Spark to pandas - Python

Introduction

Many data scientists prefer using Pandas library for data wrangling, preprocessing, and analysis instead of Apache Spark because of its simplicity, ease of use, and functionality. This guide aims to provide an overview of migrating from Spark to Pandas for Python developers.

Pandas

Pandas is an open-source Python library for data manipulation and analysis. It is built on top of NumPy, another popular library used for numerical computation. Pandas supports several data formats including CSV, Excel, SQL databases, and many others. The core component of Pandas is the DataFrame which is a two-dimensional table-like structure with columns and rows.

Installation

To install Pandas, open the terminal and use the following command:

pip install pandas

Creating a DataFrame

Pandas DataFrame can be created using different methods including reading data from files, databases, or creating from scratch. To create a new dataframe from scratch, use the following code:

import pandas as pd

data = {'name': ['John', 'Mike', 'Sarah'], 'age': [25, 30, 22], 'gender': ['M', 'M', 'F']}
df = pd.DataFrame(data)
print(df)

Selecting Data

To select one or more columns from a Pandas DataFrame, use the following syntax:

df['column_name']
df[['column_name1','column_name2']]

To select one or more rows from a DataFrame based on a condition, use the following syntax:

subset = df[df['column_name'] > value]

Aggregating Data

Pandas provides a variety of functions for aggregating data based on various grouping criteria. The following example shows how to group data based on one or more columns and perform aggregations on other columns:

avg_age_by_gender = df.groupby('gender')['age'].mean()
print(avg_age_by_gender)

Migrating from Spark to Pandas

Migrating from Spark to Pandas involves the following steps:

1. Convert the Spark DataFrame to Pandas DataFrame using the toPandas() method.

pdf = spark_df.toPandas()

2. Use Pandas operations to manipulate the DataFrame as needed.
3. Convert the modified Pandas DataFrame back to Spark DataFrame using the createDataFrame() method.

modified_spark_df = spark.createDataFrame(modified_pdf)

Conclusion

Pandas provides a more user-friendly interface for data manipulation and analysis compared to Apache Spark. By migrating from Spark to Pandas, data scientists can perform their tasks more efficiently and effectively.