Faster way to read Excel files to pandas dataframe

Question

I have a 14MB Excel file with five worksheets that I'm reading into a Pandas dataframe, and although the code below works, it takes 9 minutes!

Does anyone have suggestions for speeding it up?

import pandas as pd

def OTT_read(xl,site_name):
    df = pd.read_excel(xl.io,site_name,skiprows=2,parse_dates=0,index_col=0,
                       usecols=[0,1,2],header=None,
                       names=['date_time','%s_depth'%site_name,'%s_temp'%site_name])
    return df

def make_OTT_df(FILEDIR,OTT_FILE):
    xl = pd.ExcelFile(FILEDIR + OTT_FILE)
    site_names = xl.sheet_names
    df_list = [OTT_read(xl,site_name) for site_name in site_names]
    return site_names,df_list

FILEDIR='c:/downloads/'
OTT_FILE='OTT_Data_All_stations.xlsx'
site_names_OTT,df_list_OTT = make_OTT_df(FILEDIR,OTT_FILE)

Answer 1

As others have suggested, csv reading is faster. So if you are on windows and have Excel, you could call a vbscript to convert the Excel to csv and then read the csv. I tried the script below and it took about 30 seconds.

# create a list with sheet numbers you want to process
sheets = map(str,range(1,6))

# convert each sheet to csv and then read it using read_csv
df={}
from subprocess import call
excel='C:\\Users\\rsignell\\OTT_Data_All_stations.xlsx'
for sheet in sheets:
    csv = 'C:\\Users\\rsignell\\test' + sheet + '.csv' 
    call(['cscript.exe', 'C:\\Users\\rsignell\\ExcelToCsv.vbs', excel, csv, sheet])
    df[sheet]=pd.read_csv(csv)

Here's a little snippet of python to create the ExcelToCsv.vbs script:

#write vbscript to file
vbscript="""if WScript.Arguments.Count < 3 Then
    WScript.Echo "Please specify the source and the destination files. Usage: ExcelToCsv <xls/xlsx source file> <csv destination file> <worksheet number (starts at 1)>"
    Wscript.Quit
End If

csv_format = 6

Set objFSO = CreateObject("Scripting.FileSystemObject")

src_file = objFSO.GetAbsolutePathName(Wscript.Arguments.Item(0))
dest_file = objFSO.GetAbsolutePathName(WScript.Arguments.Item(1))
worksheet_number = CInt(WScript.Arguments.Item(2))

Dim oExcel
Set oExcel = CreateObject("Excel.Application")

Dim oBook
Set oBook = oExcel.Workbooks.Open(src_file)
oBook.Worksheets(worksheet_number).Activate

oBook.SaveAs dest_file, csv_format

oBook.Close False
oExcel.Quit
""";

f = open('ExcelToCsv.vbs','w')
f.write(vbscript.encode('utf-8'))
f.close()

This answer benefited from Convert XLS to CSV on command line and csv & xlsx files import to pandas data frame: speed issue

Answer 2

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).

Answer 3

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

Answer 4

I wrote a little quick&dirty script to just read values from .xlsx files. This does not update values (like dates) and just works for the files I use. Probably there are still some bugs, because I just wrote it down without carefully studying the xlsx definitions :-) . But it is about five to ten times faster than the default pd.read_excel.

# -*- coding: utf-8 -*-
"""
Created on Fri Jan  3 16:42:08 2020

@author: FelixKling
"""

import re
import zipfile
import pandas as pd
import datetime as dt
import numpy as np
import html

def is_number(s):
    try:
        float(s)
        return True
    except ValueError:
        return False

def read(path, sheet_name=None, header=True, index_col=False, skiprows=[], skipcolumns=[]):
    """
    Reads an .xlsx or .xlsm file and returns a Pandas DataFrame. Is much faster than pandas.read_excel().

    Parameters
    ----------
    path : str
        The path to the .xlsx or .xlsm file.
    sheet_name : str, optional
        Name of the sheet to read. If none, the first (not the active!) sheet is read. The default is None.
    header : bool, optional
        Whether to use the first line as column headers. The default is True.
    index_col : bool, optional
        Whether to use the first column as index. The default is False.
    skiprows : list of int, optional.
        The row numbers to skip ([0, 1] skips the first two rows). The default is [].
    skipcolumns : list of int, optional.
        The column numbers to skip ([0, 1] skips the first two columns). The default is [].

    Raises
    ------
    TypeError
        If the file is no .xlsx or .xlsm file.
    FileNotFoundError
        If the sheet name is not found.

    Returns
    -------
    Pandas DataFrame
        The input file as DataFrame.

    """
    # check extension
    if "." not in path:
        raise TypeError("This is no .xlsx or .xlsm file!")
    if path.rsplit(".", 1)[1] not in ["xlsx", "xlsm"]:
        raise TypeError("This is no .xlsx or .xlsm file!")

    path = path.replace("\\","/")

    tempfiles = dict()
    with zipfile.ZipFile(path, 'r') as zipObj:
        for name in zipObj.namelist():
            if name.startswith("xl/worksheets/") or name in [
                    "xl/_rels/workbook.xml.rels",
                    "xl/styles.xml",
                    "xl/workbook.xml",
                    "xl/sharedStrings.xml",                    
                    ]:
                try:
                    tempfiles[name] = zipObj.read(name).decode("utf-8")
                except UnicodeDecodeError:
                    tempfiles[name] = zipObj.read(name).decode("utf-16")

