import pandas as pd

# Check for all elements in the column "origin" whether they are equal to "europe"
mask1 = cars.origin == 'europe'
mask1.all()

mask2 = df.Country == 'ARG'
mask2.any()

#########################################################
df.loc[:,'Order_ID']

mask1 = df.Sport == "Volleyball"
df.loc[mask1]

#########################################################
mask1 = df['genres'].str.contains('Science Fiction')
mask2 = df['genres'].str.contains('Action')
mask3 = df['cast'].str.contains('Bruce Willis')
df.loc[mask1 & mask2 & mask3, ['title', 'genres', 'cast', 'vote_average']].sort_values(by=['vote_average'],ascending = False)


###########################################################
# Filter by Date
mask1 = df['release_date'].dt.date.astype(str) >= '2010'
mask2 = df['release_date'].dt.date.astype(str) <= '2015'
mask3 = df['production_companies'].str.contains('Pixar')
df.loc[mask1 & mask2 & mask3].sort_values(by=['revenue_musd'], ascending = False)

mask4 = df.release_date.between(1960, 1969, inclusive = 'both')
df.loc[mask4]

mask5 = df.release_date.isin([1960,1961,1962,1964]) # isin
df.loc[mask5]

mask6 = df.release_date >= 1992
df.loc[mask6]
df.loc[~mask6]


##########################################################
# Filter by many conditions
mask1 = df['genres'].str.contains('Action')
mask2 = df['genres'].str.contains('Thriller')
mask3 = df['spoken_languages'].str.contains('English')
df.loc[df['vote_average'] >= 7.5].loc[(mask1 | mask2) & mask3].sort_values(by=['release_date'], ascending = False)


mask1 = df.Country.isin(["ITA", "FRA", "ESP", "USA"])
df.loc[mask1]


###############################################################
mask1 = titanic.sex == male
mask2 = titanic.dtypes == object

titanic.loc[:, ~mask2] # gen only non object values (numeric)
titanic.loc[mask1,~maks2] # only males with numeric data
male_survived = titanic.loc[mask1 & mask2, :]
titanic.loc[mask1 | mask2, ['sex','survived']]

     
###########################################################
df_auto =  df_auto.loc[df_auto.Kilometes > 1111]
df_auto =  df_auto.loc[df_auto['Years Automobile'] > 0)] 

titanic.loc[titanic.sex=="Male", Age] # get only Age column        


###########################################################
index_babies = titanic.loc[titanic.age < 1, 'age'].index
titanic.loc[titanic.age < 1, 'age'] = 1 # where age < 1 changed the value to 1
titanic.loc[index_babies] # how to check the result


############################################################
not_73_74 = cars.loc[~cars.model_year.isin ([73,74]), ['mpg', 'name']] # not