Pandas add sheet to excel

I want to use excel files to store data elaborated with python. My problem is that I can’t add sheets to an existing excel file. Here I suggest a sample code to work with in order to reach this issue

import pandas as pd
import numpy as np

path = r"C:UsersfedelDesktopexcelDataPhD_data.xlsx"

x1 = np.random.randn(100, 2)
df1 = pd.DataFrame(x1)

x2 = np.random.randn(100, 2)
df2 = pd.DataFrame(x2)

writer = pd.ExcelWriter(path, engine = 'xlsxwriter')
df1.to_excel(writer, sheet_name = 'x1')
df2.to_excel(writer, sheet_name = 'x2')
writer.save()
writer.close()

This code saves two DataFrames to two sheets, named «x1» and «x2» respectively. If I create two new DataFrames and try to use the same code to add two new sheets, ‘x3’ and ‘x4’, the original data is lost.

import pandas as pd
import numpy as np

path = r"C:UsersfedelDesktopexcelDataPhD_data.xlsx"

x3 = np.random.randn(100, 2)
df3 = pd.DataFrame(x3)

x4 = np.random.randn(100, 2)
df4 = pd.DataFrame(x4)

writer = pd.ExcelWriter(path, engine = 'xlsxwriter')
df3.to_excel(writer, sheet_name = 'x3')
df4.to_excel(writer, sheet_name = 'x4')
writer.save()
writer.close()

I want an excel file with four sheets: ‘x1’, ‘x2’, ‘x3’, ‘x4’.
I know that ‘xlsxwriter’ is not the only «engine», there is ‘openpyxl’. I also saw there are already other people that have written about this issue, but still I can’t understand how to do that.

Here a code taken from this link

import pandas
from openpyxl import load_workbook

book = load_workbook('Masterfile.xlsx')
writer = pandas.ExcelWriter('Masterfile.xlsx', engine='openpyxl') 
writer.book = book
writer.sheets = dict((ws.title, ws) for ws in book.worksheets)

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

writer.save()

They say that it works, but it is hard to figure out how. I don’t understand what «ws.title», «ws», and «dict» are in this context.

Which is the best way to save «x1» and «x2», then close the file, open it again and add «x3» and «x4»?

В Pandas есть встроенная функция для сохранения датафрейма в электронную таблицу Excel. Все очень просто:

Как записать в лист с заданным именем

В этом случае будет создан xls / xlsx файл, а данные сохранятся на лист с именем Sheet1. Если хочется сохранить на лист с заданным именем, то можно использовать конструкцию:

Как записать в один файл сразу два листа

Но что делать, если хочется записать в файл сразу два листа? Логично было бы использовать две команды

df.to_excel  друг за другом, но с одним путем до файла и разными

sheet_name , однако в Pandas это так не работает. Для решения этой задачи придется использовать конструкцию посложнее:

В результате будет создан файл Excel, где будет два листа с именами Лист 1 и Лист 2.

Как добавить ещё один лист у уже существующему файлу

Если использовать предыдущий код, то текущий файл будет перезаписан и в него будет записан новый лист. Старые данные при этом, ожидаемо, будут утеряны. Выход есть, достаточно лишь добавить модификатор «a» (append):

Но что, если оставить этот код, удалить существующий файл Excel и попробовать выполнить код? Получим ошибку Файл не найден. В Python существует модификатор «a+», который создает файл, если его нет, и открывает его на редактирование, если файл существует. Но в Pandas такого модификатора не существует, поэтому мы должны выбрать модификатор для ExcelWriter в зависимости от наличия или отсутствия файла. Но это не сложно:

К сожалению в Pandas, на момент написания поста, такого функционала нет. Но это можно реализовать с помощью пакета openpyxl. Вот пример такой функции:

Как работает код и пояснения смотри в видео

Если у тебя есть вопросы, что-то не получается или ты знаешь как решить задачи в посте лучше и эффективнее (такое вполне возможно) то смело пиши в комментариях к видео.

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Use pandas to_excel() function to write a DataFrame to an excel sheet with extension .xlsx. By default it writes a single DataFrame to an excel file, you can also write multiple sheets by using an ExcelWriter object with a target file name, and sheet name to write to.

Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased.