    # read rels (paths to sheets)
    
    text = tempfiles["xl/_rels/workbook.xml.rels"]
    rels = {}
    
    relids = re.findall(r'<Relationship Id="([^"]+)"', text)
    relpaths = re.findall(r'<Relationship .*?Target="([^"]+)"', text)
    rels = dict(zip(relids, relpaths))

    # read sheet names and relation ids

    if sheet_name:
        text = tempfiles["xl/workbook.xml"]
        workbooks = {}
       
        workbookids = re.findall(r'<sheet.*? r:id="([^"]+)"', text)
        workbooknames = re.findall(r'<sheet.*? name="([^"]+)"', text)
        workbooks = dict(zip(workbooknames, workbookids))
        if sheet_name in workbooks:
            sheet = rels[workbooks[sheet_name]].rsplit("/", 1)[1]
        else:
            raise FileNotFoundError("Sheet " + str(sheet_name) + " not found in Excel file! Available sheets: " + "; ".join(workbooks.keys()))

    else:
        sheet="sheet1.xml"

    # read strings, they are numbered
    string_items = []
    if "xl/sharedStrings.xml" in tempfiles:
        text = tempfiles["xl/sharedStrings.xml"]
        
        string_items = re.split(r"<si.*?><t.*?>", text.replace("<t/>", "<t></t>").replace("</t></si>","").replace("</sst>",""))[1:]
        string_items = [html.unescape(str(i).split("</t>")[0]) if i != "" else np.nan for i in string_items]
    
    # read styles, they are numbered

    text = tempfiles["xl/styles.xml"]
    styles = re.split(r"<[/]?cellXfs.*?>", text)[1]
    styles = styles.split('numFmtId="')[1:]
    styles = [int(s.split('"', 1)[0]) for s in styles]

    numfmts = text.split("<numFmt ")[1:]
    numfmts = [n.split("/>", 1)[0] for n in numfmts]
    for i, n in enumerate(numfmts):
        n = re.sub(r"\[[^\]]*\]", "", n)
        n = re.sub(r'"[^"]*"', "", n)
        if any([x in n for x in ["y", "d", "w", "q"]]):
            numfmts[i] = "date"
        elif any([x in n for x in ["h", "s", "A", "P"]]):
            numfmts[i] = "time"
        else:
            numfmts[i] = "number"

    def style_type(x):
        if 14 <= x <= 22:
            return "date"
        if 45 <= x <= 47:
            return "time"
        if x >= 165:
            return numfmts[x - 165]
        else:
            return "number"

    styles = list(map(style_type, styles))


    text = tempfiles["xl/worksheets/" + sheet]


    def code2nr(x):
        nr = 0
        d = 1
        for c in x[::-1]:
            nr += (ord(c)-64) * d
            d *= 26
        return nr - 1

    table = []
    max_row_len = 0

    rows = [r.replace("</row>", "") for r in re.split(r"<row .*?>", text)[1:]]
    for r in rows:            
        # c><c r="AT2" s="1" t="n"><v></v></c><c r="AU2" s="115" t="inlineStr"><is><t>bla (Namensk&#252;rzel)</t></is></c>

        r = re.sub(r"</?r.*?>","", r)        
        r = re.sub(r"<(is|si).*?><t.*?>", "<v>", r)
        r = re.sub(r"</t></(is|si)>", "</v>", r)
        r = re.sub(r"</t><t.*?>","", r)

        values = r.split("</v>")[:-1]
        add = []
        colnr = 0
        for v in values:
            value = re.split("<v.*?>", v)[1]
            
            v = v.rsplit("<c", 1)[1]
            # get column number of the field
            nr = v.split(' r="')[1].split('"')[0]
            nr = code2nr("".join([n for n in nr if n.isalpha()]))
            if nr > colnr:
                for i in range(nr - colnr):
                    add.append(np.nan)
            colnr = nr + 1

            sty = "number"
            if ' s="' in v:
                sty = int(v.split(' s="', 1)[1].split('"', 1)[0])
                sty = styles[sty]
         
            # inline strings
            if 't="inlineStr"' in v:
                add.append(html.unescape(value) if value != "" else np.nan)
            # string from list
            elif 't="s"' in v:
                add.append(string_items[int(value)])
            # boolean
            elif 't="b"' in v:
                add.append(bool(int(value)))
            # date
            elif sty == "date":
                if len(value) == 0:
                    add.append(pd.NaT)
                # Texts like errors
                elif not is_number(value):
                    add.append(html.unescape(value))
                else:
                    add.append(dt.datetime(1900,1,1) + dt.timedelta(days=float(value) - 2))
            # time
            elif sty == "time":
                if len(value) == 0:
                    add.append(pd.NaT)
                # Texts like errors
                elif not is_number(value):
                    add.append(html.unescape(value))
                else:
                    add.append((dt.datetime(1900,1,1) + dt.timedelta(days=float(value) - 2)).time())
            # Null
            elif len(value) == 0:
                add.append(np.nan)
            # Texts like errors
            elif not is_number(value):
                add.append(html.unescape(value))
            # numbers
            else:
                add.append(round(float(value), 16))
        table.append(add)
        if len(add) > max_row_len:
            max_row_len = len(add)

    df = pd.DataFrame(table)

    # skip rows or columns
    df = df.iloc[[i for i in range(len(df)) if i not in skiprows], [i for i in range(len(df.columns)) if i not in skipcolumns]]
    
    if index_col:
        df = df.set_index(df.columns[0])
    if header:
        df.columns = df.iloc[0].values
        df = df.iloc[1:]

    return df

Answer 5

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

Answer 6

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

Answer 7

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 ☕.