diff --git a/README.md b/README.md
index aec0e1ca26b3a8e00ce5c9bf4baf6f471d975d92..748d5e6d9dec16f18b5910f2e5c6bb2023f2f55a 100644
--- a/README.md
+++ b/README.md
@@ -120,7 +120,7 @@ Assume, we want to calculate the square root of all these numbers. We can
 simply use numpy's `sqrt` method do perform the same operation on all elements
 of the array at the same time.
 <!-- append np_arrays.py -->
-```pyton
+```python
 # Calculte the square root for each item in the array numbers.
 roots = np.sqrt(numbers)
 ```
@@ -128,7 +128,7 @@ roots = np.sqrt(numbers)
 Since the resulting variable `roots` is also a numpy array, we can perform
 similar operations on this variable.
 <!-- append np_arrays.py -->
-```pyton
+```python
 # Perform other calucaltions for each element separately.
 something_else = 1.5 * roots - 4
 ```
@@ -439,7 +439,7 @@ free parameters of the model. The return value corresponds to the $`y`$-value, i
 our case the expected number of events.
 
 <!-- append decay.py -->
-```
+```python
 def model(channel, m, s, A, y0, b):
   return  A * np.exp(-0.5 * (channel - m)**2 / s**2) + y0 + b * channel
 ```
@@ -470,7 +470,7 @@ stable fit result. More information on the fitting method can be found in the
 [documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html).
 
 <!-- append decay.py -->
-```
+```python
 # Define the intial values of the free parameters.
 # Remember, that we defined our model as n(c; m, s, A, y0, b)
 p0 = (60, 10, 50, 20, 1)
@@ -488,7 +488,7 @@ To visualize the fitted model, we need to evaluate our model with the optimized
 parameters `popt`. 
 
 <!-- append decay.py -->
-```
+```python
 # Evaluate the model with the optimized parameter.
 fit_count = model(channel, *popt)