### Adding a new column in Pandas dataframe based on existing columns

### Adding a new column in Pandas dataframe based on existing columns

Pandas dataframe allows adding new columns to it based on the values of the existing columns.

### Example Scenario

Suppose, a dataset contains Football match results of some matches like below:

Date | Venue | Opponent | GF | GA |
---|---|---|---|---|

08-27-2017 | H | Arsenal FC | 4 | 0 |

09-16-2017 | H | Burnley FC | 1 | 1 |

Here, *GF* stands for *Goals For* and *GA* stands for *Goals Against* (i.e., number of goals conceded by a team).
Now we want to add a new column called `Result`

to this dataframe that will contain the result of the match.

The condition to generate the `Result`

column is:

- If
`GF`

>`GA`

,`Result`

should contain`W`

- If
`GF`

<`GA`

,`Result`

should contain`L`

- Otherwise
`Result`

should contain`D`

.

### Solution using Dataframe apply method

We can use `pandas.DataFrame.apply`

method to add the new `Result`

column.

```
import pandas as pd
def get_result(row):
if row['GF'] == row['GA']:
return 'D'
elif row['GF'] > row['GA']:
return 'W'
return 'L'
data = pd.read_csv("scores.csv")
data['Result'] = data.apply(lambda row: get_result(row), axis=1)
print(data)
```

**Output:**

`scores.csv`

:

```
Date,Venue,Opponent,GF,GA
08-27-2017,H,Arsenal FC,4,0
09-16-2017,H,Burnley FC,1,1
```

### Explanation

We check each row of the existing dataframe.
We assign a value to the newly created `Result`

column based on the condition given in the `get_result`

method.
`axis=1`

in the `apply`

method tells of applying the logic in all rows of the dataframe.

### Reference

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