How to read excel file using databricks

[PraveenSaini]

0

I have a excel file as source file and i want to read data from excel file and convert data in data frame using databricks. I have already added maven dependence for Excel file format. when i a tring below code it is giving error .(Error: java.io.FileNotFoundException: /FileStore/tables/Airline.xlsx (No such file or directory) But file is available. Please help me on this code.

val df = spark.read.format("com.crealytics.spark.excel")

.option("location", "/FileStore/tables/Airline.xlsx")

.option("useHeader", "true")

.option("treatEmptyValuesAsNulls", "false")

.option("inferSchema", "false")

.option("addColorColumns", "false")

.load("/FileStore/tables/Airline.xlsx")

[edwards142]

Don’t worry you have several other options to open Excel file without Excel. Here are those options, so please check it out..!

http://www.repairmsexcel.com/blog/open-excel-files-without-excel

[Devarsh]

First of all check your spark and scala version.

Then install the library with Maven coordinates according to your spark and scala version.

Check further on this link to know more about the Maven coordinates to use:

https://mvnrepository.com/artifact/com.crealytics/spark-excel_2.12

Selected Cluster --> Libraries --> Install New --> Maven -->

Coordinates- com.crealytics:spark-excel_2.12:3.2.1_0.16.4

For pyspark use the following code:

df2 = spark.read.format("com.crealytics.spark.excel").option("header", "true").option("inferSchema", "true").load("dbfs:/FileStore/shared_uploads/abc@gmail.com/book.xlsx")
display(df2)

[Ananth]

This really worked. However I see this error for larger excel files.

shadeio.poi.util.RecordFormatException: Tried to allocate an array of length 208,933,193, but the maximum length for this record type is 100,000,000.

[Anonymous]

Another way also help for your case is usign Pandas to read excel then convert Pandas Dataframe to Pyspark Dataframe 🙂

[Gaurav_Databric]

# Example: Show the first 5 rows of the DataFrame
df.head()

# For Scala
// Example: Show the first 5 rows of the DataFrame
df.show(5)

Step 7: Perform Data Visualization (Optional) If you wish to visualize the data, Databricks provides various plotting libraries and visualization tools to present your findings effectively.

Step 8: Save or Export Results (Optional) After performing your analysis, if you want to save the processed data or export the results, Databricks supports various formats such as Parquet, CSV, JSON, etc.

[MasterDataBrick]

To read an Excel file using Databricks, you can use the Databricks Runtime's built-in support for reading various file formats, including Excel. Here are the steps to do it:

1. **Upload the Excel File**: First, upload your Excel file to a location that Databricks can access, such as DBFS (Databricks File System) or an external storage system like Azure Blob Storage or AWS S3.

2. **Create a Cluster**: If you don't already have a Databricks cluster, create one.

3. **Create a Notebook**: Create a Databricks notebook where you will write your code.

4. **Load the Excel File**: Use the appropriate library and function to load the Excel file. Databricks supports multiple libraries for this purpose, but one common choice is using the `pandas` library in Python. Here's an example using `pandas`:

```python

# Import the necessary libraries

import pandas as pd

# Specify the path to your Excel file

excel_file_path = "/dbfs/path/to/your/excel/file.xlsx" # Replace with your file path

# Use pandas to read the Excel file

df = pd.read_excel(excel_file_path)

# Show the first few rows of the DataFrame to verify the data

df.head()

```

5. **Execute the Code**: Run the code in your Databricks notebook. It will read the Excel file and load it into a DataFrame (in this case, using `pandas`).

6. **Manipulate and Analyze Data**: You can now use the `df` DataFrame to perform data manipulations, analysis, or any other operations you need within your Databricks notebook.

7. **Save Results**: If you need to save any results or processed data, you can do so using Databricks' capabilities, whether it's saving to a new Excel file, a database, or another storage location.

Make sure to configure your Databricks environment and notebook with the necessary dependencies if you're using libraries other than `pandas` for reading Excel files. Also, adjust the file path to match the location of your Excel file within your Databricks environment.

[ManishChaurasia]

To read an Excel file using Databricks, you can use the Databricks runtime, which supports multiple programming languages such as Python, Scala, and R. Here are the general steps to read an Excel file in Databricks using Python:

1. **Upload the Excel File**:

   - First, upload your Excel file to a location that is accessible from your Databricks workspace. You can use Databricks DBFS (Databricks File System), AWS S3, Azure Blob Storage, or any other supported storage.

2. **Create a Databricks Cluster**:

   - If you haven't already, create a Databricks cluster to run your code.

3. **Install Required Libraries**:

   - You'll need to install the necessary libraries to work with Excel files. In Python, you can use the `pandas` library, which is commonly used for data manipulation.

   ```python

   # Install the pandas library

   %pip install pandas

   ```

4. **Read the Excel File**:

   - You can read the Excel file into a Pandas DataFrame using the `pd.read_excel()` function. Provide the path to your Excel file as the argument.

   ```python

   import pandas as pd

   # Replace 'dbfs:/path_to_your_excel_file.xlsx' with the actual path to your Excel file

   excel_file_path = 'dbfs:/path_to_your_excel_file.xlsx'

   # Read the Excel file into a Pandas DataFrame

   df = pd.read_excel(excel_file_path)

   ```

   If you're using Scala or R, you can use the respective libraries (e.g., Apache POI for Scala).

5. **Analyze or Process Data**:

   - Once you have the data in a DataFrame, you can analyze, process, or visualize it as needed within your Databricks notebook.

