To answer your question, I'm assuming you're using Python and pandas library. Here's a step-by-step guide on how to do it: 1. First, you need to calculate the average age. You can do this using the mean() function on the 'age' column of your dataframe. Let's assume your dataframe is named 'df'. ```python average_age = df['age'].mean() ``` 2. Next, you need to create a new column 'Age_Comparison'. You can do this using a for loop, but a more efficient way is to use the apply() function. This function applies a function along an axis of the DataFrame. ```python def compare_age(age): if age average_age: return 'Higher' elif age

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To answer your question, I'm assuming you're using Python and pandas library. Here's a step-by-step guide on how to do it:

1. First, you need to calculate the average age. You can do this using the mean() function on the 'age' column of your dataframe. Let's assume your dataframe is named 'df'.

```python
average_age = df['age'].mean()
```

2. Next, you need to create a new column 'Age_Comparison'. You can do this using a for loop, but a more efficient way is to use the apply() function. This function applies a function along an axis of the DataFrame.

```python
def compare_age(age):
    if age > average_age:
        return 'Higher'
    elif age < average_age:
        return 'Lower'
    else:
        return 'Same'

df['Age_Comparison'] = df['age'].apply(compare_age)
```

In the compare_age function, we compare the age with the average age and return 'Higher', 'Lower', or 'Same' accordingly. We then use the apply() function to apply this function to each element in the 'age' column. The result is stored in a new column 'Age_Comparison'.

Knowee AI · Accepted Answer

To answer your question, I'm assuming you're using Python and pandas library. Here's a step-by-step guide on how to do it:

1. First, you need to calculate the average age. You can do this using the mean() function on the 'age' column of your dataframe. Let's assume your dataframe is named 'df'.

```python
average_age = df['age'].mean()
```

2. Next, you need to create a new column 'Age_Comparison'. You can do this using a for loop, but a more efficient way is to use the apply() function. This function applies a function along an axis of the DataFrame.

```python
def compare_age(age):
    if age > average_age:
        return 'Higher'
    elif age < average_age:
        return 'Lower'
    else:
        return 'Same'

df['Age_Comparison'] = df['age'].apply(compare_age)
```

In the compare_age function, we compare the age with the average age and return 'Higher', 'Lower', or 'Same' accordingly. We then use the apply() function to apply this function to each element in the 'age' column. The result is stored in a new column 'Age_Comparison'.

Based on the dataframe generated in practice 1, use Loop to create a new columnnamed Age_Comparison indicating whether the age of a person is higher, lower or the same asthe average age in the dataset. (You can use Higher/Same/Lower)

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