Python import and export Excel data
Start the data journey: Why is Python the best partner in Excel data processing?
Imagine you are an explorer holding an ancient map (Excel file) in your hand, recording the locations of countless treasures. However, it is not easy to find these treasures—the information on the map is complicated and difficult to interpret. At this time, Python is like an experienced guide. It not only knows how to quickly understand this map, but also helps you easily locate the location of each treasure.
Python has unparalleled advantages in data processing. By writing a few lines of code, you can automate the large number of tasks that originally required manual operations, such as batch modification, finding specific values, or merging data from multiple files. More importantly, Python has powerful third-party library support, such as Pandas and Openpyxl, which provide great convenience for reading and writing Excel files. For example, in a financial company, analysts process large transaction records every day. With Python scripts, they can quickly filter out data that meets the criteria and generate reports, greatly improving work efficiency.
In addition, Python also allows users to customize functions and classes, making programs more flexible and changeable. This means that you can customize exclusive data processing processes according to your needs, not just limited to off-the-shelf features. Whether it is simple statistical analysis or complex machine learning modeling, Python is competent.
Preparation: Let Python shake hands with Excel
In order for our wizard (Python) to read and operate Excel files smoothly, we need to prepare the necessary tools first. This is as important as checking whether the equipment is complete before departure. First, make sure that your computer has a Python environment installed. If not, please visit the official website to download the latest version and follow the prompts to complete the installation.
Next, we need to install two key libraries: Pandas and Openpyxl. The former is a very popular data analysis library that provides efficient data structures and operation methods; the latter is a library specially used to process Excel files.
These two libraries can be easily installed through the pip command:
pip install pandas openpyxl
After the installation is complete, it is recommended to create a virtual environment to manage project dependencies. This will prevent conflicts between different projects.
If you are using the Anaconda distribution, you can create an environment directly through the conda command:
conda create --name myenv python=3.9 conda activate myenv
Now let's see how to introduce these libraries into your code:
import pandas as pd from openpyxl import load_workbook
To ensure everything works properly, try reading a simple CSV file as a test:
df = pd.read_csv('')
print(())If the first few lines of data can be successfully printed out, it means that the preparation work is completed successfully!
Of course, various problems may be encountered during actual application, such as compatibility between different versions or parsing errors caused by certain special characters.
When encountering these problems, don't panic and try to consult official documents or community forums for help.
Data entry: Bringing treasures from Excel tables into the Python world
It's finally time to unveil the mystery. We will lead readers deep into the interior of Excel files and mine hidden data treasures. This may seem a bit tricky for those who are first exposed to such tasks. But in fact, with the help of Python, the whole process becomes extremely simple.
The most basic operation is to read data from a single worksheet. Suppose we have a name calledExcel file containing a sales report.
To load it into a Python environment, just one line of code:
df = pd.read_excel('', sheet_name='Sheet1')Used herepandas.read_excel()function and specify the worksheet name to be read. If you want to get all the contents of the worksheet at once, you can also omit it.sheets_nameParameters, the return is a dictionary, the key is the name of each table, and the value is the corresponding DataFrame object.
However, not all Excel files are so regular in real life. Sometimes you will encounter complex documents containing multiple forms, or with merged cells, formula calculations, etc. In the face of this situation, we need to deal with it more carefully.
For example, when there is a missing value, it can be set byna_valuesParameters to specify which symbols represent null values:
df = pd.read_excel('', na_values=['NA', 'N/A'])For unstructured data, such as text description fields, regular expressions can be used for cleaning and conversion. In addition, it can also be combinedopenpyxlThe library directly manipulates the original XML format, thus achieving higher levels of control. In short, as long as you master the correct method, there will be no data puzzles that cannot be solved.
Data Outbound: Elegantly send Python analysis results back to Excel home
After a series of careful processing, it’s time to get this precious data back home. We can save the information processed by Python to a new Excel file, or update the existing file content. This process is like replacing the old house with new decoration, which not only retains the original frame but also adds a modern atmosphere.
First, let's see how to create a new Excel file. Suppose we have a sorted dataset and want to export it as namedfile.
Just callto_excel()Method:
df.to_excel('', index=False)Hereindex=FalseIndicates that the index column is not saved to avoid interfering with the layout of the original table.
If you want to output multiple worksheets at the same time, you can pass a dictionary toExcelWriterObject to implement:
with ('') as writer:
df1.to_excel(writer, sheet_name='Sheet1', index=False)
df2.to_excel(writer, sheet_name='Sheet2', index=False)In addition to basic data storage capabilities, Python can also provide us with more decorative options. For example, byopenpyxlLibrary, which can personalize cell styles, including font color, background fill, and borders.
Here is a simple example showing how to add a title line and change its appearance:
from openpyxl import Workbook
from import Font, Alignment
wb = Workbook()
ws =
# Add a title line(['Product Name', 'Sales Quantity', 'Sales'])
# Set the title line stylefor cell in ws[1]:
= Font(bold=True)
= Alignment(horizontal='center')
# Save the file('styled_output.xlsx')Not only that, Python can also help us insert charts in Excel, making data visualization more intuitive. While this is not the focus of this article, knowing this will undoubtedly make your work more appealing.
In short, with reasonable configuration, you can create Excel documents that are both beautiful and practical.
