Сохранить датафрейм в excel python

  • Редакция Кодкампа

17 авг. 2022 г.
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Часто вас может заинтересовать экспорт фрейма данных pandas в Excel. К счастью, это легко сделать с помощью функции pandas to_excel() .

Чтобы использовать эту функцию, вам нужно сначала установить openpyxl , чтобы вы могли записывать файлы в Excel:

pip install openpyxl

В этом руководстве будет объяснено несколько примеров использования этой функции со следующим фреймом данных:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'points': [25, 12, 15, 14, 19],
 'assists': [5, 7, 7, 9, 12],
 'rebounds': [11, 8, 10, 6, 6]}) 

#view DataFrame
df

 points assists rebounds
0 25 5 11
1 12 7 8
2 15 7 10
3 14 9 6
4 19 12 6

Пример 1: базовый экспорт

В следующем коде показано, как экспортировать DataFrame по определенному пути к файлу и сохранить его как mydata.xlsx :

df.to_excel (r'C:UsersZachDesktopmydata.xlsx')

Вот как выглядит фактический файл Excel:

Пример 2: Экспорт без индекса

В следующем коде показано, как экспортировать DataFrame в определенный путь к файлу и удалить столбец индекса:

df.to_excel (r'C:UsersZachDesktopmydata.xlsx', index= False )

Вот как выглядит фактический файл Excel:

Пример 3: Экспорт без индекса и заголовка

В следующем коде показано, как экспортировать DataFrame в определенный путь к файлу и удалить столбец индекса и строку заголовка:

df.to_excel (r'C:UsersZachDesktopmydata.xlsx', index= False, header= False )

Вот как выглядит фактический файл Excel:

Пример 4: Экспорт и имя листа

В следующем коде показано, как экспортировать DataFrame в определенный путь к файлу и назвать рабочий лист Excel:

df.to_excel (r'C:UsersZachDesktopmydata.xlsx', sheet_name='this_data')

Вот как выглядит фактический файл Excel:

Полную документацию по функции to_excel() можно найти здесь .

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:

Parameter Description Available Options
excel_writer= The path of the ExcelWriter to use path-like, file-like, or ExcelWriter object
sheet_name= The name of the sheet to use String representing name, default ‘Sheet1’
na_rep= How to represent missing data String, default ''
float_format= Allows you to pass in a format string to format floating point values String
columns= The columns to use when writing to the file List of strings. If blank, all will be written
header= Accepts either a boolean or a list of values. If a boolean, will either include the header or not. If a list of values is provided, aliases will be used for the column names. Boolean or list of values
index= Whether to include an index column or not. Boolean
index_label= Column labels to use for the index. String or list of strings.
startrow= The upper left cell to start the DataFrame on. Integer, default 0
startcol= The upper left column to start the DataFrame on Integer, default 0
engine= The engine to use to write. openpyxl or xlsxwriter
merge_cells= Whether to write multi-index cells or hierarchical rows as merged cells Boolean, default True
encoding= The encoding of the resulting file. String
inf_rep= How to represent infinity values (as Excel doesn’t have a representation) String, default 'inf'
verbose= Whether to display more information in the error logs. Boolean, default True
freeze_panes= Allows you to pass in a tuple of the row, column to start freezing panes on Tuple of integers with length 2
storage_options= Extra options that allow you to save to a particular storage connection Dictionary
The many parameters of the Pandas .to_excel() method

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:

01 - Saving Pandas DataFrame to Excel with Index

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:

02 - Saving Pandas DataFrame to Excel with Sheet Name

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:

03- Saving Pandas DataFrame to Excel without Index

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:

04 - Saving Pandas DataFrame to Excel with Labelled Index

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:

05 - Saving Multiple Pandas Dataframes to Excel Worksheets

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:

06 - Saving Pandas DataFrame to Excel with only some columns

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:

07 - Saving Pandas DataFrame to Excel with renamed columns.png

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:

08 - Specifying starting position for excel

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:

09 - Saving Pandas DataFrame to Excel with infinity and missing data

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:

10 - Prevent merging cells when saving Pandas to Excel

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:

11 - Saving Pandas DataFrame to Excel with frozen panes

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

Вы можете сохранить или записать DataFrame в файл Excel или конкретный лист в файле Excel, используя метод pandas.DataFrame.to_excel() класса DataFrame.

В этом руководстве мы узнаем, как записать DataFrame Pandas в файл Excel с помощью примеров программ Python.

