CODEWITHSUNDEEP
pythonpandasdataframe
toto_tico
toto_tico

Reputation: 19027

How to create a new dataframe based on dtypes from an existing dataframe?

Supposed I have this DataFrame:

import numpy as np
df = pd.DataFrame({'cat':['foo', 'bar','foo'], 
                  'val': [1,2,3]})
df['cat'] = df['cat'].astype('category')

So, its dtypes (df.dtypes) correspond to:

cat    category
val       int64
dtype: object

One option would be to simply use the columns:

new_df = pd.DataFrame(columns = df.columns)

However, this would not preserve the dtypes (new_df.dtypes):

cat    object
val    object
dtype: object

Upvotes: 1

Views: 714

Answers (2)

Ch3steR
Ch3steR

Reputation: 20669

You can do this way. You can use pandas.DataFrame.astype

new_df = pd.dataFrame(columns = df.columns).astype(dtype = df.dtypes)

new_df.dtypes
cat    category
val       int64
dtype: object

Upvotes: 2

toto_tico
toto_tico

Reputation: 19027

You can just copy an empty version of the initial DataFrame. In the example you provided, just do this:

new_df = df[df.index != df.index].copy()

Running new_df.dtypes should give you the expected categories:

cat    category
val       int64
dtype: object

Upvotes: 1

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