You can use the `fillna()`

method in pandas to fill missing values based on group. First, you need to group your dataframe using `groupby()`

and then apply the `fillna()`

method to fill the missing values within each group. This will allow you to fill missing values with the mean, median, mode, or any other value of your choice based on the group.

## What is the mode imputation method for filling missing values in pandas?

In pandas, the mode imputation method for filling missing values involves replacing missing values with the most frequent value in a column or series. This can be done using the `fillna()`

method with the `method='ffill'`

argument or by using the `fillna()`

method with the `value`

argument set to the result of the `mode()`

function applied to the column or series with missing values.

## How to identify missing values in a pandas DataFrame?

You can identify missing values in a pandas DataFrame using the `isnull()`

method in combination with the `sum()`

method.

Here's an example:

1 2 3 4 5 6 7 8 9 10 11 12 |
import pandas as pd # create a sample DataFrame with missing values data = {'A': [1, 2, None, 4], 'B': [None, 5, 6, 7], 'C': [8, 9, 10, None]} df = pd.DataFrame(data) # check for missing values in the DataFrame missing_values = df.isnull().sum() print(missing_values) |

This will output:

1 2 3 4 |
A 1 B 1 C 1 dtype: int64 |

In this example, the `isnull()`

method is used to create a boolean DataFrame where `True`

represents missing values and `False`

represents non-missing values. Then, the `sum()`

method is used to calculate the sum of missing values in each column.

## How to fill missing values based on group pattern in pandas?

You can fill missing values based on group pattern in pandas by using the `groupby`

function along with the `transform`

function.

Here is an example of how you can fill missing values in a DataFrame based on the group pattern:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |
import pandas as pd # Create a sample DataFrame data = { 'group': ['A', 'A', 'A', 'B', 'B', 'B'], 'value': [1, 2, None, 4, 5, None] } df = pd.DataFrame(data) # Define a function to fill missing values with the mean of the group def fill_missing_values(group): return group.fillna(group.mean()) # Group by 'group' column and apply the fill_missing_values function df['filled_value'] = df.groupby('group')['value'].transform(fill_missing_values) print(df) |

In this example, we first create a sample DataFrame with a 'group' column and a 'value' column that contains some missing values. We then define a function `fill_missing_values`

that fills missing values with the mean of the group. Finally, we use the `groupby`

function to group the DataFrame by the 'group' column and apply the `transform`

function to fill missing values based on the group pattern.