I am being asked to generate some Excel reports. I am currently using pandas quite heavily for my data, so naturally I would like to use the pandas.ExcelWriter method to generate these reports. However the fixed column widths are a problem.
The code I have so far is simple enough. Say I have a dataframe called df:
writer = pd.ExcelWriter(excel_file_path, engine='openpyxl')
df.to_excel(writer, sheet_name="Summary")
I was looking over the pandas docs, and I don't really see any options to set column widths. Is there a trick to make it such that the columns auto-adjust to the data? Or is there something I can do after the fact to the xlsx file to adjust the column widths?
(I am using the OpenPyXL library, and generating .xlsx files - if that makes any difference.)
Inspired by user6178746's answer, I have the following:
# Given a dict of dataframes, for example:
# dfs = {'gadgets': df_gadgets, 'widgets': df_widgets}
writer = pd.ExcelWriter(filename, engine='xlsxwriter')
for sheetname, df in dfs.items(): # loop through `dict` of dataframes
df.to_excel(writer, sheet_name=sheetname) # send df to writer
worksheet = writer.sheets[sheetname] # pull worksheet object
for idx, col in enumerate(df): # loop through all columns
series = df[col]
max_len = max((
series.astype(str).map(len).max(), # len of largest item
len(str(series.name)) # len of column name/header
)) + 1 # adding a little extra space
worksheet.set_column(idx, idx, max_len) # set column width
writer.save()
Dynamically adjust all the column lengths
writer = pd.ExcelWriter('/path/to/output/file.xlsx')
df.to_excel(writer, sheet_name='sheetName', index=False, na_rep='NaN')
for column in df:
column_length = max(df[column].astype(str).map(len).max(), len(column))
col_idx = df.columns.get_loc(column)
writer.sheets['sheetName'].set_column(col_idx, col_idx, column_length)
writer.save()
Manually adjust a column using Column Name
col_idx = df.columns.get_loc('columnName')
writer.sheets['sheetName'].set_column(col_idx, col_idx, 15)
Manually adjust a column using Column Index
writer.sheets['sheetName'].set_column(col_idx, col_idx, 15)
In case any of the above is failing with
AttributeError: 'Worksheet' object has no attribute 'set_column'
make sure to install xlsxwriter:
pip install xlsxwriter
For a more comprehensive explanation you can read the article How to Auto-Adjust the Width of Excel Columns with Pandas ExcelWriter on TDS.
I'm posting this because I just ran into the same issue and found that the official documentation for Xlsxwriter and pandas still have this functionality listed as unsupported. I hacked together a solution that solved the issue i was having. I basically just iterate through each column and use worksheet.set_column to set the column width == the max length of the contents of that column.
One important note, however. This solution does not fit the column headers, simply the column values. That should be an easy change though if you need to fit the headers instead. Hope this helps someone :)
import pandas as pd
import sqlalchemy as sa
import urllib
read_server = 'serverName'
read_database = 'databaseName'
read_params = urllib.quote_plus("DRIVER={SQL Server};SERVER="+read_server+";DATABASE="+read_database+";TRUSTED_CONNECTION=Yes")
read_engine = sa.create_engine("mssql+pyodbc:///?odbc_connect=%s" % read_params)
#Output some SQL Server data into a dataframe
my_sql_query = """ SELECT * FROM dbo.my_table """
my_dataframe = pd.read_sql_query(my_sql_query,con=read_engine)
#Set destination directory to save excel.
xlsFilepath = r'H:\my_project' + "\\" + 'my_file_name.xlsx'
writer = pd.ExcelWriter(xlsFilepath, engine='xlsxwriter')
#Write excel to file using pandas to_excel
my_dataframe.to_excel(writer, startrow = 1, sheet_name='Sheet1', index=False)
#Indicate workbook and worksheet for formatting
workbook = writer.book
worksheet = writer.sheets['Sheet1']
#Iterate through each column and set the width == the max length in that column. A padding length of 2 is also added.
for i, col in enumerate(my_dataframe.columns):
# find length of column i
column_len = my_dataframe[col].astype(str).str.len().max()
# Setting the length if the column header is larger
# than the max column value length
column_len = max(column_len, len(col)) + 2
# set the column length
worksheet.set_column(i, i, column_len)
writer.save()
There is a nice package that I started to use recently called StyleFrame.
it gets DataFrame and lets you to style it very easily...
by default the columns width is auto-adjusting.
