def find_types_of_monster():
df_copy = import_data.csv_cleaner()
#Creating seperate dataframes using user input
types = df_copy["type"].unique()
print(
"Type the number of the type of monster you would like to search through"
)
for i in range(len(types) - 1):
print("{0}: {1}".format(i, types[i]))
user_input = input("Number: ")
if user_input.isdigit() and int(user_input) < 13:
print(
"Ok: Printing a Dataframe of this type of monster, along with some information"
)
#Dataframe should be able to call different functions to create a wider picture of the DF
monster_type_df = df_copy[df_copy["type"].values == types[int(
user_input)]]
num_monsters = len(monster_type_df)
cr_avrg = round(monster_type_df["cr"].mean())
deadliest = monster_type_df.loc[monster_type_df["cr"].values ==
monster_type_df["cr"].max()]
weakest = monster_type_df[monster_type_df["cr"].values ==
monster_type_df["cr"].min()]
print(
"The DataFrame contains {0} monsters\nThe Average Cr is {1}\nThe Deadliest monster is the {2}\nThe Weakest monster is the {3}\n"
.format(num_monsters, cr_avrg, deadliest["name"].values,
weakest["name"].values), monster_type_df)
def find_average_cr():
#Finds average CR, may need to convert CR column to int/double for this
df_copy = import_data.csv_cleaner()
#print(checker, df_copy)
#print("The mean value of the challenge rating is {0:.1f}".format(df_copy["cr"].mean()), divider)
#print("The mode value of the challenge rating is {0:.1f}".format(int(df_copy["cr"].mode())))
out = round(df_copy["cr"].mean())
return out
def ranked_by_cr():
df_copy = import_data.csv_cleaner()
df_copy["rankedcr"] = df_copy["cr"].rank(ascending=1)
print(divider, "The least challenging monsters are \n",
df_copy[df_copy["rankedcr"] < 40].sort_values("rankedcr"))
print(divider, "The most challenging monsters are \n",
df_copy[df_copy["rankedcr"] > 330].sort_values("rankedcr"))
return df_copy
def find_quantile_cr():
#Finds the average CR of each quantile, and outputs a CR average that fits into the "normal" range
#Aka, between .25 and .75
df_copy = import_data.csv_cleaner()
print(checker)
print(
"The first quantile of the CR is {}".format(
df_copy["cr"].quantile(.25)),
"The second quantile of the CR is {}".format(
df_copy["cr"].quantile(.50)),
"The third quantile of the CR is {}".format(
df_copy["cr"].quantile(.75)))
def standard_dev_cr():
df_copy = import_data.csv_cleaner()
average = find_average_cr()
std_cr = df_copy["cr"].std()
top_range_monsters = average + std_cr * 1.96
low_range_monsters = average - std_cr * 1.96
df_out = df_copy
#Create dataframe for returning information
#Holds the top monsters to fight - print(df_out[df_out["cr"] > top_range_monsters].values)
#Hold the bottom mosnters to fight - print(df_out[df_out["cr"] < low_range_monsters].values)
df_out = df_out.drop(df_out[df_out["cr"] > top_range_monsters].index)
df_out = df_out.drop(df_out[df_out["cr"] < low_range_monsters].index)
#print("Testing \n", df_out)
return df_out
def iqr_monster_cr():
df_copy = import_data.csv_cleaner()
print(checker)
#Set the quantile groups
early_monsters = df_copy["cr"].quantile(.25)
later_monsters = df_copy["cr"].quantile(.75)
iqr_enemies = later_monsters - early_monsters #Changed name to enemies to avoid confusion with method
top_range_monsters = later_monsters + iqr_enemies * 1.5 #9+8*1.5
low_range_monsters = early_monsters - iqr_enemies * 1.5 #1-8*1.5
df_iqr = df_copy
df_iqr = df_iqr.drop(df_iqr[df_iqr["cr"] > top_range_monsters].index)
df_iqr = df_iqr.drop(df_iqr[df_iqr["cr"] < low_range_monsters].index)
print(
"Printing monsters withing the IQR \n",
df_iqr) #The IQR is quite compact, only 12 rows are dropped using this
def plot_cr_box():
df_copy = import_data.csv_cleaner()
print(df_copy.columns)
df_copy.boxplot(by="type", column="cr")
#Shows the Cr based on type using a box plot
plt.show()
def general_info_monsters():
df_copy = import_data.csv_cleaner()
print("Printing out basic information about the Dataframe\n",
df_copy.describe().transpose())