如何按多列过滤熊猫数据框-Python 实用宝典

如何按多列过滤熊猫数据框

要按单列过滤数据帧(df),如果我们考虑男性和女性的数据,则可以: males = df[df[Gender]=='Male'] 问题1-但是,如果数据跨越多年并且我只想看2014年的男性,该怎么办? 在其他语言中,我可能会做类似的事情: if A = "Male" and if B = "2014" then (除了我要执行此操作,并在新的数据框对象中获取原始数据框的子集) 问题2。如何循环执行此操作,并为每个唯一的年份和性别集创建一个数据框对象(例如,2013-男,2013-女,2014-男和2014-女的df for y in year: for g in gender: df = .....

问题:如何按多列过滤熊猫数据框

要按单列过滤数据帧(df),如果我们考虑男性和女性的数据,则可以:

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

问题1-但是,如果数据跨越多年并且我只想看2014年的男性,该怎么办?

在其他语言中,我可能会做类似的事情:

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

(除了我要执行此操作,并在新的数据框对象中获取原始数据框的子集)

问题2。如何循环执行此操作,并为每个唯一的年份和性别集创建一个数据框对象(例如,2013-男,2013-女,2014-男和2014-女的df

for y in year:

for g in gender:

df = .....

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 = .....

回答 0

使用&运算符时,不要忘了用():包裹子语句:

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

要将数据帧存储在dictfor循环中:

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

编辑:

您的演示getDF

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

print genDF(dic, 'male', 2014)

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)

回答 1

对于要用作过滤器且依赖于多个列的更通用的布尔函数,可以使用:

df = df[df[['col_1','col_2']].apply(lambda x: f(*x), axis=1)]

其中f是一个函数,该函数适用于col_1和col_2中的每对元素(x1,x2),并根据您要启用的任何条件(x1,x2)返回True或False。

For more general boolean functions that you would like to use as a filter and that depend on more than one column, you can use:

df = df[df[['col_1','col_2']].apply(lambda x: f(*x), axis=1)]

where f is a function that is applied to every pair of elements (x1, x2) from col_1 and col_2 and returns True or False depending on any condition you want on (x1, x2).


回答 2

pandas 0.13开始,这是最有效的方法。

df.query('Gender=="Male" & Year=="2014" ')

Start from pandas 0.13, this is the most efficient way.

df.query('Gender=="Male" & Year=="2014" ')

回答 3

如果有人想知道什么是更快的过滤方法(可接受的答案或来自@redreamality的答案):

import pandas as pd
import numpy as np

length = 100_000
df = pd.DataFrame()
df['Year'] = np.random.randint(1950, 2019, size=length)
df['Gender'] = np.random.choice(['Male', 'Female'], length)

%timeit df.query('Gender=="Male" & Year=="2014" ')
%timeit df[(df['Gender']=='Male') & (df['Year']==2014)]

100,000行的结果:

6.67 ms ± 557 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
5.54 ms ± 536 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

10,000,000行的结果:

326 ms ± 6.52 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
472 ms ± 25.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

因此,结果取决于大小和数据。在我的笔记本电脑上,query()经过50万行之后速度会更快。此外,字符串搜索Year=="2014"有不必要的开销(Year==2014更快)。

In case somebody wonders what is the faster way to filter (the accepted answer or the one from @redreamality):

import pandas as pd
import numpy as np

length = 100_000
df = pd.DataFrame()
df['Year'] = np.random.randint(1950, 2019, size=length)
df['Gender'] = np.random.choice(['Male', 'Female'], length)

%timeit df.query('Gender=="Male" & Year=="2014" ')
%timeit df[(df['Gender']=='Male') & (df['Year']==2014)]

Results for 100,000 rows:

6.67 ms ± 557 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
5.54 ms ± 536 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Results for 10,000,000 rows:

326 ms ± 6.52 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
472 ms ± 25.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

So results depend on the size and the data. On my laptop, query() gets faster after 500k rows. Further, the string search in Year=="2014" has an unnecessary overhead (Year==2014 is faster).


回答 4

您可以使用query中创建自己的过滤器功能pandas。在这里,您可以df按所有kwargs参数过滤结果。不要忘记添加一些验证器(kwargs过滤器)来获得自己的过滤器功能df

def filter(df, **kwargs):
    query_list = []
    for key in kwargs.keys():
        query_list.append(f'{key}=="{kwargs[key]}"')
    query = ' & '.join(query_list)
    return df.query(query)

You can create your own filter function using query in pandas. Here you have filtering of df results by all the kwargs parameters. Dont' forgot to add some validators(kwargs filtering) to get filter function for your own df.

def filter(df, **kwargs):
    query_list = []
    for key in kwargs.keys():
        query_list.append(f'{key}=="{kwargs[key]}"')
    query = ' & '.join(query_list)
    return df.query(query)

回答 5

您可以使用np.logical_and运算符替换&(或np.logical_or替换|)以多列(多于两列)进行过滤

如果您提供多个字段的目标值,则这是完成此任务的示例函数。您可以将其调整为适用于不同类型的过滤或其他方式:

def filter_df(df, filter_values):
    """Filter df by matching targets for multiple columns.

    Args:
        df (pd.DataFrame): dataframe
        filter_values (None or dict): Dictionary of the form:
                `{<field>: <target_values_list>}`
            used to filter columns data.
    """
    import numpy as np
    if filter_values is None or not filter_values:
        return df
    return df[
        np.logical_and.reduce([
            df[column].isin(target_values) 
            for column, target_values in filter_values.items()
        ])
    ]

用法:

df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [1, 2, 3, 4]})

filter_df(df, {
    'a': [1, 2, 3],
    'b': [1, 2, 4]
})

You can filter by multiple columns (more than two) by using the np.logical_and operator to replace & (or np.logical_or to replace |)

Here's an example function that does the job, if you provide target values for multiple fields. You can adapt it for different types of filtering and whatnot:

def filter_df(df, filter_values):
    """Filter df by matching targets for multiple columns.

    Args:
        df (pd.DataFrame): dataframe
        filter_values (None or dict): Dictionary of the form:
                `{<field>: <target_values_list>}`
            used to filter columns data.
    """
    import numpy as np
    if filter_values is None or not filter_values:
        return df
    return df[
        np.logical_and.reduce([
            df[column].isin(target_values) 
            for column, target_values in filter_values.items()
        ])
    ]

Usage:

df = pd.DataFrame({'a': [1, 2, 3, 4], 'b': [1, 2, 3, 4]})

filter_df(df, {
    'a': [1, 2, 3],
    'b': [1, 2, 4]
})

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