[人工智能]Pandas学习笔记（3）

1.理论部分

1.1 summary function

reviews

• describe函数

reviews.points.describe()

count    129971.000000
mean         88.447138
             ...      
75%          91.000000
max         100.000000
Name: points, Length: 8, dtype: float64

reviews.taster_name.describe()

count         103727
unique            19
top       Roger Voss
freq           25514
Name: taster_name, dtype: object

• mean函数

reviews.points.mean()

88.44713820775404

• unique函数

reviews.taster_name.unique()

array(['Kerin O’Keefe', 'Roger Voss', 'Paul Gregutt',
       'Alexander Peartree', 'Michael Schachner', 'Anna Lee C. Iijima',
       'Virginie Boone', 'Matt Kettmann', nan, 'Sean P. Sullivan',
       'Jim Gordon', 'Joe Czerwinski', 'Anne Krebiehl\xa0MW',
       'Lauren Buzzeo', 'Mike DeSimone', 'Jeff Jenssen',
       'Susan Kostrzewa', 'Carrie Dykes', 'Fiona Adams',
       'Christina Pickard'], dtype=object)

• value_counts函数

reviews.taster_name.value_counts()

Roger Voss           25514
Michael Schachner    15134
                     ...  
Fiona Adams             27
Christina Pickard        6
Name: taster_name, Length: 19, dtype: int64

1.2 Maps

作用：将原始数据转变为经过处理之后的数据

两种实现：一种是map()函数，一种是apply()函数

• map()函数实现

review_points_mean = reviews.points.mean()
reviews.points.map(lambda p: p - review_points_mean)

0        -1.447138
1        -1.447138
            ...   
129969    1.552862
129970    1.552862
Name: points, Length: 129971, dtype: float64

• apply()函数实现

def remean_points(row):
    row.points = row.points - review_points_mean
    return row

reviews.apply(remean_points, axis='columns')

如果将apply()函数中的axis换为’index’，那么将不是原来的对一列数据处理而是对一行

以上两种也有简单的形式，速度更快但是不灵活

review_points_mean = reviews.points.mean()
reviews.points - review_points_mean

0        -1.447138
1        -1.447138
            ...   
129969    1.552862
129970    1.552862
Name: points, Length: 129971, dtype: float64

reviews.country + " - " + reviews.region_1

0            Italy - Etna
1                     NaN
               ...       
129969    France - Alsace
129970    France - Alsace
Length: 129971, dtype: object

2.实践部分

1.What is the median of the points column in the reviews DataFrame?

median_points = reviews.points.median()

2.What countries are represented in the dataset? (Your answer should not include any duplicates.)

countries = reviews.country.unique()

3.How often does each country appear in the dataset? Create a Series reviews_per_country mapping countries to the count of reviews of wines from that country.

reviews_per_country = reviews.country.value_counts()

4.Create variable centered_price containing a version of the price column with the mean price subtracted.

reviews_price_mean = reviews.price.mean()
centered_price = reviews.price.map(lambda p : p - reviews_price_mean)

或者：

centered_price = reviews.price-reviews.price.mean()

5.I’m an economical wine buyer. Which wine is the “best bargain”? Create a variable bargain_wine with the title of the wine with the highest points-to-price ratio in the dataset.

找出性价比最高的一款酒的title
性价比：分数/价格

bargain_idx = (reviews.points/reviews.price).idxmax()
bargain_wine = reviews.loc[bargain_idx,'title']

6.There are only so many words you can use when describing a bottle of wine. Is a wine more likely to be “tropical” or “fruity”? Create a Series descriptor_counts counting how many times each of these two words appears in the description column in the dataset.

分别统计 tropical、fruity在 description列中出现的次数
以Series结构返回

n_tro = reviews.description.map(lambda desc:"tropical" in desc).sum()
n_fru = reviews.description.map(lambda desc:"fruity" in desc).sum()
descriptor_counts = pd.Series([n_tro,n_fru],index = ["tropical","fruity"])

7.We’d like to host these wine reviews on our website, but a rating system ranging from 80 to 100 points is too hard to understand - we’d like to translate them into simple star ratings. A score of 95 or higher counts as 3 stars, a score of at least 85 but less than 95 is 2 stars. Any other score is 1 star.

Also, the Canadian Vintners Association bought a lot of ads on the site, so any wines from Canada should automatically get 3 stars, regardless of points.

Create a series star_ratings with the number of stars corresponding to each review in the dataset.

points分数 >= 95 3为三颗星
points分数 大于等于85且小于95 为两颗星
小于85 为1颗星
特殊情况：country为Canada的全为三颗星

def star(row):
    if row.country == 'Canada':
        return 3
    elif row.points>=95:
        return 3
    elif row.points>=85:
        return 2
    else:
        return 1

star_ratings = reviews.apply(star,axis = 'columns')