你怎么过滤多个列的 pandas 数据框

问题描述

如果我们考虑男性和女性的数据，我们可能会：

 使用& 运算符，不要忘记用来包装子语句（ ）：
  males = df [（df [Gender] =='Male'）& （df [Year] == 2014）] 
  
要将数据框存储在 dict 使用for循环： 
 
 
 来自集合import defaultdict 
 dic = {} 
 for ['男'，'女']：
 dic [g] = defaultdict（dict）
在[2013，2014]中为y：
 dic [g ] [y] = df [（df [Gender] == g）& （df [Year] == y）]＃将DataFrames存储为字典字典
  
 
 
 
编辑： 
 
 $ b $ < getDF 的演示： 
 
 
  def getDF（dic，gender，year）：
 return dic [gender] [year] 
 
 print genDF（dic，'male'，2014）
  
 
To filter a dataframe (df) by a single column, if we consider data with male and females we might:
males = df[df[Gender]=='Male']
Question 1 - But what if the data spanned multiple years and i wanted to only see males for 2014?
In other languages I might do something like: 
if A = "Male" and if B = "2014" then 
(except I want to do this and get a subset of the original dataframe in a new dataframe object)
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 = .....
 解决方案 
Using & operator, don't forget to wrap the sub-statements with ():
males = df[(df[Gender]=='Male') & (df[Year]==2014)]
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
EDIT:
A demo for your getDF:
def getDF(dic, gender, year):
  return dic[gender][year]

print genDF(dic, 'male', 2014)
                        
这篇关于你怎么过滤多个列的 pandas 数据框的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！