Assuming the input dataframe has a single column with comma-separated values, we can use the `split()` function to split each row into 3 columns and then use the `withColumn()` function to create a new dataframe with the desired column names.

Here's the pyspark code to achieve this:

```python
from pyspark.sql.functions import split
# Create the input dataframe
input_df = spark.createDataFrame([(1,'Australia,5'),(2,'United States,7'),(3,'France,1'),(5,'United Kingdom,10')], ['id', 'data'])

# Split the data column into 3 columns and create a new dataframe with the desired column names
output_df = input_df.withColumn('data', split('data', ',')).selectExpr('id', 'data[0] as country', 'data[1] as total')

# Show the output dataframe
output_df.show()
```

Output:

```
+---+--------------+-----+
| id|       country|total|
+---+--------------+-----+
|  1|     Australia|    5|
|  2| United States|    7|
|  3|        France|    1|
|  5|United Kingdom|   10|
+---+--------------+-----+
```

The split() function splits the data column into an array of strings, using the comma as the delimiter. The selectExpr() function is used to select the 3 columns and rename them appropriately.
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