如何解决根据标题列表创建汇总列
我有一个包含调查数据的数据框。它包含其他几个具有人口统计数据的列（例如年龄，部门等）以及带有等级的列。希望根据评分列的计算向数据框添加一些列。
添加列的目的是为了提供a）获得有利答复的数量b）获得有利答复的百分比（没有有利答复/该因素中没有项目）c）获得有利因素的因子水平百分比响应（如果存在任何具有NaN的项目属于因子，则为NaN） 下表显示了如何将其应用于指导因子的示例 希望针对多样性，领导力，敬业度等其他因素进行复制。
Coach_q1 Coach_q2 Coach_q8 coach_favcount coach_fav_perc coach_agg_perc
Favourable Neutral Favourable 2 66.6% 66.6%
Favourable Favourable NaN 2 100% NaN
Favourable Favourable Unfavourable 2 66.6% 66.6%
NaN NaN Unfavourable 0 0% NaN
我使用了以下代码，但是它只能工作，但是只能获得fav_count列和fav_perc列进行辅导。想要a）获取_agg_perc列，并b）将其应用于所有其他因素。
#Get the Coaching Columns
coaching_agg = df.loc[:,df.columns.str.contains('Coaching_')]
#Create a column to store the number of favourable responses
df['coaching_fav_count'] = df[coaching_cols == 'Favourable'].notna().sum(axis=1)
#create a column to store the percentage of favourable responses
df['coaching_fav_perc'] = df['coaching_fav'] / len(coaching_agg.columns)
我猜想for循环背后的逻辑是：a）创建一个评级列的列表（请参见下面的代码），以及b）创建一个函数以计算计数，有利响应的百分比，并在以下位置查找NaN的存在项目级别； c）创建一个for循环，以将该功能应用于评分列。
#Create a list made up of rating cols
ratingcollist = ['Coaching_','Communication_','Development_','diversity_','Engagement_']
ratingcols = df.loc[:,df.columns.str.contains('|'.join(ratingcollist))]
感谢我能获得的任何形式的帮助，谢谢！
解决方法
我认为您需要分别处理列表的每个值：
df = pd.DataFrame({'Coach_q1': ['Favourable','Favourable','nan'],'Coach_q2': ['Neutral','NaN'],'Coach_q8': ['Favourable','nan','Unfavourable','Unfavourable']})
print (df)
Coach_q1 Coach_q2 Coach_q8
0 Favourable Neutral Favourable
1 Favourable Favourable nan
2 Favourable Favourable Unfavourable
3 nan NaN Unfavourable
#replace nan and NaN strings to missing values
df = df.replace(['nan',np.nan)
ratingcollist = ['Coach_','Communication_','Development_','Diversity_','Engagement_']
for rat in ratingcollist:
#filter columns by substrings
cols = df.filter(like=rat).columns
#mask for no missing values
mask = df[cols].notna().all(axis=1)
#create new columns if match
if len(cols) > 0:
df[f'{rat.lower()}fav_count'] = (df[cols] == 'Favourable').sum(axis=1)
df[f'{rat.lower()}fav_perc'] = df[f'{rat.lower()}fav_count'] / df[cols].count(axis=1)
df.loc[mask,f'{rat.lower()}agg_perc'] = df.loc[mask,f'{rat.lower()}fav_count'] / len(cols)
print (df)
Coach_q1 Coach_q2 Coach_q8 coach_fav_count coach_fav_perc \
0 Favourable Neutral Favourable 2 0.666667
1 Favourable Favourable NaN 2 1.000000
2 Favourable Favourable Unfavourable 2 0.666667
3 NaN NaN Unfavourable 0 0.000000
coach_agg_perc
0 0.666667
1 NaN
2 0.666667
3 NaN
如果将nan替换为fav_perc的单词丢失输出是错误的，则第二个值应为1，因为count排除了错误的值：
df = pd.DataFrame({'Coach_q1': ['Favourable','Unfavourable']})
print (df)
Coach_q1 Coach_q2 Coach_q8
0 Favourable Neutral Favourable
1 Favourable Favourable nan
2 Favourable Favourable Unfavourable
3 nan NaN Unfavourable
df = df.replace(['nan','Missing')
print (df)
Coach_q1 Coach_q2 Coach_q8
0 Favourable Neutral Favourable
1 Favourable Favourable Missing
2 Favourable Favourable Unfavourable
3 Missing Missing Unfavourable
#create a list of all the rating columns
ratingcollist = ['Coach_','Diversity','Leadership','Engagement']
#create a for loop to get all the columns that match the column list keyword
for rat in ratingcollist:
cols = df.filter(like=rat).columns
mask = (df[cols] != 'Missing').all(axis=1)
#create 3 new columns for each factor,one for count of Favourable responses,#one for percentage of Favourable responses,and one for Factor Level percentage of Favourable responses
if len(cols) > 0:
df[f'{rat.lower()}fav_count'] = (df[cols] == 'Favourable').sum(axis=1)
df[f'{rat.lower()}fav_perc'] = df[f'{rat.lower()}fav_count'] / df[cols].count(axis=1)
df.loc[mask,f'{rat.lower()}fav_count'] / len(cols)
print (df)
Coach_q1 Coach_q2 Coach_q8 coach_fav_count coach_fav_perc \
0 Favourable Neutral Favourable 2 0.666667
1 Favourable Favourable Missing 2 0.666667
2 Favourable Favourable Unfavourable 2 0.666667
3 Missing Missing Unfavourable 0 0.000000
coach_agg_perc
0 0.666667
1 NaN
2 0.666667
3 NaN
因此，如果有必要使用Missing，请将count更改为sum，比较不等于Missing：
#create a list of all the rating columns
ratingcollist = ['Coach_',and one for Factor Level percentage of Favourable responses
if len(cols) > 0:
df[f'{rat.lower()}fav_count'] = (df[cols] == 'Favourable').sum(axis=1)
df[f'{rat.lower()}fav_perc'] = df[f'{rat.lower()}fav_count'] / df[cols].ne('Missing').sum(axis=1)
df.loc[mask,f'{rat.lower()}fav_count'] / len(cols)
print (df)
Coach_q1 Coach_q2 Coach_q8 coach_fav_count coach_fav_perc \
0 Favourable Neutral Favourable 2 0.666667
1 Favourable Favourable Missing 2 1.000000
2 Favourable Favourable Unfavourable 2 0.666667
3 Missing Missing Unfavourable 0 0.000000
coach_agg_perc
0 0.666667
1 NaN
2 0.666667
3 NaN
我们可以尝试不使用循环：
columns_split = df.columns.str.split('_')
count = (df.set_axis(pd.MultiIndex.from_tuples(map(tuple,columns_split)),axis=1)
.stack()
.eq('Favourable')
.sum(level=0))
s = columns_split.str[0].to_series().add('_%Fav')
new_df = (df.join(count.add_suffix('_FavCount'))
.join(count.add_suffix('_%Fav').div(s.value_counts()))
)
print(new_df)
输出
Coaching_q1 Coaching_q2 Diversity_q1 Diversity_q2 Coaching_FavCount \
0 Favourable Neutral Favourable Favourable 1.0
1 Favourable Favourable Favourable Favourble 2.0
2 NaN Favourable NaN NaN 1.0
Diversity_FavCount Coaching_%Fav Diversity_%Fav
0 2.0 0.5 1.0
1 1.0 1.0 0.5
2 0.0 0.5 0.0
问题已解决，方法是将列中的NaN值重新编码为“缺少”并应用@jezrael推荐的掩码
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