英文:
Pandas groupby(pd.Grouper) is throwing error for datetime but im running it on a datetime object
问题
以下是您要翻译的代码部分：
I'm using pandas in python, and am trying to group a set of dates by month, and determine the highest value in the dates_and_grades["Grade_Values"] column for each month. I wrote the following code attempting to do this:data = pd.read_csv(input_filepath)data['Date'] = pd.to_datetime(data['Date'], format='ISO8601')roped = ["Sport", "Trad"]YDS_DICT={"N/A":"N/A",'3-4':0,'5':1,'5.0':1,'5.1':2,'5.2':3,'5.3':4,'5.4':5,'5.5':6,'5.6':7,'5.7':8,'5.8':9,'5.9':10,'5.10a':11,'5.10b':12, '5.10': 12, '5.10c':13,'5.10d':14,'5.11a':15,'5.11b':16, '5.11':16, '5.11c':17,'5.11d':18,'5.12a':19,'5.12b':20,'5.12c':21,'5.12d':22,'5.13a':23,'5.13b':24,'5.13c':25,'5.13d':26,'5.14a':27,'5.14b':28,'5.14c':29,'5.14d':30,'5.15a':31,'5.15b':32,'5.15c':33,'5.15d':34}roped_only_naive = data.loc[data['Route Type'].isin(roped)].copy()roped_only_naive["Rating"] = roped_only_naive['Rating'].map(slash_grade_converter)roped_only_naive["Rating"] = roped_only_naive['Rating'].map(flatten_plus_and_minus_grades)roped_only_naive["Rating"] = roped_only_naive['Rating'].map(remove_risk_ratings)dates_and_grades = roped_only_naive[['Date', 'Rating']]print(dates_and_grades.dtypes)dates_and_grades["Grade_Values"] = dates_and_grades["Rating"].map(lambda data: YDS_DICT[data])print(dates_and_grades.dtypes)dates_and_grades['Date'] = dates_and_grades['Date'].groupby(pd.Grouper(freq='M'))print(dates_and_grades)However, I get the following error when run.TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'What is strange is that when I check the types on my dataframe usingprint(dates_and_grades.dtypes)I get the following printoutDate datetime64[ns]Rating objectGrade_Values int64So it looks like my Date column is indeed a datetime object.My question is then, why doesn't the groupby(pd.Grouper(freq='M')) function work on my dates_and_grades['Date'] column if it does seem like dates_and_grades['Date'] is actually a datetime type?
英文:
I'm using pandas in python, and am trying to group a set of dates by month, and determine the highest value in the dates_and_grades["Grade_Values"] column for each month. I wrote the following code attempting to do this:
data = pd.read_csv(input_filepath)data['Date'] = pd.to_datetime(data['Date'], format = 'ISO8601')roped = ["Sport", "Trad"]YDS_DICT={"N/A":"N/A",'3-4':0,'5':1,'5.0':1,'5.1':2,'5.2':3,'5.3':4,'5.4':5,'5.5':6,'5.6':7,'5.7':8,'5.8':9,'5.9':10,'5.10a':11,'5.10b':12, '5.10': 12, '5.10c':13,'5.10d':14,'5.11a':15,'5.11b':16, '5.11':16, '5.11c':17,'5.11d':18,'5.12a':19,'5.12b':20,'5.12c':21,'5.12d':22,'5.13a':23,'5.13b':24,'5.13c':25,'5.13d':26,'5.14a':27,'5.14b':28,'5.14c':29,'5.14d':30,'5.15a':31,'5.15b':32,'5.15c':33,'5.15d':34}roped_only_naive = data.loc[data['Route Type'].isin(roped)].copy()roped_only_naive["Rating"] = roped_only_naive['Rating'].map(slash_grade_converter)roped_only_naive["Rating"] = roped_only_naive['Rating'].map(flatten_plus_and_minus_grades)roped_only_naive["Rating"] = roped_only_naive['Rating'].map(remove_risk_ratings)dates_and_grades = roped_only_naive[['Date', 'Rating']]print(dates_and_grades.dtypes)dates_and_grades["Grade_Values"] = dates_and_grades["Rating"].map(lambda data: YDS_DICT[data])print(dates_and_grades.dtypes)dates_and_grades['Date'] = dates_and_grades['Date'].groupby(pd.Grouper(freq='M'))print(dates_and_grades)
However, I get the following error when run.
TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'
What is strange is that when I check the types on my dataframe using
print(dates_and_grades.dtypes)
I get the following printout
Date datetime64[ns]Rating objectGrade_Values int64
So it looks like my Date column is indeed a datetime object.
My question is then, why doesn't the groupby(pd.Grouper(freq='M')) function work on my dates_and_grades['Date'] column if it does seem like dates_and_grades['Date'] is actually a datetime type?
答案1
得分: 1
在Grouper中使用参数 key，如果不使用一些转换函数，则不能将groupby的输出分配给新列 - 对于列：
dates_and_grades['new'] = dates_and_grades.groupby(pd.Grouper(freq='M', key='Date'))["Grade_Values"].transform('max')
如果省略 key 参数，Grouper 需要 DatetimeIndex，所以会产生错误。
如果需要每月按最大Grade Values获取行，请使用以下方法：
out = dates_and_grades.loc[dates_and_grades.groupby(pd.Grouper(freq='M', key='Date'))["Grade_Values"].idxmax()]
英文:
Use parameter key in Grouper, also cannot assign ouput of groupby to new column if not used some transformation function - for column :
dates_and_grades['new'] = dates_and_grades.groupby(pd.Grouper(freq='M', key='Date'))["Grade_Values"].transform('max')
If omit key parameter Grouper need DatetimeIndex, so error is expected.
If need rows by maximal Grade Values per months use:
out = dates_and_grades.loc[dates_and_grades.groupby(pd.Grouper(freq='M', key='Date'))["Grade_Values"].idxmax())
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