Data Ingestion via Excel: Comparing runtimes

Data ingestion is the process of obtaining and importing the data for the storage in the database. In this article, we explore different data ingestion techniques used to extract the data from the excel file in Python and compare their runtimes. Let's suppose the excel file looks like this -

Using xlrd library

Using xlrd module, one can retrieve information from a spreadsheet. For example, reading, writing or modifying the data can be done in Python. Also, user might have to go through various sheets and retrieve data based on some criteria or modify some rows and columns and do a lot of work. Python3

import xlrd
import time


# Time variable for finding the 
# difference
t1 = time.time()

#Open the workbook to read the
# excel file 
workbook = xlrd.open_workbook('excel.xls')
 
#Get the first sheet in the workbook 
sheet = workbook.sheet_by_index(0)

#Read row data line by line 
for i in range(sheet.nrows):
    row = sheet.row_values(i) 
    print(row)
    
t2 = time.time()
print("\nTime taken by xlrd:")
print(t2-t1)

Output:

Using Pandas

Python data analysis library is a powerful tool used by data scientists. It helps in data ingestion and data exploration. Python3

import pandas as pd 
import time


# Time variable for finding the 
# difference
t1 = time.time()

data = pd.read_excel('excel.xls') 
print(data.head())

t2 = time.time()
print("\nTime taken by xlrd:")
print(t2-t1)

Output:

Using dask dataframe

A Dask DataFrame is a large parallel DataFrame composed of many smaller Pandas DataFrames, split along the index. Python3 1==

import dask
import dask.dataframe as dd
import pandas as pd 
from dask.delayed import delayed
import time


# Time variable for finding the 
# difference
t1 = time.time()

 
parts = dask.delayed(pd.read_excel)('excel.xls', 
                                    sheet_name=0)
df = dd.from_delayed(parts)
 
print(df.head())

t2 = time.time()
print("\nTime taken by Dask:")
print(t2-t1)

Output: