The key data structure in the Pandas library is called a DataFrame. A DataFrame is a two-dimensional table-like data structure that provides a way to organize and manipulate data in a tabular format. It is similar to a spreadsheet or a SQL table, where each column represents a different variable, and each row represents a different observation.
Creating a DataFrame
To create a DataFrame, you can pass in various types of data structures including lists, dictionaries, or even other DataFrames. Let’s take a look at some examples:
From Lists
You can create a DataFrame from lists using the pandas.DataFrame() function. Each list represents a column, and all lists must have the same length. Here’s an example:
import pandas as pd
# Create lists
names = ['Alice', 'Bob', 'Charlie']
ages = [25, 30, 35]
countries = ['USA', 'Canada', 'Australia']
# Create DataFrame
df = pd.DataFrame({'Name': names, 'Age': ages, 'Country': countries})
In this example, we have three columns: Name, Age, and Country. The values in each column are defined by the corresponding lists.
From Dictionaries
You can also create a DataFrame from dictionaries. Each key-value pair in the dictionary represents a column name and its associated values. Here’s an example:
# Create dictionary
data = {
‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’],
‘Age’: [25, 30, 35],
‘Country’: [‘USA’, ‘Canada’, ‘Australia’]
}
# Create DataFrame
df = pd.DataFrame(data)
This code produces the same DataFrame as the previous example but uses a dictionary instead of separate lists.
Manipulating DataFrames
DataFrames provide a wide range of functions and methods to manipulate, filter, and transform the data. Here are some common operations:
Accessing Columns
You can access columns in a DataFrame using either dot notation or bracket notation. Here’s an example:
# Access column using dot notation
names = df.Name
# Access column using bracket notation
ages = df['Age']
In both cases, you get a Series object that represents the values in the respective column.
Filtering Rows
You can filter rows based on specific conditions using boolean indexing. Here’s an example:
# Filter rows where age is greater than 30
filtered_df = df[df.Age > 30]
This code creates a new DataFrame filtered_df that contains only the rows where the age is greater than 30.
Adding and Removing Columns
You can add new columns to a DataFrame by assigning values to them. Here’s an example:
# Add a new column
df['Salary'] = [5000, 6000, 7000]
In this case, we added a new column called Salary with the specified values.
To remove columns from a DataFrame, you can use the .drop() method. Here’s an example:
# Remove the 'Country' column
df = df.drop('Country', axis=1)
In this code, we removed the Country column using the .drop() method and specifying the axis as 1 to indicate column-wise operation.
Conclusion
The DataFrame is a powerful data structure in Pandas that allows you to organize, manipulate, and analyze data efficiently. With its intuitive syntax and extensive functionality, you can perform various operations on your data with ease. Whether you’re working with small or large datasets, Pandas DataFrame provides a flexible and efficient way to handle your data analysis tasks.