Speed up Pandas in Python with Modin

This article was published as a part of the Data Science Blogathon.

Introduction to Speeding up Pandas in Python

Apache Spark uses clusters to distribute any pandas operation and speeds up the computation.

Modin comes to the rescue!!!

What is Modin?

It is a library created by some data scientists at UC Berkeley. It uses parallel computation to process dataframe executions faster.

What Gives Modin The Edge?

High-Level Architectural View

System View

The Data Scientist executes the dataframe operations. In the backend, Modin uses Ray/Dask and distributes the operations parallelly to boost operations.

Let’s look into the code:

Step 1: Installation

We can install Modin using the pip command:

pip install modin[all] # (Recommended) Install Modin with all of Modin's currently supported engines.

Step 2: Download the data

Download the sample dataset. We will use the new york yellow trip data. We will download the parquet file (approx 100 Mb).

File Link 1

File Link 2

Step 3: Import libraries

Import the relevant packages and initialize Ray for modin.

import pandas
import time
import modin.pandas as pd
import ray
ray.init()

Step 4: Use the Downloaded File

Give the path of the parquet file we downloaded in step 2

parquet_path = 'yellow_tripdata_2022-03.parquet'

Step 5: Actual Comparison

Run the functions for pandas and Modin.

• read_parquet(): this method is used to read data from a parquet file into a dataframe.

start = time.time()
pandas_df = pandas.read_parquet(parquet_path)
end = time.time()
pandas_duration = end - start
print("Time to read with pandas: {} seconds".format(round(pandas_duration, 3)))
start = time.time()
modin_df = pd.read_parquet(parquet_path)
end = time.time()
modin_duration = end - start
print("Time to read with Modin: {} seconds".format(round(modin_duration, 3)))
print("Modin is {}x faster than pandas at `read_parquet`!".
      format(round(pandas_duration / modin_duration, 2)))

• isnull() : It returns a dataframe where NULL values are replaced with TRUE, else FALSE.

start = time.time()
pandas_df.isnull()
end = time.time()
pandas_duration = end - start
print("Time with pandas: {} seconds".format(round(pandas_duration, 3)))
start = time.time()
modin_df.isnull()
end = time.time()
modin_duration = end - start
print("Time with Modin: {} seconds".format(round(modin_duration, 3)))
print("Modin is {}x faster than pandas at `is_null()`!".
      format(round(pandas_duration / modin_duration, 2)))

• fillna() : It replaces NULL values with the value specified in the parenthesis.

pandas_df.fillna(0)
end = time.time()
pandas_duration = end - start
print("Time with pandas: {} seconds".format(round(pandas_duration, 3)))
start = time.time()
modin_df.fillna(0)
end = time.time()
modin_duration = end - start
print("Time with Modin: {} seconds".format(round(modin_duration, 3)))
print("Modin is {}x faster than pandas at `is_null()`!".
      format(round(pandas_duration / modin_duration, 2)))

• apply(): It allows to execute a function across rows or columns of a dataframe.
  It works with DataFrame or a Series.

start = time.time()
pandas_df["trip_distance"].apply(round)
end = time.time()
pandas_duration = end - start
print("Time with pandas: {} seconds".format(round(pandas_duration, 3)))
start = time.time()
modin_df["trip_distance"].apply(round)
end = time.time()
modin_duration = end - start
print("Time with Modin: {} seconds".format(round(modin_duration, 3)))
print("Modin is {}x faster than pandas at `apply()`!".
      format(round(pandas_duration / modin_duration, 2)))

• applymap(): It allows to execute a function across rows or columns of a dataframe.
  Unlike apply, it works with a DataFrame only.

start = time.time()
pandas_df[["trip_distance"]].applymap(round)
end = time.time()
pandas_duration = end - start
print("Time with pandas: {} seconds".format(round(pandas_duration, 3)))
start = time.time()
modin_df[["trip_distance"]].applymap(round)
end = time.time()
modin_duration = end - start
print("Time with Modin: {} seconds".format(round(modin_duration, 3)))
print("Modin is {}x faster than pandas at `applymap()`!".
      format(round(pandas_duration / modin_duration, 2)))

• concat(): this method appends dataframe over an axis. Here, we will concat the original dataframe to itself 20 times – thus making it 20 times bigger in size.

start = time.time()
pandas.concat([pandas_df for _ in range(20)])
end = time.time()
pandas_duration = end - start
print("Time with pandas: {} seconds".format(round(pandas_duration, 3)))
start = time.time()
pd.concat([modin_df for _ in range(20)])
end = time.time()
modin_duration = end - start
print("Time with Modin: {} seconds".format(round(modin_duration, 3)))
print("Modin is {}x faster than pandas at `concat()`!".
      format(round(pandas_duration / modin_duration, 2)))

• to_csv(): this method is used to export a dataframe to a csv format.

start = time.time()
pandas_df.to_csv('Pandas_test.csv')
end = time.time()
pandas_duration = end - start
print("Time with pandas: {} seconds".format(round(pandas_duration, 3)))
start = time.time()
modin_df.to_csv('Mobin_test.csv')
end = time.time()
modin_duration = end - start
print("Time with Modin: {} seconds".format(round(modin_duration, 3)))
print("Modin is {}x faster than pandas at `to_csv()`!".
      format(round(pandas_duration / modin_duration, 2)))

• merge(): this method is used to join dataframes, here we will do a inner self join.

start = time.time()
pandas.merge(pandas_df.head(1000),pandas_df.head(1000),how='inner',
             left_on='VendorID',right_on='VendorID')
end = time.time()
pandas_duration = end - start
print("Time with pandas: {} seconds".format(round(pandas_duration, 3)))
start = time.time()
pd.merge(modin_df.head(1000),modin_df.head(1000),how='inner',
         left_on='VendorID',right_on='VendorID')
end = time.time()
modin_duration = end - start
print("Time with Modin: {} seconds".format(round(modin_duration, 3)))
print("Modin is {}x faster than pandas at `merge()`!".
      format(round(pandas_duration / modin_duration, 2)))

Performance Summary Statistics (Modin & Pandas)

We performed several operations, let’s summarize them and see how faster Modin was  our example. We can see that except for the merge() function, Modin beats Pandas in the remaining 7 functions. It seems there are some limitations of Modin, let’s look at those next.

Limitation 

Currently, Modin can cover 90.8% of  API methods.

Ok, But Why was Merge() Slower for Modin?

As per this link, currently Modin does not have support for the merge() function. In such cases, it uses the below approach:

We first convert to a pandas DataFrame, then operate. There is a performance penalty for going from a partitioned Modin DataFrame to pandas because of the communication cost and single-threaded nature of pandas. Once the pandas operation has been completed, we convert the DataFrame back into a partitioned Modin DataFrame. This way, operations performed after something defaults to pandas will be optimized with Modin.

Modin vs. Dask DataFrame vs. Koalas (Pandas)

On operations supported by all systems, Modin provides substantial speedups. Modin can take advantage of multiple cores relative to both Koalas and DaskDF to efficiently execute its operations thanks to its optimized design. Notably, Koalasares often slower than pandas, due to the overhead of Spark.

Conclusion on Python