6. **Save or Export Results**:

   - If you want to save your results or export data back to a storage location, you can use the appropriate Databricks file APIs or libraries.

Remember to adjust the code and file paths according to your specific Databricks setup and file location. Additionally, ensure that you have the necessary permissions to access the Excel file from your Databricks cluster.

[Ajay10795]

Below code can be used

import org.apache.spark.sql.DataFrame

// Define the file path (modify this to your actual file path)
val filePath = "/dbfs/FileStore/tables/Airline.xlsx"

// Read the Excel file into a DataFrame
val df: DataFrame = spark.read
.format("com.crealytics.spark.excel")
.option("sheetName", "Sheet1") // Specify the sheet name if needed
.option("header", "true") // If the first row contains column names
.load(filePath)

// Show the DataFrame to verify the data
df.show()

[Dwiashu]

To read an Excel file using Databricks, you can use the Databricks runtime, which supports multiple programming languages such as Python, Scala, and R. Here are the general steps to read an Excel file in Databricks using Python:

1. **Upload the Excel File**:
- First, upload your Excel file to a location that is accessible from your Databricks workspace. You can use Databricks DBFS (Databricks File System), AWS S3, Azure Blob Storage, or any other supported storage.

2. **Create a Databricks Cluster**:
- If you haven't already, create a Databricks cluster to run your code.

3. **Install Required Libraries**:
- You'll need to install the necessary libraries to work with Excel files. In Python, you can use the `pandas` library, which is commonly used for data manipulation.

```python
# Install the pandas library
%pip install pandas
```

4. **Read the Excel File**:
- You can read the Excel file into a Pandas DataFrame using the `pd.read_excel()` function. Provide the path to your Excel file as the argument.

```python
import pandas as pd

# Replace 'dbfs:/path_to_your_excel_file.xlsx' with the actual path to your Excel file
excel_file_path = 'dbfs:/path_to_your_excel_file.xlsx'

# Read the Excel file into a Pandas DataFrame
df = pd.read_excel(excel_file_path)
```

If you're using Scala or R, you can use the respective libraries (e.g., Apache POI for Scala).

5. **Analyze or Process Data**:
- Once you have the data in a DataFrame, you can analyze, process, or visualize it as needed within your Databricks notebook.

6. **Save or Export Results**:
- If you want to save your results or export data back to a storage location, you can use the appropriate Databricks file APIs or libraries.

Remember to adjust the code and file paths according to your specific Databricks setup and file location. Additionally, ensure that you have the necessary permissions to access the Excel file from your Databricks cluster.

[Sachin1990]

Step 8: Save or Export Results (Optional) After performing your analysis, if you want to save the processed data or export the results, Databricks supports various formats such as Parquet, CSV, JSON, etc.

Conclusion: In this brief technical blog, we explored how to read Excel files using Databricks. By following these simple steps, you can effortlessly load your Excel data into Databricks, conduct in-depth data analysis, and uncover valuable insights. Databricks’ scalable and collaborative environment empowers data professionals to make data-driven decisions effectively, turning raw data into actionable intelligence.

Now, you’re equipped to embark on your data analysis journey with Databricks and harness the full potential of your Excel data! Happy data crunching!

[Alpesh]

The error you're encountering, "java.io.FileNotFoundException," indicates that the file specified in the "location" option cannot be found at the given path. In Databricks, the path specified should be relative to the Databricks File System (DBFS) or a mount point.

Here's how you can modify your code to read an Excel file from DBFS:

val df = spark.read.format("com.crealytics.spark.excel")
.option("location", "dbfs:/FileStore/tables/Airline.xlsx")
.option("useHeader", "true")
.option("treatEmptyValuesAsNulls", "false")
.option("inferSchema", "false")
.option("addColorColumns", "false")
.load("dbfs:/FileStore/tables/Airline.xlsx")

[ConnectWise]

Databrick is rocking in Mumbai

[NareshManik]

Microsoft Query dialog, select the Azure Databricks table that you want to load into Excel, and then click Return Data. In the Import Data dialog, select Table and Existing sheet, and then click Import. After you load your data into your Excel workbook, you can perform analytical operations on it.

[Datab]

Good product 

[Sanddepe]

Click on the "Data" tab in the Databricks workspace and select the folder where you want to upload the file.

   - Click the "Upload" button and select your Excel file from your local machine.

2. **Create a DataFrame**:

   - Once your Excel file is uploaded, you need to create a DataFrame from it. In Databricks, you typically use Apache Spark for data manipulation. You can use the `spark.read` method to read the Excel file into a DataFrame. Here's an example using Python:

   ```python

   from pyspark.sql import SparkSession

   # Create a Spark session

   spark = SparkSession.builder.appName("ExcelImport").getOrCreate()

   # Read the Excel file into a DataFrame

   excel_df = spark.read.format("com.crealytics.spark.excel") \

       .option("header", "true") \ # If your Excel file has headers

       .load("/FileStore/your_excel_file.xlsx") # Update with your file path

   ```

   Make sure to replace `"/FileStore/your_excel_file.xlsx"` with the correct path to your uploaded Excel file.

3. **Use the DataFrame**:

   - Once you have the DataFrame, you can perform various operations on it, such as filtering, aggregating, or transforming the data.

4. **Write Data Back**:

   - If you need to save the processed data back to Databricks or export it to another format, you can use the `DataFrame.write` method.

Remember to adjust the code according to your specific use case and data. Databricks provides different ways to read Excel files, and you may need to install the necessary libraries or packages depending on your Databricks environment and Spark version. The example above assumes you have the "com.crealytics.spark.excel" library available for reading Excel files.