Faster way to read Excel files to pandas dataframe

If you have less than 65536 rows (in each sheet) you can try xls (instead of xlsx. In my experience xls is faster than xlsx. It is difficult to compare to csv because it depends on the number of sheets.

Although this is not an ideal solution (xls is a binary old privative format), I have found this is useful if you are working with a lof many sheets, internal formulas with values that are often updated, or for whatever reason you would really like to keep the excel multisheet functionality (instead of csv separated files).

I know this is old but in case anyone else is looking for an answer that doesn’t involve VB. Pandas read_csv() is faster but you don’t need a VB script to get a csv file.

Open your Excel file and save as *.csv (comma separated value) format.

Under tools you can select Web Options and under the Encoding tab you can change the encoding to whatever works for your data. I ended up using Windows, Western European because Windows UTF encoding is “special” but there’s lots of ways to accomplish the same thing. Then use the encoding argument in pd.read_csv() to specify your encoding.

Encoding options are listed here

There’s no reason to open excel if you’re willing to deal with slow conversion once.

1. Read the data into a dataframe with pd.read_excel()
2. Dump it into a csv right away with pd.to_csv()

Avoid both excel and windows specific calls. In my case the one-time time hit was worth the hassle. I got a ☕.

In my experience, Pandas read_excel() works fine with Excel files with multiple sheets. As suggested in Using Pandas to read multiple worksheets, if you assign sheet_name to None it will automatically put every sheet in a Dataframe and it will output a dictionary of Dataframes with the keys of sheet names.

But the reason that it takes time is for where you parse texts in your code. 14MB excel with 5 sheets is not that much. I have a 20.1MB excel file with 46 sheets each one with more than 6000 rows and 17 columns and using read_excel it took like below:

t0 = time.time()

def parse(datestr):
    y,m,d = datestr.split("/")
    return dt.date(int(y),int(m),int(d))

data = pd.read_excel("DATA (1).xlsx", sheet_name=None, encoding="utf-8", skiprows=1, header=0, parse_dates=[1], date_parser=parse)

t1 = time.time()

print(t1 - t0)
## result: 37.54169297218323 seconds

In code above data is a dictionary of 46 Dataframes.

As others suggested, using read_csv() can help because reading .csv file is faster. But consider that for the fact that .xlsx files use compression, .csv files might be larger and hence, slower to read. But if you wanted to convert your file to comma-separated using python (VBcode is offered by Rich Signel), you can use: Convert xlsx to csv

I used xlsx2csv to virtually convert excel file to csv in memory and this helped cut the read time to about half.

from xlsx2csv import Xlsx2csv
from io import StringIO
import pandas as pd


def read_excel(path: str, sheet_name: str) -> pd.DataFrame:
    buffer = StringIO()
    Xlsx2csv(path, outputencoding="utf-8", sheet_name=sheet_name).convert(buffer)
    buffer.seek(0)
    df = pd.read_csv(buffer)
    return df

I encourage you to do the comparison yourself and see which approach is appropriate in your situation.

For instance, if you are processing a lot of XLSX files and are only going to ever read each one once, you may not want to worry about the CSV conversion. However, if you are going to read the CSVs over and over again, then I would highly recommend saving each of the worksheets in the workbook to a csv once, then read them repeatedly using pd.read_csv().

Below is a simple script that will let you compare Importing XLSX Directly, Converting XLSX to CSV in memory, and Importing CSV. It is based on Jing Xue’s answer.

Spoiler alert: If you are going to read the file(s) multiple times, it’s going to be faster to convert the XLSX to CSV.

I did some testing with some files I’m working on are here are my results:

5,874 KB xlsx file (29,415 rows, 58 columns)
Elapsed time for [Import XLSX with Pandas]:    0:00:31.75
Elapsed time for [Convert XLSX to CSV in mem]: 0:00:22.19
Elapsed time for [Import CSV file]:            0:00:00.21

********************
202,782 KB xlsx file (990,832 rows, 58 columns)
Elapsed time for [Import XLSX with Pandas]:    0:17:04.31
Elapsed time for [Convert XLSX to CSV in mem]: 0:12:11.74
Elapsed time for [Import CSV file]:            0:00:07.11

YES! the 202MB file really did take only 7 seconds compared to 17 minutes for the XLSX!!!

If you’re ready to set up your own test, just open you XLSX in Excel and save one of the worksheets to CSV. For a final solution, you would obviously need to loop through the worksheets to process each one.

You will also need to pip install rich pandas xlsx2csv.

from rich import print
import pandas as pd
from datetime import datetime
from xlsx2csv import Xlsx2csv
from io import StringIO

def timer(name, startTime = None):
    if startTime:
        print(f"Timer: Elapsed time for [{name}]: {datetime.now() - startTime}")
    else:
        startTime = datetime.now()
        print(f"Timer: Starting [{name}] at {startTime}")
        return startTime


def read_excel(path: str, sheet_name: str) -> pd.DataFrame:
    buffer = StringIO()
    Xlsx2csv(path, outputencoding="utf-8", sheet_name=sheet_name).convert(buffer)
    buffer.seek(0)
    df = pd.read_csv(buffer)
    return df


xlsxFileName = "MyBig.xlsx"
sheetName = "Sheet1"
csvFileName = "MyBig.csv"

startTime = timer(name="Import XLSX with Pandas")
df = pd.read_excel(xlsxFileName, sheet_name=sheetName)
timer("Import XLSX with Pandas", startTime)

startTime = timer(name="Convert XLSX to CSV first")
df = read_excel(path=xlsxFileName, sheet_name=sheetName)
timer("Convert XLSX to CSV first", startTime)

startTime = timer(name="Import CSV")
df = pd.read_csv(csvFileName)
timer("Import CSV", startTime)