Do you often find yourself wondering about the data types of columns in a DataFrame while working with Python? Don’t worry; you’re not alone!
It’s a common query that many data analysts and scientists come across. In this tutorial, we’ll explore various methods to find the DataFrame data type in Python.
Using the dtypes Attribute
The easiest way to determine the data types of columns in a DataFrame is by using the dtypes attribute. This attribute returns a Series object, where each column name is paired with its corresponding data type.
Let’s consider a simple example:
“`python
import pandas as pd
# Create a DataFrame
data = {‘Column1’: [1, 2, 3],
‘Column2’: [‘A’, ‘B’, ‘C’],
‘Column3’: [True, False, True]}
df = pd.DataFrame(data)
# Use the dtypes attribute to get column data types
column_types = df.dtypes
print(column_types)
“`
The output will be:
“`
Column1 int64
Column2 object
Column3 boolean
dtype: object
“`
In this example, we created a DataFrame with three columns: Column1, Column2, and Column3. By applying the dtypes attribute on our DataFrame, we obtained the respective data types for each column.
Using the info() Method
An alternative method to find the DataFrame data type is by utilizing the info() method. This method provides more detailed information about a DataFrame, including column names, non-null values count, and of course, their respective data types.
Let’s modify our previous example:
# Use the info() method to get detailed information
df.info()
“`
“`
RangeIndex: 3 entries, 0 to 2
Data columns (total 3 columns):
Column1 3 non-null int64
Column2 3 non-null object
Column3 3 non-null bool
dtypes: bool(1), int64(1), object(1)
memory usage: 152.0+ bytes
“`
As you can see, the info() method provides a summary of the DataFrame with additional details such as the total number of entries and memory usage. It also displays each column name along with its respective data type.
Conclusion
In this tutorial, we explored two methods to find the DataFrame data type in Python. The dtypes attribute is simple and returns a Series object containing column names and their corresponding data types. On the other hand, the info() method provides more comprehensive information about a DataFrame, including column names, non-null values count, memory usage, and data types.
Now that you know how to find DataFrame data types in Python using different approaches, you can easily analyze and manipulate your data based on their respective types.