如何在不覆盖数据的情况下写入现有的 excel 文件（使用熊猫）？

Question

I use pandas to write to excel file in the following fashion:

import pandas

writer = pandas.ExcelWriter('Masterfile.xlsx') 

data_filtered.to_excel(writer, "Main", cols=['Diff1', 'Diff2'])

writer.save()

Masterfile.xlsx already consists of number of different tabs. However, it does not yet contain "Main".

Pandas correctly writes to "Main" sheet, unfortunately it also deletes all other tabs.

Answer 1

Pandas docs says it uses openpyxl for xlsx files. Quick look through the code in ExcelWriter gives a clue that something like this might work out:

import pandas
from openpyxl import load_workbook

book = load_workbook('Masterfile.xlsx')
writer = pandas.ExcelWriter('Masterfile.xlsx', engine='openpyxl') 
writer.book = book

## ExcelWriter for some reason uses writer.sheets to access the sheet.
## If you leave it empty it will not know that sheet Main is already there
## and will create a new sheet.

writer.sheets = dict((ws.title, ws) for ws in book.worksheets)

data_filtered.to_excel(writer, "Main", cols=['Diff1', 'Diff2'])

writer.save()

Answer 2

UPDATE: Starting from Pandas 1.3.0 the following function will not work properly, because functions DataFrame.to_excel() and pd.ExcelWriter() have been changed - a new if_sheet_exists parameter has been introduced, which has invalidated the function below.

Here you can find an updated version of the append_df_to_excel() , which is working for Pandas 1.3.0+.

---

Here is a helper function:

import os
from openpyxl import load_workbook


def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None,
                       truncate_sheet=False, 
                       **to_excel_kwargs):
    """
    Append a DataFrame [df] to existing Excel file [filename]
    into [sheet_name] Sheet.
    If [filename] doesn't exist, then this function will create it.

    @param filename: File path or existing ExcelWriter
                     (Example: '/path/to/file.xlsx')
    @param df: DataFrame to save to workbook
    @param sheet_name: Name of sheet which will contain DataFrame.
                       (default: 'Sheet1')
    @param startrow: upper left cell row to dump data frame.
                     Per default (startrow=None) calculate the last row
                     in the existing DF and write to the next row...
    @param truncate_sheet: truncate (remove and recreate) [sheet_name]
                           before writing DataFrame to Excel file
    @param to_excel_kwargs: arguments which will be passed to `DataFrame.to_excel()`
                            [can be a dictionary]
    @return: None

    Usage examples:

    >>> append_df_to_excel('d:/temp/test.xlsx', df)

    >>> append_df_to_excel('d:/temp/test.xlsx', df, header=None, index=False)

    >>> append_df_to_excel('d:/temp/test.xlsx', df, sheet_name='Sheet2',
                           index=False)

    >>> append_df_to_excel('d:/temp/test.xlsx', df, sheet_name='Sheet2', 
                           index=False, startrow=25)

    (c) [MaxU](https://stackoverflow.com/users/5741205/maxu?tab=profile)
    """
    # Excel file doesn't exist - saving and exiting
    if not os.path.isfile(filename):
        df.to_excel(
            filename,
            sheet_name=sheet_name, 
            startrow=startrow if startrow is not None else 0, 
            **to_excel_kwargs)
        return
    
    # ignore [engine] parameter if it was passed
    if 'engine' in to_excel_kwargs:
        to_excel_kwargs.pop('engine')

    writer = pd.ExcelWriter(filename, engine='openpyxl', mode='a')

    # try to open an existing workbook
    writer.book = load_workbook(filename)
    
    # get the last row in the existing Excel sheet
    # if it was not specified explicitly
    if startrow is None and sheet_name in writer.book.sheetnames:
        startrow = writer.book[sheet_name].max_row

