Pandas vs Dask -Which One is Better?
Find the main differences between Pandas and Dask.
Pandas is the first tool that comes to mind when discussing data processing with Python. Most Python libraries for data analytics, such as NumPy, Pandas, and Scikit-learn, are not made to scale beyond a single machine, which is an issue. Here is when Dask comes into the picture, offering advanced analytics parallelisation.
In this blog on Pandas vs Dask, we will compare the two popular Python libraries for data manipulation and data analysis.
Table of Contents
- What is Pandas?
- Working of Pandas
- Using Pandas Library
- Challenges with Pandas
- What is Dask?
- How Dask Works?
- How to Use Dask?
- Challenges with Dask
- Comparing Pandas and Dask
- What Performs Better – Pandas or Dask?
What is Pandas?
Pandas is a popular open-source Python library for handling and analyzing data. It offers several functions for data cleansing, transformation, and analysis, as well as high-performance data structures including Series and DataFrame.
You can quickly import, modify, and analyze data in a variety of formats using pandas, including CSV, Excel, SQL databases, and more.
Pandas is frequently used in various domains where data analysis and manipulation are essential, such as data science, finance, social networking sites, healthcare, and many more.
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Working of Pandas
Pandas offers simple data structures and tools for data analysis on top of the NumPy library.
Series and DataFrame are the two main data structures in Pandas. A DataFrame is a 2-dimensional labeled data structure with columns of possibly varied types, whereas a Series is a 1-dimensional labeled array. It can carry any form of data.
Here is a quick explanation of how Pandas work:
Data Import: Pandas can read data from many different file types, including CSV, Excel, SQL databases, and even online APIs. To read data from many sources, Pandas offers functions like read_csv(), read_excel(), read_sql(), etc.
Data Cleaning and Manipulation: This involves a variety of operations on the data by using functions offered by Pandas. A few popular functions are fillna(), which fills in missing values, merge(), which joins two or more DataFrames based on a shared column, and many others.
Data Analysis: Pandas has a wide range of statistical analysis tools, including mean(), median(), mode(), std(), and var(). Additionally, Pandas has functions for plotting data using Matplotlib or other libraries.
Data Export: After manipulating and analyzing the data, you can export it to different file formats using functions like to_csv(), to_excel(), to_sql(), etc. provided by Pandas.
Using Pandas Library
Let’s see how we can use Pandas.
- Using Pandas to create a DataFrame using a CSV file.
import pandas as pd
df = pd.read_csv('data.csv')
- Using Pandas to save a DataFrame using as a CSV file
df.to_csv('file1.csv')
Challenges with Pandas
Let’s all agree, before we continue, that Pandas is awesome. Pandas is the best option if it works for the use case in front of you and completes the task. Let’s discuss some of the challenges you may face while working with Pandas.
Memory Usage: Pandas data structures like DataFrames and Series, especially when working with big datasets, can use a lot of memory. Pandas succeeds at handling tabular data, but it struggles with complex data types including hierarchical data, graphs, and time-series data.
Speed: Pandas is not speed-optimized, especially when working with big datasets. Large dataset processing can be time-consuming, which might be a bottleneck in some applications.
Limited Scalability: Pandas can handle big datasets, but its scalability may be constrained by the fact that distributed computing settings are not ideal for it.
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What is Dask?
Dask is a Python-based parallel computing library for data analytics that can run on anything from a single-core computer to a sizable cluster of machines. By offering a high-level interface for parallelism and distributed computing.
Dask offers Dask Arrays and Dask DataFrames as its two primary data structures. Dask DataFrames are an extension of Pandas DataFrames, just as Dask Arrays are an extension of NumPy arrays.
These data structures offer a mechanism to distribute computations over numerous cores or machines to parallelize operations on huge datasets.
What is Lazy Evaluation in Dask?
The “lazy evaluation” or “delayed” functions concept is the key differentiator of dask. A group of transformations or computations would queue up for later, parallel execution by delaying a task with dask.
This means that until specifically instructed to do so, Python will not evaluate the computations that have been requested. This is different from other functions, which begin computing immediately after being called.
In dask, many useful functions convert to native. This means that they will automatically be lazy (delayed computation) without your ever having to ask.
You can switch from that to something like this, where your tasks run concurrently when it’s feasible, greatly enhancing the speed and effectiveness of your work.
How Dask Works?
Dask provides a parallel computing framework that allows you to scale your computations to multiple cores of your machine or even across multiple machines. The way Dask works is through the following steps.
1. Create a Dask DataFrame or Dask Array: To start using Dask, you need to create a Dask DataFrame or Dask Array. These data structures are similar to Pandas DataFrames or NumPy arrays, but they are divided into smaller chunks, which can be processed in parallel across multiple cores or machines.
2. Break up The Data into Smaller Chunks: When you create a Dask DataFrame or Dask Array, Dask automatically breaks up the data into smaller chunks. This allows each chunk to be processed independently and in parallel, which improves performance and enables distributed computing.
