[英]pandas: use value_counts in an apply function
Here is a toy example of my pandas dataframe:这是我的熊猫数据框的一个玩具示例:
country_market language_market
0 United States English
1 United States French
2 Not used Not used
3 Canada OR United States English
4 Germany English
5 United Kingdom French
6 United States German
7 United Kingdom English
8 United Kingdom English
9 Not used Not used
10 United States French
11 United States English
12 United Kingdom English
13 United States French
14 Not used English
15 Not used English
16 United States French
17 United States Not used
18 Not used English
19 United States German
I want to add a column top_country
that shows whether the value in country_market
is one of the top two most commonly seen countries in the data.我想添加一列
top_country
来显示country_market
的值是否是数据中最常见的两个国家之一。 If it is, I want the new top_country
column show the value in country_market
and if not, then I want it to show "Other".如果是,我希望新的
top_country
列显示country_market
的值,如果不是,那么我希望它显示“其他”。 I want to repeat this process for language_market
(and a whole load of other market columns I don't show here).我想为
language_market
重复这个过程(以及我没有在此处显示的大量其他市场列)。
This is how I'd like the data to look after processing:这是我希望数据在处理后的样子:
country_market language_market top_country top_language
0 United States English United States English
1 United States French United States French
2 Not used Not used Not used Other
3 Canada OR United States English Other English
4 Germany English Other English
5 United Kingdom French Other French
6 United States German United States Other
7 United Kingdom English Other English
8 United Kingdom English Other English
9 Not used Not used Not used Other
10 United States French United States French
11 United States English United States English
12 United Kingdom English Other English
13 United States French United States French
14 Not used English Not used English
15 Not used English Not used English
16 United States French United States French
17 United States Not used United States Other
18 Not used English Not used English
19 United States German United States Other
I made a function original_top_markets_function
to do this, but I couldn't figure how to pass the value_counts
part of my function to pandas apply
.我创建了一个函数
original_top_markets_function
来执行此操作,但我无法弄清楚如何将函数的value_counts
部分传递给 pandas apply
。 I kept getting AttributeError: 'str' object has no attribute 'value_counts'
.我不断收到
AttributeError: 'str' object has no attribute 'value_counts'
。
def original_top_markets_function(x):
top2 = x.value_counts().nlargest(2).index
for i in x:
if i in top2:
return i
else:
return 'Other'
I know this is because apply
is looking at each element in my target column, but I also need the function to consider the whole column at once, so that I can use value_counts
.我知道这是因为
apply
正在查看目标列中的每个元素,但我还需要该函数一次考虑整个列,以便我可以使用value_counts
。 I don't know how to do that.我不知道该怎么做。
So I have come up with this top_markets
function as a solution, using a list, which does what I want, but isn't very efficient.所以我想出了这个
top_markets
函数作为解决方案,使用一个列表,它top_markets
我的需求,但效率不高。 I'll need to apply this function to lots of different market columns, so I'd like something more pythonic.我需要将此函数应用于许多不同的市场列,所以我想要更pythonic 的东西。
def top_markets(x):
top2 = x.value_counts().nlargest(2).index
results = []
for i in x:
if i in top2:
results.append(i)
else:
results.append('Other')
return results
Here's a reproducible example.这是一个可重现的示例。 Please can somehow help me fix my
top_markets
function so I can use it with apply
?请以某种方式帮助我修复我的
top_markets
函数,以便我可以将它与apply
一起apply
?
import pandas as pd
d = {0: {'country_market': 'United States', 'language_market': 'English'},
1: {'country_market': 'United States', 'language_market': 'French'},
2: {'country_market': 'Not used', 'language_market': 'Not used'},
3: {'country_market': 'Canada OR United States',
'language_market': 'English'},
4: {'country_market': 'Germany', 'language_market': 'English'},
5: {'country_market': 'United Kingdom', 'language_market': 'French'},
6: {'country_market': 'United States', 'language_market': 'German'},
7: {'country_market': 'United Kingdom', 'language_market': 'English'},
8: {'country_market': 'United Kingdom', 'language_market': 'English'},
9: {'country_market': 'Not used', 'language_market': 'Not used'},
10: {'country_market': 'United States', 'language_market': 'French'},
11: {'country_market': 'United States', 'language_market': 'English'},
12: {'country_market': 'United Kingdom', 'language_market': 'English'},
13: {'country_market': 'United States', 'language_market': 'French'},
14: {'country_market': 'Not used', 'language_market': 'English'},
15: {'country_market': 'Not used', 'language_market': 'English'},
16: {'country_market': 'United States', 'language_market': 'French'},
17: {'country_market': 'United States', 'language_market': 'Not used'},
18: {'country_market': 'Not used', 'language_market': 'English'},
19: {'country_market': 'United States', 'language_market': 'German'}}
df = pd.DataFrame.from_dict(d, orient='index')
def top_markets(x):
top2 = x.value_counts().nlargest(2).index
results = []
for i in x:
if i in top2:
results.append(i)
else:
results.append('Other')
return results
df['top_country'] = top_markets(df['country_market'])
df['top_language'] = top_markets(df['language_market'])
df
I think u can just use:我认为你可以使用:
df['top_country'] = np.where(df['country_market'].isin(df['country_market'].value_counts().nlargest(2).index), df['country_market'], 'Other')
df['top_language'] = np.where(df['language_market'].isin(df['language_market'].value_counts().nlargest(2).index), df['language_market'], 'Other')
If u wish to use your own function, you can use:如果你想使用你自己的函数,你可以使用:
df['top_country'] = df[['country_market']].apply(top_markets)
df['top_language'] = df[['language_market']].apply(top_markets)
#OR
df[['top_country', 'top_language']] = df[['country_market', 'language_market']].apply(top_markets)
Edit as per discussion in comments:根据评论中的讨论进行编辑:
def top_markets(x, top):
if x in top:
return x
else:
'Other'
top_country = df['country_market'].value_counts().nlargest(2).index
top_languages = df['language_market'].value_counts().nlargest(2).index
df['top_country'] = df['country_market'].apply(lambda x: top_markets(x, top_country))
df['top_language'] = df['language_market'].apply(lambda x: top_markets(x, top_languages))
If need working by multiple columns by DataFrame.apply
in some function, eg here lambda function
use:如果需要在某些函数中通过
DataFrame.apply
处理多列,例如这里的lambda function
使用:
cols = ['language_market', 'country_market']
f = lambda x: np.where(x.isin(x.value_counts().nlargest(2).index), x, 'Other')
df = df.join(df[cols].apply(f).add_prefix('total_'))
Solution without lambda function:没有 lambda 函数的解决方案:
def top_markets(x):
return np.where(x.isin(x.value_counts().nlargest(2).index), x, 'Other')
df = df.join(df[cols].apply(top_markets).add_prefix('total_'))
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