In this short post we are going to revisit the topic on how to carry out summary/descriptive statistics in Python. In the previous post, I used Pandas (but also SciPy and Numpy, see Descriptive Statistics Using Python) but now we are only going to use Numpy. The descriptive statistics we are going to calculate are the central tendency (in this case only the mean), standard deviation, percentiles (25 and 75), min, and max.
Loading the data
In this example I am going to use the Toothgrowth dataset (download here). It is pretty easy to load a CSV file using the genfromtxt method:
Notice the arguments we pass. The first row has the names and that is why we set the argument ‘names’ to True. One of the columns, further, has strings. Setting ‘dtype‘ to None enables us to load both floats and integers into our data.
One of the great things with programming is that you can automate things that is boring. As a student I often get schedules in the form of Word documents. I prefer to have all my activities in a calendar and used to manually put every time in a course into my calendar. Today I got a new schedule and thought: I could probably do this using Python.
After some searching around on the Internet I found the Python packages python-docx and iCalendar. In this post I will show you how to use these to packages to create an iCalender file that can be loaded in to a lot of available calendars.
Installing python-docx & iCalendar
Both Python packages can be installed using pip:
Install python-docx and icalendar
pip install python-docx icalendar
In the example code I used a table from a Word document containing 4 columns. It is a pretty simple example but in the first column store the date, the second the time, third the room (location), and the last the activity of the event (e.g., lecture).
Extracting a table from a Word Document
In the first code chunk, below, we start by importing the needed modules. Apart from using Document from python-docx, Calendar and Event from iCalendar, we are going to use datetime from datetime. Datetime is used to store the date in a format that icalendar “likes”.
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In the next chunk of code (in the same loop as above) we split the date and time. We do this since due to the formatting of the date and time in the example (“5/4” and “9-12). As previously mentioned the date need to be formatted in a certain way (e.g., using Pythons datetime). In the table from the Word document, some of the events are deadlines and, thus, have no set time. Therefore, we need to see if we have a time to use. If not, we set the time to 17:00-17:01. There is probably a better way to do this but this will do for now. The last line adds each event (as a Python dictionary) to our list containing all data.
Now that we have a list of dictionaries containing our lectures/seminars (one for each dictionary) we can use iCalendar to create the calendar. First we create the calendar object and the continue with looping through our list of dictionaries. In the loop we create an event and add the information. In the example here we use the activity as both summary and description but we could have had a summary of the activity and a more detailed description if we’d liked.
The crucial parts, may be, are the ‘dtstart’ and ‘dtend’. This is the starting time and ending time of the event (e.g., a lecture). We continue to add the location (e.g., the room of the event) and add the event to our calender. Finally, we create a file (‘schedule.ics’), write the calender to the file, and close the file.
Now we have our iCalendar file (course_schedule.ics) and can load it into our calender software. I typically use Lightning (a calendar addon for Thunderbird). To open the iCalendar file we created using Python go to File, Open, and Calendar File. Finally select the your iCalendar file:
After you done that your new schedule should be loaded into Lightning. Your schedule will be loaded as a separate calendar. As you can see in the image below your lecture and computer labs will show up.
In this post, we learned how to use Python (python-docx) to extract a schedule from a table in a Word Document (.docx). We used the data to create an iCalendar file that we can load into many Calendar applications (e.g., Google, Lightning).
In this video you learn how to create a Flanker task using the Python package Expyriment. If you don’t know Expyriment it is an open-source library. You can program your experiments and run on Linux, Windows, and OS-x computers as well as on Android devices.
In the tutorial you will get familiar with Expyriment and get to create a commonly used task in Psychology – the Flanker task. In this task, you are to respond on the direction of an arrow surrounded by distractors (arrows pointing in either the same or the other direction). It shows how hard it can be to ignore irrelevant information (arrows pointing in the wrong direction).
Inspired by my post for the JEPS Bulletin (Python programming in Psychology), where I try to show how Python can be used from collecting to analyzing and visualizing data, I have started to learn more data exploring techniques for Psychology experiments (e.g., response time and accuracy). Here are some methods, using Python, for visualization of distributed data that I have learned; kernel density estimation, cumulative distribution functions, delta plots, and conditional accuracy functions. These graphing methods let you explore your data in a way just looking at averages will not (e.g., Balota & Yap, 2011).
