If you are leaning towards a career as a Data Scientist or just a coder looking to expand your skillset, the art of pickling is a must-have. This article focuses on creating, saving, and reading various object types to/from a pickle file. Syntax pandas.read_pickle(filepath_or_buffer, compression=’infer’, storage_options=None) The return value is an unpickled object of the … Read more
Background Part 2 of this series centers around running more complex MySQL queries. This skill is a must-have if you are interested in pursuing a career as a Data Scientist. After completing Part 1 of this series, you should be comfortable: Installing Libraries Downloading and saving a CSV file from the web. Creating a Pandas … Read more
Background To provide a solid understanding of data manipulation using the Pandas DataFrame and MySQL, the Finxter Academy has created a 3-part series to take the user from beginner to advanced. This article assumes you have access to a localhost/server with MySQL. Part 2 and Part 3 also assumes you possess the login credentials with … Read more
In this video and blog tutorial, we will learn how to apply a function to a Pandas data frame or series using the apply() function. Using this tool, we can apply any kind of function to segregate our data and change it with a very limited amount of code. Here’s the syntax from the official … Read more
Syntax pandas.pivot(data, index=None, columns=None, values=None) Return Value: The return value for the pivot() function is a reshaped DataFrame organized by index/column values. Background Direct quote from the Pandas Documentation website: This function does not support data aggregation. If there are multiple values, they will result in a Multi-Index in the columns. This article delves into … Read more
Syntax pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name=’value’, col_level=None, ignore_index=True) Return Value The return value for the melt() function is an unpivoted DataFrame. Background Direct quote from the Pandas Documentation website: “This function massages a DataFrame into a format where one or more columns are identifier variables (id_vars). While all other columns are considered measured variables (value_vars), … Read more
In this tutorial, we will learn how to transform a JSON object into a Pandas data frame and the other way around. For these approaches, we will have a look at the Pandas functions read_json() and to_json(). Working with JSON objects in Pandas is a very essential skill because we often find data stored in … Read more
In this tutorial, we will learn how to read in HTML tables using the read_html() function and how to turn these tables into Pandas data frames to analyze them. Furthermore, we will see how to render Pandas data frames as HTML tables applying the to_html() function. As you go through the article, you can also … 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) The j-greats.xlsx file should exist on your system. If you do not have this particular … Read more
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 … Read more
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, … Read more
The data we use in Pandas for real-world projects often comes from outside sources. One popular and repeatedly used source is Excel. In this tutorial, we will learn how to read files from Excel, how to modify these files, and how to write them to Excel. We will use several Pandas functions for these approaches. … Read more