How to transform a dataset type to int in Pandas

In Pandas, you can transform a dataset to int by using the astype() method. Here is an example of how to convert a Pandas DataFrame column to an integer data type:

import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({'A': ['1', '2', '3', '4'], 'B': [10.5, 20.3, 30.1, 40.0]})

# print the data types of the columns
print(df.dtypes)

# output:
# A object
# B float64
# dtype: object

# convert column 'A' to integer data type
df['A'] = df['A'].astype(int)

# print the data types of the columns after conversion
print(df.dtypes)

# output:
# A int64
# B float64
# dtype: object

In the example above, the astype() method is used to convert the values in the ‘A’ column from object data type (which in this case is a string) to an integer data type. The new integer column replaces the original string column in the DataFrame.

Note that if the ‘A’ column had missing or non-numeric values, an error would be raised during the conversion process. It is therefore important to handle such cases beforehand using appropriate data cleaning techniques.