Related: pandas read Excel Sheet

pandas to Excel key Points

• By default, it uses xlsxwriter if it is installed otherwise it uses openpyxl
• Supports saving multiple DataFrames to single sheet.
• Save multiple sheets, append existing sheet or file.
• Use ExcelWriter()

Let’s create a pandas DataFrame from list and explore usingto_excel() function by using multiple parameters.

import pandas as pd
import numpy as np

# Create multiple lists
technologies =  ['Spark','Pandas','Java','Python', 'PHP']
fee = [25000,20000,15000,15000,18000]
duration = ['5o Days','35 Days',np.nan,'30 Days', '30 Days']
discount = [2000,1000,800,500,800]
columns=['Courses','Fee','Duration','Discount']

# Create DataFrame from multiple lists
df = pd.DataFrame(list(zip(technologies,fee,duration,discount)), columns=columns)
print(df)

# Outputs
#  Courses    Fee Duration  Discount
#0   Spark  25000  5o Days      2000
#1  Pandas  20000  35 Days      1000
#2    Java  15000      NaN       800
#3  Python  15000  30 Days       500
#4     PHP  18000  30 Days       800

1. pandas DataFrame to Excel

Use to_excel() function to write or export pandas DataFrame to excel sheet with extension xslx. Using this you can write excel files to the local file system, S3 e.t.c. Not specifying any parameter it default writes to a single sheet.

to_excel() takes several optional params that can be used skip columns, skip rows, not to write index, set column names, formatting, and many more.

# Write DataFrame to Excel file
df.to_excel('Courses.xlsx')

This creates an excel file with the contents as below. By default, It exports column names, indexes, and data to a sheet named 'Sheet1'.

You can change the name of the sheet from Sheet1 to something that makes sense to your data by using sheet_name param. The below example exports it to the sheet named ‘Technologies‘.

# Write DataFrame to Excel file with sheet name
df.to_excel('Courses.xlsx', sheet_name='Technologies')

2. Write to Multiple Sheets

The ExcelWriter class allows you to write or export multiple pandas DataFrames to separate sheets. First, you need to create an object for ExcelWriter.

The below example writes data from df object to a sheet named Technologies and df2 object to a sheet named Schedule.

# Write to Multiple Sheets
with pd.ExcelWriter('Courses.xlsx') as writer:
    df.to_excel(writer, sheet_name='Technologies')
    df2.to_excel(writer, sheet_name='Schedule')

3. Append to Existing Excel File

ExcelWriter can be used to append DataFrame to an excel file. Use mode param with value 'a' to append. The code below opens an existing file and adds data from the DataFrame to the specified sheet.

# Append DataFrame to existing excel file
with pd.ExcelWriter('Courses.xlsx',mode='a') as writer:  
    df.to_excel(writer, sheet_name='Technologies')

4. Save Selected Columns

use param columns to save selected columns from DataFrame to excel file. The below example only saves columns Fee, Duration to excel file.

# Save Selected Columns to Excel File
df.to_excel('Courses.xlsx', columns = ['Fee','Duration'])

Use header param with a list of values if you wanted to write with different column names.

5. Skip Index

To skip Index from writing use index=False param. By default, it is set to True meaning write numerical Index to excel sheet.

# Skip Index
df.to_excel('Courses.xlsx', index = False)

Conclusion

In this article, you have learned how to write pandas DataFrame to excel file by using to_excel(). Also explored how to write to specific sheets, multiple sheets, and append to existing excel file.

Related Articles

• pandas ExcelWriter Usage with Examples
• pandas write CSV file
• pandas read Excel
• Pandas ExcelWriter Explained with Examples
• Pandas Read Multiple CSV Files into DataFrame
• How to Read Excel Multiple Sheets in Pandas
• Pretty Print Pandas DataFrame or Series?
• Pandas Handle Missing Data in Dataframe
• How to read CSV without headers in pandas

References

• https://stackoverflow.com/questions/38074678/append-existing-excel-sheet-with-new-dataframe-using-python-pandas/38075046#38075046

In this tutorial, you’ll learn how to save your Pandas DataFrame or DataFrames to Excel files. Being able to save data to this ubiquitous data format is an important skill in many organizations. In this tutorial, you’ll learn how to save a simple DataFrame to Excel, but also how to customize your options to create the report you want!

