outliers detection and handling in Python

cars.plot(figsize = (15,15), subplots = True)
plt.show()

cars.hist(figsize = (20,12), bins = 100)
plt.show()

#####################################################
# Outliers Detection
titanic.describe() # use describe method to see minimum and maximum values

plt.figure(figsize = (12,6)) # boxplot
titanic.boxplot("Age")
plt.show()

plt.figure(figsize = (12,6)) # line plot
titanic.Age.plot()
plt.show()

titanic.Age.sort_values(ascending = False) # sorting to see if there are any outliers
titanic.Age.sort_values(ascending = True)

titanic.loc[titanic.Age > 90] # filter df only with outliers

#############################################
# Outliers Handling
index_outliers = titanic.loc[titanic.Age > 90].index
index_outliers

titanic.loc[titanic.Age > 90, "Age"] = titanic.loc[titanic.Age > 90, "Age"] / 10 # correction of the values
titanic.loc[index_outliers]

titanic.loc[217, "Age"] = 42.0

plt.figure(figsize = (12,6)) # last checking
titanic.Age.plot()
plt.show()

##########################################################################
# Change all values with outliers in dataframe
titanic.fare.plot(figsize = (12,8))
plt.show()
titanic.fare.describe()
titanic.fare.sort_values(ascending = False)

fare_cap = 250
titanic.loc[titanic.fare > fare_cap, "fare"] = fare_cap

fare_floor = 5
titanic.loc[titanic.fare < fare_floor, "fare"] = fare_floor