In Python, a global DataFrame refers to a DataFrame object that is accessible and modifiable from anywhere within the code. This means that the DataFrame is not limited to a specific function or scope and can be used and manipulated throughout the program.
Here is an example of using a global DataFrame in Python:
import pandas as pd
# Create a global DataFrame
global_df = pd.DataFrame({'Name': ['John', 'Jane', 'Mike'],
'Age': [25, 30, 35],
'City': ['New York', 'London', 'Paris']})
In the above example, the global_df DataFrame is created outside of any function, making it accessible globally. It consists of columns for Name, Age, and City with corresponding data.
Since the global_df is not limited to any specific scope, it can be accessed and modified from any part of the program. For example, you can perform operations such as filtering, sorting, or appending data to the global DataFrame.
# Accessing and modifying the global DataFrame
print(global_df)
# Output:
# Name Age City
# 0 John 25 New York
# 1 Jane 30 London
# 2 Mike 35 Paris
# Adding a new row to the global DataFrame
global_df = global_df.append({'Name': 'Kate', 'Age': 28, 'City': 'Berlin'}, ignore_index=True)
# Filtering the global DataFrame
filtered_df = global_df[global_df['Age'] > 25]
# Sorting the global DataFrame by Age
sorted_df = global_df.sort_values(by='Age')
print(filtered_df)
# Output:
# Name Age City
# 1 Jane 30 London
# 2 Mike 35 Paris
# 3 Kate 28 Berlin
print(sorted_df)
# Output:
# Name Age City
# 0 John 25 New York
# 3 Kate 28 Berlin
# 1 Jane 30 London
# 2 Mike 35 Paris
It’s important to note that while using global variables can provide convenience in terms of accessibility, it also introduces potential risks related to unintentional modifications or conflicts. It’s advisable to use global DataFrames carefully and consider alternative approaches when necessary.