import pandas as pd

path = r'F:\数据分析专用\数据分析与机器学习\food_info.csv'

with open(path, 'r') as f:

    data = pd.read_csv(f)

    print(type(data))

    print(data.dtypes)

    print(help(pd.read_csv))

print(data.head(3))

print(data.tail(3))

print(data.columns)

data.sort_values('Carbohydrt_(g)', inplace=True)

print(data['Carbohydrt_(g)'])

# print(data)

data.sort_values('Carbohydrt_(g)', inplace=True, ascending=False)

print(data['Carbohydrt_(g)'])

age = t_s['Age']

age_is_null = pd.isnull(age)

age_null_true = age[age_is_null]

age_null_count = len(age_null_true)

print(age_null_count)

#----------------------------------------

count_list = []

for i in age:

    if i != i:

        count_list.append(i)

print(len(count_list))

mean_age = sum(t_s['Age'])/len(t_s['Age'])

print(mean_age)

#--------------------------------------------------

good_ages = t_s['Age'][age_is_null==False]

correct_mean_age = sum(good_ages)/len(good_ages)

print(correct_mean_age)

#---------------------------------------------

correct_mean_age = t_s['Age'].mean()

print(correct_mean_age)

passenger_classes = {1, 2, 3}

faces_by_class = {}

for this_class in passenger_classes:

    pclass_rows = t_s[t_s['Pclass']==this_class]

    pclass_fares = pclass_rows['Fare']

    fare_for_class = pclass_fares.mean()

    faces_by_class[this_class] = fare_for_class

print(faces_by_class)

#--------------------------------------------------------

passenger_s = t_s.pivot_table(index='Pclass', values='Survived', aggfunc=np.mean)

print(passenger_s)

#--------------------------------------------------------

passenger_age = t_s.pivot_table(index='Pclass', values='Age')

print(passenger_age)

#--------------------------------------------------------

passenger_price = t_s.pivot_table(index='Pclass', values='Fare')

print(passenger_price)

#--------------------------------------------------------

port_stats = t_s.pivot_table(index='Embarked', values=['Fare', 'Survived'], aggfunc=np.sum)

print(port_stats)

def hundredth_row(column):

    hundredth_item = column.loc[99]

    return hundredth_item

hundredth_row = t_r.apply(hundredth_row)

print(hundredth_row)

def which_class(row):

    pclass = row['Pclass']

    if pd.isnull(pclass):

        return 'Unknown'

    elif pclass == 1:

        return "First Class"

    elif pclass == 2:

        return "Second Class"

    elif pclass == 3:

        return "Third Class"

classes = t_r.apply(which_class, axis=1)

print(classes)

import pandas as pd

path = r'F:\数据分析专用\数据分析与机器学习\fandango_score_comparison.csv'

with open(path, 'r', encoding='utf-8') as f:

    data = pd.read_csv(f)

    # print(data.dtypes)

    series_film = data['FILM']

    print(type(series_film))

    series_rt = data['RottenTomatoes']

    print(series_rt[0:5])

#--------------------------------------------------------------

from pandas import Series

film_names = series_film.values

print(type(film_names))

rt_scores = series_rt.values

series_custom = Series(rt_scores, index=film_names)

series_custom[['Minions (2015)', 'Leviathan (2014)']]