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

I found this question after running into the same issue. I prefer the following solution (Python 3) for creating an empty DataFrame with no index.

import numpy as np
import pandas as pd

def make_empty_typed_df(dtype):
    tdict = np.typeDict
    types = tuple(tdict.get(t, t) for (_, t, *__) in dtype)
    if any(t == np.void for t in types):
        raise NotImplementedError('Not Implemented for columns of type "void"')
    return pd.DataFrame.from_records(np.array([tuple(t() for t in types)], dtype=dtype)).iloc[:0, :]

Testing this out ...

from itertools import chain

dtype = [('col%d' % i, t) for i, t in enumerate(chain(np.typeDict, set(np.typeDict.values())))]
dtype = [(c, t) for (c, t) in dtype if (np.typeDict.get(t, t) != np.void) and not isinstance(t, int)]

print(make_empty_typed_df(dtype))

Out:

Empty DataFrame

Columns: [col0, col6, col16, col23, col24, col25, col26, col27, col29, col30, col31, col32, col33, col34, col35, col36, col37, col38, col39, col40, col41, col42, col43, col44, col45, col46, col47, col48, col49, col50, col51, col52, col53, col54, col55, col56, col57, col58, col60, col61, col62, col63, col64, col65, col66, col67, col68, col69, col70, col71, col72, col73, col74, col75, col76, col77, col78, col79, col80, col81, col82, col83, col84, col85, col86, col87, col88, col89, col90, col91, col92, col93, col95, col96, col97, col98, col99, col100, col101, col102, col103, col104, col105, col106, col107, col108, col109, col110, col111, col112, col113, col114, col115, col117, col119, col120, col121, col122, col123, col124, ...]
Index: []

[0 rows x 146 columns]

And the datatypes ...

print(make_empty_typed_df(dtype).dtypes)

Out:

col0      timedelta64[ns]
col6               uint16
col16              uint64
col23                int8
col24     timedelta64[ns]
col25                bool
col26           complex64
col27               int64
col29             float64
col30                int8
col31             float16
col32              uint64
col33               uint8
col34              object
col35          complex128
col36               int64
col37               int16
col38               int32
col39               int32
col40             float16
col41              object
col42              uint64
col43              object
col44               int16
col45              object
col46               int64
col47               int16
col48              uint32
col49              object
col50              uint64
               ...       
col144              int32
col145               bool
col146            float64
col147     datetime64[ns]
col148             object
col149             object
col150         complex128
col151    timedelta64[ns]
col152              int32
col153              uint8
col154            float64
col156              int64
col157             uint32
col158             object
col159               int8
col160              int32
col161             uint64
col162              int16
col163             uint32
col164             object
col165     datetime64[ns]
col166            float32
col167               bool
col168            float64
col169         complex128
col170            float16
col171             object
col172             uint16
col173          complex64
col174         complex128
dtype: object

Adding an index gets tricky because there isn't a true missing value for most data types so they end up getting cast to some other type with a native missing value (e.g., ints are cast to floats or objects), but if you have complete data of the types you've specified, then you can always insert rows as needed, and your types will be respected. This can be accomplished with:

df.loc[index, :] = new_row

Again, as @Hun pointed out, this NOT how Pandas is intended to be used.