Required Python packages
I used the following Python packages; Pandas for data storing/manipulation, NumPy for some calculations, Seaborn for most of the plotting, and Matplotlib for some tweaking of the plots. Any script using these functions should import them:
Python is gaining popularity in many fields of science. This means that there also are many applications and libraries specifically for use in Psychological research. For instance, there are packages for collecting data & analysing brain imaging data. In this post, I have collected some useful Python packages for researchers within the field of Psychology and Neuroscience. I have used and tested some of them but others I have yet to try.
In this post I will describe, shortly, how to use InLine scripts in E-prime to save your data in comma-separated values (CSV) files. For those who are not familiar with E-prime it is an experiment generating software based on visual basic (i.e., it has its own scripting language called e-basic). Its main purpose is to make building experiment easy/easier. It offers a drag-and-drop graphical user interface (GUI) which is fairly easy to use (although I prefer OpenSesame and PsychoPy – which both offers drag-and-drop GUIs). See the Wikipedia article if you want to know more about e-prime.
This guide will assume that you have worked with e-prime before. That is, you should already have a, more or less, ready experiment that you can add the scripts to. In the guide I use a Simon task created in e-prime as an example.
PsychoPy, as I have previously written about (e.g., Free and Useful Software and PsychoPy tutorial) is really a great Python tool for creating Psychology experiments. You can write Python code by either using “code view” or import the package in your favourite IDE. Furthermore, you can use the builder mode and just drag and drop different items and PsychoPy will create a Python Script for you. If needed inline scripts (in Python, of course) can be inserted. That is, you can combine drag-and-drop building with some coding. In this post I have collected some tutorial videos that can be useful for someone unfamiliar with PsychoPy.
I created a playlist with 4 youtube tutorials. In the first video you will learn how to create a classical psychology experiment; the stroop task. The second is more into psycholinguistics and you will learn how to create a language experiment using PsychoPy. In the third you get to know how to create text input fields in PsychoPy. In this tutorial inline Python coding is used so you will also get to know how you may use programming. In the forth video you will get acquainted with using video stimuli in PsychoPy.
Programming for Psychology & Vision Science Tutorial series
Recently I found the following playlist on youtube and it is amazing. In this series of tutorial videos you will learn how to use PsychoPy as a Python package. For instance, it is starting at a very basic level; importing the visual module to create windows. In this video he uses my favourite Python IDE Spyder. The videos are actually screencasts from the course Programming for Psychology & Vision Science and contains 10 videos. The first 5 videos covers drawing stimuli on the screen (i.e., drawing to a video, gratings, shapes, images, dots). Watching these videos you will also learn how to collect responses, providing input, and saving your data.
That was all for now. If you know more good video tutorial for PsychoPy please leave a comment. Preferably the tutorials should cover coding but just building experiments with the builder mode is also fine. I may update my playlist with more PsychoPy tutorials (the first playlist).
Previously I have shown how to analyze data collected using within-subjects designs using rpy2 (i.e., R from within Python) and Pyvttbl. In this post I will extend it into a factorial ANOVA using Python (i.e., Pyvttbl). In fact, we are going to carry out a Two-way ANOVA but the same method will enable you to analyze any factorial design. I start with importing the Python libraries that are going to be use. Continue reading →
An important advantage of the two-way ANOVA is that it is more efficient compared to the one-way. There are two assignable sources of variation – supp and dose in our example – and this helps to reduce error variation thereby making this design more efficient. Two-way ANOVA (factorial) can be used to, for instance, compare the means of populations that are different in two ways. It can also be used to analyse the mean responses in an experiment with two factors. Unlike One-Way ANOVA, it enables us to test the effect of two factors at the same time. One can also test for independence of the factors provided there are more than one observation in each cell. The only restriction is that the number of observations in each cell has to be equal (there is no such restriction in case of one-way ANOVA).
The current post will focus on how to carry out between-subjects ANOVA using Python. As mentioned in an earlier post (Repeated measures ANOVA with Python) ANOVAs are commonly used in Psychology.
We start with some brief introduction on theory of ANOVA. If you are more interested in the four methods to carry out one-way ANOVA with Python click here. ANOVA is a means of comparing the ratio of systematic variance to unsystematic variance in an experimental study. Variance in the ANOVA is partitioned in to total variance, variance due to groups, and variance due to individual differences.