PySpark : Getting int representing the number of array dimensions


The Pandas API on Spark opens doors to seamless data manipulation and analysis. One fundamental feature within this integration is, which serves a crucial role in identifying and organizing data. Let’s delve into its significance through practical examples.


In Pandas, a Series is a one-dimensional labeled array capable of holding data of any type. Each element in the Series has a label or index. is an attribute that allows assigning a name to the Series, aiding in its identification and interpretation.

Example 1: Naming a Series

Consider a scenario where we have a Series representing the sales figures for different products. Assigning a name to this Series enhances its readability and context.

import pandas as pd
from pyspark.sql import SparkSession

# Initialize Spark session
spark = SparkSession.builder \
    .appName("SeriesNameExample") \

# Sample data
data = {'Product': ['A', 'B', 'C', 'D'],
        'Sales': [1000, 1500, 800, 2000]}

# Creating a Pandas DataFrame
df = pd.DataFrame(data)

# Converting Pandas DataFrame to Spark DataFrame
spark_df = spark.createDataFrame(df)

# Converting Spark DataFrame to Pandas DataFrame
pandas_df = spark_df.toPandas()

# Creating a Series from Pandas DataFrame
sales_series = pandas_df['Sales'] = 'Sales Figures'  # Assigning a name to the Series



0    1000
1    1500
2     800
3    2000
Name: Sales Figures, dtype: int64

In this example, Sales Figures serves as a descriptive label for the series, providing clarity on its contents.

Example 2: Retrieving Series Name

Another utility of is retrieving the assigned name programmatically.

# Retrieving the name of the Series
series_name =
print("Series Name:", series_name)
Series Name: Sales Figures

Here, series_name holds the name of the Sales series, allowing further processing based on the context.

Author: user