How to read txt file in pandas

The steps for pandas to read txt files: 1. Install the Pandas library; 2. Use the "read_csv" function to read the txt file and specify the file path and file delimiter; 3. Pandas reads the data into a An object named DataFrame; 4. If the first row contains column names, you can specify it by setting the header parameter to 0, if not, set it to None; 5. If the txt file contains missing values ​​or empty values, you can Use "na_values" to specify these missing values.

# Operating system for this tutorial: Windows 10 system, Dell G3 computer.

Pandas is a powerful Python library for data analysis and data processing. It provides many convenient methods to read and process various data files, including txt files. In this article, I will show you how to use Pandas to read txt files.

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

After the installation is complete, we can start using Pandas to read txt files. Suppose we have a txt file named "data.txt" which contains some data. The following is the content of an example txt file:

Name Age Gender
John 25 Male
Emily 28 Female

To read this txt file, we can use Pandas's read_csv function and specify the file path and file delimiter. Although our file is space-delimited, the read_csv function uses commas as the delimiter by default. Therefore, we need to set the delimiter parameter to " ", which means using spaces as the delimiter. The following is a code example for reading a txt file:

import pandas as pd
# 读取txt文件
data = pd.read_csv('data.txt', sep=' ')
# 打印数据
print(data)

After running the above code, the following results will be output:

   Name  Age  Gender
0  John   25    Male
1  Emily  28  Female

Pandas reads the data as an object named DataFrame. DataFrame is the most commonly used data structure in Pandas, similar to tables in Excel. Each column is parsed as a column of the DataFrame, and each row is parsed as a record of the DataFrame.

If the first line of the txt file contains column names, this can be specified by setting the header parameter to 0. If the txt file has no column names, you can set the header parameter to None. Here is an example:

import pandas as pd
# 读取txt文件，指定列名
data = pd.read_csv('data.txt', sep=' ', header=0)
# 打印数据
print(data)

If the txt file contains missing or empty values, you can use the na_values ​​parameter to specify these missing values. Here is an example that demonstrates how to identify "NA" and "-" as missing values:

import pandas as pd
# 读取txt文件，指定缺失值
data = pd.read_csv('data.txt', sep=' ', header=0, na_values=['NA', '-'])
# 打印数据
print(data)

The above is the basic method of reading txt files using Pandas. In addition to the above parameters, the read_csv function also provides many other parameters for handling different data situations. You can find more details about the read_csv function in the official Pandas documentation.

Reading txt files using Pandas is very simple. Just use the read_csv function and specify the file path, delimiter and other necessary parameters to read the txt file into a DataFrame object to facilitate subsequent data processing and analysis. Hope this article can help you!

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