    # truncate sheet
    if truncate_sheet and sheet_name in writer.book.sheetnames:
        # index of [sheet_name] sheet
        idx = writer.book.sheetnames.index(sheet_name)
        # remove [sheet_name]
        writer.book.remove(writer.book.worksheets[idx])
        # create an empty sheet [sheet_name] using old index
        writer.book.create_sheet(sheet_name, idx)
    
    # copy existing sheets
    writer.sheets = {ws.title:ws for ws in writer.book.worksheets}

    if startrow is None:
        startrow = 0

    # write out the new sheet
    df.to_excel(writer, sheet_name, startrow=startrow, **to_excel_kwargs)

    # save the workbook
    writer.save()

Tested with the following versions:

• Pandas 1.2.3
• Openpyxl 3.0.5

Answer 3

With openpyxl version 2.4.0 and pandas version 0.19.2 , the process @ski came up with gets a bit simpler:

import pandas
from openpyxl import load_workbook

with pandas.ExcelWriter('Masterfile.xlsx', engine='openpyxl') as writer:
    writer.book = load_workbook('Masterfile.xlsx')
    data_filtered.to_excel(writer, "Main", cols=['Diff1', 'Diff2'])
#That's it!

Answer 4

Starting in pandas 0.24 you can simplify this with the mode keyword argument of ExcelWriter :

import pandas as pd

with pd.ExcelWriter('the_file.xlsx', engine='openpyxl', mode='a') as writer: 
     data_filtered.to_excel(writer) 

Answer 5

I know this is an older thread, but this is the first item you find when searching, and the above solutions don't work if you need to retain charts in a workbook that you already have created. In that case, xlwings is a better option - it allows you to write to the excel book and keeps the charts/chart data.

simple example:

import xlwings as xw
import pandas as pd

#create DF
months = ['2017-01','2017-02','2017-03','2017-04','2017-05','2017-06','2017-07','2017-08','2017-09','2017-10','2017-11','2017-12']
value1 = [x * 5+5 for x in range(len(months))]
df = pd.DataFrame(value1, index = months, columns = ['value1'])
df['value2'] = df['value1']+5
df['value3'] = df['value2']+5

#load workbook that has a chart in it
wb = xw.Book('C:\\data\\bookwithChart.xlsx')

ws = wb.sheets['chartData']

ws.range('A1').options(index=False).value = df

wb = xw.Book('C:\\data\\bookwithChart_updated.xlsx')

xw.apps[0].quit()

Answer 6

There is a better solution in pandas 0.24:

with pd.ExcelWriter(path, mode='a') as writer:
    s.to_excel(writer, sheet_name='another sheet', index=False)

before:

after:

so upgrade your pandas now:

pip install --upgrade pandas

Answer 7

Old question, but I am guessing some people still search for this - so...

I find this method nice because all worksheets are loaded into a dictionary of sheet name and dataframe pairs, created by pandas with the sheetname=None option. It is simple to add, delete or modify worksheets between reading the spreadsheet into the dict format and writing it back from the dict. For me the xlsxwriter works better than openpyxl for this particular task in terms of speed and format.

Note: future versions of pandas (0.21.0+) will change the "sheetname" parameter to "sheet_name".

# read a single or multi-sheet excel file
# (returns dict of sheetname(s), dataframe(s))
ws_dict = pd.read_excel(excel_file_path,
                        sheetname=None)

# all worksheets are accessible as dataframes.

# easy to change a worksheet as a dataframe:
mod_df = ws_dict['existing_worksheet']

# do work on mod_df...then reassign
ws_dict['existing_worksheet'] = mod_df

# add a dataframe to the workbook as a new worksheet with
# ws name, df as dict key, value:
ws_dict['new_worksheet'] = some_other_dataframe

# when done, write dictionary back to excel...
# xlsxwriter honors datetime and date formats
# (only included as example)...
with pd.ExcelWriter(excel_file_path,
                    engine='xlsxwriter',
                    datetime_format='yyyy-mm-dd',
                    date_format='yyyy-mm-dd') as writer:

    for ws_name, df_sheet in ws_dict.items():
        df_sheet.to_excel(writer, sheet_name=ws_name)

