Last updated on January 9, 2020

The current post will focus on how to carry out between-subjects ANOVA in **Python**. As mentioned in an earlier post (Repeated measures ANOVA with Python) ANOVAs are commonly used in Psychology. We start with some brief introduction to the theory of ANOVA.

If you are more interested in the four methods to carry out one-way ANOVA with Python click here.

In this post we will learn how to carry out ANOVA using SciPy, calculating it “by hand” in Python, using Statsmodels, and Pyvttbl.

**Update**: the Python package Pyvttbl is not maintained for a couple of years but there’s a new package called Pingouin. As a bonus, how to use this package is added at the end of the post.

## Introduction to ANOVA

Before we learn how to do ANOVA in Python, we are briefly discussing what ANOVA is. ANOVA is a means of comparing the ratio of systematic variance to unsystematic variance in an experimental study. Variance in the ANOVA is partitioned into total variance, variance due to groups, and variance due to individual differences.

The ratio obtained when doing this comparison is known as the *F*-ratio. A one-way ANOVA can be seen as a regression model with a single categorical predictor. This predictor usually has two plus categories. A one-way ANOVA has a single factor with *J *levels. Each level corresponds to the groups in the independent measures design.

The general form of the model, which is a regression model for a categorical factor with *J *levels, is:

$latex y_i = b_0+b_1X_{1,i} +…+b_{j-1,i} + e_i&s=2$

There is a more elegant way to parametrize the model. In this way, the group means are represented as deviations from the grand mean by grouping their coefficients under a single term. I will not go into detail on this equation:

$latex y_{ij} = \mu_{grand} + \tau_j + \varepsilon_{ij}&s=2$

As for all parametric tests the data need to be normally distributed (each group’s data should be roughly normally distributed) for the *F*-statistic to be reliable. Each experimental condition should have roughly the same variance (i.e., homogeneity of variance), the observations (e.g., each group) should be independent, and the dependent variable should be measured on, at least, an interval scale.

### A Priori Tests

When conducting ANOVA in Python, it is usually best to restrict the testing to a small set of possible hypotheses. Furthermore, these tests should be motivated by theory and are known as a priori or planned comparisons. As the names imply, these tests should be planned before the data is collected.

### Post-Hoc Tests (Pairwise Comparisons) in Python

Even though studies can have a strong theoretical motivation, as well as a priori hypotheses, there will be times when the pattern occurs after the data is collected. Note, if there are many possible tests, these post-hoc tests, the error rate is determined by the number of tests that might have been carried out.

There are a number of possible post-hoc tests that can be carried out. In this ANOVA in Python tutorial, we will use the Tukey’s honestly significant difference (Tukey-HSD) test

## ANOVA using Python

In the four Python ANOVA examples in this tutorial we are going to use the dataset “PlantGrowth” that originally was available in R. However, it can be downloaded using this link: PlantGrowth. In the first three examples, we are going to use Pandas DataFrame. All three Python ANOVA examples below are using Pandas to load data from a CSV file. Note, we can also use Pandas read excel if we have our data in an Excel file (e.g., .xlsx).

```
import pandas as pd
datafile = "PlantGrowth.csv"
data = pd.read_csv(datafile)
#Create a boxplot
data.boxplot('weight', by='group', figsize=(12, 8))
ctrl = data['weight'][data.group == 'ctrl']
grps = pd.unique(data.group.values)
d_data = {grp:data['weight'][data.group == grp] for grp in grps}
k = len(pd.unique(data.group)) # number of conditions
N = len(data.values) # conditions times participants
n = data.groupby('group').size()[0] #Participants in each condition
```

Judging by the Boxplot there are differences in the dried weight for the two treatments. However, easy to visually determine whether the treatments are different from the control group.

## Python ANOVA YouTube Video:

- If you want to learn how to work with Pandas dataframe see the post A Basic Pandas Dataframe Tutorial
- Also see the Python Pandas Groupby Tutorial for more about working with the groupby method.

