To apply the groupby function on multiple columns in pandas, you can use the groupby method followed by the names of the columns you want to group by in a list. For example, if you have a DataFrame called df and you want to group by columns 'A' and 'B', you can do this by writing df.groupby(['A', 'B']). This will group the data based on unique combinations of values in columns 'A' and 'B'. You can then apply various aggregate functions on the grouped data to perform analysis and generate insights.

## How to calculate minimum value within each group using groupby in pandas?

You can calculate the minimum value within each group using the `groupby`

method in pandas along with the `min`

function. Here is an example:

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import pandas as pd # Create a sample dataframe data = {'group': ['A', 'A', 'B', 'B', 'B'], 'value': [10, 15, 5, 12, 8]} df = pd.DataFrame(data) # Calculate the minimum value within each group min_values = df.groupby('group')['value'].min() print(min_values) |

This will output:

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group A 10 B 5 Name: value, dtype: int64 |

In this example, we group the dataframe by the 'group' column and then calculate the minimum value within each group using the `min`

function. The result is a Series with the minimum value for each group.

## How to apply multiple aggregation functions to grouped data in pandas?

To apply multiple aggregation functions to grouped data in pandas, you can use the `agg()`

method in combination with a dictionary that specifies the column(s) and corresponding aggregation functions you want to apply.

Here's an example of how you can apply multiple aggregation functions to grouped data in pandas:

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import pandas as pd # Create a sample DataFrame data = {'group': ['A', 'A', 'B', 'B', 'B'], 'value1': [1, 2, 3, 4, 5], 'value2': [10, 20, 30, 40, 50]} df = pd.DataFrame(data) # Group the data by the 'group' column grouped = df.groupby('group') # Apply multiple aggregation functions to the grouped data result = grouped.agg({'value1': ['sum', 'mean'], 'value2': 'max'}) print(result) |

In this example, we first create a sample DataFrame with columns 'group', 'value1', and 'value2'. We then group the data by the 'group' column using the `groupby()`

method. Finally, we use the `agg()`

method with a dictionary that specifies we want to calculate the sum and mean of the 'value1' column, as well as the maximum value in the 'value2' column.

The resulting DataFrame will show the grouped data with the specified aggregation functions applied.

## How to calculate count of values within each group using groupby in pandas?

To calculate the count of values within each group using `groupby`

in pandas, you can use the `count()`

function. Here is an example:

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import pandas as pd # Creating a sample DataFrame data = {'Group': ['A', 'A', 'B', 'B', 'B', 'C'], 'Value': [10, 20, 15, 30, 25, 5]} df = pd.DataFrame(data) # Grouping by 'Group' column and calculating count of values in each group grouped_df = df.groupby('Group').count() print(grouped_df) |

This will output:

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Value Group A 2 B 3 C 1 |

In this example, the `count()`

function is used to calculate the count of values within each group based on the 'Group' column in the DataFrame.