The `loc` function in pandas is primarily used for accessing a group of rows and columns by labels or a boolean array. It is not typically used to add a new column in an existing dataframe.

However, you can add a new column to an existing dataframe in pandas using the following methods:

1. Direct assignment: This is the simplest way to add a new column. Here is an example:

```python
dataframe['new_column_label'] = value
```

2. Using `assign()`: This function returns a new dataframe with the new column added to the old dataframe. Here is an example:

```python
dataframe = dataframe.assign(new_column_label = value)
```

3. Using `insert()`: This function allows you to insert a column at a specific location. Here is an example:

```python
dataframe.insert(location, 'new_column_label', value)
```

In all these examples, `dataframe` is your existing dataframe, `new_column_label` is the label of your new column, and `value` is the value(s) you want to add in the new column. The `value` can be a single value (which will be repeated for all rows), a list of values, or a Series object.

Question

The `loc` function in pandas is primarily used for accessing a group of rows and columns by labels or a boolean array. It is not typically used to add a new column in an existing dataframe.

However, you can add a new column to an existing dataframe in pandas using the following methods:

1. Direct assignment: This is the simplest way to add a new column. Here is an example:

```python
dataframe['new_column_label'] = value
```

2. Using `assign()`: This function returns a new dataframe with the new column added to the old dataframe. Here is an example:

```python
dataframe = dataframe.assign(new_column_label = value)
```

3. Using `insert()`: This function allows you to insert a column at a specific location. Here is an example:

```python
dataframe.insert(location, 'new_column_label', value)
```

In all these examples, `dataframe` is your existing dataframe, `new_column_label` is the label of your new column, and `value` is the value(s) you want to add in the new column. The `value` can be a single value (which will be repeated for all rows), a list of values, or a Series object.

Knowee AI · Accepted Answer

The `loc` function in pandas is primarily used for accessing a group of rows and columns by labels or a boolean array. It is not typically used to add a new column in an existing dataframe.

However, you can add a new column to an existing dataframe in pandas using the following methods:

1. Direct assignment: This is the simplest way to add a new column. Here is an example:

```python
dataframe['new_column_label'] = value
```

2. Using `assign()`: This function returns a new dataframe with the new column added to the old dataframe. Here is an example:

```python
dataframe = dataframe.assign(new_column_label = value)
```

3. Using `insert()`: This function allows you to insert a column at a specific location. Here is an example:

```python
dataframe.insert(location, 'new_column_label', value)
```

In all these examples, `dataframe` is your existing dataframe, `new_column_label` is the label of your new column, and `value` is the value(s) you want to add in the new column. The `value` can be a single value (which will be repeated for all rows), a list of values, or a Series object.

Which of the following can be used to add a new column in existing dataframe:locatiloc<dataframe_object>.<column_lable>

Question

Which of the following can be used to add a new column in existing dataframe:

Solution

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