Revealing the efficient data deduplication method in Pandas: Tips for quickly removing duplicate data

The secret of Pandas deduplication method: a fast and efficient data deduplication method requires specific code examples

In the process of data analysis and processing, we often encounter There are duplicates in the data. Duplicate data may mislead the analysis results, so deduplication is a very important step. Pandas, a powerful data processing library, provides a variety of methods to achieve data deduplication. This article will introduce some commonly used deduplication methods, and attach specific code examples.

1. Deduplication based on a single column

The most common situation is to perform deduplication based on whether the value of a certain column is repeated. In Pandas, you can use the .duplicated() method to determine whether the values ​​of a certain column are duplicated, and then use the .drop_duplicates() method to remove duplicate values.

For example, we have a DataFrame containing student information, one of which is the student's student number. We hope to remove duplicate rows based on the student number:

import pandas as pd

data = {'学号': [1001, 1002, 1003, 1002, 1004, 1003],
        '姓名': ['张三', '李四', '王五', '李四', '赵六', '王五'],
        '年龄': [18, 19, 20, 19, 21, 20]}

df = pd.DataFrame(data)

df.drop_duplicates(subset='学号', inplace=True)
print(df)

Running results:

    学号  姓名  年龄
0  1001  张三  18
1  1002  李四  19
2  1003  王五  20
4  1004  赵六  21

In this way, rows with duplicate student IDs are removed, and only the rows that appear for the first time are retained.

2. Deduplication based on multiple columns

Sometimes we need to deduplicate based on whether the values ​​​​of multiple columns are repeated. In the .drop_duplicates() method, you can specify which columns to deduplicate based on the subset parameter.

For example, we still use the above DataFrame of student information, and now remove duplicate rows based on student ID and name:

import pandas as pd

data = {'学号': [1001, 1002, 1003, 1002, 1004, 1003],
        '姓名': ['张三', '李四', '王五', '李四', '赵六', '王五'],
        '年龄': [18, 19, 20, 19, 21, 20]}

df = pd.DataFrame(data)

df.drop_duplicates(subset=['学号', '姓名'], inplace=True)
print(df)

Running results:

    学号  姓名  年龄
0  1001  张三  18
1  1002  李四  19
2  1003  王五  20
4  1004  赵六  21

In this way, based on student number and name Duplicate lines are removed from numbers and names at the same time.

3. Deduplication based on all columns

Sometimes we want to deduplicate based on whether the values ​​of all columns in the entire DataFrame are repeated. You can use the .duplicated() method to set the keep parameter to False, and all duplicate rows will be marked. Then use the .drop_duplicates() method to remove these duplicate rows.

For example, we still use the above DataFrame of student information, and now remove duplicate rows based on all columns of the entire DataFrame:

import pandas as pd

data = {'学号': [1001, 1002, 1003, 1002, 1004, 1003],
        '姓名': ['张三', '李四', '王五', '李四', '赵六', '王五'],
        '年龄': [18, 19, 20, 19, 21, 20]}

df = pd.DataFrame(data)

df.drop_duplicates(keep=False, inplace=True)
print(df)

Running results:

    学号  姓名  年龄
0  1001  张三  18
4  1004  赵六  21

This removes All duplicate rows in the entire DataFrame.

Summary:

This article introduces three commonly used deduplication methods in Pandas: deduplication based on a single column, deduplication based on multiple columns, and deduplication based on all columns. Select the appropriate method according to actual needs to remove duplicate data quickly and efficiently. In practical applications, these methods can be flexibly used according to specific data and needs to improve the efficiency of data processing and analysis.

The above is the entire content of this article. I hope readers can benefit from it and be able to better apply Pandas for data deduplication.

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