initialize pandas DataFrame with defined dtypes
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initialize pandas DataFrame with defined dtypes
提问by Dima Lituiev
The pd.DataFramedocstring specifies a scalar argument for the whole dataframe:
The pd.DataFramedocstring specifies a scalar argument for the whole dataframe:
dtype : dtype, default None
Data type to force, otherwise infer
dtype : dtype, default None
Data type to force, otherwise infer
Seemingly it is indeed intended to be a scalar, as following leads to an error:
Seemingly it is indeed intended to be a scalar, as following leads to an error:
dfbinseq = pd.DataFrame([],
columns = ["chr", "centre", "seq_binary"],
dtype = ["O", pd.np.int64, "O"])
dfbinseq = pd.DataFrame([],
columns = ["chr", "centre", "seq_binary"],
dtype = [pd.np.object, pd.np.int64, pd.np.object])
The only workaround for creating an empty data frame (which I need to put in a HDF5 store for further appends) for me was
The only workaround for creating an empty data frame (which I need to put in a HDF5 store for further appends) for me was
dfbinseq.centre.dtype = np.int64
Is there a way to set dtypesarguments at once?
Is there a way to set dtypesarguments at once?
回答by jezrael
You can set dtypeto Series:
You can set dtypeto Series:
import pandas as pd
df = pd.DataFrame({'A':pd.Series([], dtype='str'),
'B':pd.Series([], dtype='int'),
'C':pd.Series([], dtype='float')})
print (df)
Empty DataFrame
Columns: [A, B, C]
Index: []
print (df.dtypes)
A object
B int32
C float64
dtype: object
With data:
With data:
df = pd.DataFrame({'A':pd.Series([1,2,3], dtype='str'),
'B':pd.Series([4,5,6], dtype='int'),
'C':pd.Series([7,8,9], dtype='float')})
print (df)
A B C
0 1 4 7.0
1 2 5 8.0
2 3 6 9.0
print (df.dtypes)
A object
B int32
C float64
dtype: object