Необходимым условием для работы с функциями файлов Excel в pandas является установка модуля openpyxl. Чтобы установить openpyxl с помощью pip, выполните следующую команду.

pip install openpyxl

Пример 1

Вы можете записать DataFrame в файл Excel без упоминания имени листа. Пошаговый процесс представлен ниже:

  1. Подготовьте свой DataFrame. В этом примере мы инициализируем DataFrame с несколькими строками и столбцами.
  2. Создайте модуль записи Excel с именем выходного файла Excel, в который вы хотите записать наш DataFrame.
  3. Вызов функции to_excel() в DataFrame с помощью модуля записи Excel, переданного в качестве аргумента.
  4. Сохраните файл Excel, используя метод save() Excel Writer.
import pandas as pd

# create dataframe
df_marks = pd.DataFrame({'name': ['Somu', 'Kiku', 'Amol', 'Lini'],
     'physics': [68, 74, 77, 78],
     'chemistry': [84, 56, 73, 69],
     'algebra': [78, 88, 82, 87]})

# create excel writer object
writer = pd.ExcelWriter('output.xlsx')
# write dataframe to excel
df_marks.to_excel(writer)
# save the excel
writer.save()
print('DataFrame is written successfully to Excel File.')

Запустите указанную выше программу, и файл Excel будет создан с именем, указанным при создании модуля записи Excel.

Выходной файл Excel

Откройте файл Excel, и вы увидите индекс, метки столбцов и данные строк, записанные в файл.

Запись DataFrame в лист Excel

Вы можете записать DataFrame на конкретный лист Excel. Пошаговый процесс:

  1. Подготовьте свой DataFrame.
  2. Создайте модуль записи Excel с именем желаемого выходного файла Excel.
  3. Вызовите функцию to_excel() в DataFrame с записывающим устройством и именем листа Excel, переданными в качестве аргументов.
  4. Сохраните файл Excel, используя метод save() Excel Writer.
import pandas as pd

# create dataframe
df_marks = pd.DataFrame({'name': ['Somu', 'Kiku', 'Amol', 'Lini'],
     'physics': [68, 74, 77, 78],
     'chemistry': [84, 56, 73, 69],
     'algebra': [78, 88, 82, 87]})

# create excel writer
writer = pd.ExcelWriter('output.xlsx')
# write dataframe to excel sheet named 'marks'
df_marks.to_excel(writer, 'marks')
# save the excel file
writer.save()
print('DataFrame is written successfully to Excel Sheet.')

Запустите программу:

Выходной файл Excel

Откройте файл Excel. Обратите внимание на название листа Excel. Он назван в честь строки, которую мы указали в качестве второго аргумента функции to_excel().

Работа в листе Excel

В этом руководстве по Pandas мы узнали, как написать Pandas DataFrame в лист Excel с помощью примеров программ Python.

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    Let us see how to export a Pandas DataFrame to an Excel file. 

    Algorithm:

    1. Create the DataFrame.
    2. Determine the name of the Excel file.
    3. Call to_excel() function with the file name to export the DataFrame.

    Example 1:

    Python3

    import pandas as pd

    marks_data = pd.DataFrame({'ID': {0: 23, 1: 43, 2: 12,

                                     3: 13, 4: 67, 5: 89,

                                     6: 90, 7: 56, 8: 34},

                              'Name': {0: 'Ram', 1: 'Deep',

                                       2: 'Yash', 3: 'Aman',

                                       4: 'Arjun', 5: 'Aditya',

                                       6: 'Divya', 7: 'Chalsea',

                                       8: 'Akash' },

                              'Marks': {0: 89, 1: 97, 2: 45, 3: 78,

                                        4: 56, 5: 76, 6: 100, 7: 87,

                                        8: 81},

                              'Grade': {0: 'B', 1: 'A', 2: 'F', 3: 'C',

                                        4: 'E', 5: 'C', 6: 'A', 7: 'B',

                                        8: 'B'}})

    file_name = 'MarksData.xlsx'

    marks_data.to_excel(file_name)

    print('DataFrame is written to Excel File successfully.')

    Output:

    DataFrame is written to Excel File successfully.

    The Excel file is:

    Example 2: We can also first use the ExcelWriter() method to save it.

