pandas tutorial: Detailed explanation of how to use this library to read Excel files

Pandas Tutorial: Detailed explanation of how to use this library to read Excel files, specific code examples are required

Pandas is a commonly used data processing library with many powerful functions , especially very convenient in data processing. In the actual data processing process, it is often necessary to read Excel files. This article will explain in detail how to use the Pandas library to read Excel files and provide specific code examples.

1. Import Pandas library

To use the Pandas library, you need to import the library first:

import pandas as pd

Among them, pd is the alias of the Pandas library, which is more convenient Use Pandas related methods appropriately.

2. Reading Excel files

It is very convenient to use Pandas to read Excel files. It only requires one line of code to achieve:

data = pd.read_excel('file_name.xlsx')

Among them, file_name. xlsx is the name of the Excel file, which is in the same directory as the Python script.

If the Excel file is not in the same directory, you need to specify the complete path, for example:

data = pd.read_excel('C:/Users/username/Desktop/file_name.xlsx')

After reading the Excel file, you can view the data in the file in the following way:

The

print(data.head())

head() method can view the first 5 rows of data in the Excel file. If you need to view more rows, you can change the number in brackets to the number of rows you need to view, for example:

print(data.head(10))

3. Specify the Excel table that needs to be read

When When the Excel file contains multiple tables, you need to specify the table that needs to be read, for example:

data = pd.read_excel('file_name.xlsx', sheet_name='Sheet1')

Among them, sheet_name is used to specify the name of the table that needs to be read. If you need to read multiple sheets, you can change sheet_name to a list, for example:

data = pd.read_excel('file_name.xlsx', sheet_name=['Sheet1', 'Sheet2'])

In this way, the data of Sheet1 and Sheet2 can be read out at one time and stored in a dictionary.

4. Read specific rows or columns

When there is a lot of data in the Excel table, we sometimes only need to read some of the rows or columns. You can use Pandas' loc and iloc method implementation:

• loc method can read specified row or column data, the example is as follows:

  data = pd.read_excel('file_name.xlsx')
  # 读取第 3 行数据
  print(data.loc[2])
  # 读取名称为 'column_name' 的列数据
  print(data.loc[:, 'column_name'])
  # 读取第 3 行、名称为 'column_name' 的数据
  print(data.loc[2, 'column_name'])

• iloc method can read Specified row or column data, but you need to use an integer position index. The example is as follows:

  data = pd.read_excel('file_name.xlsx')
  # 读取第 3 行数据
  print(data.iloc[2])
  # 读取第 3 行、第 4 列数据
  print(data.iloc[2, 3])
  # 读取第 2-4 行、第 1-3 列的数据
  print(data.iloc[1:4, 0:3])

5. Read the column names in the Excel file

In the process of reading Excel files, sometimes you need to get the column names in the Excel file. You can use the following method:

data = pd.read_excel('file_name.xlsx')
# 读取所有列名
print(data.columns.values)
# 读取第 3 列的列名
print(data.columns.values[2])

Among them, columns.values ​​is used to return the column name list. In Python, list indexes start from 0.

6. Write data to Excel files

In addition to reading Excel files, Pandas also provides methods for writing data to Excel files. The example is as follows:

data = pd.DataFrame({'姓名': ['张三', '李四', '王五'], '年龄': [18, 22, 25]})
# 将数据写入名为 'MySheet' 的表格中
data.to_excel('file_name.xlsx', sheet_name='MySheet', index=False)

Among them, the to_excel() method is used to write data to an Excel file. The first parameter is the Excel file name, and the second parameter is the name of the table to be written. Index=False means No need to write to index columns.

7. Conclusion

This article mainly introduces how to use the Pandas library to read Excel files and provides specific code examples. Of course, Pandas has many other functions, which can be further understood in daily data processing.

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