How to Convert JSON to Excel in Python with Pandas
Here you will learn how to covnert JSON to Excel with Python and Pandas.
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Programming
Programming related posts
In this category you will find posts that are related to programming and should be interesting for psychologists, cognitive scientists, and neuroscientists. Well, almost every researcher would probably find some of the information useful at some time!
Every research psychologist, cognitive scientist, and neuroscientist, should know how to program.. Knowing how to program and write scripts will make many of a researchers everyday tasks much easier. For instance, instead of going through line after line of raw data you can write a Python script that runs through each cell in each column. Furthermore, you get the possibility to use more advanced, and cutting edge, statistical techniques by using R statistical programming environment.
Another example might be to create experiments using PsychoPy (either by coding using Python or using the drag-and-drop interface) and the cheap and open-source Arduino microcontroller. Also, coding is fun and relaxing!
How to Create Dummy Variables in R (with Examples)
In this tutorial, we will learn how to create dummy variables in R. Now, creating dummy/indicator variables can be carried out in many ways. For example, we can write code using the ifelse() function, we can install the R-package fastDummies, and we can work with other packages and functions (e.g. model.matrix). In this post, however,
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How to Import Historical Stock Prices Into A Python Script Using the IEX Cloud API
Python is one of the world’s most popular programming languages. Specifically, Python for finance is arguably the world’s most popular language-application pair. This is because of the robust ecosystem of packages and libraries that makes it easy for developers to build robust financial applications. In this tutorial, you will learn how to import historical stock
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Seaborn Line Plots: A Detailed Guide with Examples (Multiple Lines)
In this Python data visualization tutorial, we will learn how to create line plots with Seaborn. First, we’ll start with the simplest example (with one line), and then we’ll look at how to change the look of the graphs and how to plot multiple lines, among other things.
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Create a Correlation Matrix in Python with NumPy and Pandas
In this post, we will calculate a correlation matrix in Python with NumPy and Pandas. Now, there will be several Python correlation matrix examples in this tutorial. First, we will read data from a CSV file so we can simply have a look at the numpy.corrcoef and Pandas DataFrame.corr methods. Now, building a correlation table
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Pandas Tutorial: Renaming Columns in Pandas Dataframe
Renaming columns in Pandas can be a lifesaver when working with data. Sometimes, we might receive data from a colleague or download it from the internet, and the column names might not be informative or consistent with our analysis. This could lead to confusion and errors in data interpretation, so it’s essential to know how
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How to use Pandas read_html to Scrape Data from HTML Tables
Here you will learn how to use Pandas read_html to scrape tables from web pages such as Wikipedia.
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How to use Pandas Scatter Matrix (Pair Plot) to Visualize Trends in Data
In this Python data visualization tutorial, we will work with Pandas scatter_matrix method to explore trends in data. Previously, we have learned how to create scatter plots with Seaborn and histograms with Pandas. This post will focus on scatter matrices (pair plots) using Pandas.
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How to get Absolute Value in Python with abs() and Pandas
In this Python tutorial, we will learn how to get the absolute value in Python. First, we will use one of Pythons built-in functions abs() to do this. In this section, we will go through a couple of examples of how to get the absolute value. Second, we will import data with Pandas and use
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How to Convert a Pandas DataFrame to a NumPy Array
In this short Python Pandas tutorial, we will learn how to convert a Pandas dataframe to a NumPy array. Specifically, we will learn how easy it is to transform a dataframe into an array using the two methods values and to_numpy, respectively. Furthermore, we will also learn how to import data from an Excel file
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