For the example in the 2013 question:

ws_dict = pd.read_excel('Masterfile.xlsx',
                        sheetname=None)

ws_dict['Main'] = data_filtered[['Diff1', 'Diff2']]

with pd.ExcelWriter('Masterfile.xlsx',
                    engine='xlsxwriter') as writer:

    for ws_name, df_sheet in ws_dict.items():
        df_sheet.to_excel(writer, sheet_name=ws_name)

Answer 8

The solution of @MaxU is not working for the updated version of python and related packages. It raises the error: "zipfile.BadZipFile: File is not a zip file"

I generated a new version of the function that works fine with the updated version of python and related packages and tested with python: 3.9 | openpyxl: 3.0.6 | pandas: 1.2.3

In addition I added more features to the helper function:

1. Now It resize all columns based on cell content width AND all variables will be visible (SEE "resizeColumns")
2. You can handle NaN, if you want that NaN are displayed as NaN or as empty cells (SEE "na_rep")
3. Added "startcol", you can decide to start to write from specific column, oterwise will start from col = 0

Here the function:

import pandas as pd

def append_df_to_excel(filename, df, sheet_name='Sheet1', startrow=None, startcol=None,
    truncate_sheet=False, resizeColumns=True, na_rep = 'NA', **to_excel_kwargs):
    """
    Append a DataFrame [df] to existing Excel file [filename]
    into [sheet_name] Sheet.
    If [filename] doesn't exist, then this function will create it.

    Parameters:
      filename : File path or existing ExcelWriter
                 (Example: '/path/to/file.xlsx')
      df : dataframe to save to workbook
      sheet_name : Name of sheet which will contain DataFrame.
                   (default: 'Sheet1')
      startrow : upper left cell row to dump data frame.
                 Per default (startrow=None) calculate the last row
                 in the existing DF and write to the next row...
      truncate_sheet : truncate (remove and recreate) [sheet_name]
                       before writing DataFrame to Excel file

      resizeColumns: default = True . It resize all columns based on cell content width
      to_excel_kwargs : arguments which will be passed to `DataFrame.to_excel()`
                        [can be dictionary]
      na_rep: default = 'NA'. If, instead of NaN, you want blank cells, just edit as follows: na_rep=''


    Returns: None

    *******************

    CONTRIBUTION:
    Current helper function generated by [Baggio]: https://stackoverflow.com/users/14302009/baggio?tab=profile
    Contributions to the current helper function: https://stackoverflow.com/users/4046632/buran?tab=profile
    Original helper function: (c) [MaxU](https://stackoverflow.com/users/5741205/maxu?tab=profile)


    Features of the new helper function:
    1) Now it works with python 3.9 and latest versions of pandas and openpxl
    ---> Fixed the error: "zipfile.BadZipFile: File is not a zip file".
    2) Now It resize all columns based on cell content width AND all variables will be visible (SEE "resizeColumns")
    3) You can handle NaN,  if you want that NaN are displayed as NaN or as empty cells (SEE "na_rep")
    4) Added "startcol", you can decide to start to write from specific column, oterwise will start from col = 0

    *******************



    """
    from openpyxl import load_workbook
    from string import ascii_uppercase
    from openpyxl.utils import get_column_letter
    from openpyxl import Workbook

    # ignore [engine] parameter if it was passed
    if 'engine' in to_excel_kwargs:
        to_excel_kwargs.pop('engine')

    try:
        f = open(filename)
        # Do something with the file
    except IOError:
        # print("File not accessible")
        wb = Workbook()
        ws = wb.active
        ws.title = sheet_name
        wb.save(filename)

    writer = pd.ExcelWriter(filename, engine='openpyxl', mode='a')


