Practical tips and examples of Pandas data filtering

Practical tips for using Pandas for data filtering

Pandas is a powerful data processing library that is widely used in data analysis and data science. Data filtering is a common task during data processing. This article will introduce how to use Pandas for data filtering and provide specific code examples.

1. Filter data based on conditions

1. Use conditional operators to filter

Pandas provides a variety of conditional operators to filter data based on conditions. . Commonly used operators include equal (==), not equal (!=), greater than (>), less than (=), less than or equal to (

For example, assuming there is a DataFrame object df, which contains the student's name (name), age (age) and score (score), we can use the following code to filter out student data with scores greater than or equal to 90 points :

df_filtered = df[df['score'] >= 90]

2. Filtering using multiple conditions

In addition to a single condition, Pandas also supports using multiple conditions for data filtering. Conditions can be combined using the logical operators and, or and not.

For example, suppose we want to filter out the data of students who are between 18 and 25 years old and whose scores are greater than or equal to 80 points. You can use the following code:

df_filtered = df[(df['age'] >= 18) & (df['age'] <= 25) & (df['score'] >= 80)]

2. Filter data based on index

The DataFrame object in Pandas automatically generates an integer index by default, and you can use the index to filter data.

1. Filtering using positional index

You can use the iloc attribute to filter data based on the positional index of rows and columns.

For example, assuming we want to filter out the data from rows 2 to 5, we can use the following code:

df_filtered = df.iloc[2:6, :]

2. Use tag index to filter

If the label index is set in the DataFrame object, you can use the loc attribute to filter the data based on the label index.

For example, assuming we want to filter out student data that is 20 years or older, we can use the following code:

df_filtered = df.loc[df['age'] >= 20, :]

3. Filter data based on fields

In addition to using conditions and Filter by index, and you can also filter data based on fields.

1. Filter data based on column names

You can use column names to filter out specified column data.

For example, assuming we only want to filter out data in the two columns of name and grades, you can use the following code:

df_filtered = df[['name', 'score']]

2. Filter data based on field values

You can use the value of the field to filter out the data corresponding to the field value.

For example, suppose we want to filter out student data with scores between 80 and 90 points, you can use the following code:

df_filtered = df[df['score'].between(80, 90)]

The above are practical techniques for using Pandas for data filtering, through flexible Using conditions, indexes and fields, you can easily filter out the data you need. I hope this article will help you in your data processing process!

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