Loading Data with Pandas

 Dependencies or libraries are pre-written code that helps solve various problems. In this context, we are introducing Pandas, a popular library for data analysis. To use Pandas, we import the library using the import command followed by the name of the library. Once imported, we have access to a wide range of pre-built classes and functions provided by Pandas.

For example, if we want to load a CSV file using Pandas, we can use the built-in function read_csv(). We can do this by typing Pandas, then a dot, followed by the name of the function with its inputs. However, typing Pandas repeatedly can become cumbersome. To avoid this, we can use the as statement to assign a shorter name to the library, known as an alias. Common aliases for Pandas include pd.

Once we have imported Pandas with an alias (import pandas as pd), we can then use pd followed by a dot and the name of the function we want to use, such as read_csv().

import pandas as pd # Load a CSV file df = pd.read_csv('file.csv') # Display the first five rows of the data frame print(df.head())

This code snippet imports Pandas as pd, loads a CSV file named file.csv into a Pandas DataFrame called df, and then displays the first five rows of the DataFrame using the head() method.

Pandas allows us to work with data using data frames, which are tabular data structures consisting of rows and columns. We can create data frames from various sources such as CSV files, Excel files, dictionaries, or even individual columns of existing data frames. This makes Pandas a powerful tool for data manipulation and analysis.