    # Python 2.x: define [FileNotFoundError] exception if it doesn't exist
    try:
        FileNotFoundError
    except NameError:
        FileNotFoundError = IOError


    try:
        # try to open an existing workbook
        writer.book = load_workbook(filename)

        # get the last row in the existing Excel sheet
        # if it was not specified explicitly
        if startrow is None and sheet_name in writer.book.sheetnames:
            startrow = writer.book[sheet_name].max_row

        # truncate sheet
        if truncate_sheet and sheet_name in writer.book.sheetnames:
            # index of [sheet_name] sheet
            idx = writer.book.sheetnames.index(sheet_name)
            # remove [sheet_name]
            writer.book.remove(writer.book.worksheets[idx])
            # create an empty sheet [sheet_name] using old index
            writer.book.create_sheet(sheet_name, idx)

        # copy existing sheets
        writer.sheets = {ws.title:ws for ws in writer.book.worksheets}
    except FileNotFoundError:
        # file does not exist yet, we will create it
        pass

    if startrow is None:
        # startrow = -1
        startrow = 0

    if startcol is None:
        startcol = 0

    # write out the new sheet
    df.to_excel(writer, sheet_name, startrow=startrow, startcol=startcol, na_rep=na_rep, **to_excel_kwargs)


    if resizeColumns:

        ws = writer.book[sheet_name]

        def auto_format_cell_width(ws):
            for letter in range(1,ws.max_column):
                maximum_value = 0
                for cell in ws[get_column_letter(letter)]:
                    val_to_check = len(str(cell.value))
                    if val_to_check > maximum_value:
                        maximum_value = val_to_check
                ws.column_dimensions[get_column_letter(letter)].width = maximum_value + 2

        auto_format_cell_width(ws)

    # save the workbook
    writer.save()

Example Usage:

# Create a sample dataframe
df = pd.DataFrame({'numbers': [1, 2, 3],
                    'colors': ['red', 'white', 'blue'],
                    'colorsTwo': ['yellow', 'white', 'blue'],
                    'NaNcheck': [float('NaN'), 1, float('NaN')],
                    })

# EDIT YOUR PATH FOR THE EXPORT 
filename = r"C:\DataScience\df.xlsx"   

# RUN ONE BY ONE IN ROW THE FOLLOWING LINES, TO SEE THE DIFFERENT UPDATES TO THE EXCELFILE 
  
append_df_to_excel(filename, df, index=False, startrow=0) # Basic Export of df in default sheet (Sheet1)
append_df_to_excel(filename, df, sheet_name="Cool", index=False, startrow=0) # Append the sheet "Cool" where "df" is written
append_df_to_excel(filename, df, sheet_name="Cool", index=False) # Append another "df" to the sheet "Cool", just below the other "df" instance
append_df_to_excel(filename, df, sheet_name="Cool", index=False, startrow=0, startcol=5) # Append another "df" to the sheet "Cool" starting from col 5
append_df_to_excel(filename, df, index=False, truncate_sheet=True, startrow=10, na_rep = '') # Override (truncate) the "Sheet1", writing the df from row 10, and showing blank cells instead of NaN

Answer 9

def append_sheet_to_master(self, master_file_path, current_file_path, sheet_name):
    try:
        master_book = load_workbook(master_file_path)
        master_writer = pandas.ExcelWriter(master_file_path, engine='openpyxl')
        master_writer.book = master_book
        master_writer.sheets = dict((ws.title, ws) for ws in master_book.worksheets)
        current_frames = pandas.ExcelFile(current_file_path).parse(pandas.ExcelFile(current_file_path).sheet_names[0],
                                                               header=None,
                                                               index_col=None)
        current_frames.to_excel(master_writer, sheet_name, index=None, header=False)

        master_writer.save()
    except Exception as e:
        raise e

This works perfectly fine only thing is that formatting of the master file(file to which we add new sheet) is lost.