for example:
from StyleFrame import StyleFrame
import pandas as pd
df = pd.DataFrame({'aaaaaaaaaaa': [1, 2, 3],
'bbbbbbbbb': [1, 1, 1],
'ccccccccccc': [2, 3, 4]})
excel_writer = StyleFrame.ExcelWriter('example.xlsx')
sf = StyleFrame(df)
sf.to_excel(excel_writer=excel_writer, row_to_add_filters=0,
columns_and_rows_to_freeze='B2')
excel_writer.save()
you can also change the columns width:
sf.set_column_width(columns=['aaaaaaaaaaa', 'bbbbbbbbb'],
width=35.3)
UPDATE 1
In version 1.4 best_fit argument was added to StyleFrame.to_excel.
See the documentation.
UPDATE 2
Here's a sample of code that works for StyleFrame 3.x.x
from styleframe import StyleFrame
import pandas as pd
columns = ['aaaaaaaaaaa', 'bbbbbbbbb', 'ccccccccccc', ]
df = pd.DataFrame(data={
'aaaaaaaaaaa': [1, 2, 3, ],
'bbbbbbbbb': [1, 1, 1, ],
'ccccccccccc': [2, 3, 4, ],
}, columns=columns,
)
excel_writer = StyleFrame.ExcelWriter('example.xlsx')
sf = StyleFrame(df)
sf.to_excel(
excel_writer=excel_writer,
best_fit=columns,
columns_and_rows_to_freeze='B2',
row_to_add_filters=0,
)
excel_writer.save()
There is probably no automatic way to do it right now, but as you use openpyxl, the following line (adapted from another answer by user Bufke on how to do in manually) allows you to specify a sane value (in character widths):
writer.sheets['Summary'].column_dimensions['A'].width = 15
By using pandas and xlsxwriter you can do your task, below code will perfectly work in Python 3.x. For more details on working with XlsxWriter with pandas this link might be useful https://xlsxwriter.readthedocs.io/working_with_pandas.html
import pandas as pd
writer = pd.ExcelWriter(excel_file_path, engine='xlsxwriter')
df.to_excel(writer, sheet_name="Summary")
workbook = writer.book
worksheet = writer.sheets["Summary"]
#set the column width as per your requirement
worksheet.set_column('A:A', 25)
writer.save()
I found that it was more useful to adjust the column with based on the column header rather than column content.
Using df.columns.values.tolist() I generate a list of the column headers and use the lengths of these headers to determine the width of the columns.
See full code below:
import pandas as pd
import xlsxwriter
writer = pd.ExcelWriter(filename, engine='xlsxwriter')
df.to_excel(writer, index=False, sheet_name=sheetname)
workbook = writer.book # Access the workbook
worksheet= writer.sheets[sheetname] # Access the Worksheet
header_list = df.columns.values.tolist() # Generate list of headers
for i in range(0, len(header_list)):
worksheet.set_column(i, i, len(header_list[i])) # Set column widths based on len(header)
writer.save() # Save the excel file
At work, I am always writing the dataframes to excel files. So instead of writing the same code over and over, I have created a modulus. Now I just import it and use it to write and formate the excel files. There is one downside though, it takes a long time if the dataframe is extra large.
So here is the code:
def result_to_excel(output_name, dataframes_list, sheet_names_list, output_dir):
out_path = os.path.join(output_dir, output_name)
writerReport = pd.ExcelWriter(out_path, engine='xlsxwriter',
datetime_format='yyyymmdd', date_format='yyyymmdd')
workbook = writerReport.book
# loop through the list of dataframes to save every dataframe into a new sheet in the excel file
for i, dataframe in enumerate(dataframes_list):
sheet_name = sheet_names_list[i] # choose the sheet name from sheet_names_list
dataframe.to_excel(writerReport, sheet_name=sheet_name, index=False, startrow=0)
# Add a header format.
format = workbook.add_format({
'bold': True,
'border': 1,
'fg_color': '#0000FF',
'font_color': 'white'})
