你如何通过多列过滤 pandas 数据框 [英] how do you filter pandas dataframes by multiple columns

问题描述

要按单列过滤数据框 (df)，如果我们考虑包含男性和女性的数据，我们可能会:

To filter a dataframe (df) by a single column, if we consider data with male and females we might:

males = df[df[Gender]=='Male']

问题 1 - 但如果数据跨越多年并且我只想看到 2014 年的男性怎么办?

Question 1 - But what if the data spanned multiple years and i wanted to only see males for 2014?

在其他语言中，我可能会这样做:

In other languages I might do something like:

if A = "Male" and if B = "2014" then 

(除非我想这样做并在新的数据帧对象中获取原始数据帧的子集)

(except I want to do this and get a subset of the original dataframe in a new dataframe object)

问题 2. 我如何在循环中执行此操作，并为每个独特的年份和性别集创建一个数据框对象(即 df 用于:2013-Male、2013-Female、2014-Male 和 2014-Female

Question 2. How do I do this in a loop, and create a dataframe object for each unique sets of year and gender (i.e. a df for: 2013-Male, 2013-Female, 2014-Male, and 2014-Female

for y in year:

for g in gender:

df = .....

推荐答案

使用 & 操作符，不要忘记用 () 包裹子语句:

Using & operator, don't forget to wrap the sub-statements with ():

males = df[(df[Gender]=='Male') & (df[Year]==2014)]

使用 for 循环将数据帧存储在 dict 中:

To store your dataframes in a dict using a for loop:

from collections import defaultdict
dic={}
for g in ['male', 'female']:
  dic[g]=defaultdict(dict)
  for y in [2013, 2014]:
    dic[g][y]=df[(df[Gender]==g) & (df[Year]==y)] #store the DataFrames to a dict of dict

getDF 的演示:

def getDF(dic, gender, year):
  return dic[gender][year]

print genDF(dic, 'male', 2014)

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