By the end of this tutorial, you’ll have learned:

• How to save a Pandas DataFrame to Excel
• How to customize the sheet name of your DataFrame in Excel
• How to customize the index and column names when writing to Excel
• How to write multiple DataFrames to Excel in Pandas
• Whether to merge cells or freeze panes when writing to Excel in Pandas
• How to format missing values and infinity values when writing Pandas to Excel

Let’s get started!

The Quick Answer: Use Pandas to_excel

To write a Pandas DataFrame to an Excel file, you can apply the .to_excel() method to the DataFrame, as shown below:

# Saving a Pandas DataFrame to an Excel File
# Without a Sheet Name
df.to_excel(file_name)

# With a Sheet Name
df.to_excel(file_name, sheet_name='My Sheet')

# Without an Index
df.to_excel(file_name, index=False)

Understanding the Pandas to_excel Function

Before diving into any specifics, let’s take a look at the different parameters that the method offers. The method provides a ton of different options, allowing you to customize the output of your DataFrame in many different ways. Let’s take a look:

# The many parameters of the .to_excel() function
df.to_excel(excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None, storage_options=None)

Let’s break down what each of these parameters does:

How to Save a Pandas DataFrame to Excel

The easiest way to save a Pandas DataFrame to an Excel file is by passing a path to the .to_excel() method. This will save the DataFrame to an Excel file at that path, overwriting an Excel file if it exists already.

Let’s take a look at how this works:

# Saving a Pandas DataFrame to an Excel File
import pandas as pd
df = pd.DataFrame.from_dict(
    {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
)

df.to_excel('filename.xlsx')

Running the code as shown above will save the file with all other default parameters. This returns the following image:

You can specify a sheetname by using the sheet_name= parameter. By default, Pandas will use 'sheet1'.

# Specifying a Sheet Name When Saving to Excel
import pandas as pd
df = pd.DataFrame.from_dict(
    {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
)

df.to_excel('filename.xlsx', sheet_name='Your Sheet')

This returns the following workbook:

In the following section, you’ll learn how to customize whether to include an index column or not.

How to Include an Index when Saving a Pandas DataFrame to Excel

By default, Pandas will include the index when saving a Pandas Dataframe to an Excel file. This can be helpful when the index is a meaningful index (such as a date and time). However, in many cases, the index will simply represent the values from 0 through to the end of the records.

If you don’t want to include the index in your Excel file, you can use the index= parameter, as shown below:

# How to exclude the index when saving a DataFrame to Excel
import pandas as pd
df = pd.DataFrame.from_dict(
    {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
)

df.to_excel('filename.xlsx', index=False)

This returns the following Excel file:

In the following section, you’ll learn how to rename an index when saving a Pandas DataFrame to an Excel file.

How to Rename an Index when Saving a Pandas DataFrame to Excel

By default, Pandas will not named the index of your DataFrame. This, however, can be confusing and can lead to poorer results when trying to manipulate the data in Excel, either by filtering or by pivoting the data. Because of this, it can be helpful to provide a name or names for your indices.

Pandas makes this easy by using the index_label= parameter. This parameter accepts either a single string (for a single index) or a list of strings (for a multi-index). Check out below how you can use this parameter:

# Providing a name for your Pandas index
import pandas as pd
df = pd.DataFrame.from_dict(
    {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
)

df.to_excel('filename.xlsx', index_label='Your Index')

This returns the following sheet:

How to Save Multiple DataFrames to Different Sheets in Excel

One of the tasks you may encounter quite frequently is the need to save multi Pandas DataFrames to the same Excel file, but in different sheets. This is where Pandas makes it a less intuitive. If you were to simply write the following code, the second command would overwrite the first command:

# The wrong way to save multiple DataFrames to the same workbook
import pandas as pd
df = pd.DataFrame.from_dict(
    {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
)

df.to_excel('filename.xlsx', sheet_name='Sheet1')
df.to_excel('filename.xlsx', sheet_name='Sheet2')

Instead, we need to use a Pandas Excel Writer to manage opening and saving our workbook. This can be done easily by using a context manager, as shown below:

# The Correct Way to Save Multiple DataFrames to the Same Workbook
import pandas as pd
df = pd.DataFrame.from_dict(
    {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
)

with pd.ExcelWriter('filename.xlsx') as writer:
    df.to_excel(writer, sheet_name='Sheet1')
    df.to_excel(writer, sheet_name='Sheet2')

This will create multiple sheets in the same workbook. The sheets will be created in the same order as you specify them in the command above.

