将Timestamp转换为str值python pandas dataframe

Convert Timestamp to str value python pandas dataframe

我有数据框架看起来像这样

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Date Player Fee
0 2017-01-08 Steven Berghuis 6500000
1 2017-07-18 Jerry St. Juste 4500000
2 2017-07-18 Ridgeciano Haps 600000
3 2017-01-07 Sofyan Amrabat 400000

如果符合条件，我想将每个日期值都更改为str

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def is_in_range(x):
ses1 = pd.to_datetime('2013-02-01')
ses2 = pd.to_datetime('2014-02-01')
ses3 = pd.to_datetime('2015-02-01')
ses4 = pd.to_datetime('2016-02-01')
ses5 = pd.to_datetime('2017-02-01')
ses6 = pd.to_datetime('2018-02-01')

if x < ses1 :
x = '2012-13'
if x > ses2 and x < ses3 :
x = '2013-14'
if x > ses3 and x < ses4 :
x = '2014-15'
if x > ses4 and x < ses5 :
x = '2015-16'
if x > ses5 and x < ses6 :
x = '2016-17'
return ses6
aj = ajax_t['Date'].apply(is_in_range)
aj

TypeError Traceback (most recent call last)
in ()
18 x = '2016-17'
19 return ses6
---> 20 aj = ajax_t['Date'].apply(is_in_range)
21 aj

/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/series.py
in apply(self, func, convert_dtype, args, **kwds) 2353
else: 2354 values = self.asobject
-> 2355 mapped = lib.map_infer(values, f, convert=convert_dtype) 2356 2357 if len(mapped) and
isinstance(mapped[0], Series):

pandas/_libs/src/inference.pyx in pandas._libs.lib.map_infer
(pandas/_libs/lib.c:66645)()

in is_in_range(x)
15 if x > ses4 and x < ses5 : 16 x = '2015-16' ---> 17 if x > ses5 and x < ses6 : 18 x = '2016-17' 19 return ses6

pandas/_libs/tslib.pyx in pandas._libs.tslib._Timestamp.richcmp
(pandas/_libs/tslib.c:20281)()

TypeError: Cannot compare type 'Timestamp' with type 'str'

我得到这个错误的任何建议，亲切地

相关讨论

如果需要，您需要转换到列to_datetime，并将变量x更改为另一个，如y，因为在循环中被覆盖。

变量y也应从函数返回：

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ajax_t['Date'] = pd.to_datetime(ajax_t['Date'])

def is_in_range(x):
print (x)
ses1 = pd.to_datetime('2013-02-01')
ses2 = pd.to_datetime('2014-02-01')
ses3 = pd.to_datetime('2015-02-01')
ses4 = pd.to_datetime('2016-02-01')
ses5 = pd.to_datetime('2017-02-01')
ses6 = pd.to_datetime('2018-02-01')

if x < ses1 :
y = '2012-13'
if x > ses2 and x < ses3 :
y = '2013-14'
if x > ses3 and x < ses4 :
y = '2014-15'
if x > ses4 and x < ses5 :
y = '2015-16'
if x > ses5 and x < ses6 :
y = '2016-17'
return y
aj = ajax_t['Date'].apply(is_in_range)
print (aj)
0 2015-16
1 2016-17
2 2016-17
3 2015-16
Name: Date, dtype: object

显然，您没有将Date列作为DateTime加载到DataFrame ajax_t中。尝试转换它

1	ajax_t['Date'] = pd.to_datetime(ajax_t.Date)

或者，如果从文件(例如，data.csv文件)加载数据帧ajax_t，则可以指定参数以强制分析Date列为DateTime类型。

1	ajax_t = pd.read_csv('data.csv', parse_dates=['Date'])

希望这会有所帮助。

您可以尝试更改日期的格式：

1	ses1 = pd.to_datetime('2017-01-08', format='%Y%b/%d')