Gili In today’s recipe we’ll learn about Seaborn title objects for charts and axes. We’ll see how we can quickly set and change them and apply some customization to improve your Python charts look and feel. Using set_title to add a title to your chart Seaborn leverages the Matplotlib plt.set_title method to define your chart title … Read more
In this short recipe we’ll learn how to correctly set the size of a Seaborn chart in Jupyter notebooks/Lab. Well first go a head and load a csv file into a Pandas DataFrame and then explain how to resize it so it fits your screen for clarity and readability. Use plt figsize to resize your … Read more
In this recipe we’ll learn how to add numeric lists into NumPy ndarrays. We’ll look into two cases: appending a Python list to the end an existing array (which oculd be either 1d / 2d or more). Insert a list into a specific position in the array Use np.append() to concatenate a list and an … Read more
In this recipe we’ll look into the topic of loading text files in Pandas dataframes. for further data wrangling for visualization purposes or as a preparatory step for Machine Learning. Use pd.read_csv() to load text file with tab delimiters Let’s outline this using a simple example. Suppose we have a text file that has several … Read more
Our quick data wrangling recipe today covers the topic of adding Python lists to Pandas DataFrames as columns and rows. Creating the data We’ll start with a simple dataset that we’ll use throughout this tutorial examples. Go ahead and copy this code into your data analysis Python environment. Let’s also define a simple list for … Read more
In this data visualization recipe we’ll learn how to visualize grouped data using the Pandas library as part of your Data wrangling workflow. Data acquisition We’ll start by creating representative data. Copy the code below and paste it into your notebook: # Python3 # Import Pandas import pandas as pd # Create Dataframe budget = … Read more
In today’s recipe we’ll learn about how to quickly draw barplots to analyze data which we already acquired into a Pandas DataFrame. We’ll look into several scenarios: Simple bar chart Using groupby to summarize values in our chart. Horizontal and Stacked Charts Formatting options: color maps, titles, font sizes, labels, etc’ Data Preparation Let’s start … Read more
In today’s recipe we’ll discuss how to fix the pretty ubiquitous modulenotfounderror: no module named ‘numpy’ error on MAC , Linux Ubuntu and Windows 10 operating systems. You typically get the error when working on Jupyter Notebook / Lab or when using your favorite Python IDE such as PyCharm, Spyder or others. The error can … Read more
Today’s Python data wrangling recipe is about counting number of non unique entries in a Pandas DataFrame groupby object. We’ll start by importing the Pandas library and then acquiring our dataset from a comma separated value file into a DataFrame. We’ll use the Pandas read_csv function: Let’s take a quick look at the DataFrame Header: … Read more
In today’s recipe we’ll touch on the basics of adding numeric values in a pandas DataFrame. We’ll cover the following cases: Sum all rows of one or multiple columns Sum by column name/label into a new column Adding values by index Dealing with nan values Sum values that meet a certain condition Creating the dataset … Read more