Pandas笔记（二）

本文介绍常用Pandas列（Series）数据特征提取方法

我们以一组酒的数据为例，将数据保存到reviews，然后用heads()预览一下:

import pandas as pd
pd.set_option("display.max_rows", 5)
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)

reviews.head()

后面列出一些针对Series的方法：

• 求平均值

mean_points = reviews.points.means()

• 求中值

median_points = reviews.points.median()

• 输出集合

countries = reviews.country.unique()

这里返回一个list

• 输出元素个数

reviews_per_country = reviews.country.value_counts()

注意输出格式：
US 54504
France 22093
...
China 1
Egypt 1
Name: country, Length: 43, dtype: int64

• 最大索引和最小索引
  idxmax()和idxmin()

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

• 用map匹配数据
  通过lambda函数创建新的Series

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

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

• 用apply匹配数据
  通过传递函数创建新的Series，可以指定对象为行或者列

def stars(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(stars, axis='columns')