Loading different types of data from CSV files with proper encoding in Python 3

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I have a CSV file with different types of data. For example: Some columns are categorical (e.g. name of city) Some are numerical (e.g. price of a product)

I would like to read the data file using Python 3 in such a way that all the categorical data are 1-hot encoded and the numerical data are simply encoded as a scalar value.

Something like this: 

import numpy as np

x = np.loadtxt(d, dtype={'names': ('city', 'price')
       'formats': (string, int)})

But here I want to one-hot encode the 'city' column as well.

Is there any dataloader/preprocessor in numpy/pandas/scikit that will help read the csv as well as 1-hot encode some of the columns as well?

dataloader scikit-learn pandas numpy python
1 Answers

i think you should use pandas package to do this

import pandas as pd
df = pd.read_csv('file_name.csv')
df['city'] = df['city'].astype('str')
df['price'] = df['price'].astype('int')
print(df)
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