Advanced Pandas Data Types Guide

Welcome to the advanced guide on Pandas data types. In this section, we will delve deeper into the various data types available in Pandas and how to effectively use them to manage and manipulate your data.

Overview of Pandas Data Types

Pandas offers a variety of data types to handle different kinds of data. These include:

Numeric Data Types: Int64, Float64, Int32, Int16, Int8, etc.
Object Data Types: Typically used for mixed data types, including strings, dates, and categorical data.
Categorical Data Types: Efficient for handling a large number of categories with a limited number of unique values.
Boolean Data Types: Ideal for binary data, such as True/False or Yes/No.

Working with Numeric Data Types

Numeric data types are used to store numerical values. Pandas provides several numeric data types, each with its own specific use case.

Int64: Used for integer values.
Float64: Used for floating-point numbers.
Int32, Int16, Int8: Used for integers with different sizes, depending on the range of values you need to store.

For example, if you are working with large datasets and need to store integer values, you can use Int64 to ensure efficient memory usage.

import pandas as pd

# Creating a DataFrame with Int64 data type
df = pd.DataFrame({'Age': [25, 30, 45, 50]})
print(df.dtypes)

Object Data Types

Object data types are used for mixed data types, including strings, dates, and categorical data. They are the default data type for columns in a Pandas DataFrame.

import pandas as pd

# Creating a DataFrame with Object data type
df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 45]})
print(df.dtypes)

Categorical Data Types

Categorical data types are efficient for handling a large number of categories with a limited number of unique values. They are especially useful when dealing with data that has repetitive text values.

import pandas as pd

# Creating a DataFrame with Categorical data type
df = pd.DataFrame({'City': ['New York', 'Los Angeles', 'Chicago', 'New York', 'Los Angeles']})
df['City'] = df['City'].astype('category')
print(df.dtypes)

Boolean Data Types

Boolean data types are ideal for binary data, such as True/False or Yes/No. They are commonly used for filtering and conditional operations.

import pandas as pd

# Creating a DataFrame with Boolean data type
df = pd.DataFrame({'Pass': [True, False, True, False, True]})
print(df.dtypes)