Apply xlsxwriter formatting to Excel file in AWS lambda function

Question

In a jupyter notebook I am able to create two Pandas dataframes and export them to individual sheets of an Excel workbook with some additional formatting, including text-wrapping, frozen pane, bold headers and auto-filters.

Code below:

# MODULES
import pandas as pd 
import numpy as np
from datetime import date

#CONSTRUCT DATAFRAME
df = pd.DataFrame({'Data': [10, 22, 31, 43, 57, 99, 65, 74, 88],
                  'Data2':[10, 22, 31, 43, 57, 99, 65, 74, 88],
                  'Data3':[10, 22, 31, 43, 57, 99, 65, 74, 88]})

#CONSTRUCT DATAFRAME
df2 = pd.DataFrame({'df2_Data': ['blue', 'yellow', 'purple', 'orange', 'green', 'brown', 'gray', 'white', 'red'],
                  'df2_Data2':['bike', 'car', 'bus', 'train', 'boat', 'truck', 'plane', 'scooter', 'skateboard'],
                  'df2_Data3':['chicken', 'cow', 'dog', 'crocodile', 'snake', 'pig', 'rat', 'mouse', 'monkey']})


#DATE FOR INCLUSION IN FILENAME 
today = date.today()
d2 = today.strftime("%B %d, %Y")


writer = pd.ExcelWriter('ExcelExample{}.xlsx'.format(d2), engine='xlsxwriter')
sheets_in_writer=['Sheet1','sheet2']

data_frame_for_writer=[df, df2]

for i,j in zip(data_frame_for_writer,sheets_in_writer):
    i.to_excel(writer,j,index=False)
    


### Assign WorkBook
workbook=writer.book

# Add a header format
header_format = workbook.add_format({'bold': True,'text_wrap': True,'size':10,
                                                      'valign': 'top','fg_color': '#c7e7ff','border': 1})


### Apply same format on each sheet being saved
for i,j in zip(data_frame_for_writer,sheets_in_writer):
    for col_num, value in enumerate(i.columns.values):
        writer.sheets[j].set_column(0, max_col - 1, 12)
        writer.sheets[j].write(0, col_num, value, header_format)
        writer.sheets[j].autofilter(0,0,0,i.shape[1]-1)
        writer.sheets[j].freeze_panes(1,0)
writer.save()

This produces the following Excel workbook in which both/all sheets have the formatting applied.

I'm struggling to reproduce this functionality within an AWS lambda function due to my unfamiliarity with boto3

My attempt is as follows, which does save an Excel file to the specified destination but of course has none of the required formatting.

import boto3 

import numpy as np
import pandas as pd

import io 
from io import BytesIO
from io import StringIO 

from datetime import date

def lambda_handler(event, context):

    s3 = boto3.client('s3') 

    # STORE TODAY'S DATE 
    today = date.today()
    d2 = today.strftime("%B %d, %Y")
    print("d2 =", d2)
    
    #SPECIFY OUTPUT EXCEL FILE NAME AND FILEPATH 
    bucket = 'brnddmn-s3'
    filepath = 'output/'ExcelExample{}.xlsx'.format(d2)
    
    # GENERATE AND EXPORT EXCEL FILE TO BUCKET 
    with io.BytesIO() as output:
        with pd.ExcelWriter(output, engine='xlsxwriter') as writer:
            df.to_excel(writer, sheet_name = 'Sheet1')
            df2.to_excel(writer, sheet_name = 'Sheet2')
        data = output.getvalue()
    s3 = boto3.resource('s3')
    s3.Bucket(bucket).put_object(Key=filepath, Body=data)

Any help greatly appreciated.

user_248823 · Answer

I tried to verify the issue, but apart from syntax errors and missing definitions of data-frames, it all works as expected. The corrected example version that works is below.

The only other comment I can make is to make sure that you have pandas and XlsxWriter setup for you lambda, for example, in a lambda layer.

import boto3

import json
import io

from datetime import date

import pandas as pd

s3 = boto3.resource('s3')

def lambda_handler(event, context):
        
    
    #CONSTRUCT DATAFRAME
    df = pd.DataFrame({'Data': [10, 22, 31, 43, 57, 99, 65, 74, 88],
                      'Data2':[10, 22, 31, 43, 57, 99, 65, 74, 88],
                      'Data3':[10, 22, 31, 43, 57, 99, 65, 74, 88]})
    
    #CONSTRUCT DATAFRAME
    df2 = pd.DataFrame({'df2_Data': ['blue', 'yellow', 'purple', 'orange', 'green', 'brown', 'gray', 'white', 'red'],
                      'df2_Data2':['bike', 'car', 'bus', 'train', 'boat', 'truck', 'plane', 'scooter', 'skateboard'],
                      'df2_Data3':['chicken', 'cow', 'dog', 'crocodile', 'snake', 'pig', 'rat', 'mouse', 'monkey']})
    
    today = date.today()
    d2 = today.strftime("%B %d, %Y")
    
    
    io_buffer = io.BytesIO()
    
    writer = pd.ExcelWriter(io_buffer, engine='xlsxwriter')
    sheets_in_writer=['Sheet1','sheet2']
    
    data_frame_for_writer=[df, df2]
    
    for i,j in zip(data_frame_for_writer,sheets_in_writer):
        i.to_excel(writer,j,index=False)
    
    ### Assign WorkBook
    workbook=writer.book
    
    # Add a header format
    header_format = workbook.add_format({'bold': True,'text_wrap': True,'size':10,
                                                          'valign': 'top','fg_color': '#c7e7ff','border': 1})
    max_col=3
    
    ### Apply same format on each sheet being saved
    for i,j in zip(data_frame_for_writer,sheets_in_writer):
        for col_num, value in enumerate(i.columns.values):
            writer.sheets[j].set_column(0, max_col - 1, 12)
            writer.sheets[j].write(0, col_num, value, header_format)
            writer.sheets[j].autofilter(0,0,0,i.shape[1]-1)
            writer.sheets[j].freeze_panes(1,0)
    writer.save()
    
    bucket = 'brnddmn-s3' 
    filepath = 'output/ExcelExample{}.xlsx'.format(d2)
    
    data = io_buffer.getvalue()        
    
    s3.Bucket(bucket).put_object(Key=filepath, Body=data)
    
    return  {
        'statusCode': 200,  
        'body': json.dumps(filepath)
        }

user_650756 · Answer

AWS Lambdas are guaranteed to have 512mb /tmp space, so a potential solution that will help you develop locally is to change

writer = pd.ExcelWriter('ExcelExample{}.xlsx'.format(d2), engine='xlsxwriter')

to

output_file_name = "ExcelExample{}.xlsx".format(d2)
local_file_path = f"/tmp/{output_file_name}"
writer = pd.ExcelWriter(local_file_path, engine="xlsxwriter")

which then would allow you to use

import os

import boto3


bucket = "brnddmn-s3"
s3_file_path = f"/output/{output_file_name}"

boto3.resource("s3").Object(bucket, s3_file_path).upload_file(local_file_path)
os.remove(local_file_path)

That should be the simplest change to keep you going, assuming ~500mb is enough for each workbook.

Apply xlsxwriter formatting to Excel file in AWS lambda function

2 Answers