As part of your data preparation process you might need to drop one ore multiple redundant columns, calculate new column values or simply manipulate column names or values. In this tutorial we’ll learn how to convert columns names and values to lowercase. Creating example data Let’s assume that we have this very simple example DataFrame. … Read more
In this short Data Analysis tutorial we’ll learn how to use Python in order to access the first column of a Pandas DataFrame object so we can further process it as needed. Creating an example DataFrame Here’s our Data: month salary days_to_hire 0 Jan 140 45 1 Feb 145 34 2 March 145 23 3 … Read more
In today’s quick tutorial we’ll learn how to sum columns in Pandas DataFrames which contains missing or non available data, which might be marked as NAN by Pandas. Create a DataFrame We’ll start by creating a simple DataFrame for you to follow along with this example. year h1 h2 0 2012 NaN 235.0 1 2014 … Read more
There might be cases in which you’ll need to perform calculations across several columns in your Pandas DataFrame. In this post we’ll learn how to easily use the max() DataFrame to retrieve the maximum value across two or more Pandas column. Example DataFrame We’ll get started with a very simple DataFrame that you can use … Read more
Today we’ll learn how to replace empty values in Pandas DataFrame columns with a string object. We’ll show how to use the Pandas DataFrame replace() method to easily convert We’ll start by defining a sample DataFrame that you can use in order to follow along this example: Let’s look into our DataFrame: month office salary … Read more
Today we’ll learn how to convert timestamps to Python datetime objects. In Pandas we use those terms almost interchangeably though, which renders this a bit confusing. Creating Pandas timestamps We can easily create timestamps using the Pandas pd.TimeStamp function. Here is a very simple example to get us started with the tutorial: This will return … Read more
In today’s tutorial we’ll learn how to select DataFrame rows by specific or multiple conditions. For people new to Pandas but experienced in SQL, we’ll learn how to write where statements aimed at selecting data from our DataFrames. We’ll look into several cases: Filtering rows by column value Selecting by multiple boolean conditions Selecting only … Read more
In this Data Analysis tutorial we’ll learn how to use Python to search for a specific or multiple strings in a Pandas DataFrame column. In a nutshell we can easily check whether a string is contained in a column using the .str.contains() Series function: df_name[‘col_name’].str.contains(‘string’).sum() Read on for several examples of using this capability. Creating … Read more
In this tutorial we’ll learn lots of very useful information about using the Pandas data range objects. We’ll learn about two very versatile Pandas functions: data_range and bdate_range that allow to create data ranges with ease. This will come very handy when pre-process / wrangling your data before analysis and visualization. Preparation Before starting to … Read more
In today’s tutorial we will learn to troubleshoot one common error we encounter when working with external files in Python3. Error message In order to make your Python code more robust and platform agnostic you might have decided to open any file that you interact with in binary mode. Then, when trying to interact with … Read more