Pandas pd.cut()-合并日期时间列/系列 [英] Pandas pd.cut() - binning datetime column / series
问题描述
尝试使用pd.cut()进行垃圾箱，但相当复杂-
Attempting to do a bin using pd.cut() but it is fairly elaborate-
一个同事向我发送了多个文件，这些文件的报告日期如下:
A collegue sends me multiple files with report dates such as:
'03-16-2017 to 03-22-2017'
'03-23-2017 to 03-29-2017'
'03-30-2017 to 04-05-2017'
它们全部合并为一个数据帧，并赋予列名称df ['Filedate']，以便文件中的每个记录都具有正确的文件日期.
They are all combined into a single dataframe and given a column name, df['Filedate'] so that every record in the file has the correct filedate.
最后一天是一个截止点，因此我创建了一个新列df ['Filedate_bin']，将最后一天转换为字符串，格式为3/22/2017，3/29/2017，4/05/2017.
The last day is a cutoff point, so I created a new column df['Filedate_bin'] which converts the last day to 3/22/2017, 3/29/2017, 4/05/2017 as a string.
然后，我创建了一个列表:Filedate_bin_list = df.Filedate_bin.unique().因此，我有一个唯一的字符串截止日期列表，我想将其用作垃圾箱.
Then I created a list: Filedate_bin_list= df.Filedate_bin.unique(). As a result I have a unique list of string cutoff dates that I would like to use as bins.
将不同的数据导入数据框中，有一列交易日期:3/28/2017、3/29/2017、3/30/2017、4/1/2017、4/2/2017等.分配将它们放到垃圾箱很困难，它尝试过:
Importing different data into dataframe, there is a column of transaction dates: 3/28/2017, 3/29/2017, 3/30/2017, 4/1/2017, 4/2/2017, etc. Assigning them to a bin is difficult, it tried:
df['bin'] = pd.cut(df.Processed_date, Filedate_bin_list)
已收到TypeError: unsupported operand type for -: 'str' and 'str'
返回并尝试将Filedate_bin转换为datetime，格式为'％m/％d/％Y'并获取
Went back and tried converting the Filedate_bin to datetime, format='%m/%d/%Y' and get
TypeError: Cannot cast ufunc less input from dtype('<m8[ns]') to dtype ('<m8') with casting rule 'same_kind'.
是否有更好的方法将我的处理日期与文本容器进行分箱?
Is there a better way to bin my processed_date(s) to text bins?
我想将我的处理日期与2017年3月27日绑定为"2017年3月23日至2017年3月29日"
Am trying to tie in my processed dates 3/27/2017 to '03-23-2017 to 03-29-2017'
推荐答案
UPDATE: starting from Pandas v0.20.1 (May 5, 2017) pd.cut and pd.qcut support datetime64 and timedelta64 dtypes (GH14714, GH14798).
旧答案:
考虑这种方法:
df = pd.DataFrame(pd.date_range('2000-01-02', freq='1D', periods=15), columns=['Date'])
bins_dt = pd.date_range('2000-01-01', freq='3D', periods=6)
bins_str = bins_dt.astype(str).values
labels = ['({}, {}]'.format(bins_str[i-1], bins_str[i]) for i in range(1, len(bins_str))]
df['cat'] = pd.cut(df.Date.astype(np.int64)//10**9,
bins=bins_dt.astype(np.int64)//10**9,
labels=labels)
结果:
In [59]: df
Out[59]:
Date cat
0 2000-01-02 (2000-01-01, 2000-01-04]
1 2000-01-03 (2000-01-01, 2000-01-04]
2 2000-01-04 (2000-01-01, 2000-01-04]
3 2000-01-05 (2000-01-04, 2000-01-07]
4 2000-01-06 (2000-01-04, 2000-01-07]
5 2000-01-07 (2000-01-04, 2000-01-07]
6 2000-01-08 (2000-01-07, 2000-01-10]
7 2000-01-09 (2000-01-07, 2000-01-10]
8 2000-01-10 (2000-01-07, 2000-01-10]
9 2000-01-11 (2000-01-10, 2000-01-13]
10 2000-01-12 (2000-01-10, 2000-01-13]
11 2000-01-13 (2000-01-10, 2000-01-13]
12 2000-01-14 (2000-01-13, 2000-01-16]
13 2000-01-15 (2000-01-13, 2000-01-16]
14 2000-01-16 (2000-01-13, 2000-01-16]
In [60]: df.dtypes
Out[60]:
Date datetime64[ns]
cat category
dtype: object
说明:
df.Date.astype(np.int64)//10**9-将datetime值转换为UNIX时期(时间戳-自1970-01-01 00:00:00起的秒数):
df.Date.astype(np.int64)//10**9 - converts datetime values into UNIX epoch (timestamp - # of seconds since 1970-01-01 00:00:00):
In [65]: df.Date.astype(np.int64)//10**9
Out[65]:
0 946771200
1 946857600
2 946944000
3 947030400
4 947116800
5 947203200
6 947289600
7 947376000
8 947462400
9 947548800
10 947635200
11 947721600
12 947808000
13 947894400
14 947980800
Name: Date, dtype: int64
同样适用于bins:
In [66]: bins_dt.astype(np.int64)//10**9
Out[66]: Int64Index([946684800, 946944000, 947203200, 947462400, 947721600, 947980800], dtype='int64')
标签:
In [67]: labels
Out[67]:
['(2000-01-01, 2000-01-04]',
'(2000-01-04, 2000-01-07]',
'(2000-01-07, 2000-01-10]',
'(2000-01-10, 2000-01-13]',
'(2000-01-13, 2000-01-16]']
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