Part 6 in Working with Excel focuses on styling. Impress your customers by styling the Worksheet to match their brand by: adding in their unique logo, using their color scheme, using their preferred font style, formatting the Worksheet using their preferred report style. Taking the time to do this makes you stand above the crowd. …
Part 5 in the Working with Excel series focuses on formulas. Background After completing Part 4 of this series, you should be comfortable using Python and openpyxl to: add worksheets rename worksheets reorder worksheets delete Worksheets Remember to add the Required Starter Code (lines [1-4]) to the top of each code snippet. This snippet …
Python Excel – Sum, Average, Max, and Date Formulas Read More »
Part 4 in the Working with Excel series focuses on Worksheet(s) manipulation. Background After completing Part 3 of this series, you should be comfortable using Python and openpyxl to: append row(s), modify data, insert column(s), delete row(s) and column(s) Remember to add the Required Starter Code (lines [1-4]) to the top of each code …
Building on the skill(s) you learned earlier, Part 3 centers around manipulating the data. This skill is another must-have if you are interested in pursuing a career as a Data Scientist. Background After completing Part 2 of this series, you should be comfortable using Python to: access a single row/column value, access a range of …
Part 2 of Working with Excel centers around accessing data from the Active Worksheet. This skill is one of the must-haves if you are interested in pursuing a career as a Data Scientist. Background After completing Part 1 of this series, you should be comfortable using Python to: create an Excel file, import a library, …
To provide a solid understanding of data manipulation with Excel, the Finxter Academy has created a 6-part series to take the user from beginner to advanced. This article assumes that Microsoft Excel is available on the computer. Quick Intro In most cases, working with large amounts of data requires some manipulation to make it more …
A Python nested dictionary is a dictionary with another dictionary or dictionaries nested within (dictionary of dictionaries or collection of collections). Nested dictionaries are one way to represent structured data (similar to a database table(s) relationship). An analogy for this concept is the Russian Nesting Dolls. Our article focuses on various ways to retrieve …
The Python dictionary is a structure used to store data. This data type is appropriate when the data needs to contain labels (unique keys) associated with a value (key:value). Python lists are not able to handle this format. The list(s) will require conversion to a dictionary format (key:value). In the code samples below, there are …
Python defines the dictionary data type as an unordered collection that contains key:value pairs. The key:value pairs (separated by commas) are wrapped inside curly brackets {}. Each key inside the dictionary must be unique and immutable. Dictionary keys can be one of the following data types: integer, string, or tuple. Dictionary keys can not …
Python Dictionary: How to Create, Add, Replace, Retrieve, Remove Read More »
There are various ways to skip the first entry in Python loops. You will need to determine which example best satisfies your coding needs. Skip First Entry using Slicing Python offers a unique feature, slicing. This feature enables the coder to quickly access various data types, such as lists, tuples, and strings. This feature is …
During your career as a Pythonista, you will most likely find yourself required to work with binary data. To efficiently read/write to a Binary file, see the examples outlined below. Read a Binary File to an Array For this example, a list of integers is saved to a new binary file. The file is then …