Answer 10

writer = pd.ExcelWriter('prueba1.xlsx'engine='openpyxl',keep_date_col=True)

“keep_date_col”希望对你有帮助

Answer 11

book = load_workbook(xlsFilename)
writer = pd.ExcelWriter(self.xlsFilename)
writer.book = book
writer.sheets = dict((ws.title, ws) for ws in book.worksheets)
df.to_excel(writer, sheet_name=sheetName, index=False)
writer.save()

Answer 12

Method:

• Can create file if not present
• Append to existing excel as per sheet name

import pandas as pd
from openpyxl import load_workbook

def write_to_excel(df, file):
    try:
        book = load_workbook(file)
        writer = pd.ExcelWriter(file, engine='openpyxl') 
        writer.book = book
        writer.sheets = dict((ws.title, ws) for ws in book.worksheets)
        df.to_excel(writer, **kwds)
        writer.save()
    except FileNotFoundError as e:
        df.to_excel(file, **kwds)

Usage:

df_a = pd.DataFrame(range(10), columns=["a"])
df_b = pd.DataFrame(range(10, 20), columns=["b"])
write_to_excel(df_a, "test.xlsx", sheet_name="Sheet a", columns=['a'], index=False)
write_to_excel(df_b, "test.xlsx", sheet_name="Sheet b", columns=['b'])

Answer 13

Solution by @MaxU worked very well. I have just one suggestion:

If truncate_sheet=True is specified than "startrow" should NOT be retained from existing sheet. I suggest:

        if startrow is None and sheet_name in writer.book.sheetnames:
            if not truncate_sheet: # truncate_sheet would use startrow if provided (or zero below)
                startrow = writer.book[sheet_name].max_row

Answer 14

I used the answer described here

from openpyxl import load_workbook
writer = pd.ExcelWriter(p_file_name, engine='openpyxl', mode='a')
writer.book = load_workbook(p_file_name)
writer.sheets = {ws.title:ws for ws in writer.book.worksheets}
df.to_excel(writer, 'Data', startrow=10, startcol=20)
writer.save()

Answer 15

I'd reccommend using xlwings ( https://docs.xlwings.org/en/stable/api.html ), it is really powerful for this application... This is how I use it:

import xlwings as xw
import pandas as pd
import xlsxwriter

# function to get the active workbook
def getActiveWorkbook():
    try:
        # logic from xlwings to grab the current excel file
        activeWb = xw.books.active
    except:
        # print error message if unable to get the current workbook
        print('Unable to grab the current Workbook')
        pause()
        exitProgram()
    else:
        return activeWb

# function that returns the last row number and last cell of a sheet
def getLastRow(myBook, sheetName):
    lastRow = myBook.sheets[sheetName].range("A1").current_region.last_cell.row
    lastCol = str(xlsxwriter.utility.xl_col_to_name(myBook.sheets[sheetName].range("A1").current_region.last_cell.column))
    return str(lastRow), lastCol + str(lastRow)

activeWb = getActiveWorkbook()
df = pd.DataFrame(data=[1,2,3])

# look at worksheet = Part Number Status
sheetName = "Sheet1"
ws = activeWb.sheets[sheetName]
lastRow, lastCell = getLastRow(activeWb, sheetName)
if int(lastRow) > 1:
    ws.range("A1:" + lastCell).clear()
ws.range("A1").options(index=False, header=False).value = df.fillna('')

This seems to work very well for my applications because.xlsm workbooks can be very tricky. You can execute this as a python script or turn it into and executable with pyinstaller and then run the.exe through an excel macro. You can also call VBA macros from Python using xlwings which is very useful.

Answer 16

You can write to an existing Excel file without overwriting data using pandas by using the pandas.DataFrame.to_excel() method and specifying the mode parameter as 'a' (append mode).

Here's an example:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Write the DataFrame to an existing Excel file in append mode
df.to_excel('existing_file.xlsx', engine='openpyxl', mode='a', index=False, sheet_name='Sheet1')