At the heart of Pandas lies the DataFrame, a two-dimensional table resembling a spreadsheet or SQL table. With labeled rows and columns, the DataFrame enables easy manipulation and analysis of data. Importing data from various sources, such as CSV files or SQL databases, is a breeze with Pandas, making it an invaluable tool for data preprocessing and cleaning.
#Pandas Dataframe
dict = {"country": ["Brazil", "Russia", "India", "China", "South Africa"],
"capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"],
"area": [8.516, 17.10, 3.286, 9.597, 1.221],
"population": [200.4, 143.5, 1252, 1357, 52.98] }
import pandas as pd
brics = pd.DataFrame(dict)
print(brics)
# Set the index for brics
brics.index = ["BR", "RU", "IN", "CH", "SA"]
# Print out brics with new index values
print(brics)
Pandas provides an extensive array of functions and methods for efficient data manipulation. Whether you need to extract specific rows and columns, perform mathematical operations, or aggregate data based on specific criteria, Pandas has you covered. It seamlessly integrates with NumPy, ensuring fast and efficient calculations even on large datasets.
# Import pandas and cars.csv
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
# Print out country column as Pandas Series
print(cars['cars_per_cap'])
# Print out country column as Pandas DataFrame
print(cars[['cars_per_cap']])
# Print out DataFrame with country and drives_right columns
print(cars[['cars_per_cap', 'country']])
Pandas goes beyond data manipulation and offers powerful analytical capabilities. You can perform statistical computations, generate descriptive statistics, and handle time series data with ease. With its intuitive syntax and rich library of functions, Pandas simplifies complex tasks like grouping data and creating pivot tables, empowering data analysts and scientists to extract valuable insights.
# Import pandas as pd
import pandas as pd
# Import the cars.csv data: cars
cars = pd.read_csv('cars.csv')
# Print out cars
print(cars)
Pandas provides convenient methods to handle missing data and duplicates in datasets. It allows you to detect missing values, either dropping them or filling them with appropriate values. Pandas also offers functions to identify and remove duplicate rows, ensuring data integrity and accuracy in your analyses.
Pandas Basic Exercise Solution
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
# Print out first 4 observations
print(cars[0:4])
# Print out fifth and sixth observation
print(cars[4:6])
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)
# Print out observation for Japan
print(cars.iloc[1])
# Print out observations for Australia and Egypt
print(cars.loc[['AUS', 'EG']])
Pandas is a powerful data manipulation tool in Python, offering a range of functionalities for efficient data handling and analysis. Its intuitive data structures, extensive set of functions, and seamless integration with other libraries make it a go-to choice for data professionals. By mastering Pandas, you unlock the ability to clean, transform, and analyze data effectively, facilitating data-driven decision-making in various domains.
