pandas groupby
时间: 2023-08-27 11:05:11 浏览: 48
Pandas groupby is a powerful function that is used for grouping data based on one or more columns. It allows you to group data in a way that you can perform various aggregate functions on the grouped data. This function is widely used in data analysis and is one of the most important tools in the Pandas library.
The groupby function in Pandas is used to group a Pandas DataFrame by one or more columns. It returns a GroupBy object that can be used to perform various aggregate functions on the grouped data. The syntax for the groupby function is as follows:
```
df.groupby('column_name')
```
Here, 'column_name' is the name of the column that you want to group the data by. You can also group the data by multiple columns by passing a list of column names to the groupby function.
Once you have grouped the data, you can perform various aggregate functions on the grouped data. Some of the commonly used aggregate functions are:
- sum(): Calculates the sum of the values in the grouped data.
- mean(): Calculates the mean of the values in the grouped data.
- count(): Calculates the count of the values in the grouped data.
- max(): Calculates the maximum value in the grouped data.
- min(): Calculates the minimum value in the grouped data.
To apply these functions to the grouped data, you can call them on the GroupBy object. For example, to calculate the sum of the values in the grouped data, you can use the following code:
```
df.groupby('column_name').sum()
```
This will return a new DataFrame with the sum of the values in the 'column_name' column for each group.
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