In pandas, you can group by one column or another by using the `groupby()`

function along with specifying the columns you want to group by. Simply pass the column name or column names as arguments to the `groupby()`

function to group the data based on those columns. This will create groups based on the unique values in the specified column(s) and allow you to perform operations on each group separately.

## How to group by a column and calculate the mean in pandas?

You can use the `groupby()`

function in pandas along with the `mean()`

function to group by a column and calculate the mean of another column. Here's an example:

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import pandas as pd # Create a sample dataframe data = { 'category': ['A', 'B', 'A', 'B', 'A'], 'value': [10, 20, 30, 40, 50] } df = pd.DataFrame(data) # Group by the 'category' column and calculate the mean of the 'value' column mean_values = df.groupby('category')['value'].mean() print(mean_values) |

Output:

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category A 30.0 B 30.0 Name: value, dtype: float64 |

This code groups the dataframe by the 'category' column and calculates the mean of the 'value' column for each group. The result is a Series with the mean values for each category.

## How to group by one column and drop duplicates within each group in pandas?

You can achieve this by using the `groupby`

and `drop_duplicates`

methods in pandas.

Here's an example code snippet to group by one column and drop duplicates within each group:

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import pandas as pd # Create a sample DataFrame data = {'Group': ['A', 'A', 'B', 'B', 'C', 'C'], 'Value': [1, 2, 3, 3, 4, 5]} df = pd.DataFrame(data) # Group by 'Group' column and drop duplicates within each group output_df = df.groupby('Group').apply(lambda x: x.drop_duplicates()) print(output_df) |

In this code snippet, we first create a sample DataFrame with two columns 'Group' and 'Value'. We then use the `groupby`

method to group the DataFrame by the 'Group' column. Next, we use the `apply`

method along with a lambda function to apply the `drop_duplicates`

method to each group within the DataFrame. Finally, we print the resulting DataFrame `output_df`

.

## How to group by a numeric column in pandas?

To group by a numeric column in pandas, you can use the `groupby()`

function along with the column you want to group by. Here is an example:

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import pandas as pd # Create a sample dataframe data = {'Category': ['A', 'B', 'A', 'B', 'A'], 'Value': [10, 20, 30, 40, 50]} df = pd.DataFrame(data) # Group by the 'Category' column grouped = df.groupby('Category') # Sum the values in each group sum_values = grouped['Value'].sum() print(sum_values) |

This will group the dataframe by the 'Category' column and calculate the sum of the 'Value' column for each group. You can also perform other aggregations such as mean, count, max, min, etc. by using different aggregation functions with the `agg()`

method.

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

You can group by one column and calculate the cumulative sum in pandas using the `groupby()`

and `cumsum()`

functions. 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, 15, 25, 30, 35]} df = pd.DataFrame(data) # Group by 'Category' and calculate the cumulative sum df['Cumulative Sum'] = df.groupby('Category')['Value'].cumsum() print(df) |

Output:

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Category Value Cumulative Sum 0 A 10 10 1 A 20 30 2 B 15 15 3 B 25 40 4 A 30 60 5 B 35 75 |

In this example, we first create a DataFrame with two columns 'Category' and 'Value'. We then use the `groupby()`

function to group the DataFrame by the 'Category' column, and calculate the cumulative sum of the 'Value' column within each group using the `cumsum()`

function. The resulting cumulative sum values are stored in a new column 'Cumulative Sum' in the DataFrame.

## How to group by one column and sort the results in pandas?

You can group by one column and sort the results in pandas using the following steps:

- First, import pandas library:

```
1
``` |
```
import pandas as pd
``` |

- Create a DataFrame:

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data = {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar'], 'B': [1, 2, 3, 4, 5, 6], 'C': [7, 8, 9, 10, 11, 12]} df = pd.DataFrame(data) |

- Group by column 'A' and apply the sort_values() method to sort the results within each group:

```
1
``` |
```
sorted_df = df.groupby('A').apply(lambda x: x.sort_values('B')).reset_index(drop=True)
``` |

In this code snippet, we first group the DataFrame `df`

by column 'A'. Then, we use the `apply()`

method to apply the `sort_values()`

method on column 'B' within each group. Finally, we reset the index of the resulting DataFrame using the `reset_index()`

method with `drop=True`

to remove the original index.

Now, `sorted_df`

will be a new DataFrame with the rows grouped by column 'A' and sorted within each group based on the values in column 'B'.

## How to group by one column and aggregate multiple columns in pandas?

To group by one column and aggregate multiple columns in Pandas, you can use the `groupby()`

function in combination with the `agg()`

function.

Here's an example of how to do this:

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import pandas as pd # Sample data data = { 'group': ['A', 'A', 'B', 'B', 'C'], 'value1': [10, 20, 15, 25, 30], 'value2': [5, 10, 8, 12, 15] } df = pd.DataFrame(data) # Group by 'group' column and aggregate 'value1' and 'value2' columns agg_df = df.groupby('group').agg({ 'value1': 'sum', 'value2': 'mean' }) print(agg_df) |

This will output:

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value1 value2 group A 30 7.5 B 40 10.0 C 30 15.0 |

In this example, we are grouping the data by the 'group' column and aggregating the 'value1' column using the sum function and the 'value2' column using the mean function. You can also use other aggregation functions such as 'max', 'min', 'count', etc. to aggregate the values in the columns.