将日期归入月份
Bin the date into month
现在我有一个table：
Score Customer ID my_dates Threshold Model_name is_alert
50 8 2017-08-05 50 Mod1 yes
50 9 2017-12-05 50 Mod1 yes
50 28 2017-05-22 50 Mod2 yes
50 28 2017-05-26 50 Mod2 yes
50 36 2017-06-20 50 Mod2 yes
如果分数等于或超过阈值，is_alert将显示'yes'
现在我想将日期放入以下格式中，并打印每个型号下每个箱子中的警报数量，但如果在 7 天内对一个客户多次发出警报，则只有第一次点击有助于总分:
Model_name Jan-17 Feb-17 Mar-17 APR-17 May-17 Jun-17
Mod1
Mod2
有人可以帮我吗？谢谢
使用crosstab with convert datetimes to month periods by Series.dt.to_period, last convert to names of months by PeriodIndex.strftime, but before get difference per groups by DataFrameGroupBy.diff and filter rows with missing values (first rows per groups) and less or equal like 7 by Series.ge and boolean indexing:
df['my_dates'] = pd.to_datetime(df['my_dates'])
m = df['my_dates'].dt.to_period('m')
df['diff'] = df.groupby(['Model_name'])['my_dates'].diff().dt.days
print (df)
Score Customer ID my_dates Threshold Model_name is_alert diff
0 50 8 2017-08-05 50 Mod1 yes NaN
1 50 9 2017-12-05 50 Mod1 yes 122.0
2 50 28 2017-05-22 50 Mod2 yes NaN
3 50 28 2017-05-26 50 Mod2 yes 4.0
4 50 36 2017-06-20 50 Mod2 yes 25.0
df = df[df['diff'].ge(7) | df['diff'].isna()]
df1 = pd.crosstab(df['Model_name'], m)
df1.columns = df1.columns.strftime('%b-%y')
print (df1)
my_dates May-17 Jun-17 Aug-17 Dec-17
Model_name
Mod1 0 0 1 1
Mod2 1 1 0 0
现在我有一个table：
Score Customer ID my_dates Threshold Model_name is_alert
50 8 2017-08-05 50 Mod1 yes
50 9 2017-12-05 50 Mod1 yes
50 28 2017-05-22 50 Mod2 yes
50 28 2017-05-26 50 Mod2 yes
50 36 2017-06-20 50 Mod2 yes
如果分数等于或超过阈值，is_alert将显示'yes'
现在我想将日期放入以下格式中，并打印每个型号下每个箱子中的警报数量，但如果在 7 天内对一个客户多次发出警报，则只有第一次点击有助于总分:
Model_name Jan-17 Feb-17 Mar-17 APR-17 May-17 Jun-17
Mod1
Mod2
有人可以帮我吗？谢谢
使用crosstab with convert datetimes to month periods by Series.dt.to_period, last convert to names of months by PeriodIndex.strftime, but before get difference per groups by DataFrameGroupBy.diff and filter rows with missing values (first rows per groups) and less or equal like 7 by Series.ge and boolean indexing:
df['my_dates'] = pd.to_datetime(df['my_dates'])
m = df['my_dates'].dt.to_period('m')
df['diff'] = df.groupby(['Model_name'])['my_dates'].diff().dt.days
print (df)
Score Customer ID my_dates Threshold Model_name is_alert diff
0 50 8 2017-08-05 50 Mod1 yes NaN
1 50 9 2017-12-05 50 Mod1 yes 122.0
2 50 28 2017-05-22 50 Mod2 yes NaN
3 50 28 2017-05-26 50 Mod2 yes 4.0
4 50 36 2017-06-20 50 Mod2 yes 25.0
df = df[df['diff'].ge(7) | df['diff'].isna()]
df1 = pd.crosstab(df['Model_name'], m)
df1.columns = df1.columns.strftime('%b-%y')
print (df1)
my_dates May-17 Jun-17 Aug-17 Dec-17
Model_name
Mod1 0 0 1 1
Mod2 1 1 0 0