This returns the following workbook:

How to Save Only Some Columns when Exporting Pandas DataFrames to Excel

When saving a Pandas DataFrame to an Excel file, you may not always want to save every single column. In many cases, the Excel file will be used for reporting and it may be redundant to save every column. Because of this, you can use the columns= parameter to accomplish this.

Let’s see how we can save only a number of columns from our dataset:

# Saving Only a Subset of Columns to Excel
import pandas as pd
df = pd.DataFrame.from_dict(
    {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
)

df.to_excel('filename.xlsx', columns=['A', 'B'])

This returns the following Excel file:

How to Rename Columns when Exporting Pandas DataFrames to Excel

Continuing our discussion about how to handle Pandas DataFrame columns when exporting to Excel, we can also rename our columns in the saved Excel file. The benefit of this is that we can work with aliases in Pandas, which may be easier to write, but then output presentation-ready column names when saving to Excel.

We can accomplish this using the header= parameter. The parameter accepts either a boolean value of a list of values. If a boolean value is passed, you can decide whether to include or a header or not. When a list of strings is provided, then you can modify the column names in the resulting Excel file, as shown below:

# Modifying Column Names when Exporting a Pandas DataFrame to Excel
import pandas as pd
df = pd.DataFrame.from_dict(
    {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
)

df.to_excel('filename.xlsx', header=['New_A', 'New_B', 'New_C'])

This returns the following Excel sheet:

How to Specify Starting Positions when Exporting a Pandas DataFrame to Excel

One of the interesting features that Pandas provides is the ability to modify the starting position of where your DataFrame will be saved on the Excel sheet. This can be helpful if you know you’ll be including different rows above your data or a logo of your company.

Let’s see how we can use the startrow= and startcol= parameters to modify this:

# Changing the Start Row and Column When Saving a DataFrame to an Excel File
import pandas as pd
df = pd.DataFrame.from_dict(
    {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
)

df.to_excel('filename.xlsx', startcol=3, startrow=2)

This returns the following worksheet:

How to Represent Missing and Infinity Values When Saving Pandas DataFrame to Excel

In this section, you’ll learn how to represent missing data and infinity values when saving a Pandas DataFrame to Excel. Because Excel doesn’t have a way to represent infinity, Pandas will default to the string 'inf' to represent any values of infinity.

In order to modify these behaviors, we can use the na_rep= and inf_rep= parameters to modify the missing and infinity values respectively. Let’s see how we can do this by adding some of these values to our DataFrame:

# Customizing Output of Missing and Infinity Values When Saving to Excel
import pandas as pd
import numpy as np

df = pd.DataFrame.from_dict(
    {'A': [1, np.NaN, 3], 'B': [4, 5, np.inf], 'C': [7, 8, 9]}
)

df.to_excel('filename.xlsx', na_rep='NA', inf_rep='INFINITY')

This returns the following worksheet:

How to Merge Cells when Writing Multi-Index DataFrames to Excel

In this section, you’ll learn how to modify the behavior of multi-index DataFrames when saved to Excel. By default Pandas will set the merge_cells= parameter to True, meaning that the cells will be merged. Let’s see what happens when we set this behavior to False, indicating that the cells should not be merged:

# Modifying Merge Cell Behavior for Multi-Index DataFrames
import pandas as pd
import numpy as np
from random import choice

df = pd.DataFrame.from_dict({
    'A': np.random.randint(0, 10, size=50),
    'B': [choice(['a', 'b', 'c']) for i in range(50)],
    'C': np.random.randint(0, 3, size=50)})

pivot = df.pivot_table(index=['B', 'C'], values='A')

pivot.to_excel('filename.xlsx', merge_cells=False)

This returns the Excel worksheet below:

How to Freeze Panes when Saving a Pandas DataFrame to Excel

In this final section, you’ll learn how to freeze panes in your resulting Excel worksheet. This allows you to specify the row and column at which you want Excel to freeze the panes. This can be done using the freeze_panes= parameter. The parameter accepts a tuple of integers (of length 2). The tuple represents the bottommost row and the rightmost column that is to be frozen.

