Getting Started#

Note

Estimated Reading Time: 10 minutes
You can follow along this tutorial in a Jupyter notebook here.

Quick Start Guide#

To install the most recent stable release for Modin run the following:

pip install modin[all]

For further instructions on how to install Modin with conda or for specific platforms or engines, see our detailed installation guide.

Modin acts as a drop-in replacement for pandas so you simply have to replace the import of pandas with the import of Modin as follows to speed up your pandas workflows:

# import pandas as pd
import modin.pandas as pd

Example: Instant Scalability with No Extra Effort#

When working on large datasets, pandas becomes painfully slow or runs out of memory. Modin automatically scales up your pandas workflows by parallelizing the dataframe operations, so that you can more effectively leverage the compute resources available.

For the purpose of demonstration, we will load in modin as pd and pandas as pandas.

import modin.pandas as pd
import pandas

#############################################
### For the purpose of timing comparisons ###
#############################################
import time
import ray
ray.init()
#############################################

In this toy example, we look at the NYC taxi dataset, which is around 200MB in size. You can download this dataset to run the example locally.

# This may take a few minutes to download
import urllib.request
s3_path = "https://modin-test.s3.us-west-1.amazonaws.com/yellow_tripdata_2015-01.csv"
urllib.request.urlretrieve(s3_path, "taxi.csv")

Faster Data Loading with read_csv#

start = time.time()

pandas_df = pandas.read_csv(s3_path, parse_dates=["tpep_pickup_datetime", "tpep_dropoff_datetime"], quoting=3)

end = time.time()
pandas_duration = end - start
print("Time to read with pandas: {} seconds".format(round(pandas_duration, 3)))

By running the same command read_csv with Modin, we generally get around 4X speedup for loading in the data in parallel.

start = time.time()

modin_df = pd.read_csv(s3_path, parse_dates=["tpep_pickup_datetime", "tpep_dropoff_datetime"], quoting=3)

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_csv`!".format(round(pandas_duration / modin_duration, 2)))

Faster concat across multiple dataframes#

Our previous read_csv example operated on a relatively small dataframe. In the following example, we duplicate the same taxi dataset 100 times and then concatenate them together, resulting in a dataset around 19GB in size.

start = time.time()

big_pandas_df = pandas.concat([pandas_df for _ in range(25)])

end = time.time()
pandas_duration = end - start
print("Time to concat with pandas: {} seconds".format(round(pandas_duration, 3)))
start = time.time()

big_modin_df = pd.concat([modin_df for _ in range(25)])

end = time.time()
modin_duration = end - start
print("Time to concat with Modin: {} seconds".format(round(modin_duration, 3)))

print("Modin is {}x faster than pandas at `concat`!".format(round(pandas_duration / modin_duration, 2)))

Modin speeds up the concat operation by more than 60X, taking less than a second to create the large dataframe, while pandas took close to a minute.

Faster apply over a single column#

The performance benefits of Modin becomes aparent when we operate on large gigabyte-scale datasets. For example, let’s say that we want to round up the number across a single column via the apply operation.

start = time.time()
rounded_trip_distance_pandas = big_pandas_df["trip_distance"].apply(round)

end = time.time()
pandas_duration = end - start
print("Time to apply with pandas: {} seconds".format(round(pandas_duration, 3)))
start = time.time()

rounded_trip_distance_modin = big_modin_df["trip_distance"].apply(round)

end = time.time()
modin_duration = end - start
print("Time to apply with Modin: {} seconds".format(round(modin_duration, 3)))

print("Modin is {}x faster than pandas at `apply` on one column!".format(round(pandas_duration / modin_duration, 2)))

Modin is more than 30X faster at applying a single column of data, operating on 130+ million rows in a second.

In short, Modin provides orders of magnitude speed up over pandas for a variety of operations out of the box.

../_images/quickstart_speedup.svg

Summary#

Hopefully, this tutorial demonstrated how Modin delivers significant speedup on pandas operations without the need for any extra effort. Throughout example, we moved from working with 100MBs of data to 20GBs of data all without having to change anything or manually optimize our code to achieve the level of scalable performance that Modin provides.

Note that in this quickstart example, we’ve only shown read_csv, concat, apply, but these are not the only pandas operations that Modin optimizes for. In fact, Modin covers more than 90% of the pandas API, yielding considerable speedups for many common operations.