循环数据框 Python 中的列

Question

我想知道您是否能够根据以下情况进行循环（因为我试图这样做，但似乎无法弄清楚）。

我的数据框有以下标题：

female2['fiscal_year','ratio_loanofficers', 'ratio_female_borrowers', 'ratio_male_borrowers']

我想要做的是在fiscal_year 下获得每年每个比率的平均值。所以，我可能需要为信贷员、女性借款人和男性借款人做三个循环。在fiscal_year 下，有多个 2010、2011、...2019 条目。所以我实际上为实现我的答案所做的是创建一个数据库子集并每年分组，然后得到平均值。它有效...但我想看看循环是否有效（我对 Python 很陌生）。

这是我的初始代码：

for y in range(2010, 2020):
    if y == 2010:
        loan2010 += round(female2.ratio_floanofficers.mean(), 3)
    elif y == 2011:
        loan2011 += round(female2.ratio_floanofficers.mean(), 3)
    elif y == 2012:
        loan2012 += round(female2.ratio_floanofficers.mean(), 3)
    elif y == 2013:
        loan2013 += round(female2.ratio_floanofficers.mean(), 3)
    elif y == 2014:
        loan2014 += round(female2.ratio_floanofficers.mean(), 3)
    elif y == 2015:
        loan2015 += round(female2.ratio_floanofficers.mean(), 3)
    elif y == 2016:
        loan2016 += round(female2.ratio_floanofficers.mean(), 3)
    elif y == 2017:
        loan2017 += round(female2.ratio_floanofficers.mean(), 3)
    elif y == 2018:
        loan2018 += round(female2.ratio_floanofficers.mean(), 3)
    else:
        loan2019 += round(female2.ratio_floanofficers.mean(), 3)

print(loan2010, loan2011, loan2012, loan2013, loan2014, loan2015, loan2016, loan2017, loan2018, loan2019)

然而，我得到的是每年的相同结果，这向我表明循环没有按我想要的那样工作。

谢谢！

Answer 1

• round(female2.ratio_floanofficers.mean() 对于每个循环都是相同的，因此您每年都会得到相同的值。 y 尚未用于选择与特定年份对应的数据。
  • 如果需要使用for-loop，请将round(female2.ratio_floanofficers.mean() 替换为以下内容
  • round(female2[female2.fiscal_year == y]['ratio_loanofficers'].mean(), 3)
• 当需要创建多个变量时，使用dict
• f'loan{year}' 是 f-String: A New and Improved Way to Format Strings in Python
  • PEP 498 - Literal String Interpolation
• {year: 'some value' for year in range(2010, 2020)} 是 dictionary comprehension
• female2[female2.fiscal_year == year] 是 Boolean indexing

import pandas as pd

# dataframe
female2 = pd.DataFrame({'fiscal_year': [2018, 2018, 2018, 2018, 2019, 2019, 2019, 2019],
                        'ratio_female_borrowers': [1, 2, 3, 4, 5, 6, 7, 8]})

   fiscal_year  ratio_female_borrowers
0         2018                       1
1         2018                       2
2         2018                       3
3         2018                       4
4         2019                       5
5         2019                       6
6         2019                       7
7         2019                       8

# calculate mean for loan year into dict
loans = {f'loan{year}': round(female2[female2.fiscal_year == year]['ratio_female_borrowers'].mean(), 3) for year in range(2010, 2020)}

print(loans)

{'loan2010': nan,
 'loan2011': nan,
 'loan2012': nan,
 'loan2013': nan,
 'loan2014': nan,
 'loan2015': nan,
 'loan2016': nan,
 'loan2017': nan,
 'loan2018': 2.5,
 'loan2019': 6.5}

print(loans['loan2019'])

>>> 6.5

与dict comprehension 等效的for-loop

loans = dict()

for year in range(2010, 2020):
    loans[f'loan{year}'] = round(female2[female2.fiscal_year == year]['ratio_female_borrowers'].mean(), 3)

使用pandas.DataFrame.groupby

ratio_female_borrowers_mean = female2.groupby(['fiscal_year'], as_index=False)['ratio_female_borrowers'].agg(['mean'])

print(ratio_female_borrowers_mean)

             mean
fiscal_year      
2018          2.5
2019          6.5