BA 7060 M4: Working with Data in Python

Introduction

MBA students who have a good understanding of Python and its data manipulation capabilities are better positioned to succeed in their business pursuits. Python provides powerful tools for managing, manipulating, and analyzing data which can help businesses make informed decisions that lead to success. It also offers the ability to quickly build prototypes of new products and services, allowing businesses to quickly test and execute changes. With Python, MBA students can quickly read, save, load, and analyze data to gain insight into the performance of their business. Furthermore, understanding how to use Python for data manipulation provides MBA students with a valuable skill set that will give them an edge in their future careers.

Reading Files with Open

Reading files with "open" in the Python programming language is a straightforward process. The open() method takes two parameters: the file name and mode. The mode can be set to "r" for reading, "w" for writing or "a" for appending. Once the open() method has been called, it will return a file object which contains functions and attributes that can be used to perform various operations on the file. After calling open(), the read() function can be used to read content from the opened file. The read() function uses different parameters depending on how much of the file should be read and what type of data should be returned.

Writing Files with Open

Writing files using open and pandas is a relatively simple process. To write a file using Python, the first step is to open a file object by calling the built-in open() function. The two main arguments to consider when opening a file are the filename and access mode. Filenames should be string objects, and access modes determine the type of operation that can be performed on the file. Once a file has been opened, data can then be written to it using the appropriate operations for writing such as write(), writelines(), and print().

For more complex operations, pandas is an incredibly powerful library that can read and write files in many different formats. Pandas provides a convenient read_csv() and to_csv() function that can be used to quickly read and write data from common file formats such as comma-separated values (CSV) files. To use these functions, simply pass the desired filename and access mode into the open() function. Pandas also provides several other functions for writing data to other file formats such as Excel, JSON, and HTML. With the combination of open() and pandas functions, writing files in Python can be made easy, efficient, and comprehensive.

Loading Data with Pandas

Pandas is a powerful Python library that provides easy-to-use data structures and data analysis tools for handling large datasets. It makes it simple to load data from various sources, such as text files, CSV files, Excel files, databases and more. To load data with pandas, it is necessary to import the pandas library as well as the data source. The read_csv() method is used to read in CSV files, and the DataFrame object is then used to manipulate the data. Additionally, if the dataset has different types of values such as strings and numbers, they can be converted into numerical values using the astype() function. Finally, it is possible to save the DataFrame object to a file by using the to_csv() method. This saves time when dealing with large datasets, as it eliminates the need for manual data entry and data manipulation. With pandas, loading data is quick and easy!

Programming Languages

Learning Objectives:

Table of Contents

Introduction

Discussion of Python

Reading Files with Open

Writing Files with Open

Loading Data with Pandas

Working with and Saving Data with Pandas

Conclusion

Discussion of Python