrame(data)
    modin_df = mpd.DataFrame(data)
   
    print(f"Dataset created: {rows:,} rows × {len(data)} columns")
    print(f"Memory usage: {pandas_df.memory_usage(deep=True).sum() / 1024**2:.1f} MB")
   
    return {'pandas': pandas_df, 'modin': modin_df}


dataset = create_large_dataset(500_000)  


print("n" + "="*60)
print("ADVANCED MODIN OPERATIONS BENCHMARK")
print("="*60)

We define a create_large_dataset function to generate a rich synthetic dataset with 500,000 rows that mimic real-world transaction data including customer information, purchase patterns, and timestamps. We created both the pandas and modin versions of this dataset, so we can benchmark them side by side. After the data is generated, we display its size and memory footprint, setting up stages for advanced Modin operations.

def complex_groupby(df):
    return df.groupby(['category', 'region']).agg({
        'transaction_amount': ['sum', 'mean', 'std', 'count'],
        'rating': ['mean', 'min', 'max'],
        'quantity': 'sum'
    }).round(2)


groupby_results = benchmark_operation(
    complex_groupby, complex_groupby, dataset, "Complex GroupBy Aggregation"
)

We define a complex _roupby function that performs multi-level grouping operations on the dataset by category and region. We then summarize multiple columns using functions such as sum, mean, standard deviation, and count. Finally, we benchmarked this operation in Pandas and Modin to measure how fast Modin performed such heavy collective aggregation.

def advanced_cleaning(df):
    df_clean = df.copy()
   
    Q1 = df_clean['transaction_amount'].quantile(0.25)
    Q3 = df_clean['transaction_amount'].quantile(0.75)
    IQR = Q3 - Q1
    df_clean = df_clean[
        (df_clean['transaction_amount'] >= Q1 - 1.5 * IQR) &
        (df_clean['transaction_amount']  df_clean['transaction_amount'].median()
   
    return df_clean


cleaning_results = benchmark_operation(
    advanced_cleaning, advanced_cleaning, dataset, "Advanced Data Cleaning"
)

We define the Advanced_Cleaning function to simulate real-world data preprocessing pipelines. First, we use the IQR method to remove outliers to ensure a cleaner insight. We then perform functional engineering by creating a new metric called TransAction_Score and tagging high-value transactions. Finally, we cleaned this using Pandas and Modin to see how they handle complex transformations on large datasets.

def time_series_analysis(df):
    df_ts = df.copy()
    df_ts = df_ts.set_index('date')
   
    daily_sum = df_ts.groupby(df_ts.index.date)['transaction_amount'].sum()
    daily_mean = df_ts.groupby(df_ts.index.date)['transaction_amount'].mean()
    daily_count = df_ts.groupby(df_ts.index.date)['transaction_amount'].count()
    daily_rating = df_ts.groupby(df_ts.index.date)['rating'].mean()
   
    daily_stats = type(df)({  
        'transaction_sum': daily_sum,
        'transaction_mean': daily_mean,
        'transaction_count': daily_count,
        'rating_mean': daily_rating
    })
   
    daily_stats['rolling_mean_7d'] = daily_stats['transaction_sum'].rolling(window=7).mean()
   
    return daily_stats


ts_results = benchmark_operation(
    time_series_analysis, time_series_analysis, dataset, "Time Series Analysis"
)

We defined the Time_Series_Analysis function to explore daily trends by resampling transaction data over time. We set the date column to index, calculate daily aggregations such as sum, mean, count and average score, and compile them into a new data frame. To capture the long-term pattern, we also added a 7-day rolling average. Finally, we benchmarked the pipeline of time series with Pandas and Modin to compare their efficiency on time data.

def create_lookup_data():
    """Create lookup tables for joins"""
    categories_data = {
        'category': ['Electronics', 'Clothing', 'Food', 'Books', 'Sports'],
        'commission_rate': [0.15, 0.20, 0.10, 0.12, 0.18],
        'target_audience': ['Tech Enthusiasts', 'Fashion Forward', 'Food Lovers', 'Readers', 'Athletes']
    }
   
    regions_data = {
        'region': ['North', 'South', 'East', 'West'],
        'tax_rate': [0.08, 0.06, 0.09, 0.07],
        'shipping_cost': [5.99, 4.99, 6.99, 5.49]
    }
   
    return {
        'pandas': {
            'categories': pd.DataFrame(categories_data),
            'regions': pd.DataFrame(regions_data)
        },
        'modin': {
            'categories': mpd.DataFrame(categories_data),
            'regions': mpd.DataFrame(regions_data)
        }
    }


lookup_data = create_lookup_data()