# Write the column headers with the defined format.
worksheet = writerReport.sheets[sheet_name]
for col_num, col_name in enumerate(dataframe.columns.values):
worksheet.write(0, col_num, col_name, format)
worksheet.autofilter(0, 0, 0, len(dataframe.columns) - 1)
worksheet.freeze_panes(1, 0)
# loop through the columns in the dataframe to get the width of the column
for j, col in enumerate(dataframe.columns):
max_width = max([len(str(s)) for s in dataframe[col].values] + [len(col) + 2])
# define a max width to not get to wide column
if max_width > 50:
max_width = 50
worksheet.set_column(j, j, max_width)
writerReport.save()
return output_dir + output_name
Combining the other answers and comments and also supporting multi-indices:
def autosize_excel_columns(worksheet, df):
autosize_excel_columns_df(worksheet, df.index.to_frame())
autosize_excel_columns_df(worksheet, df, offset=df.index.nlevels)
def autosize_excel_columns_df(worksheet, df, offset=0):
for idx, col in enumerate(df):
series = df[col]
max_len = max((
series.astype(str).map(len).max(),
len(str(series.name))
)) + 1
worksheet.set_column(idx+offset, idx+offset, max_len)
sheetname=...
df.to_excel(writer, sheet_name=sheetname, freeze_panes=(df.columns.nlevels, df.index.nlevels))
worksheet = writer.sheets[sheetname]
autosize_excel_columns(worksheet, df)
writer.save()
you can solve the problem by calling the following function, where df is the dataframe you want to get the sizes and the sheetname is the sheet in excel where you want the modifications to take place
def auto_width_columns(df, sheetname):
workbook = writer.book
worksheet= writer.sheets[sheetname]
for i, col in enumerate(df.columns):
column_len = max(df[col].astype(str).str.len().max(), len(col) + 2)
worksheet.set_column(i, i, column_len)
import re
import openpyxl
..
for col in _ws.columns:
max_lenght = 0
print(col[0])
col_name = re.findall('\w\d', str(col[0]))
col_name = col_name[0]
col_name = re.findall('\w', str(col_name))[0]
print(col_name)
for cell in col:
try:
if len(str(cell.value)) > max_lenght:
max_lenght = len(cell.value)
except:
pass
adjusted_width = (max_lenght+2)
_ws.column_dimensions[col_name].width = adjusted_width
Yes, there is there is something you can do subsequently to the xlsx file to adjust the column widths.
Use xlwings to autofit columns. It's a pretty simple solution, see the 6 last lines of the example code. The advantage of this procedure is that you don't have to worry about font size, font type or anything else.
Requirement: Excel installation.
import pandas as pd
import xlwings as xw
path = r"test.xlsx"
# Export your dataframe in question.
df = pd._testing.makeDataFrame()
df.to_excel(path)
# Autofit all columns with xlwings.
with xw.App(visible=False) as app:
wb = xw.Book(path)
for ws in wb.sheets:
ws.autofit(axis="columns")
wb.save(path)
wb.close()
Easiest solution is to specify width of column in set_column method.
for worksheet in writer.sheets.values():
worksheet.set_column(0,last_column_value, required_width_constant)
This function works for me, also fixes the index width
def write_to_excel(writer, X, sheet_name, sep_only=False):
#writer=writer object
#X=dataframe
#sheet_name=name of sheet
#sep_only=True:write only as separate excel file, False: write as sheet to the writer object
if sheet_name=="":
print("specify sheet_name!")
else:
X.to_excel(f"{output_folder}{prefix_excel_save}_{sheet_name}.xlsx")
if not sep_only:
X.to_excel(writer, sheet_name=sheet_name)
#fix column widths
worksheet = writer.sheets[sheet_name] # pull worksheet object
for idx, col in enumerate(X.columns): # loop through all columns
series = X[col]
max_len = max((
series.astype(str).map(len).max(), # len of largest item
len(str(series.name)) # len of column name/header
)) + 1 # adding a little extra space
worksheet.set_column(idx+1, idx+1, max_len) # set column width (=1 because index = 1)
#fix index width
max_len=pd.Series(X.index.values).astype(str).map(len).max()+1
worksheet.set_column(0, 0, max_len)
if sep_only:
print(f'{sheet_name} is written as seperate file')
else:
print(f'{sheet_name} is written as seperate file')
print(f'{sheet_name} is written as sheet')
return writer
call example:
writer = write_to_excel(writer, dataframe, "Statistical_Analysis")
I may be a bit late to the party but this code works when using 'openpyxl' as your engine, sometimes pip install xlsxwriter wont solve the issue. This code below works like a charm. Edit any part as you wish.