### ANOVA in Python using SciPy

We start this Python ANOVA tutorial using SciPy and its method f_oneway from stats.

```
from scipy import stats
F, p = stats.f_oneway(d_data['ctrl'], d_data['trt1'], d_data['trt2'])
```

One problem with using SciPy is that following APA guidelines we should also effect size (e.g., eta squared) as well as Degree of freedom (DF). DFs needed for the example data is easily obtained

```
DFbetween = k - 1
DFwithin = N - k
DFtotal = N - 1
```

However, if we want to calculate eta-squared we need to do some more computations. Thus, the next section will deal with how to calculate a one-way ANOVA using the Pandas DataFrame and Python code.

### Calculating using Python (i.e., pure Python ANOVA)

A one-way ANOVA in Python is quite easy to calculate so below I am going to show how to do it. First, we need to calculate the sum of squares between (SSbetween), sum of squares within (SSwithin), and sum of squares total (SSTotal).

#### Sum of Squares Between

We start by calculating the Sum of Squares between. Sum of Squares Between is the variability due to interaction between the groups. Sometimes known as the Sum of Squares of the Model.

$latex SSbetween = \frac{\sum(\sum k_i) ^2} {n} – \frac{T^2}{N}&s=2$

```
SSbetween = (sum(data.groupby('group').sum()['weight']**2)/n) \
- (data['weight'].sum()**2)/N
```

#### How to Calculate the Sum of Squares Within

The variability in the data due to differences within people. The calculation of Sum of Squares Within can be carried out according to this formula:

$latex SSwithin = \sum Y^2 – \frac{\sum (\sum a_i)^2}{n}&s=2$

```
sum_y_squared = sum([value**2 for value in data['weight'].values])
SSwithin = sum_y_squared - sum(data.groupby('group').sum()['weight']**2)/n
```

#### Calculation of Sum of Squares Total

Sum of Squares Total will be needed to calculate eta-squared later. This is the total variability in the data.

$latex SStotal = \sum Y^2 – \frac{T^2}{N}&s=2$

`SStotal = sum_y_squared - (data['weight'].sum()**2)/N`

#### How to Calculate Mean Square Between

Mean square between is the sum of squares within divided by degree of freedom between.

`MSbetween = SSbetween/DFbetween`

#### Calculation of Mean Square Within

Mean Square within is also an easy calculation;

`MSwithin = SSwithin/DFwithin`

#### Calculating the F-value` `

`F = MSbetween/MSwithin`

To reject the null hypothesis we check if the obtained F-value is above the critical value for rejecting the null hypothesis. We could look it up in an F-value table based on the DFwithin and DFbetween. However, there is a method in SciPy for obtaining a p-value.

`p = stats.f.sf(F, DFbetween, DFwithin)`

Finally, we are also going to calculate the effect size. We start with the commonly used eta-squared (*η²* ):

`eta_sqrd = SSbetween/SStotal`

However, eta-squared is somewhat biased because it is based purely on sums of squares from the sample. No adjustment is made for the fact that what we aiming to do is to estimate the effect size in the population. Thus, we can use the less biased effect size measure Omega squared:

`om_sqrd = (SSbetween - (DFbetween * MSwithin))/(SStotal + MSwithin)`

The results we get from both the SciPy and the above method can be reported according to APA style; *F*(2, 27) = 4.846, *p *= .016, *η²* = .264. If you want to report Omega Squared: *ω ^{2}* = .204. That was it, now we know how to do ANOVA in Python by calculating everything “by hand”.

### ANOVA in Python using Statsmodels

In this section of the Python ANOVA tutorial, we will use Statsmodels. First, we start by using the ordinary least squares (*ols*) method and then the *anova_lm *method. Also, if you are familiar with R-syntax, Statsmodels have a formula APIwhere our model is very intuitively formulated.

Here’s three simple step for carrying out ANOVA using Statsmodels:

Time needed: 1 minute.

In the ANOVA how-to below, it is assumed that the data is in a Pandas dataframe (i.e., df).

