Simple operation: quickly delete row data of pandas data frame

Title: pandas data processing tips: easily delete rows of data

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Introduction:
In the process of data analysis and processing In the database, we often encounter situations where we need to delete some useless rows of data. Using the pandas library for data processing is one of the quite common practices. This article will introduce some simple and practical methods to help you easily delete row data in pandas data frame. At the same time, we will provide specific code examples for better understanding and practice.

Method 1: Delete row data based on conditions

The pandas library provides many flexible methods that allow us to delete row data based on specific conditions. We can use the drop method and the loc method to achieve this function.

import pandas as pd

# 示例数据
data = {'Name': ['Tom', 'Nick', 'John', 'Jerry'],
        'Age': [25, 32, 19, 45],
        'Department': ['HR', 'IT', 'Marketing', 'Finance']}

df = pd.DataFrame(data)

# 删除年龄大于30岁的员工数据
df = df.drop(df[df['Age'] > 30].index)
print(df)

In the above code, we use the drop method and Boolean index to delete the data of employees older than 30 years old. The parameter of the drop method is an index list specifying the index of the row to be deleted.

Method 2: Delete row data based on index

In addition to deleting row data based on conditions, we can also delete specific rows based on index. At this time, we can use the drop method or directly use the index tag.

import pandas as pd

# 示例数据
data = {'Name': ['Tom', 'Nick', 'John', 'Jerry'],
        'Age': [25, 32, 19, 45],
        'Department': ['HR', 'IT', 'Marketing', 'Finance']}

df = pd.DataFrame(data)

# 删除索引为2的行数据
df = df.drop(2)
print(df)

In the above code, we use the drop method to delete the row data with index 2. In addition, we can also directly use index tags to delete, as shown below:

import pandas as pd

# 示例数据
data = {'Name': ['Tom', 'Nick', 'John', 'Jerry'],
        'Age': [25, 32, 19, 45],
        'Department': ['HR', 'IT', 'Marketing', 'Finance']}

df = pd.DataFrame(data)

# 删除索引为2的行数据
df = df.drop(df.index[2])
print(df)

Method 3: Delete row data based on duplicate values

Sometimes, we may need to delete rows based on duplicate values ​​in a column Delete row data. The pandas library provides the duplicated method to find duplicate rows, and we can combine it with the drop_duplicates method to delete duplicate rows.

import pandas as pd

# 示例数据
data = {'Name': ['Tom', 'Nick', 'John', 'Tom'],
        'Age': [25, 32, 19, 28],
        'Department': ['HR', 'IT', 'Marketing', 'HR']}

df = pd.DataFrame(data)

# 删除重复行数据
df = df.drop_duplicates()
print(df)

In the above example, we used the drop_duplicates method to remove duplicate rows of data. In this way we can easily remove duplicate rows in pandas dataframe.

Conclusion:
Through the introduction of this article, we have learned three common methods to delete row data in pandas data frames. You can select the appropriate method to delete row data based on your specific needs. I hope these tips will be helpful to you in your data processing. Practice is the best way to learn. We encourage you to try the above code examples to gain a deeper understanding of the use and effects of these methods.

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