def text_length(text):
"""
Get the effective text length in characters, taking into account newlines
"""
if not text:
return 0
lines = text.split("\n")
return max(len(line) for line in lines)
def _to_str_for_length(v, decimals=3):
"""
Like str() but rounds decimals to predefined length
"""
if isinstance(v, float):
# Round to [decimal] places
return str(Decimal(v).quantize(Decimal('1.' + '0' * decimals)).normalize())
else:
return str(v)
def auto_adjust_xlsx_column_width(df, writer, sheet_name, margin=3, length_factor=1.0, decimals=3, index=False):
sheet = writer.sheets[sheet_name]
_to_str = functools.partial(_to_str_for_length, decimals=decimals)
# Compute & set column width for each column
for column_name in df.columns:
# Convert the value of the columns to string and select the
column_length = max(df[column_name].apply(_to_str).map(text_length).max(), text_length(column_name)) + 5
# Get index of column in XLSX
# Column index is +1 if we also export the index column
col_idx = df.columns.get_loc(column_name)
if index:
col_idx += 1
# Set width of column to (column_length + margin)
sheet.column_dimensions[openpyxl.utils.cell.get_column_letter(col_idx + 1)].width = column_length * length_factor + margin
# Compute column width of index column (if enabled)
if index: # If the index column is being exported
index_length = max(df.index.map(_to_str).map(text_length).max(), text_length(df.index.name))
sheet.column_dimensions["A"].width = index_length * length_factor + margin
An openpyxl version based on #alichaudry's code.
The code 1) loads an excel file, 2) adjusts column widths and 3) saves it.
def auto_adjust_column_widths(excel_file : "Excel File Path", extra_space = 1) -> None:
"""
Adjusts column widths of the excel file and replaces it with the adjusted one.
Adjusting columns is based on the lengths of columns values (including column names).
Parameters
----------
excel_file :
excel_file to adjust column widths.
extra_space :
extra column width in addition to the value-based-widths
"""
from openpyxl import load_workbook
from openpyxl.utils import get_column_letter
wb = load_workbook(excel_file)
for ws in wb:
df = pd.DataFrame(ws.values,)
for i,r in (df.astype(str).applymap(len).max(axis=0) + extra_space).iteritems():
ws.column_dimensions[get_column_letter(i+1)].width = r
wb.save(excel_file)
In a Pandas DataFrame i have some "cells" with values and some that need to contain excel formulas. I have read that i can get formulas with
link = 'HYPERLINK("#Groups!A' + str(someInt) + '"; "LINKTEXT")'
xlwt.Formula(link)
and store them in the dataframe.
When i try to save my dataframe as an xlsx file with
writer = pd.ExcelWriter("pandas" + str(fileCounter) + ".xlsx", engine = "xlsxwriter")
df.to_excel(writer, sheet_name = "Paths", index = False)
# insert more sheets here
writer.save()
i get the error:
TypeError: Unsupported type <class 'xlwt.ExcelFormula.Formula'> in write()
So i tried to write my formula as a string to my dataframe but Excel wants to restore the file content and then fills all formula cells with 0's.
Edit: I managed to get it work with regular strings but nevertheless would be interested in a solution for xlwt formulas.
So my question is: How do i save dataframes with formulas to xlsx files?
Since you are using xlsxwriter, strings are parsed as formulas by default ("strings_to_formulas: Enable the worksheet.write() method to convert strings to formulas. The default is True"), so you can simply specify formulas as strings in your dataframe.
Example of a formula column which references other columns in your dataframe:
d = {'col1': [1, 2], 'col2': [3, 4]}
df = pd.DataFrame(data=d)
writer = pd.ExcelWriter("foo.xlsx", engine="xlsxwriter")
df["product"] = None
df["product"] = (
'=INDIRECT("R[0]C[%s]", 0)+INDIRECT("R[0]C[%s]", 0)'
% (
df.columns.get_loc("col1") - df.columns.get_loc("product"),
df.columns.get_loc("col2") - df.columns.get_loc("product"),
)
)
df.to_excel(writer, index=False)
writer.save()
Produces the following output:
After writing the df using table.to_excel(writer, sheet_name=...), I use write_formula() as in this example (edited to add the full loop). To write all the formulas in your dataframe, read each formula in your dataframe.
