How to read excel files with pandas

Steps for pandas to read excel files: 1. Make sure the Pandas library has been installed; 2. Import the Pandas library and other libraries that may be needed; 3. Use Pandas's "read_excel()" function to read Excel File; 4. Operate and analyze data, such as viewing the first few rows of data, viewing basic statistics of data, selecting specific columns, filtering, sorting data, grouping and aggregating data, and visualizing data etc.

Operating system for this tutorial: Windows 10 system, Python version 3.11.4, Dell G3 computer.

Pandas is a powerful data processing library that can be used to read, analyze and process various types of data, including Excel files. In this article, I will answer how to read Excel files using Pandas and explain the relevant code.

First, we need to make sure the Pandas library is installed. Pandas can be installed in a Python environment using the following command:

pip install pandas

Next, we need to import the Pandas library and other libraries that may be needed:

import pandas as pd

Now, we can use Pandas’ read_excel() function to read Excel files. The following is a sample code:

df = pd.read_excel('example.xlsx')

In the above code, the read_excel() function accepts one parameter, which is the path to the Excel file. This will return a Pandas DataFrame object named df containing the data from the Excel file.

In addition to the file path, the read_excel() function has other optional parameters, which can be used to specify the specific worksheet to be read, the number of rows to be skipped, the columns to be parsed, etc. For example:

df = pd.read_excel('example.xlsx', sheet_name='Sheet1', skiprows=2, usecols='A:C')

In the above code, the sheet_name parameter specifies the name of the worksheet to be read, the skiprows parameter specifies the number of rows to be skipped, and the usecols parameter specifies the column range to be parsed.

After reading the Excel file, we can use various functions and methods provided by Pandas to operate and analyze the data. Here are some common examples of operations:

View the first few rows of the data:

df.head()

View basic statistics of the data:

df.describe()

Select specific columns:

df['Column1']

Filter:

df[df['Column1'] > 10]

Sort your data:

df.sort_values('Column1', ascending=False)

Group and aggregate data:

df.groupby('Column1').mean()

Visualize data:

df.plot(x='Column1', y='Column2', kind='scatter')

Column1 and Column2 is the column name in the Excel file and can be replaced according to the actual situation.

To summarize, the basic steps for using Pandas to read Excel files include importing the library, using the read_excel() function to read the file, and operating and analyzing the data. Through these operations, we can easily read and process data in Excel files and perform further analysis and visualization.

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