3. Schedule Computations: When you perform operations on a Dask DataFrame or Dask Array, Dask creates a task graph that represents the computation. This task graph is a directed acyclic graph (DAG) that defines the dependencies between the operations.
4. Execute The Computations: Once there is the creation of a task graph, Dask can schedule and execute the computations. Dask uses a scheduler to coordinate the tasks and distribute them across the available workers. The scheduler ensures that each task executes only once and that the results combine correctly.
5. Aggregate The Results: Finally, Dask aggregates the results from the computations and returns them to the user. The results typically return as a Dask DataFrame or Dask Array, but they can also convert to a Pandas DataFrame or NumPy array if needed.
How to Use Dask?
You can create a dask Dataframe by:
- Converting an existing pandas DataFrame
dataframe.from_pandas()
- Loading data directly into a dask DataFrame: for example,
dataframe.read_csv()
Dask can read a wide variety of data storage formats, including Parquet and JSON, in addition to CSV, and this feature is built-in.
Converting an Existing Pandas DataFrame
import dask.dataframe as ddimport pandas as pddf = pd.read_csv(" /File Path ") ddf = dd.from_pandas(df, npartitions=10)
Loading Data Directly into A dask DataFrame
import dask.dataframe as dd ddf = dd.read_csv(" /File Path ")
Challenges with Dask
Although Dask has many benefits, users may encounter the following difficulties as well:
Debugging: Dask programs can be difficult to debug due to the distributed structure of the system. When anything goes wrong, the source of the issue might not be immediately evident, and it might take some investigation to find it.
Performance Tuning: While Dask may significantly boost the performance of applications that require a lot of data, doing so might be challenging. To maximize the use of the resources, it becomes necessary to understand the underlying system and optimize the code.
Cluster Management: Memory and CPU time are quick to use in Dask. It can be difficult to manage these resources, especially when operating Dask on a big cluster.
Data Locality: Dask deals with dispersed data, but it can be difficult to make sure that the data is there at the appropriate time and location. Inefficient resource utilization and slow performance can stem from improper data distribution.
Comparing Pandas and Dask
| Parameter | Pandas | Dask |
| Performance | Pandas is made to operate on single computers and, small to medium-sized datasets that fit in memory. | Dask can spread out computations across numerous threads or computers, which can make it significantly faster. |
| Scalability | Pandas is meant to run on a single system and has trouble with huge data sets that don’t fit in memory, | Dask is designed for parallel and distributed computing and can scale out computations across numerous machines. |
| Memory Management | Pandas loads entire datasets into memory which can have performance issues when working with large datasets, | Dask handles large datasets more effectively since it employs lazy evaluation and dynamically loads data into memory as needed |
| Integration with other libraries and tools | Pandas offers a significantly larger range of third-party libraries and tools. | Dask offers comparatively fewer third-party libraries and tools. |
| Community Support | Pandas is one of the most used tools for data analytics, making it simpler to get answers and support from community. | Dask is gaining popularity and has a growing community |
In this section, let’s compare some of the most popular DataFrame functions using both pandas and dask to figure out which one is better.
Note: We are going to use
%%time
%%time
Loading Multiple CSV Files in a Dask DataFrame
%%timeimport dask.dataframe as dddask_df = dd.read_csv(f"/content/Files_Folder/*.csv")
Output
Loading Multiple CSV Files in a Pandas DataFrame
%%timeimport globimport osimport pandas as pdall_files = glob.glob("/content/Files_Folder/*.csv")pandas_df = pd.concat((pd.read_csv(f) for f in all_files))
Output
GroupBy Function using Dask
%%timedask_df.groupby("company_location").salary_in_usd.mean()
Output
GroupBy Function using Pandas
%%timePandas_df.groupby("company_location").salary_in_usd.mean()
Output
Analyzing a Single Column using Dask
%%timedask_df['salary_in_usd'].mean()
Output
Analyzing a Single Column Pandas
%%timedf['salary_in_usd'].mean()
Output
What Performs Better – Pandas or Dask?
As we can see, Dask performs this operation on a larger dataset much more quickly than Pandas. Dask is a better option for managing bigger datasets due to its scalability.
Dask is more difficult to use than Pandas, which has a more user-friendly library. Pandas’ functions operate together naturally, and its syntax is clear and straightforward to understand. Dask, on the other hand, calls for more specialized expertise and can necessitate further education.
Pandas can only manage datasets that fit in memory, but Dask can scale to handle datasets that are larger than memory. Dask can handle significantly larger datasets than Pandas since it can split calculations across numerous cores or even computers.
Conclusion
In this blog, we attempted to demystify a few aspects of pandas and dask by comparing them, and we hope that it will inspire you to try these innovative, exciting technologies for your data science projects.
If you are familiar with pandas and other PyData libraries, dask clusters are more affordable than you might expect, they operate incredibly quickly and are simple to use.
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