Let’s see how we can use the freeze_panes= parameter to freeze our panes in Excel:

# Freezing Panes in an Excel Workbook Using Pandas
import pandas as pd
import numpy as np

df = pd.DataFrame.from_dict(
    {'A': [1, np.NaN, 3], 'B': [4, 5, np.inf], 'C': [7, 8, 9]}
)

df.to_excel('filename.xlsx', freeze_panes=(3,4))

This returns the following workbook:

Conclusion

In this tutorial, you learned how to save a Pandas DataFrame to an Excel file using the to_excel method. You first explored all of the different parameters that the function had to offer at a high level. Following that, you learned how to use these parameters to gain control over how the resulting Excel file should be saved. For example, you learned how to specify sheet names, index names, and whether to include the index or not. Then you learned how to include only some columns in the resulting file and how to rename the columns of your DataFrame. You also learned how to modify the starting position of the data and how to freeze panes.

Additional Resources

To learn more about related topics, check out the tutorials below:

• How to Use Pandas to Read Excel Files in Python
• Pandas Dataframe to CSV File – Export Using .to_csv()
• Introduction to Pandas for Data Science
• Official Documentation: Pandas to_excel

1. Syntax of pandas.DataFrame.to_excel()
2. Example Codes: Pandas DataFrame.to_excel()
3. Example Codes: Pandas DataFrame.to_excel() With ExcelWriter
4. Example Codes: Pandas DataFrame.to_excel to Append to an Existing Excel File
5. Example Codes: Pandas DataFrame.to_excel to Write Multiple Sheets
6. Example Codes: Pandas DataFrame.to_excel With header Parameter
7. Example Codes: Pandas DataFrame.to_excel With index=False
8. Example Codes: Pandas DataFrame.to_excel With index_label Parameter
9. Example Codes: Pandas DataFrame.to_excel With float_format Parameter
10. Example Codes: Pandas DataFrame.to_excel With freeze_panes Parameter

Python Pandas DataFrame.to_excel(values) function dumps the dataframe data to an Excel file, in a single sheet or multiple sheets.

Syntax of pandas.DataFrame.to_excel()

DataFrame.to_excel(excel_writer, 
               sheet_name='Sheet1', 
               na_rep='', 
               float_format=None, 
               columns=None, 
               header=True, 
               index=True, 
               index_label=None, 
               startrow=0, 
               startcol=0, 
               engine=None, 
               merge_cells=True, 
               encoding=None, 
               inf_rep='inf', 
               verbose=True, 
               freeze_panes=None) 

Parameters

Return

None

Example Codes: Pandas DataFrame.to_excel()

import pandas as pd

dataframe= pd.DataFrame({'Attendance': [60, 100, 80, 78, 95],
                    'Name': ['Olivia', 'John', 'Laura', 'Ben', 'Kevin'],
                    'Marks': [90, 75, 82, 64, 45]})

dataframe.to_excel('test.xlsx')

The caller DataFrame is

   Attendance    Name  Marks
0          60  Olivia     90
1         100    John     75
2          80   Laura     82
3          78     Ben     64
4          95   Kevin     45

test.xlsx is created.

Example Codes: Pandas DataFrame.to_excel() With ExcelWriter

The above example uses the file path as the excel_writer, and we could also use pandas.Excelwriter to specify the excel file the dataframe dumps.

import pandas as pd

dataframe= pd.DataFrame({'Attendance': [60, 100, 80, 78, 95],
                    'Name': ['Olivia', 'John', 'Laura', 'Ben', 'Kevin'],
                    'Marks': [90, 75, 82, 64, 45]})

with pd.ExcelWriter('test.xlsx') as writer:
    dataframe.to_excel(writer)

Example Codes: Pandas DataFrame.to_excel to Append to an Existing Excel File

import pandas as pd
import openpyxl

dataframe= pd.DataFrame({'Attendance': [60, 100, 80, 78, 95],
                    'Name': ['Olivia', 'John', 'Laura', 'Ben', 'Kevin'],
                    'Marks': [90, 75, 82, 64, 45]})

with pd.ExcelWriter('test.xlsx', mode='a', engine='openpyxl') as writer:
    dataframe.to_excel(writer, sheet_name="new")

We should specify the engine as openpyxl but not default xlsxwriter; otherwise, we will get the error that xlswriter doesn’t support append mode.