# replace the right side below with reading the formula from your dataframe
# e.g., formula_to_write = df.loc(...)`
rows = table.shape[0]
for row_num in range(1 + startrow, rows + startrow + 1):
formula_to_write = '=I{} * (1 - AM{})'.format(row_num+1, row_num+1)
worksheet.write_formula(row_num, col, formula_to_write)`
Later in the code (I seem to recall one of these might be redundant, but I haven't looked it up):
writer.save() workbook.close()
Documentation is here.
you need to save in as usual just keep in mind to write the formula as string.
you can use also f strings with vars.
writer = pd.ExcelWriter(FILE_PATH ,mode='a', if_sheet_exists='overlay')
col_Q_index = 3
best_formula = f'=max(L1,N98,Q{col_Q_index})'
formula_df = pd.DataFrame([[best_formula]])
formula_df.to_excel(writer, sheet_name=SHEET_NAME, startrow=i, startcol=17, index=False, header=False)
writer.save()
I am able to combine multiple excel files having one sheet currently.
I want to combine multiple sheets having two different sheets in each excel file with giving name to each sheets How can I achieve this?
Here below is my current code for combining single sheet in multiple excel files without giving sheet name to Combined excel file
import pandas as pd
# filenames
excel_names = ["xlsx1.xlsx", "xlsx2.xlsx", "xlsx3.xlsx"]
# read them in
excels = [pd.ExcelFile(name) for name in excel_names]
# turn them into dataframes
frames = [x.parse(x.sheet_names[0], header=None,index_col=None) for x in excels]
# delete the first row for all frames except the first
# i.e. remove the header row -- assumes it's the first
frames[1:] = [df[1:] for df in frames[1:]]
# concatenate them..
combined = pd.concat(frames)
# write it out
combined.to_excel("c.xlsx", header=False, index=False)
First combine the first and the second sheet separately
import pandas as pd
# filenames
excel_names = ["xlsx1.xlsx", "xlsx2.xlsx", "xlsx3.xlsx"]
def combine_excel_to_dfs(excel_names, sheet_name):
sheet_frames = [pd.read_excel(x, sheet_name=sheet_name) for x in excel_names]
combined_df = pd.concat(sheet_frames).reset_index(drop=True)
return combined_df
df_first = combine_excel_to_dfs(excel_names, 0)
df_second = combine_excel_to_dfs(excel_names, 1)
Use pd.ExcelWriter
And write these sheets to the same excel file:
# Create a Pandas Excel writer using XlsxWriter as the engine.
writer = pd.ExcelWriter('two_sheets_combined.xlsx', engine='xlsxwriter')
# Write each dataframe to a different worksheet.
df_first.to_excel(writer, sheet_name='Sheet1')
df_second.to_excel(writer, sheet_name='Sheet2')
# Close the Pandas Excel writer and output the Excel file.
writer.save()
You can use:
#number of sheets
N = 2
#get all sheets to nested lists
frames = [[x.parse(y, index_col=None) for y in x.sheet_names] for x in excels]
#print (frames)
#combine firt dataframe from first list with first df with second list...
combined = [pd.concat([x[i] for x in frames], ignore_index=True) for i in range(N)]
#print (combined)
#write to file
writer = pd.ExcelWriter('c.xlsx', engine='xlsxwriter')
for i, x in enumerate(combined):
x.to_excel(writer, sheet_name='Sheet{}'.format(i + 1))
writer.save()
# Set the working folder to the same folder as the script
os.chdir(os.path.dirname(os.path.abspath(__file__)))
test = send_request().content
df = pd.read_csv(io.StringIO(test.decode('utf-8')))
writer = pd.ExcelWriter('NHL_STATS_JSB_final.xlsx', \
engine = 'xlsxwriter')
df.to_excel(writer, 'Player statistics', index=False)
writer.save()
I don't understand why, but I am trying to add the worksheet Player statistics to my current NHL_STATS_JSB_final.xlsx file, but it is not working. Instead of adding the worksheet to the file, my code use the current file and erase all previous worksheet to add the new one.
How could I add Player statistics to my current Excel file with erasing all other worksheets?
Here is a snippet of code from one of my projects. This should do exactly what you want. You need to use openpyxl rather than xlsxwriter to allow you to update an existing file.
writer = pd.ExcelWriter(file_name, engine='openpyxl')
if os.path.exists(file_name):
book = openpyxl.load_workbook(file_name)
writer.book = book
df.to_excel(writer, sheet_name=key)
writer.save()
writer.close()
As the OP mentioned, xlsxwriter will overwrite your existing workbook. Xlsxwriter is for writing original .xlsx files. Openpyxl, on the other hand, can modify existing .xlsx files.
