Alternatives Pandas pour lire Excel (sans utiliser Interop) | IronXL pour Python

DataFrame

Pandas introduces the DataFrame data structure, which is essentially a two-dimensional labeled data structure with columns of potentially different types. It's similar to a spreadsheet or SQL table, making it easy to perform operations like filtering, grouping, and aggregation on tabular data.

Data manipulation

Pandas offers a wide range of functions for data manipulation, including merging, reshaping, slicing, indexing, and pivoting data. These operations allow users to clean, transform, and prepare data for analysis or visualization efficiently.

Time series functionality

Pandas provides robust support for working with time series data, including tools for date/time indexing and resampling, and convenient methods for handling missing data and time zone conversion.

Integration with libraries

Pandas can seamlessly collaborate with various Python libraries frequently employed in data analysis and scientific computations, including NumPy, Matplotlib, and Scikit-learn. This interoperability allows users to leverage the strengths of different libraries within a single analysis workflow.

Overall, Pandas is a powerful tool for data manipulation and analysis in Python, and it's widely used in various domains, including finance, economics, biology, and social sciences.

Intuitive Python 3+ Excel Document API

IronXL offers a Python 3+ Excel document API that's intuitive and easy to use, allowing developers to seamlessly read, edit, and create Excel spreadsheet files.

Cross-Platform Support

Designed for Python 3+ and compatible with Windows, Mac, Linux, and cloud platforms, IronXL ensures flexibility in deployment environments.

No Need for Microsoft Office or Excel Interop

Developers can work with Excel files in Python without installing Microsoft Office or dealing with Excel Interop, simplifying the integration process and minimizing dependencies.

Compatibility

Supports Python 3.7+ on various operating systems including Microsoft Windows, macOS, Linux, Docker, Azure, and AWS. Compatible with popular IDEs like JetBrains PyCharm and other Python IDEs.

Versatile Workbooks Handling

Create, load, save, and export spreadsheets in various formats including XLS, XLSX, XSLT, XLSM, CSV, TSV, JSON, HTML, Binary, and Byte Array.

Powerful Worksheet Editing

Edit metadata, set permissions and passwords, create and remove worksheets, manipulate sheet layout, handle images, and more.

Advanced Cell Range Operations

Perform various operations on cell ranges such as sorting, trimming, clearing, copying, finding and replacing values, setting hyperlinks, and merging and unmerging cells.

Flexible Cell Styling

Customize cell styles including font, size, border, alignment, and background pattern, and apply conditional formatting.

Math Functions and Data Formats

Utilize math functions like average, sum, min, and max, and set cell data formats including text, number, formula, date, currency, scientific, time, boolean, and custom formats.

Installing Pandas

To install Pandas in your Project, you can follow these steps:

1. Open Command Prompt or Terminal: In PyCharm, from View->Tool Windows->Terminal.

   

2. Install Pandas via pip: Pandas can be installed using the pip package manager. Run the following command in the terminal:

   pip install pandas

   pip install pandas

   SHELL

   This command installs the Pandas library and its dependencies from the Python Package Index (PyPI).

   

3. Install OpenPyXL via pip: OpenPyXL is the library that helps read and write Excel files. It is one of the dependencies used by Pandas. When you install Pandas, OpenPyXL is automatically installed if not present already. If somehow it isn't installed, then you can install it using the following command in the terminal:

   pip install openpyxl

   pip install openpyxl

   SHELL

Installing IronXL

To install IronXL in a Python project, follow these steps:

1. Ensure Prerequisites: Before installing IronXL, make sure you have the necessary prerequisites installed on your system.

   .NET 6.0 SDK: IronXL relies on the IronXL .NET library, specifically .NET 6.0, as its underlying technology. Ensure that you have the .NET 6.0 SDK installed on your machine. You can download it from the official .NET website.

2. Open Command Prompt or Terminal: Do the same as before.

3. Install IronXL via pip: IronXL can be installed using the pip package manager. Run the following command:

   
   :ProductInstall
   W```
   
   This command will collect, download, and install the IronXL library and its dependencies from the Python Package Index (PyPI).
   
   ![Pandas Read Excel Alternatives (Without Using Interop) | IronXL for Python: Figure 5 - Console output from installing IronXL](/static-assets/excel/blog/pandas-read-excel/pandas-read-excel-5.webp)

   
   :ProductInstall
   W```
   
   This command will collect, download, and install the IronXL library and its dependencies from the Python Package Index (PyPI).
   
   ![Pandas Read Excel Alternatives (Without Using Interop) | IronXL for Python: Figure 5 - Console output from installing IronXL](/static-assets/excel/blog/pandas-read-excel/pandas-read-excel-5.webp)

   SHELL

Using Pandas

import pandas as pd

# Read the Excel file
df = pd.read_excel("file.xlsx")

import pandas as pd

# Read the Excel file
df = pd.read_excel("file.xlsx")

print(df)

print(df)

Using IronXL

Step 1: Import the IronXL library and use the WorkBook.Load() method to load the Excel file. In the Load method parameter, you can pass the valid file URL, local file path object, or filename if it is in the same directory as the script.

from ironxl import WorkBook

# Load the Excel file as a WorkBook object
workbook = WorkBook.Load("file.xlsx")

from ironxl import WorkBook

# Load the Excel file as a WorkBook object
workbook = WorkBook.Load("file.xlsx")

Step 2: With IronXL, you can access multiple sheets and print column labels. Access the worksheets and cells to read the column-stored data. The cells can be of any data type like numeric columns or string columns. The cell values can be converted to int by parsing string columns to numeric values using the IntValue property and vice versa.

# Access the first worksheet
worksheet = workbook.DefaultWorkSheet

# Select a specific cell and return the converted value
cell_value = worksheet["A2"].IntValue
print(cell_value)

# Read from the entire worksheet and print each cell's address and value
for cell in worksheet:
    print(f"Cell {cell.AddressString} has value '{cell.Text}'")

# Access the first worksheet
worksheet = workbook.DefaultWorkSheet

# Select a specific cell and return the converted value
cell_value = worksheet["A2"].IntValue
print(cell_value)

# Read from the entire worksheet and print each cell's address and value
for cell in worksheet:
    print(f"Cell {cell.AddressString} has value '{cell.Text}'")

Here is the output of the above code with a proper display format showcasing the versatility of IronXL:

For more information on working with Excel files, please visit this code examples page.