animales = ['gato', 'perro', 'serpiente']
for animal in animales:
print "El animal es: {0}, tamaño de palabra es: {1}".format(
animal, len(animal))
Here is what the above code is Doing:
1. We create a list of animals.
2. We create a for loop that iterates over each animal in the list.
3. We print out a string that tells us the animal and the length of the animal’s name.
df = pd.DataFrame(old_df.groupby([‘groupby_attribute’])[‘mean_attribute’].mean()) df = df.reset_index() df Here is what the above code is Doing: 1. We’re grouping the dataframe by the groupby_attribute column. 2. We’re calculating the mean of the mean_attribute column. 3. We’re resetting the index of the dataframe. The output of the code is a dataframe that looks like this: groupby_attribute mean_attribute…
self.postreqparse = reqparse.RequestParser() self.postreqparse.add_argument(“shopping_cart”, type=dict, action=”append”) Here is what the above code is Doing: 1. We’re creating a new RequestParser object. 2. We’re adding a new argument to the RequestParser object. 3. We’re specifying that the argument is a dictionary. 4. We’re specifying that the argument is an array.
import tkinter window = tkinter.Tk() # creating the window object window.title(‘my first GUI program’) window.minsize(width=600, height=500) # label # adding text to the middle of the window my_label = tkinter.Label(text=’I am a Label’, font=(‘Cursive’, 24, ‘bold’)) my_label.pack() window.mainloop() # keeping the window until we close it Here is what the above code is Doing: 1….
#import all from myfile.py (in same directory) from myfile import * Here is what the above code is Doing: 1. It’s creating a new module object named myfile. 2. It’s adding that module object to the sys.modules dictionary as myfile. 3. It’s executing the code in myfile.py and assigning the resulting module object to myfile…
if (type(img) is np.ndarray): #do stuff with image Here is what the above code is Doing: 1. It’s checking if the image is an ndarray. 2. If it is, it’s doing something with the image. You can use this same technique to check if a variable is a list, tuple, or dictionary. Here’s how you…
df.groupby(‘year’).case_status.value_counts().unstack().plot.barh() Here is what the above code is Doing: 1. We are grouping the dataframe by year and case_status. 2. We are counting the number of rows in each group. 3. We are unstacking the case_status column. 4. We are plotting the result as a bar chart.