[英]float() argument must be a string or a number, not 'Timestamp'
I can't make scilearn work with a datetime series. 我不能让scilearn使用日期时间系列。
found this post but did not help me = Pandas : TypeError: float() argument must be a string or a number 发现这篇文章但没有帮助我= Pandas:TypeError:float()参数必须是字符串或数字
the csv file has 2 date columns with a date, dates are in the following format: 2017-07-21 06:19:53 (string) csv文件有2个日期列和日期,日期格式如下:2017-07-21 06:19:53(string)
i converted the string to an datetime64[ns], so the date became a long value and i could do calculations on it. 我将字符串转换为datetime64 [ns],因此日期变为长值,我可以对其进行计算。 scilearn refuses this type and gives the error float() argument must be a string or a number, not 'Timestamp' scilearn拒绝这种类型并给出错误float()参数必须是字符串或数字,而不是'Timestamp'
also tried with pandas.to_datetime() no luck. 也尝试过pandas.to_datetime()没有运气。
the model i use in scilearn is the KMeans clustering model. 我在scilearn中使用的模型是KMeans聚类模型。 when printing the dtypes this is the result: 打印dtypes时,结果如下:
ip int64
date datetime64[ns]
succesFlag int64
app int64
enddate datetime64[ns]
user_userid int64
dtype: object
Here is my code: 这是我的代码:
def getDataframe():
df = pd.read_csv(filename)
df['date']=df['date'].astype('datetime64[ns]',inplace=True)
df['enddate']=df['enddate'].astype('datetime64[ns]',inplace=True)
df['app']=df['app'].replace({
"Azure": 0 ,
"Peoplesoft":1,
"Office":2 ,
"DevOps":3 ,
"Optima":4 ,
"Ada-Tech": 5
},inplace=True)
df['ip']=df['ip'].apply(lambda x: int(ip4.ip_address(x))).to_frame('ip')
print(df.dtypes)
return df
the expectation was that KMeans clustering model would work with numerical values as i converted them but it did not. 期望是KMeans聚类模型可以使用数值,因为我转换它们但它没有。
what did i wrong ? 我错了什么?
I suggest change your solution - a but simplify also: 我建议改变你的解决方案 - 但也要简化:
parse_dates
for converting columns to datetimes and then to numeric unix datetimes 添加参数parse_dates
以将列转换为日期时间,然后再转换为数字unix日期时间 inplace=True
or use faster map
- it also create NaNs for non matched values, so output is numeric too 用于转换remove inplace=True
或使用更快的map
- 它还为非匹配值创建NaN,因此输出也是数字 def getDataframe():
df = pd.read_csv(filename, parse_dates=['date','enddate'])
df[['date','enddate']] = df[['date','enddate']].astype(np.int64) // 10**9
df['app']=df['app'].map({
"Azure": 0 ,
"Peoplesoft":1,
"Office":2 ,
"DevOps":3 ,
"Optima":4 ,
"Ada-Tech": 5
})
df['ip']=df['ip'].apply(lambda x: int(ip4.ip_address(x))).to_frame('ip')
print(df.dtypes)
return df
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