Create Empty Dataframe in Pandas specifying column types

Question

I\'m trying to create an empty data frame with an index and specify the column types. The way I am doing it is the following:

df = pd.DataFrame(index=[\'pbp\         


        

Answer 1

You can do this by passing a dictionary into the DataFrame constructor:

df = pd.DataFrame(index=['pbp'],
                  data={'contract' : np.full(1, "", dtype=str),
                        'starting_membership' : np.full(1, np.nan, dtype=float),
                        'projected_membership' : np.full(1, np.nan, dtype=int)
                       }
                 )

This will correctly give you a dataframe that looks like:

     contract  projected_membership   starting_membership
pbp     ""             NaN           -9223372036854775808

With dtypes:

contract                 object
projected_membership    float64
starting_membership       int64

That said, there are two things to note:

1) str isn't actually a type that a DataFrame column can handle; instead it falls back to the general case object. It'll still work properly.

2) Why don't you see NaN under starting_membership? Well, NaN is only defined for floats; there is no "None" value for integers, so it casts np.NaN to an integer. If you want a different default value, you can change that in the np.full call.