[英]Using Pandas .diff() on a time series column with a groupby
I have a CSV
file of customer purchases in no particular order that I read into a Pandas
Dataframe
. 我有一个客户购买的
CSV
文件,没有按照我读入Pandas
Dataframe
特定顺序。 I'd like to add a column for each purchase and show how much time has passed since the last purchase, grouped by customer. 我想为每次购买添加一个列,并显示自上次购买以来已经过了多少时间,按客户分组。 I'm not sure where it's getting the differences, but they are much too large (even if in seconds).
我不确定它在哪里得到差异,但它们太大了(即使在几秒钟内)。
CSV: CSV:
Customer Id,Purchase Date
4543,1/1/2015
4543,2/5/2015
4543,3/15/2015
2322,1/1/2015
2322,3/1/2015
2322,2/1/2015
Python: 蟒蛇:
import pandas as pd
import time
start = time.time()
data = pd.read_csv('data.csv', low_memory=False)
data = data.sort_values(by=['Customer Id', 'Purchase Date'])
data['Purchase Date'] = pd.to_datetime(data['Purchase Date'])
data['Purchase Difference'] = (data.groupby(['Customer Id'])['Purchase Date']
.diff()
.fillna('-')
)
print data
Output: 输出:
Customer Id Purchase Date Purchase Difference
3 2322 2015-01-01 -
5 2322 2015-02-01 2678400000000000
4 2322 2015-03-01 2419200000000000
0 4543 2015-01-01 -
1 4543 2015-02-05 3024000000000000
2 4543 2015-03-15 328320000000000
Desired Output: 期望的输出:
Customer Id Purchase Date Purchase Difference
3 2322 2015-01-01 -
5 2322 2015-02-01 31 days
4 2322 2015-03-01 28 days
0 4543 2015-01-01 -
1 4543 2015-02-05 35 days
2 4543 2015-03-15 38 days
I think you can add to read_csv
parameter parse_dates
for parsing datetime
, sort_values
and last groupby
with diff
: 我想你可以添加
read_csv
参数parse_dates
来解析datetime
, sort_values
和last groupby
with diff
:
import pandas as pd
import io
temp=u"""Customer Id,Purchase Date
4543,1/1/2015
4543,2/5/2015
4543,3/15/2015
2322,1/1/2015
2322,3/1/2015
2322,2/1/2015"""
#after testing replace io.StringIO(temp) to filename
data = pd.read_csv(io.StringIO(temp), parse_dates=['Purchase Date'])
data.sort_values(by=['Customer Id', 'Purchase Date'], inplace=True)
data['Purchase Difference'] = data.groupby(['Customer Id'])['Purchase Date'].diff()
print data
Customer Id Purchase Date Purchase Difference
3 2322 2015-01-01 NaT
5 2322 2015-02-01 31 days
4 2322 2015-03-01 28 days
0 4543 2015-01-01 NaT
1 4543 2015-02-05 35 days
2 4543 2015-03-15 38 days
You can just apply diff
to the Purchase Date
column once it has been converted to a Timestamp. 一旦转换为时间戳,您就可以将
diff
应用于Purchase Date
列。
df['Purchase Date'] = pd.to_datetime(df['Purchase Date'])
df.sort_values(['Customer Id', 'Purchase Date'], inplace=True)
df['Purchase Difference'] = \
[str(n.days) + ' day' + 's' if n > pd.Timedelta(days=1) else '' if pd.notnull(n) else ""
for n in df.groupby('Customer Id', sort=False)['Purchase Date'].diff()]
>>> df
Customer Id Purchase Date Purchase Difference
3 2322 2015-01-01
5 2322 2015-02-01 31 days
4 2322 2015-03-01 28 days
0 4543 2015-01-01
1 4543 2015-02-05 35 days
2 4543 2015-03-15 38 days
6 4543 2015-03-15
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