Difference between Spark Dataframe and Pandas Dataframe

Spark DataFrame

Spark DataFrame is a distributed data collection established into named columns. it's a key statistics structure in Apache Spark, a quick and distributed computing device optimised for huge data processing. In a distributed computing context, Spark DataFrames provide a better-stage API for operating with established and semi-structured information.

Pandas DataFrame

A Pandas DataFrame is a two-dimensional labelled data structure that represents tabular data. It is one of the core data structures provided by the Pandas library in Python. The DataFrame organizes data in a row-column format, similar to a table or spreadsheet.

Advantages

Spark DataFrames	Pandas DataFrames
Can cope with massive datasets that exceed the memory potential of a single device and leverage the computing capabilities of Spark.	user-friendly API with intuitive syntax, making it smooth to govern and examine structured data.
Optimises information processing minimises data shuffling, and optimises the execution plan.	Have a wealthy environment of libraries presenting effective tools for statistics manipulation, visualization, and machine learning.
automatically recovers from screw-ups by means of redistributing the workload to other nodes inside the cluster.	guides numerous data formats, permitting seamless integration with local data sources.
Supports numerous data sources, allowing seamless integration with other data formats.	Can be operated completely in memory, enabling fast and efficient data processing.
Enables parallel processing across a cluster of machines, making it well-suited for large data processing tasks.	Offers a rich set of features and operations for data manipulation, exploration, and analysis.

Disadvantages

Spark DataFrames	Pandas DataFrames
Requires distributed computing surroundings and cluster configuration, which provides complexity compared to a single-machine solution like Pandas DataFrames.	Memory limits caused by a single computer's memory capacity make it less efficient for working with massive datasets.
Due to the distributed nature of computing, it incurs overhead, which may introduce additional delay, making it much less efficient for small to medium-sized datasets.	lacks built-in distributed computing features, making it less efficient than Spark DataFrames for running with large datasets.

Example 1

we'll show the variations in developing a Spark DataFrame with the use of PySpark and a Pandas DataFrame with pandas.

Algorithm

• Bring in the essential libraries

• SparkSession creation: To construct a builder item, use the SparkSession.builder.

• define the data by way of developing a list of dictionaries

• To construct a Spark DataFrame, use createDataFrame(data).

• Create a Pandas DataFrame using pd.DataFrame(data)

• show every Individual Dataframes.

Example

from pyspark.sql import SparkSession
import pandas as pd
# Creating a SparkSession
spark = SparkSession.builder.appName("SparkDataFrameExample").getOrCreate()

# Creating the DataFrames from a list of dictionaries
data = [{"name": "Ashwin", "age": 25}, {"name": "Pooja", "age": 30}, {"name": 
"John", "age": 28}]
Sdf = spark.createDataFrame(data)
Pdf = pd.DataFrame(data)

# Displaying the Spark DataFrame
print("Structure of Spark DataFrame")
Sdf.show()
# Displaying the Pandas DataFrame
print("Structure of Pandas DataFrame")
print(Pdf)

Output

Structure of Spark DataFrame
+------+---+
|  name|age|
+------+---+
|Ashwin| 25|
| Pooja| 30|
|  John| 28|
+------+---+

Structure of Pandas DataFrame
 name  age
0  Ashwin   25   #displays along with the index number
1   Pooja   30
2   John   28

The Spark DataFrame is displayed in a tabular format, while the Pandas DataFrame is printed as a table with automatic indexing starting from 0

We can also see that in the pandas dataframe the output is displayed with the corresponding index number.

Example 2

We will create a spark dataframe and pandas dataframe with data containing jobs then perform aggregation in both the dataframes to figure out the differences in the syntax and to find the count of each job.

Algorithm

• start via importing the pyspark and pandas.

• Initiate a SparkSession:

• outline the data with lists of dictionary and create pandas and spark dateframes with that records.

• Aggregate data in the Spark DataFrame:

• Sdf.groupby("job") to organise the DataFrame through the "job" column

• count() is used to count the number of occurrences of each job.

• Aggregate data in the Pandas DataFrame:

• Pdf.groupby("job") to seperate the DataFrame by the "job" column

• size() to rely the occurrences of every job

• reset_index(name="count") to reset the index and rename the aggregated column as "count"

• Print the aggregated Pandas and spark dataframe.

Example

from pyspark.sql import SparkSession
import pandas as pd

# Creating a SparkSession
spark = SparkSession.builder.appName("SparkDataFrameExample").getOrCreate()

# Creating a Spark DataFrame from a list of dictionaries representing jobs
data = [{"job": "Engineer"}, {"job": "Analyst"}, {"job": "Analyst"}, {"job": 
"Manager"}, {"job": "Engineer"}]
Sdf = spark.createDataFrame(data)

# Creating a Pandas DataFrame representing jobs
Pdf = pd.DataFrame(data)

# Aggregating data in Spark DataFrame
grouped_df_spark = Sdf.groupby("job").count()

# Aggregating data in Pandas DataFrame
grouped_df_pandas = Pdf.groupby("job").size().reset_index(name="count")

# Displaying the aggregated Pandas DataFrame
print(grouped_df_pandas)

# Displaying the aggregated Spark DataFrame
grouped_df_spark.show()

Output

   job  count
0   Analyst   2
1   Engineer   2
2   Manager   1

+--------+-----+
|   job|count|
+--------+-----+
| Analyst|   2|
|Engineer|   2|
| Manager|   1|
+--------+-----+

Table comparing Spark DataFrame vs Pandas DataFrame

Feature	Spark DataFrame	Pandas DataFrame
Computing Environment	Distributed computing framework for big data processing, multiple nodes.	Single-node environment for smaller datasets.
Performance and Scalability	Highly scalable and efficient for big data.	Excellent performance for small to medium-sized datasets.
Data Processing Model	Lazy evaluation and optimized execution plan.	Immediate computation for interactive data exploration.
Language Support	Supports Scala, Java,Python, and R.	Primarily built for Python with extensive Python ecosystem integration.
Indexing	This doesn't provide the output with index	They provide default index numbers starting from 0
Data Manipulation	has a Wide range of transformations and actions.	a Rich set of functions for data manipulation and analysis.
Ecosystem and Integration	Seamless integration with the Apache Spark ecosystem.	Integrates well with Python libraries (e.g., NumPy, Matplotlib).
Data Partitioning	Supports partitioning and parallel processing at a partition level.	Does not have built-in partitioning capabilities.
Memory Usage	Optimized memory management for distributed processing.	Relies on available memory in a single-node environment.

Conclusion

Both Spark and Pandas Dataframe are powerful tools for working with structured data, but they have some key differences. If we are working with small to medium-sized datasets on a single machine, Pandas DataFrames provide a convenient and efficient solution. If you are dealing with large-scale data processing or working in a distributed computing environment, Spark DataFrames are better suited due to their scalability and fault tolerance.