**Import the needed Python packages**First, we import statsmodels API and ols:

**Set up the ANOVA model**Second, we use ols to set up our model using a formula

**Carry out the ANOVA**We can now use anova_lm to carry out the ANOVA in Python

In the ANOVA example below, we import the API and the formula API. Second, we use ordinary least squares regression with our data. The object obtained is a fitted model that we later use with the anova_lm method to obtain an ANOVA table.

In the final part of this section, we are going to carry out pairwise comparisons using Statsmodels.

```
import statsmodels.api as sm
from statsmodels.formula.api import ols
mod = ols('weight ~ group',
data=data).fit()
aov_table = sm.stats.anova_lm(mod, typ=2)
print(aov_table)
```

#### ANOVA Python table:

Note, no effect sizes are calculated when we use Statsmodels. To calculate eta squared we can use the sum of squares from the table:

`esq_sm = aov_table['sum_sq'][0]/(aov_table['sum_sq'][0]+aov_table['sum_sq'][1])aov_table['EtaSq'] = [esq_sm, 'NaN']print(aov_table)`

#### Python ANOVA: Pairwise Comparisons

It is, of course, also possible to calculate pairwise comparisons for our Python ANOVA using Statsmodels. In the next example, we are going to use the *t_test_pairwise *method. Conducting post-hoc tests, corrections for familywise error can be carried out using a number of methods (e.g., Bonferroni, Šidák)

```
pair_t = mod.t_test_pairwise('group')
pair_t.result_frame
```

Note, if we want to use another correction method, we add the parameter *method* and add “bonferroni” or “sidak”, for instance (e.g., method=”sidak”).

If we were to carry out regression analysis, using Python, we might have to convert the categorical variables to dummy variables using Pandas get_dummies() method.

### Python ANOVA using pyvttbl anova1way

In this section, we are going to learn how to carry out an ANOVA in Python using the method anova1way from the Python package pyvttbl. This package also has a DataFrame method. We have to use this method instead of Pandas DataFrame to be able to carry out the one-way ANOVA in Python. Note, Pyvttbl is old and outdated. It requires Numpy to be at most version 1.1.x or else you will run into an error ( “unsupported operand type(s) for +: ‘float’ and ‘NoneType’”).

This can, of course, be solved by downgrading Numpy (see my solution using a virtual environment Step-by-step guide for solving the Pyvttbl Float and NoneType error). However, it may be better to use pingouin for carrying out Python ANOVAs (see the next section of this blog post).

```
from pyvttbl import DataFrame
df=DataFrame()
df.read_tbl(datafile)
aov_pyvttbl = df.anova1way('weight', 'group')
print aov_pyvttbl
```

#### Output anova1way

```
Anova: Single Factor on weight
SUMMARY
Groups Count Sum Average Variance
============================================
ctrl 10 50.320 5.032 0.340
trt1 10 46.610 4.661 0.630
trt2 10 55.260 5.526 0.196
O'BRIEN TEST FOR HOMOGENEITY OF VARIANCE
Source of Variation SS df MS F P-value eta^2 Obs. power
===============================================================================
Treatments 0.977 2 0.489 1.593 0.222 0.106 0.306
Error 8.281 27 0.307
===============================================================================
Total 9.259 29
ANOVA
Source of Variation SS df MS F P-value eta^2 Obs. power
================================================================================
Treatments 3.766 2 1.883 4.846 0.016 0.264 0.661
Error 10.492 27 0.389
================================================================================
Total 14.258 29
POSTHOC MULTIPLE COMPARISONS
Tukey HSD: Table of q-statistics
ctrl trt1 trt2
=================================
ctrl 0 1.882 ns 2.506 ns
trt1 0 4.388 *
trt2 0
=================================
+ p < .10 (q-critical[3, 27] = 3.0301664694)
* p < .05 (q-critical[3, 27] = 3.50576984879)
** p < .01 (q-critical[3, 27] = 4.49413305084)
```

We get a lot of more information using the anova1way method. What may be of particular interest here is that we get results from a post-hoc test (i.e., Tukey HSD). Whereas the ANOVA only lets us know that there was a significant effect of treatment the post-hoc analysis reveals where this effect may be (between which groups).