We define the create_lookup_data function to generate two reference tables: one for product categories and the other for zones, each containing relevant metadata such as commission rate, tax rate, and shipping cost. We prepared these lookup tables in pandas and modin formats so that they can be used for joining operations later and benchmarking their performance in both libraries.

def advanced_joins(df, lookup):
    result = df.merge(lookup['categories'], on='category', how='left')
    result = result.merge(lookup['regions'], on='region', how='left')
   
    result['commission_amount'] = result['transaction_amount'] * result['commission_rate']
    result['tax_amount'] = result['transaction_amount'] * result['tax_rate']
    result['total_cost'] = result['transaction_amount'] + result['tax_amount'] + result['shipping_cost']
   
    return result


join_results = benchmark_operation(
    lambda df: advanced_joins(df, lookup_data['pandas']),
    lambda df: advanced_joins(df, lookup_data['modin']),
    dataset,
    "Advanced Joins & Calculations"
)

We define the Advanced_joins function to enrich our main dataset by combining the category and region lookup tables. After performing the connection, we compute other fields such as commission_amount, sax_amount, and total_cost to simulate financial calculations in the real world. Finally, we benchmark the entire join and compute pipeline using Pandas and Modin to evaluate how Modin handles complex multi-step operations.

print("n" + "="*60)
print("MEMORY EFFICIENCY COMPARISON")
print("="*60)


def get_memory_usage(df, name):
    """Get memory usage of dataframe"""
    if hasattr(df, '_to_pandas'):
        memory_mb = df.memory_usage(deep=True).sum() / 1024**2
    else:
        memory_mb = df.memory_usage(deep=True).sum() / 1024**2
   
    print(f"{name} memory usage: {memory_mb:.1f} MB")
    return memory_mb


pandas_memory = get_memory_usage(dataset['pandas'], "Pandas")
modin_memory = get_memory_usage(dataset['modin'], "Modin")

Now, we shift the focus to memory usage and print a partial title to highlight this comparison. In the GET_MEMORY_USAGE function, we use its internal MONEME_USAGE method to calculate the memory footprints of PANDAS and MODIN DATAFAMES. We ensure compatibility with Modin by checking the _to_pandas property. This helps us evaluate how Modin handles memory efficiently compared to giant pandas, especially in the case of large data sets.

print("n" + "="*60)
print("PERFORMANCE SUMMARY")
print("="*60)


results = [groupby_results, cleaning_results, ts_results, join_results]
avg_speedup = sum(r['speedup'] for r in results) / len(results)


print(f"nAverage Speedup: {avg_speedup:.2f}x")
print(f"Best Operation: {max(results, key=lambda x: x['speedup'])['operation']} "
      f"({max(results, key=lambda x: x['speedup'])['speedup']:.2f}x)")


print("nDetailed Results:")
for result in results:
    print(f"  {result['operation']}: {result['speedup']:.2f}x speedup")


print("n" + "="*60)
print("MODIN BEST PRACTICES")
print("="*60)


best_practices = [
    "1. Use 'import modin.pandas as pd' to replace pandas completely",
    "2. Modin works best with operations on large datasets (>100MB)",
    "3. Ray backend is most stable; Dask for distributed clusters",
    "4. Some pandas functions may fall back to pandas automatically",
    "5. Use .to_pandas() to convert Modin DataFrame to pandas when needed",
    "6. Profile your specific workload - speedup varies by operation type",
    "7. Modin excels at: groupby, join, apply, and large data I/O operations"
]


for tip in best_practices:
    print(tip)


ray.shutdown()
print("n✅ Tutorial completed successfully!")
print("🚀 Modin is now ready to scale your pandas workflows!")

We summarize our tutorial by summarizing the performance benchmarks of all tested operations to calculate the average speed of Modin on Pandas. We also highlighted the best performing operations, which gave us a clear understanding of where Modin is good at. We then share a set of best practices for using Modin effectively, including tips on compatibility, performance analysis, and conversion between pandas and modin. Finally, we closed the thunder.

All in all, we’ve seen how Modin changed to the pandas workflow boost with minimal code. Whether it’s complex aggregation, time series analysis or memory-intensive connections, Modin provides scalable performance for everyday tasks, especially on platforms like Google Colab. With under the hood rays and almost complete Panda API compatibility, Modin makes it effortless to use larger datasets.


Check Code. All credits for this study are to the researchers on the project. Also, please feel free to follow us twitterand Youtube And don’t forget to join us 100K+ ml reddit And subscribe Our newsletter.


Nikhil is an intern consultant at Marktechpost. He is studying for a comprehensive material degree in integrated materials at the Haragpur Indian Technical College. Nikhil is an AI/ML enthusiast and has been studying applications in fields such as biomaterials and biomedical sciences. He has a strong background in materials science, and he is exploring new advancements and creating opportunities for contribution.