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I need to obtain the type for each column to properly preprocess it.

Currently I do this via the following method:

import pandas as pd

# input is of type List[List[any]]

# but has one type (int, float, str, bool) per column

df = pd.DataFrame(input, columns=key_labels)

column_types = dict(df.dtypes)

matrix = df.values

Since I only use pandas for obtaining the dtypes (per column) and use numpy for everything else I want to cut pandas from my project.

In summary: Is there a way to obtain (specific) dtypes per column from numpy

!Or: Is there a fast way to recompute the dtype of ndarray (after splicing the matrix)

1 Answer

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by (20.4k points)

If you want to change the data type of a particular column you can use pandas:

In this below example I am using the customer-churn dataset.

import pandas as pd

df_data = pd.read_csv("Customer-Churn.csv")

I am checking the data type of a particular column:

In [18]:df_data['PaymentMethod'].dtype

Out[18]:

dtype('O')

Changing the column dtype to bool

In[19]:df_data['PaymentMethod']=df_data['PaymentMethod'].astype(bool)

df_data['PaymentMethod'].dtype

Out[19]:

dtype('bool')

I hope this will help you.

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