How to read and write data with Pandas in Microsoft Fabric

Microsoft Fabric notebooks support seamless interaction with Lakehouse data using Pandas, the most popular Python library for data exploration and processing. Within a notebook, you can quickly read data from, and write data back to, their Lakehouse resources in various file formats. This guide provides code samples to help you get started in your own notebook.

Prerequisites

• Get a Microsoft Fabric subscription. Or, sign up for a free Microsoft Fabric trial.

• Sign in to Microsoft Fabric.

• Use the experience switcher on the left side of your home page to switch to the Synapse Data Science experience.

  

Load Lakehouse data into a notebook

Once you attach a Lakehouse to your Microsoft Fabric notebook, you can explore stored data without leaving the page, and read it into your notebook, all with a few steps. Selection of any Lakehouse file surfaces options to "Load data" into a Spark or a Pandas DataFrame. You can also copy the file's full ABFS path or a friendly relative path.

Selection of one of the "Load data" prompts generates a code cell to load that file into a DataFrame in your notebook.

Converting a Spark DataFrame into a Pandas DataFrame

For reference, this command shows how to convert a Spark DataFrame into a Pandas DataFrame:

# Replace "spark_df" with the name of your own Spark DataFrame
pandas_df = spark_df.toPandas() 

Reading and writing various file formats

These code samples describe the Pandas operations to read and write various file formats.

Note

You must replace the file paths in these code samples. Pandas supports both relative paths, as shown here, and full ABFS paths. Paths of either type can be retrieved and copied from the interface according to the previous step.

Read data from a CSV file

import pandas as pd

# Read a CSV file from your Lakehouse into a Pandas DataFrame
# Replace LAKEHOUSE_PATH and FILENAME with your own values
df = pd.read_csv("/LAKEHOUSE_PATH/Files/FILENAME.csv")
display(df)

Write data as a CSV file

import pandas as pd 

# Write a Pandas DataFrame into a CSV file in your Lakehouse
# Replace LAKEHOUSE_PATH and FILENAME with your own values
df.to_csv("/LAKEHOUSE_PATH/Files/FILENAME.csv") 

Read data from a Parquet file

import pandas as pd 
 
# Read a Parquet file from your Lakehouse into a Pandas DataFrame
# Replace LAKEHOUSE_PATH and FILENAME with your own values
df = pandas.read_parquet("/LAKEHOUSE_PATH/Files/FILENAME.parquet") 
display(df)

Write data as a Parquet file

import pandas as pd 
 
# Write a Pandas DataFrame into a Parquet file in your Lakehouse
# Replace LAKEHOUSE_PATH and FILENAME with your own values
df.to_parquet("/LAKEHOUSE_PATH/Files/FILENAME.parquet") 

Read data from an Excel file

import pandas as pd 
 
# Read an Excel file from your Lakehouse into a Pandas DataFrame
# Replace LAKEHOUSE_PATH and FILENAME with your own values
df = pandas.read_excel("/LAKEHOUSE_PATH/Files/FILENAME.xlsx") 
display(df) 

Write data as an Excel file

import pandas as pd 

# Write a Pandas DataFrame into an Excel file in your Lakehouse
# Replace LAKEHOUSE_PATH and FILENAME with your own values
df.to_excel("/LAKEHOUSE_PATH/Files/FILENAME.xlsx") 

Read data from a JSON file

import pandas as pd 
 
# Read a JSON file from your Lakehouse into a Pandas DataFrame
# Replace LAKEHOUSE_PATH and FILENAME with your own values
df = pandas.read_json("/LAKEHOUSE_PATH/Files/FILENAME.json") 
display(df) 

Write data as a JSON file

import pandas as pd 
 
# Write a Pandas DataFrame into a JSON file in your Lakehouse
# Replace LAKEHOUSE_PATH and FILENAME with your own values
df.to_json("/LAKEHOUSE_PATH/Files/FILENAME.json") 

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