Play with data: Use Python to clean, convert and analyze Excel data
Now that we have mastered how to bring data into the world of Python and know how to send them home gracefully, then the next time is the time to really get creative. Python is more like a magician, who can turn boring data into story-filled information.
Taking data cleaning as an example, this is the most basic and important step in any data analysis project. Imagine you are sorting out a bunch of messy puzzle pieces, and only by bringing them back to the bottom can you see the complete picture. Python provides a variety of ways to clean up data, such as deleting duplicates, filling missing values, correcting incorrect inputs, etc.
For example, to remove duplicate rows in DataFrame, you can usedrop_duplicates()function:
df_cleaned = df.drop_duplicates()
Then comes the data conversion stage. In this process, we will perform some transformations on the original data to make it more suitable for subsequent analysis work. Common operations include renaming column names, adjusting data types, creating new calculated fields, etc.
For example, if you find that some values are stored in strings, you canastype()Method converts it to a numeric type:
df['Sales'] = df['Sales'].(',', '').astype(float)Finally, and the most exciting part - data analysis. Python has many excellent scientific computing libraries, such as NumPy, SciPy, etc., which can help us perform various tasks from simple descriptive statistics to complex model construction.
For example, calculate the mean value, standard deviation and other statistics:
mean_sales = df['Sales'].mean() std_sales = df['Sales'].std()
Or draw histograms, scatter plots and other graphical display results. In this way, you can better understand the meaning behind the data and discover potential trends and patterns. Encourage readers to boldly try different techniques and methods and explore more possibilities!
Automation magic: Write Python scripts to automate Excel data processing
When we talk about automation, it is like giving Python the superpowers to automate a series of complex data processing tasks without human intervention. This is a godsend opportunity for data sets that need to be updated regularly. Imagine waking up every morning and receiving a freshly released sales report, all of which are silently prepared for you by Python.
To achieve such a miracle, you must first build a Python script that can run independently and complete a specific data processing process. For example, collect the latest sales data from multiple sources, then clean, convert and analyze, and finally generate a unified format Excel file.
Here is a simplified example:
import pandas as pd
from datetime import datetime
def process_data():
# Read source files df1 = pd.read_excel('')
df2 = pd.read_excel('')
# Merge data combined_df = ([df1, df2])
# Clean the data cleaned_df = combined_df.drop_duplicates()
#Analyze data summary_stats = cleaned_df.describe()
# Save the results timestamp = ().strftime('%Y%m%d')
output_filename = f'report_{timestamp}.xlsx'
with (output_filename) as writer:
cleaned_df.to_excel(writer, sheet_name='Data', index=False)
summary_stats.to_excel(writer, sheet_name='Summary')
if __name__ == '__main__':
process_data()This code implements a series of operations from reading, merging, cleaning, analysis, and saving. In order to enable this script to be executed regularly, we can use the task scheduling tools provided by the operating system, such as cron jobs under Linux or Windows task scheduler. After setting the time interval, Python will automatically start and complete the task according to the scheduled schedule.
Of course, in order to ensure the stability and security of the script, logging and error handling mechanisms need to be considered. For example, whenever an exception occurs, the error message is captured in time and the notification is sent to the administrator. Doing so will not only help track the root cause of the problem, but will also prevent task interruptions due to unexpected situations.
Cross-border cooperation: Integrate other tools and technologies to improve efficiency
With the development of technology, more and more tools and services have begun to integrate into each other, forming a huge ecosystem. In this environment, Python is no longer alone, but can work closely with other software and services to create greater value together. It's like forming a team of superheroes, each member has his own unique abilities, and when they join forces, there are few difficulties that cannot be overcome.
Taking database connections as an example, many enterprise-level applications need to frequently interact with relational databases. Through ORM (Object Relational Mapping) libraries such as SQLAlchemy, Python can easily establish connections with mainstream databases such as MySQL and PostgreSQL, and perform queries, inserts, and updates. This not only improves development efficiency, but also enhances the scalability of the system.
For example, to retrieve data from a database and save it to an Excel file, you can do this:
from sqlalchemy import create_engine
import pandas as pd
engine = create_engine('mysql+pymysql://user:password@localhost/dbname')
query = "SELECT * FROM sales"
df = pd.read_sql(query, engine)
df.to_excel('sales_report.xlsx', index=False)Let’s take a look at the application scenarios of API calls. Today, almost all online services provide a RESTful API interface that allows external programs to communicate with them. Python also has a powerful HTTP request library, such as requests, which can easily send GET/POST requests and obtain the required data. For example, obtain real-time temperature information from the weather forecast website and save it to Excel for further analysis:
import requests
import pandas as pd
response = ('/v1/location/your_location:4:CN/observations/?apiKey=your_api_key')
weather_data = ()
df = (weather_data['observation'])
df.to_excel('weather_report.xlsx', index=False)Finally, don't forget the powerful features of the cloud storage platform. When it comes to massive data, local disk space often seems to be short of limits. At this time, you can consider using Alibaba Cloud OSS, Tencent Cloud COS and other services, which provide unlimited storage capacity and efficient transmission speed. Through the Python SDK, you can easily upload and download files, and even process data directly in the cloud. This not only saves hardware costs, but also brings great convenience to teamwork.
Anyway
Through cross-border cooperation, Python can show a more colorful side in the field of data processing. Readers are encouraged to actively explore more innovative application scenarios and continuously broaden their skills boundaries.
The above is personal experience. I hope you can give you a reference and I hope you can support me more.