Quickly get started with pandas: a quick way to use this library to read Excel files

pandas is an important data analysis library in Python, which can simplify the process of reading, cleaning and processing data. It has now become the standard for data analysis work. In the process of data analysis, Excel is often one of the data sources, so this article will introduce a quick method to read Excel files using pandas.

Several advantages of using pandas to read Excel files:

1. You can quickly read and process a large number of Excel files.
2. Thanks to the efficiency and flexibility provided by pandas, it supports various types of data operations, including, but not limited to, data filtering, data splicing, pivot tables, data visualization, etc.
3. By using pandas, we can easily write the read Excel data to various types of output files, such as CSV, SQL DATABASES, etc.

So, how to use pandas to read Excel files? The following will introduce in detail the entire process from reading Excel files to data cleaning and manipulation.

First, you need to install the pandas library and related dependent libraries. You can use the following statement to install:

pip install pandas openpyxl

After installing the necessary libraries, you can directly use pandas to read the Excel file. The method of using pandas to read Excel files is flexible. When reading Excel files, you can read only one sheet or all sheets of the Excel file. At the same time, you can also name each column, define data types, etc. operate.

1. Read a single sheet

Use the pandas.read_excel function to read a single sheet from an Excel file. For example, we have an Excel file named test.xlsx, which contains a sheet named Sheet1, which can be read using the following code:

import pandas as pd

df = pd.read_excel('test.xlsx', sheet_name='Sheet1')

2. Read all sheets

If we need to read all sheets in the Excel file, we can use the following code:

import pandas as pd

xls = pd.read_excel('test.xlsx', sheet_name=None)

Set the sheet_name parameter to None to return a dictionary with the sheet name as the key and DataFrame as the value.

You can use xls.keys() to view the names of all sheets, and use xls.values() to view the contents of all sheets.

3. Rename columns

In pandas, we can simplify the operation by renaming the columns in the DataFrame. Here is an example of renaming a column in an Excel file. The sample code is as follows:

import pandas as pd

df = pd.read_excel('test.xlsx', sheet_name='Sheet1')

df.rename(columns={'原列名':'新列名'}, inplace=True)

4. Write data to a file

Use pandas Easily write data from Excel files to various types of files, including CSV, SQL database, and more.

Take writing data from an Excel file to a CSV file as an example. The sample code is as follows:

import pandas as pd

df = pd.read_excel('test.xlsx', sheet_name='Sheet1')

df.to_csv('output.csv')

Save the data read from Excel to DataFrame, and then use the to_csv function of DataFrame to directly The data is written into a CSV file.

Through the above examples, I hope that everyone can understand the superiority of pandas in reading and processing Excel data, and can quickly get started with this library, and use this skill in subsequent data analysis work to handle it gracefully. data.

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