You can group by a specific column in a pandas DataFrame using the `groupby()`

function. Once you have grouped the data, you can then calculate the sum of a particular column using the `sum()`

function. For example, if you have a DataFrame named `df`

and you want to group by the column `category`

and calculate the sum of the column `value`

, you can use the following code:

```
1
``` |
```
df.groupby('category')['value'].sum()
``` |

This will group the data by the values in the `category`

column and calculate the sum of the `value`

column for each group. The result will be a Series with the sum of the `value`

column for each unique value in the `category`

column.

## How to group by and calculate the cumulative sum in pandas?

You can use the `groupby()`

function in pandas along with the `cumsum()`

function to group by a column and calculate the cumulative sum in pandas. Here's an example:

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import pandas as pd # Create a sample DataFrame data = {'Category': ['A', 'A', 'B', 'B', 'A', 'B'], 'Value': [10, 20, 30, 40, 50, 60]} df = pd.DataFrame(data) # Group by 'Category' and calculate the cumulative sum of 'Value' df['Cumulative Sum'] = df.groupby('Category')['Value'].cumsum() # Display the DataFrame print(df) |

This code will output:

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Category Value Cumulative Sum 0 A 10 10 1 A 20 30 2 B 30 30 3 B 40 70 4 A 50 80 5 B 60 130 |

In this example, we grouped the DataFrame by the 'Category' column and calculated the cumulative sum of the 'Value' column within each group. The result is stored in a new column called 'Cumulative Sum'.

## What is the syntax for groupby in pandas?

The syntax for groupby in pandas is:

```
1
``` |
```
df.groupby(by=grouping_columns)[columns_to_show].function()
``` |

Where:

- df is the pandas DataFrame that you want to group
- grouping_columns is the column or list of columns by which you want to group the data
- columns_to_show is the column or list of columns that you want to display the results for
- function() is the function that you want to apply to the grouped data, such as mean(), sum(), count(), etc.

## How to group by and calculate the maximum value in pandas?

You can group by a column in a pandas DataFrame and calculate the maximum value for each group using the following code:

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import pandas as pd # Create a sample DataFrame data = {'Group': ['A', 'B', 'A', 'B', 'A', 'C'], 'Value': [10, 20, 15, 25, 30, 5]} df = pd.DataFrame(data) # Group by 'Group' column and calculate maximum value max_values = df.groupby('Group')['Value'].max() print(max_values) |

This code will output:

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Group A 30 B 25 C 5 Name: Value, dtype: int64 |

In this example, we are grouping the DataFrame by the 'Group' column and calculating the maximum value for each group in the 'Value' column.