    Python3

    import pandas as pd

    cars_data = pd.DataFrame({'Cars': ['BMW', 'Audi', 'Bugatti'

                                       'Porsche', 'Volkswagen'],

                              'MaxSpeed': [220, 230, 240, 210, 190],

                              'Color': ['Black', 'Red', 'Blue'

                                        'Violet', 'White']})

    datatoexcel = pd.ExcelWriter('CarsData1.xlsx')

    cars_data.to_excel(datatoexcel)

    datatoexcel.save()

    print('DataFrame is written to Excel File successfully.')

    Output:

    DataFrame is written to Excel File successfully.

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    Excel files can be a great way of saving your tabular data particularly when you want to display it (and even perform some formatting to it) in a nice GUI like Microsoft Excel. In this tutorial, we’ll look at how to save a pandas dataframe to an excel .xlsx file.

    Note: The terms “excel file” and “excel workbook” are used interchangeably in this tutorial.

    The to_excel() function

    The pandas DataFrame to_excel() function is used to save a pandas dataframe to an excel file. It’s like the to_csv() function but instead of a CSV, it writes the dataframe to a .xlsx file. The following is its syntax:

    df.to_excel("pathfile_name.xlsx")

    Here, df is a pandas dataframe and is written to the excel file file_name.xlsx present at the location path. By default, the dataframe is written to Sheet1 but you can also give custom sheet names. You can also write to multiple sheets in the same excel workbook as well (See the examples below).

    Note that once the excel workbook is saved, you cannot write further data without rewriting the whole workbook.

    Examples

    First, we’ll create a sample dataframe that we’ll be using throughout this tutorial.

    import pandas as pd
    
    data = {
        'Name': ['Microsoft Corporation', 'Google, LLC', 'Tesla, Inc.',
                 'Apple Inc.', 'Netflix, Inc.'],
        'Symbol': ['MSFT', 'GOOG', 'TSLA', 'AAPL', 'NFLX'],
        'Shares': [100, 50, 150, 200, 80]
    }
    
    # create dataframe from data
    df = pd.DataFrame(data)
    # display the dataframe
    df

    pandas dataframe containing information on a stock portfolio

    Now, let’s look at examples of some of the different use-cases where the to_excel() function might be useful.

    1. Save dataframe to an excel file with default parameters

    df.to_excel("portfolio.xlsx")

    If you just pass the file name to the to_excel() function and use the default values for all the other parameters, the resulting Excel file gets saved in your current working directory with the given file name. Here’s a snapshot of the file when opened in Excel.

    Snapshot of the excel file saved

    You can see that by default, the dataframe is saved to the sheet Sheet1. Also, note that the index of the dataframe is saved as a separate column. Pass index=False if you don’t want the index as a separate column in the excel file.

    # to not include index as a column
    df.to_excel("portfolio.xlsx", index=False)

    Here’s how the saved excel file looks now.

    Snapshot of the saved pandas dataframe without index in excel.

    2. Save dataframe to an excel file with custom sheet name

    You can specify the name of the worksheet using the sheet_name parameter.

    # with custom sheet name
    df.to_excel("portfolio.xlsx", sheet_name="stocks")

    Snapshot of the saved dataframe with custom worksheet name

    You can see in the above snapshot that the resulting excel file has stocks as its sheet name.

    3. Save to multiple sheets in the same workbook

    You can also save dataframes to multiple worksheets within the same workbook using the to_excel() function. For this, you need to specify an ExcelWriter object which is a pandas object used to write to excel files. See the example below:

    # write to multiple sheets
    df2 = df.copy()
    with pd.ExcelWriter("portfolio.xlsx") as writer:
        df.to_excel(writer, sheet_name="stocks1")
        df2.to_excel(writer, sheet_name="stocks2")

    Here’s how the saved excel file looks.

    Snapshot of excel file with dataframes saved to two worksheets

    In the above example, an ExcelWriter object is used to write the dataframes df and df2 to the worksheets stocks1 and stocks2 respectively.

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

    For more on the pandas dataframe to_excel() function, refer to its official documentation.

    You might also be interested in –

    • Write a Pandas DataFrame to a JSON File
    • Copy Pandas DataFrame to the Clipboard
    • Save Pandas DataFrame to a CSV file

    With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5

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    • Piyush Raj

      Piyush is a data professional passionate about using data to understand things better and make informed decisions. He has experience working as a Data Scientist in the consulting domain and holds an engineering degree from IIT Roorkee. His hobbies include watching cricket, reading, and working on side projects.

      View all posts

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