What are the methods for pandas data analysis?

Pandas data analysis methods include: 1. Data reading and processing; 2. Data filtering and selection; 3. Data sorting and organization; 4. Data aggregation and grouping; 5. Data pivot and pivot table; 6. Merge and connect data; 7. Data persistence and storage. The Pandas library provides a wealth of data analysis and processing functions, covering data reading, processing, filtering, sorting, aggregation, pivoting, etc. By flexibly using these methods and functions, various types of data can be easily analyzed and processed. .

The operating system for this tutorial: Windows 10 system, DELL G3 computer.

Pandas is a popular Python data analysis library that provides many powerful methods and functions, allowing you to easily process, filter, and analyze data. The following are some common methods when using Pandas for data analysis:

1. Data reading and processing

• Read data: use read_csv() in the Pandas library , read_excel(), read_sql() and other methods to read different types of data files.

• Data cleaning: Preprocessing of data, such as filling in missing values, deleting duplicate values, processing outliers, etc.

• Data conversion: Convert data from one format to another, for example, convert CSV files to Excel files.

2. Data filtering and selection

• Filter based on conditions: use loc and iloc attributes and logical operators (such as &, |, ~ etc.) to filter data that meets specific criteria.

• Filtering based on tags: Use the loc attribute to filter data for specific tags.

• Filter by range: Use the loc attribute to filter data within a specific range.

• Multi-condition filtering: Use the query method to filter data that meets multiple conditions.

3. Data sorting and sorting

• Data sorting: Use the sort_values() method to sort the data, which can be based on a certain column or multiple columns Sort.

• Data wrangling: Reorganizing or reshaping data, for example, converting data from wide format to long format.

4. Data aggregation and grouping

• Data aggregation: perform aggregation operations on data, for example, calculate the average and sum of each category wait.

• Data grouping: Use the groupby() method to group the data and perform aggregate calculations on each group.

• Group statistics: Perform statistics on grouped data, for example, calculate the average population, GDP, etc. of each region.

5. Pivot table and pivot table

• Pivot table: Use the pivot_table() method in the Pandas library to create a pivot table. Analyze data from multiple angles.

• Pivot Conversion: Convert Pivot Table to other data formats, for example, to CSV file or Excel file.

6. Merge and join data

• Merge data: Use the concat() method to merge multiple data frames along a certain row or column directions to merge.

• Connect data: Use the merge() method to connect two data frames according to a certain column or row.

7. Data persistence and saving

• Persistent storage: Use various methods in the Pandas library to save data to disk. For example, use to_csv(), to_excel(), to_sql() and other methods to save data into CSV, Excel, SQL database and other formats.

• Data reading: Use various methods in the Pandas library to read data from the disk, for example, use read_csv(), read_excel(), read_sql() and other methods to read CSV , Excel, SQL database and other formats of data.

In short, the Pandas library provides a wealth of data analysis and processing functions, covering data reading, processing, filtering, sorting, aggregation, perspective, etc. By leveraging these methods and features, you can easily analyze and process various types of data.

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