Here is a Python function that fulfills the requirements:

```python
import pandas as pd

def clean_mean_df(name, col_name):
# Read the dataset into a pandas dataframe
df = pd.read_csv(name)

# Count the number of missing values in the chosen column
total_removed = df[col_name].isnull().sum()

# Remove any missing values from the chosen column
df = df.dropna(subset=[col_name])

# Compute the mean of the cleaned column
mean = df[col_name].mean()

return total_removed, mean
```

You can use this function as follows:

```python
dataset_filename = 'penguins.csv'
data_col = 'bill_depth_mm'
tr, mn = clean_mean_df(name=dataset_filename, col_name=data_col)
```

This will return the total number of missing values removed from the column and the mean of the cleaned column.

Question

Here is a Python function that fulfills the requirements:

```python
import pandas as pd

def clean_mean_df(name, col_name):
    # Read the dataset into a pandas dataframe
    df = pd.read_csv(name)
    
    # Count the number of missing values in the chosen column
    total_removed = df[col_name].isnull().sum()
    
    # Remove any missing values from the chosen column
    df = df.dropna(subset=[col_name])
    
    # Compute the mean of the cleaned column
    mean = df[col_name].mean()
    
    return total_removed, mean
```

You can use this function as follows:

```python
dataset_filename = 'penguins.csv'
data_col = 'bill_depth_mm'
tr, mn = clean_mean_df(name=dataset_filename, col_name=data_col)
```

This will return the total number of missing values removed from the column and the mean of the cleaned column.

Knowee AI · Accepted Answer

Here is a Python function that fulfills the requirements:

```python
import pandas as pd

def clean_mean_df(name, col_name):
    # Read the dataset into a pandas dataframe
    df = pd.read_csv(name)
    
    # Count the number of missing values in the chosen column
    total_removed = df[col_name].isnull().sum()
    
    # Remove any missing values from the chosen column
    df = df.dropna(subset=[col_name])
    
    # Compute the mean of the cleaned column
    mean = df[col_name].mean()
    
    return total_removed, mean
```

You can use this function as follows:

```python
dataset_filename = 'penguins.csv'
data_col = 'bill_depth_mm'
tr, mn = clean_mean_df(name=dataset_filename, col_name=data_col)
```

This will return the total number of missing values removed from the column and the mean of the cleaned column.

Question

Solution

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