ValueError: Append mode is not supported with xlsxwriter!

openpyxl shall be installed and imported because it is not part of pandas.

Example Codes: Pandas DataFrame.to_excel to Write Multiple Sheets

import pandas as pd

dataframe= pd.DataFrame({'Attendance': [60, 100, 80, 78, 95],
                    'Name': ['Olivia', 'John', 'Laura', 'Ben', 'Kevin'],
                    'Marks': [90, 75, 82, 64, 45]})

with pd.ExcelWriter('test.xlsx') as writer:
    dataframe.to_excel(writer, sheet_name="Sheet1")
    dataframe.to_excel(writer, sheet_name="Sheet2")

It dumps the dataframe object to both Sheet1 and Sheet2.

You could also write different data to multiple sheets if you specify the columns parameter.

import pandas as pd

dataframe= pd.DataFrame({'Attendance': [60, 100, 80, 78, 95],
                    'Name': ['Olivia', 'John', 'Laura', 'Ben', 'Kevin'],
                    'Marks': [90, 75, 82, 64, 45]})

with pd.ExcelWriter('test.xlsx') as writer:
    dataframe.to_excel(writer, 
                       columns=["Name","Attendance"],
                       sheet_name="Sheet1")
    dataframe.to_excel(writer, 
                       columns=["Name","Marks"],
                       sheet_name="Sheet2")

Example Codes: Pandas DataFrame.to_excel With header Parameter

import pandas as pd

dataframe= pd.DataFrame({'Attendance': [60, 100, 80, 78, 95],
                    'Name': ['Olivia', 'John', 'Laura', 'Ben', 'Kevin'],
                    'Marks': [90, 75, 82, 64, 45]})

with pd.ExcelWriter('test.xlsx') as writer:
    dataframe.to_excel(writer, header=["Student", "First Name", "Score"])

The default header in the created Excel file is the same as dataframe’s column names. The header parameter specifies the new header to replace the default one.

Example Codes: Pandas DataFrame.to_excel With index=False

import pandas as pd

dataframe= pd.DataFrame({'Attendance': [60, 100, 80, 78, 95],
                    'Name': ['Olivia', 'John', 'Laura', 'Ben', 'Kevin'],
                    'Marks': [90, 75, 82, 64, 45]})

with pd.ExcelWriter('test.xlsx') as writer:
    dataframe.to_excel(writer, index=False)

index = False specifies that DataFrame.to_excel() generates an Excel file without header row.

Example Codes: Pandas DataFrame.to_excel With index_label Parameter

import pandas as pd

dataframe= pd.DataFrame({'Attendance': [60, 100, 80, 78, 95],
                    'Name': ['Olivia', 'John', 'Laura', 'Ben', 'Kevin'],
                    'Marks': [90, 75, 82, 64, 45]})

with pd.ExcelWriter('test.xlsx') as writer:
    dataframe.to_excel(writer, index_label='id')

index_label='id' sets the column name of the index column to be id.

Example Codes: Pandas DataFrame.to_excel With float_format Parameter

import pandas as pd

dataframe= pd.DataFrame({'Attendance': [60, 100, 80, 78, 95],
                    'Name': ['Olivia', 'John', 'Laura', 'Ben', 'Kevin'],
                    'Marks': [90, 75, 82, 64, 45]})

with pd.ExcelWriter('test.xlsx') as writer:
    dataframe.to_excel(writer, float_format="%.1f")

float_format="%.1f" specifies the floating number to have two floating digits.

Example Codes: Pandas DataFrame.to_excel With freeze_panes Parameter

import pandas as pd

dataframe= pd.DataFrame({'Attendance': [60, 100, 80, 78, 95],
                    'Name': ['Olivia', 'John', 'Laura', 'Ben', 'Kevin'],
                    'Marks': [90, 75, 82, 64, 45]})

with pd.ExcelWriter('test.xlsx') as writer:
    dataframe.to_excel(writer, freeze_panes=(1,1))

freeze_panes=(1,1) specifies that the excel file has the frozen top row and frozen first column.