#Brad Campbell answer using openpyxl is the best way to do this. Since the OP was using the xlsxwriter engine, I wanted to demonstrate that it is possible to read in your existing .xlsx file and then create a new workbook (of the same name) containing that data from the original sheets and the new sheet that you'd like to add on.
import pandas as pd
import os
xl = pd.ExcelFile('NHL_STATS_JSB_final.xlsx')
sheet_names = xl.sheet_names # a list of existing sheet names
#the next three lines are OPs original code
os.chdir(os.path.dirname(os.path.abspath(__file__)))
test = send_request().content
df = pd.read_csv(io.StringIO(test.decode('utf-8')))
#beginning the process of creating new workbook with the same name
writer = pd.ExcelWriter('NHL_STATS_JSB_final.xlsx', engine = 'xlsxwriter')
d = {} #creating an empty dictionary
for i in range (0, len(sheet_names)):
current_sheet_name = sheet_names[i]
d[current_sheet_name] = pd.read_excel('NHL_STATS_JSB_final.xlsx', sheetname = i)
d[current_sheet_name].to_excel(writer, '%s' % (current_sheet_name), index=False)
# adding in the new worksheet
df.to_excel(writer, 'Player statistics', index=False)
writer.save()
# I needed to append tabs to a workbook only if data existed
# OP wants to append sheets to a workbook.
# using mode 'a' appends if the file exists
# mode 'w' creates a new file if failed to append.
# ended up with this:
def create_POC_file_tab(df, sheetname):
# within function before the 'if' code below, prep data.
# Like extracting df_SA values from df,
# building POC_file name using df_SA+date, etc.
#
# might not have data after filtering so check length.
if len(df_SA) > 0: # extracted dataframe contains data
# Have data so finalize workbook path/name
POC_file = PATH + POC_file # build file path
try:
# mode='a' tries to append a new tab if the
# workbook exists already
writer_SA = pd.ExcelWriter(POC_file + ' ' +
process_date + '.xlsx', engine='openpyxl', mode='a')
print(POC, 'File exists. Appending to POC',POC,sheetname)
except:
# mode='w' creates a new workbook if one does not exist
writer_SA = pd.ExcelWriter(POC_file + ' ' +
process_date + '.xlsx', engine='openpyxl', mode='w')
print(POC, ' !!! Creating !!! ', sheetname)
try:
df_SA.to_excel(writer_SA, sheet_name=sheetname,
index=False)
writer_SA.save()
except:
print ("error on writing sheetname: ", sheetname,
"for: ",POC)
return
# when I exit the file seems to be closed properly.
# In brief, to append a new tab to a workbook use:
writer=pd.ExcelWriter('filename.xlsx',engine='openpyxl', mode='a')
df.to_excel(writer, sheet_name='my_sheet_name', index=False)
writer_SA.save()
I need to write a program to scrap daily quote from a certain web page and collect them into a single excel file. I wrote something which finds next empty row and starts writing new quotes on it but deletes previous rows too:
wb = openpyxl.load_workbook('gold_quote.xlsx')
sheet = wb.get_sheet_by_name('Sheet1')
.
.
.
z = 1
x = sheet['A{}'.format(z)].value
while x != None:
x = sheet['A{}'.format(z)].value
z += 1
writer = pd.ExcelWriter('quote.xlsx')
df.to_excel(writer, sheet_name='Sheet1',na_rep='', float_format=None,columns=['Date', 'Time', 'Price'], header=True,index=False, index_label=None, startrow=z-1, startcol=0, engine=None,merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None)
writer.save()
Question: How to write on existing excel files without losing previous information
openpyxl uses append to write after last used Row:
wb = openpyxl.load_workbook('gold_quote.xlsx')
sheet = wb.get_sheet_by_name('Sheet1')
rowData = ['2017-08-01', '16:31', 1.23]
sheet.append(rowData)
wb.save('gold_quote.xlsx')
writer.book = wb
writer.sheets = dict((ws.title, ws) for ws in wb.worksheets)
I figured it out, first we should define a reader to read existing data of excel file then concatenate recently extracted data from web with a defined writer, and we should drop duplicates otherwise any time the program is executed there will be many duplicated data. Then we can write previous and new data altogether:
excel_reader = pd.ExcelFile('gold_quote.xlsx')
to_update = {"Sheet1": df}
excel_writer = pd.ExcelWriter('gold_quote.xlsx')
for sheet in excel_reader.sheet_names:
sheet_df = excel_reader.parse(sheet)
append_df = to_update.get(sheet)
if append_df is not None:
sheet_df = pd.concat([sheet_df, df]).drop_duplicates()
sheet_df.to_excel(excel_writer, sheet, index=False)
excel_writer.save()