If you have more than one dependent variable a multivariate method may be more suitable. Learn more on how to carry out a Multivariate Analysis of Variance (ANOVA) using Python:

### Python ANOVA using Pingouin (bonus)

In this section, we are going to learn how to carry out ANOVA in Python using the package *pingouin.* This package is, as with Statsmodels, very simple to use. If we want to carry out an ANOVA we just use the method called *anova*.

```
import pandas as pd
import pingouin as pg
data = "https://vincentarelbundock.github.io/Rdatasets/csv/datasets/PlantGrowth.csv"
df = pd.read_csv(data, index_col=0)
aov = pg.anova(data=df, dv='weight', between='group', detailed=True)
print(aov)
```

As can be seen in the ANOVA table above, we get the degrees of freedom, the mean square error, F- and p-values, as well as the partial eta squared when using pingouin.

#### Pairwise Comparisons in Python (Tukey-HSD)

One neat thing with Pingouin is that we can also carry post-hoc tests. We are now going to carry out the Tukey-HSD test as a follow up on our ANOVA. This is also very simple we use the *pairwise_tukey *method to carry out the pairwise comparisons:

```
pt = pg.pairwise_tukey(dv='weight', between='group', data=df)
print(pt)
```

Note, if we want another type of effect size we can add the argument *effsize* and choose between six different effect sizes (or none): cohen, hedges, glass, eta-square, odds-ratio, and AUC. In the last code example we change the default effect size (hedges) to cohen:

```
cpt = pg.pairwise_tukey(dv='weight', between='group', effsize='cohen', data=df)
print(pt)
```

## Conclusion: Python ANOVA

That is it! In this tutorial you learned 4 methods that let you carry out one-way ANOVAs using Python. There are, of course, other ways to deal with the tests between the groups (e.g., the post-hoc analysis).

One could carry out Multiple Comparisons (e.g., t-tests between each group. Just remember to correct for familywise error!) or Planned Contrasts. In conclusion, doing ANOVAs in Python is pretty simple.

## 8 Comments

Heck of a job there, it aboesutlly helps me out.

Thank you for your effort, very clearly set.

However, I am hitting a problem using ANOVA1Way, I wonder if you have any suggestions. When I make a copy of PlantGrowth.csv and type in new numbers for “weight” and then run your code, I get:

Error: new-line character seen in unquoted field – do you need to open the file in universal-newline mode?

Thanks and Regards

Hey Umit,

I cannot really answer your question since the error does not happen on my computer. I did find this: http://stackoverflow.com/questions/17315635/csv-new-line-character-seen-in-unquoted-field-error. Maybe you could test that and see if it works. If you solve your problem, or have already solved it, please let me know how.

Thanks and regards,

Erik

Hi Erik,

thanks for the great post. I wanted to offer an update to part 2 (python based ANOVA) for when the groups have different sample sizes.

First, rewrite the calculation for n:

n = data.groupby(var).size().values

Then the calculation for SSbetween and SSwithin needs to be modified:

SSbetween = (sum(data.groupby(var).sum()[‘LogSalePrice’].values**2/n)) – (data[‘LogSalePrice’].sum()**2)/N

SSwithin = sum_y_squared – sum(data.groupby(var).sum()[‘LogSalePrice’].values**2/n)

It just takes the division by n (element-wise) inside the outer sum in both cases.

I tested this by comparing with the output from f_oneway and it seems to work. It should also generalize well to the case where n is the same for all groups.

Thanks again for the write-up!

Hi Joel,

thanks for your comment and thanks for the update! I’ll add this to the post (with a reference to your comment, of course).

Erik

Thanks for your post… It was super useful for me

Hi Erik!

Thank you for the post. I’ve been working recently on a Python stats package that implements several ANOVA-related functions and post-hocs tests. Just thought I’d mention it in case this would turn useful to you or others: https://pingouin-stats.org/

All the best,

Raphael

Hey Raphael,

This looks really interesting! Will install this later today and play around with it. I might just add it to one of my posts listing useful Python packages. We’ll see!

Maybe I’ll also update this post (or write a new one). I’ll send you an email, if I do.

Thanks for letting us know about the package,

Best,

Erik