How Do You Find the Data Type of a Column in Python?

How Do You Find the Data Type of a Column in Python?

When working with data in Python, it is essential to know the data type of each column. The data type determines how the values in a column are stored and processed. Python provides several ways to find the data type of a column, depending on the library or package you are using.

Using Pandas

If you are working with tabular data, the Pandas library is an excellent choice. Pandas provides a DataFrame object that allows you to store and manipulate tabular data efficiently. To find the data type of a column in Pandas, you can use the dtypes attribute.

• Create a DataFrame:

import pandas as pd

data = {'Name': ['John', 'Emma', 'Sam'],
        'Age': [25, 30, 35],
        'Salary': [50000.0, 60000.0, 70000.0]}

df = pd.DataFrame(data)
  

• Find the data types:

column_data_types = df.dtypes
print(column_data_types)
  

The output will display the data types of each column:

Name       object
Age         int64
Salary    float64
dtype: object

In this example, we have three columns: ‘Name’, ‘Age’, and ‘Salary’. The ‘Name’ column has an object (string) data type, while both ‘Age’ and ‘Salary’ have numeric types.

Using NumPy

If you are working with numerical data and arrays, the NumPy library is a powerful tool. NumPy provides a dtype attribute that allows you to find the data type of an array or column.

• Create an array:

import numpy as np

data = np.array([1, 2, 3, 4, 5])
  

• Find the data type:

array_data_type = data.dtype
print(array_data_type)
  

The output will display the data type of the array:

int64

In this example, our array contains integers, so the data type is ‘int64’.

Using Built-in Functions

In addition to libraries like Pandas and NumPy, Python provides built-in functions to determine the data type of an object. The most commonly used function for this purpose is type().

• Create a variable:

text = "Hello World!"
number = 42
boolean = True
list_of_numbers = [1, 2, 3]
tuple_of_strings = ('apple', 'banana', 'cherry')
dictionary = {'name': 'John', 'age': 25}
set_of_colors = {'red', 'green', 'blue'}
none_value = None
function_pointer = print
custom_class_instance = MyClass()
file_object = open("example.txt", "r")

• Find the data types:

print(type(text))
print(type(number))
print(type(boolean))
print(type(list_of_numbers))
print(type(tuple_of_strings))
print(type(dictionary))
print(type(set_of_colors))
print(type(none_value))
print(type(function_pointer))
print(type(custom_class_instance))
print(type(file_object))  
  

The output will display the data types of each variable:

<class 'str'>
<class 'int'>
<class 'bool'>
<class 'list'>
<class 'tuple'>
<class 'dict'>
<class 'set'>
<class 'NoneType'>
<class 'builtin_function_or_method'>
<class '__main__.MyClass'>
<class '_io.TextIOWrapper'>

In this example, we have different variables with various data types, such as string, integer, boolean, list, tuple, dictionary, set, NoneType, built-in function or method, custom class instance, and file object.

Knowing the data type of each column is crucial for performing appropriate operations and ensuring the accuracy of your analysis. Whether you are working with tabular data using Pandas or numerical arrays using NumPy or even general Python objects, these methods will help you find the data type effortlessly.

Now that you know how to determine the data type of a column in Python using various libraries and functions, you can confidently handle your data and make informed decisions based on its characteristics.