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from sympy import *

x, y = symbols('x y')
f = (y**2 + x**2 - 1)**3 - x**2*y**3

plot_implicit(f, (x, -1.5, 1.5), (y, -1.5, 1.5), title='<3')

import matplotlib.pyplot as plt
import numpy as np 

# Opening the file, "r" is to specify that we want to read it
fid = open("Stortingsvalg 2009 2013 2017.csv", "r")

# Using "loadtxt" to import our data from the file
    # "dtype = "str"" defines the data as strings
    # "skiprows=3" makes us skip the 3 first lines, as they are useless to our problem
    # "usecols=(0, 3)" makes us only read the first and fourth column, as we only need them in our data base
    # "delimiter=";"" sets the dilimiter that seperates our variabels.
        # That is because our csv-file (comma-seperated values file) is seperated by ";"
        # To doublecheck this, open up the csv-file in notepad.
data = np.loadtxt(fid, dtype='str', skiprows=3, usecols=(0, 3), delimiter=';')

# Closing the file
fid.close()

# The way we imported our data, the labels are stored in the first column,
    # and the number of votes in the second column
labels = data[:, 0]
votes = data[:, 1]

import matplotlib.pyplot as plt
import numpy as np 

# Opening the file, "r" is to specify that we want to read it
fid = open("Stortingsvalg 2009 2013 2017.csv", "r")

# Using "loadtxt" to import our data from the file
    # "dtype = "str"" defines the data as strings
    # "skiprows=3" makes us skip the 3 first lines, as they are useless to our problem
    # "usecols=(0,3)" makes us only read the first and fourth column, as we only need them in our data base
    # "delimiter=";"" sets the dilimiter that seperates our variabels.
        # That is because our csv-file (comma-seperated values file) is seperated by ";"
        # To doublecheck this, open up the csv-file in notepad.
data = np.loadtxt(fid, dtype='str', skiprows=3, usecols=(0, 3), delimiter=';')

# Closing the file
fid.close()

# The way we imported our data, the labels are stored in the first column,
    # and the number of votes in the second column
labels = data[:, 0]
votes = data[:, 1]

# Sets the title of our plot
plt.title('Stortingsvalg 2017')  

# Equal aspect ratio ensures that pie is drawn as a circle.  
plt.gca().axis("equal")    

# Plotting the pie chart:
    # "autopct" converts the values in terms of percentage
    # "startangle" sets the angle at which we starts the first "slice", in this case "Arbeiderpartiet"
pie = plt.pie(votes, autopct='%1.1f%%', startangle=0)

# "legend" is used to set the labels on the side, not around the pie
    # "labels" are the labels we want to display in our legend.
    # "fontsize" sets the size of the label text
    # "bbox_to_anchor=(1, 0.75)" specifies the legends location
    # "bbox_transform=plt.gcf().transFigure" makes sure our location specified in "bbox_to_anchor" 
        # is in reference to the reference system of the figure, not the axes (the actual pie)   
plt.legend(labels, fontsize=10, bbox_to_anchor=(1, 0.75), bbox_transform=plt.gcf().transFigure)

# This section offsets the pie-figure to the left, in order to make space to display the legend
# Try removing/playing with this function and see what happens.
    # If removed, you will have to drag the pie away to show the legend.
plt.subplots_adjust(left=0.0, bottom=0.1, right=0.65)

# To make sure our data import is correct, I like to include the following "print(data)" as well
# print(data)

# Display our figure
plt.show()

The legend methods used/described in this exercise does not only apply to pie charts, but to most plots!

import numpy as np 
import matplotlib.pyplot as plt

fibNum = np.array([0, 1, 1, 2, 3, 5, 8, 13, 21, 34])

plt.plot(fibNum)
plt.title("First 10 Fibonacci numbers")

plt.show()