In this lesson, we enter MedinPowerful Instead of Pandas Pandas will develop compatible Computing to speed up the performance of the most data. Comparing the Modin.Pandas as a PD, we convert our PANDAS code to computation powerhouse distributed. Our goal here is to understand how Modin works in real-life data, such as Groupby, joining, vomiting, and time analysis, all during the Google Colab. We look at each job against the standard PANDAS library to see if the fastest quick modin and working with instant memory.
!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 start by installing Modin with a Ray Backend, enabling the functioning of the seams pandas on the Google Colab. We press unnecessary alerts to keep the outworking clean and clear. Then we import all libraries needed and implement Ray by 2 CPUS, preparing our DataFree distribution distribution area.
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 describe the Benchmark_peration activity to compare the work period for a particular work using pandas and Modin. By using each work and recording its time, we calculate the Speedup Modin that offers. This provides a clear and comparative way to assessing the operation of each operation we test.
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 creative_large_dataset production data generating data. We create both pandas and the Modin types of this data so that we can put aside aside. After producing details, we show its size and foot memory, set up the improved Modin service category.
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 Complex_groupby function to perform multi-level groups of multi-level groupby in the dataset by distinguishing it by category. We put many columns using jobs such as the sum, means deviation, regular deviation, and count. Finally, we write this job well in both pandas and modin to measure how fast Modin works for a heavy groupby modin.
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'] <= Q3 + 1.5 * IQR)
]
df_clean['transaction_score'] = (
df_clean['transaction_amount'] * df_clean['rating'] * df_clean['quantity']
)
df_clean['is_high_value'] = 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 explain the best work for the first time, removing the sellers using the IQR method of confirming a clean understanding. Then, we make the engineering feature by creating a new metric called transactions_score and high labeling of the high value. Finally, we look at this logical cleaning using pandas and Modin to see how they treat the complex conversion to large dataset.
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 define Time_Series' activity to check daily styles by resetting shopping data later. We put the daily column as an indicator, computing daily as a SU, means, counting, and average measures, and then including new data. Holding long-term patterns, we also add a measure of folding for 7 days. Finally, we referred to the sign in this pipe with Pandas and Modin to compare their proper functioning in temporary 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 describe the creative_to-producing work – one of the products of products: each of the regions, each contains the appropriate metadata as the commission, tax rates, and shipping costs. We prepare for these tables to look at both pandas and modin formats for later use to join the functions and to target their performance in all 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"
)
It describes the highest quality_joins to enrich our main data by combining the regional tables. After making joins, we calculate the additional fields, as a_a Mission, Tax_amount, and the actual financial value. Finally, we look at all this joining and a combined pipeline using pandas and modin to check how well Modin deals with multi-step functions various functions.
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")We now focus on memory usage and print the header of the section to highlight this comparisons. Work in Get_Metory_sage, calculating the memory of the foot of Pandas Both Pandas and Modin Dataframes using their internal_sage methods. We ensure compliance with Modin by viewing the Quality of the _ito_Pandas. This helps us to check how well Modin treats memory compared to the pandas, especially with large datasets.
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 conclude our tutorials for summarizing the benches to work in all the audited activities, calculating the Middle Modin and the Modin and the Panda. We also reveal the work that performs a lot better, provides a clear idea where Modin exceeds. After that, we share the best set of modern practices, including compliance tips, working and conversion between pandas and Modin. Finally, we close Ray.
In conclusion, we see for personally how Modin can change our pandas movements with small changes in our code. Whether it is a complicated sides, analysis of a series of time, or to join the memory, Modin distributes a powerful functionality of daily activities, especially on the platforms such as Google Colab. Strength of Ray under the Hood and Pandas API Compliance, Modin makes it easy to work with large datasets.
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Nikhil is a student of students in MarktechPost. Pursuing integrated graduates combined in the Indian Institute of Technology, Kharagpur. Nikhl is a UI / ML enthusiasm that searches for applications such as biomoutomostoments and biomedical science. After a solid in the Material Science, he examines new development and developing opportunities to contribute.
