Coding Ref

In Pandas, the `GroupBy.mean`

method is used to compute the mean of groups within a DataFrame.

This method is called on a `DataFrameGroupBy`

object and returns a new `DataFrame`

object containing the mean of each group.

For example, consider the following DataFrame:

main.py

```
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 1, 2, 3],
'B': [10, 20, 30, 10, 20, 30],
'C': [100, 200, 300, 100, 200, 300]
})
```

This DataFrame has three columns `A`

, `B`

, and `C`

, with six rows of data.

To compute the mean of each group in this DataFrame, you would first need to use the `groupby`

method to group the rows by a specific column or columns.

For example, to group the rows by column `A`

, you could do the following:

main.py

```
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 1, 2, 3],
'B': [10, 20, 30, 10, 20, 30],
'C': [100, 200, 300, 100, 200, 300]
})
# Group the rows by column A
grouped = df.groupby('A')
```

In the code above, the `groupby`

method is applied to the DataFrame and the `A`

column is specified as the grouping key.

This returns a `DataFrameGroupBy`

object that can be used to apply various aggregation functions to the groups.

Once the rows have been grouped, you can use the `mean`

method to compute the mean of each group.

For example:

main.py

```
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 1, 2, 3],
'B': [10, 20, 30, 10, 20, 30],
'C': [100, 200, 300, 100, 200, 300]
})
# Group the rows by column A
grouped = df.groupby('A')
# Compute the mean of each group
mean_values = grouped.mean()
# Print the mean values
print(mean_values)
```

output

```
A B C
1 10.0 100.0
2 20.0 200.0
3 30.0 300.0
```

In the code above, the `mean`

method is applied to the `DataFrameGroupBy`

object, which computes the mean of each group.

This returns a new `DataFrame`

object containing the mean values for each group.

In this case, the resulting DataFrame has three rows, one for each group (1, 2, and 3), and three columns (`A`

, `B`

, and `C`

), with the mean value for each group and column.

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