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#Now let´s convert those into balances using the .where function from numpy
np_balances = np.where(np_set_rolls == 5, 10,-5)
np_list_balances= []

for x in np_balances:
    np_list_balances.append(sum(x))

#Let´s plot a histogram with the possible economic results of the game
pd_list_balances= pd.Series(np_list_balances)
pd_list_balances.plot.hist(bins=6, cmap="twilight_shifted_r")

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#Setting a variable for the seed generation 
seedgen=3  

#Applying the seed function with our variable as a parameter
np.random.seed(seedgen)

#Creating the lists to be populated by the for loops. 
rolls= []
set_rolls = []

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list generated in order to manipulate it more easily
df = pd.DataFrame(thousand_scenarios)
df.columns=["Probability"]
df.head(1)
df.plot.hist(bins=10, cmap="plasma")

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#Setting a variable for the seed generation 
seedgen=1  

#Applying the seed function with our variable as a parameter
np.random.seed(seedgen)

#Creating the lists to be populated by the for loops. 
fives = []
thousand_scenarios = []

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#Setting up the seed for the excercise 
np.random.seed(123)

#Creating a list to be populated with the amount of fives obtained in 5000 rolls. 
fives = []

#Loop to roll the dices 5000 times and append a "1" for each time a 5 is obtained. 
for x in range (0,5000):
    one_roll = np.random.randint(1,7) + np.random.randint(1,7)
    if one_roll == 5: 

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np.random.seed(123) 
print("Seed 123 gives us: " + str(np.random.randint(0,100)) + " as first result") 
print("Seed 123 gives us: " + str(np.random.randint(0,100)) + " as second result") 

np.random.seed(1234)
print("Seed 1234 gives us: " + str(np.random.randint(0,100)) + " as first result") 
print("Seed 1234 gives us: " + str(np.random.randint(0,100)) + " as second result") 

np.random.seed(123)
print("Seed 123 gives us: " + str(np.random.randint(0,100)) + " as first result again") 

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import numpy as np
import pandas as pd 
from pandas.plotting import bootstrap_plot

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#Creating subset of our dataset, to include only deaths ocurred in Winterfell
data_winterfell = dataset[dataset["Location"].isin(["Winterfell"])].sort_values("Killer")
winterfell_killers = data_winterfell["Killer"].value_counts()

#Setting the x & y axis to the index and values of our "winterfell_killers" series 
wfk_index= winterfell_killers.index
wfk_values= winterfell_killers.values

#Plotting the bargraph with some customizations
fig, ax = plt.subplots()

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fig, ax = plt.subplots()
data = dataset["Killers House"].value_counts().head(10).index

#splitting the data in two to highlight in a vibrant color only the most important datapoint 
data1 = dataset["Killers House"].value_counts().head(1)
data2 = dataset["Killers House"].value_counts().head(10).tail(9) #the only way I could come up with to select the top2-9 

x_data1 = data1.index
y_data1 = data1.values

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#Creating the plot & setting up the data we´re plotting, in this case, the value count of deaths ocurred per Killers House, as each log represents one death. 
fig, ax = plt.subplots()
data = dataset["Killers House"].value_counts().head(10)

#Setting the x & y axis to the index and values of our "data" series 
x_data = data.index
y_data = data.values

#Plotting the data itself 
ax.bar(x_data , y_data)