# Modify a DataFrames

## Adding a Column

One way that we can add a new column is by giving a list of the same length as the existing DataFrame.

```python
df = pd.DataFrame([
  [1, '3 inch screw', 0.5, 0.75],
  [2, '2 inch nail', 0.10, 0.25],
  [3, 'hammer', 3.00, 5.50],
  [4, 'screwdriver', 2.50, 3.00]
],
  columns=['Product ID', 'Description', 
           'Cost to Manufacture', 'Price']
)

# Add one column
df['Sold in Bulk?'] = ['Yes', 'Yes', 'No', 'No']
```

We can also add a new column that is the same for all rows in the DataFrame.

```python
df = pd.DataFrame([
  [1, '3 inch screw', 0.5, 0.75],
  [2, '2 inch nail', 0.10, 0.25],
  [3, 'hammer', 3.00, 5.50],
  [4, 'screwdriver', 2.50, 3.00]
],
  columns=['Product ID', 'Description', 
           'Cost to Manufacture', 'Price']
)

# Add one column
df['Sold in Bulk?'] = 'Yes'
```

Finally, you can add a new column by performing a function on the existing columns.

```python
df = pd.DataFrame([
  [1, '3 inch screw', 0.5, 0.75],
  [2, '2 inch nail', 0.10, 0.25],
  [3, 'hammer', 3.00, 5.50],
  [4, 'screwdriver', 2.50, 3.00]
],
  columns=['Product ID', 'Description', 
           'Cost to Manufacture', 'Price']
)

# Add column here
df['Margin'] = df['Price'] - df['Cost to Manufacture']
```

Note: when adding a new column, we can only use `df['new_column_name']` to refer to the new column, and `df.new_column_name` will not work.

### Adding a column using apply() function

The Pandas `apply()` function can be used to apply a function on every value in a column or row of a DataFrame, and transform that column or row to the resulting values. The function used in `apply()` originally only work for one element, but because of `apply()` it will apply the function to all values of a column. To perform it on a row instead, you can specify the argument `axis=1` in the `apply()` function call.

```python
# This function doubles the input value
def double(x):
  return 2*x

# Apply this function to double every value in a specified column
df.column1 = df.column1.apply(double)

# Lambda functions can also be supplied to `apply()`
df.column2 = df.column2.apply(lambda x : 3*x)

# Applying to a row requires it to be called on the entire DataFrame
df['newColumn'] = df.apply(lambda row: 
  row['column1'] * 1.5 + row['column2'],
  axis=1
)
```

## Renaming Columns

#### Rename All Columns

We can change all of the column names at once by setting the `.columns` property to a different list. This is great when you need to change all of the column names at once.

```python
df = pd.DataFrame({
    'name': ['John', 'Jane', 'Sue', 'Fred'],
    'age': [23, 29, 21, 18]
})
df.columns = ['First Name', 'Age']
```

#### Renaming Individual Columns

You also can rename individual columns by using the `.rename` method. Pass a dictionary like the one below to the `columns` keyword argument:

```python
{'old_column_name1': 'new_column_name1', 
'old_column_name2': 'new_column_name2'}
```

Here’s an example:

```python
df = pd.DataFrame({
    'name': ['John', 'Jane', 'Sue', 'Fred'],
    'age': [23, 29, 21, 18]
})
df.rename(columns={
    'name': 'First Name',
    'age': 'Age'},
    inplace=True)
```

The code above will rename `name` to `First Name` and `age` to `Age`.

Using `rename` with only the `columns` keyword will create a **new** DataFrame, leaving your original DataFrame unchanged. That’s why we also passed in the keyword argument **`inplace=True`**. Using `inplace=True` lets us edit the **original** DataFrame.

There are several reasons why `.rename` is preferable to `.columns`:

* You can rename just one column
* You can be specific about which column names are getting changed (with `.column` you can accidentally switch column names if you’re not careful)

*Note:* If you misspell one of the original column names, this command won’t fail. It just won’t change anything.


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