英文:
Use key of groupby to create another column pandas python
问题
You can resolve the "keyword error: month" by renaming the 'month' column in your DataFrame before adding the 'Month Name' column. Here's the corrected code:
import calendar
# Rename the 'month' column to avoid the keyword error
df1 = df1.rename(columns={'month': 'Month'})
# Add the 'Month Name' column using the renamed 'Month' column
df1['Month Name'] = df1['Month'].apply(lambda x: calendar.month_abbr[x])
# Now your DataFrame should have the 'Month Name' column
This code renames the 'month' column to 'Month' and then adds the 'Month Name' column based on the renamed column, which should resolve the keyword error you encountered.
英文:
df
order_date month year Days Data
2015-12-20 12 2014 1 3
2016-1-21 1 2014 2 3
2015-08-20 8 2015 1 1
2016-04-12 4 2016 4 1
and so on
Code:(finding mean, min, and median of days column and finding number of order_dates month wise for each respective year)
df1 = (df.groupby(["year", "month"])
.agg(Min_days=("days", 'min'),
Avg_days=("days", 'mean'),
Median_days=('days','median'),
Count = ('order_date', 'count'))
.reset_index())
df1
year month Min_days Avg_days Median_days Count
2015 1 9 12.56666666 10 4
2015 2 10 13.67678788 9 3
........................................................
2016 12 12 15.7889990 19 2
and so on...
Issue at hand:
I want to have another column month name in the table using key month from df1. Im doing this:
Output I want:
year month Min_days Avg_days Median_days Count Month Name
2015 1 9 12.56666666 10 4 Jan
2015 2 10 13.67678788 9 3 Feb
........................................................
2016 12 12 15.7889990 19 2 Dec
and so on...
import calendar
df1['Month Name']=df1['month'].apply(lambda x:calendar.month_abbr[x])
But I am getting keyword error: month. I am unable to use key month to create another column month name. Pls help
答案1
得分: 1
似乎没有month列，原因可能是month是MultiIndex的level。
检查一下：
print(df1.index.names)
print(df1.columns.tolist())
所以需要：
df1 = df1.reset_index()
df1['Month Name'] = df1['month'].apply(lambda x: calendar.month_abbr[x])
英文:
It seems there is no column month, resaon should be month is level of MultiIndex.
Check it :
print (df1.index.names)
print (df1.columns.tolist())
So need:
df1 = df1.reset_index()
df1['Month Name']=df1['month'].apply(lambda x:calendar.month_abbr[x])
答案2
得分: 1
你可以尝试使用get_level_values和多级索引映射：
s = df1.index.get_level_values(1).map({i:e for i,e in enumerate([*calendar.month_abbr])})
df1 = df1.assign(Month=pd.Series(s,index=df1.index))
或者更简单的方法，如果数据框不是多级索引并且已经重置，只需使用：
df1 = df1.assign(Month=np.array(calendar.month_abbr)[df1['month']])
英文:
You can try map with get_level_values for multiindex mapping:
s = df1.index.get_level_values(1).map({i:e for i,e in enumerate([*calendar.month_abbr])})
df1 = df1.assign(Month=pd.Series(s,index=df1.index)
Or even simpler without apply ,if the dataframe is not a multi index and already reset, just use
df1 = df1.assign(Month=np.array(calendar.month_abbr)[df1['month']])
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