You can select one column by doing df[column_name], such as df['age'], or multiple columns as df[[column_name1, column_name2]]. For a single column, you can also select it using the attribute syntax, df.<column_name>, as in, df.age. Note, a single column in Pandas is called a Series and operates differently from a DataFrame. Later exercises will explore the difference.
Suppose you constructed a DataFrame by
import pandas as pd df = pd.DataFrame({'name': ['Jeff', 'Esha', 'Jia'], 'age': [30, 56, 8]})
Giving you the DataFrame
| name | age | |
|---|---|---|
| 1 | Jeff | 30 |
| 2 | Esha | 56 |
| 3 | Jia | 8 |
Now, you want to select just the name column from the DataFrame.
Complete the function, name_column(df), by having it return only the name column.
df = pd.DataFrame({'name': ['Jeff', 'Esha', 'Jia'], 'age': [30, 56, 8]})
| name | age | |
|---|---|---|
| 0 | Jeff | 30 |
| 1 | Esha | 56 |
| 2 | Jia | 8 |
| name | |
|---|---|
| 0 | Jeff |
| 1 | Esha |
| 2 | Jia |