Convert PySpark DataFrames to and from pandas DataFrames

 The convertion is ok look like on pyspark - Python API for Apache Spark


All Spark SQL data types are supported by Arrow-based conversion except ArrayType of TimestampType. 

Arrow-based conversion in PySpark currently does not support ArrayType of TimestampType. This means that when attempting to convert a PySpark DataFrame containing a column of ArrayType(TimestampType) to a Pandas DataFrame using Arrow-based optimization, the conversion will either fail or automatically fall back to a non-Arrow based method if spark.sql.execution.arrow.fallback.enabled is set to true.

Code: 

import numpy as np
import pandas as pd

# Enable Arrow-based columnar data transfers
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")

# Generate a pandas DataFrame
pdf = pd.DataFrame(np.random.rand(100, 3))

pdf

# Create a Spark DataFrame from a pandas DataFrame using Arrow
df = spark.createDataFrame(pdf)

# Convert the Spark DataFrame back to a pandas DataFrame using Arrow
result_pdf = df.select("*").toPandas()

result_pdf


The running clip

A Spark DataFrame is equivalent to a relational table in Spark SQL, and can be created using various functions in SparkSession:

(In Spark, DataFrames are the distributed collections of data, organized into rows and columns. Each column in a DataFrame has a name and an associated type. DataFrames are similar to traditional database tables, which are structured and concise.)

Example:

people = spark.createDataFrame([

    {"deptId": 1, "age": 40, "name": "Hyukjin Kwon", "gender": "M", "salary": 50},

    {"deptId": 1, "age": 50, "name": "Takuya Ueshin", "gender": "M", "salary": 100},

    {"deptId": 2, "age": 60, "name": "Xinrong Meng", "gender": "F", "salary": 150},

    {"deptId": 3, "age": 20, "name": "Haejoon Lee", "gender": "M", "salary": 200}

])

A pandas DataFrame is a two-dimensional, size-mutable, and potentially heterogeneous tabular data structure with labeled axes (rows and columns). It is the primary data structure in the pandas library for Python and is widely used for data manipulation and analysis. 

Example:

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'],

        'Age': [25, 30, 35],

        'City': ['New York', 'London', 'Paris']}

df = pd.DataFrame(data)

print(df)

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