如何遍历 pandas dataframe 并检查日期时间索引中的日期

Question

I have a large dataframe with this datetimeindex:

...   Date                 A       B

190   2019-09-13 21:50:00  1       2
191   2019-09-13 21:55:00  3       2
192   2019-09-13 22:00:00  1       2
193   2019-09-13 22:05:00  3       2
194   2019-09-13 22:10:00  1       2
195   2019-09-16 06:00:00  1       2
196   2019-09-16 06:05:00  1       2
197   2019-09-16 06:10:00  4       2
198   2019-09-16 06:15:00  1       2
199   2019-09-16 06:20:00  4       2
200   2019-09-16 06:25:00  1       2

.....
Name: Date, dtype: datetime64[ns]

Now I need to count if A is larger or equal to B, but only the first time on each day. How can I achieve it that the list is getting filled only with the first hit per day?

count = []

for i in df.index:
    if A[i] >= B[i]:
       count.append('A is larger than B' + f" on {df.Date[i]}")

My desired output according to this example would be

A is larger than B on 2019-09-13 21:55:00  
A is larger than B on 2019-09-16 06:10:00  

Answer 1

You can first filter rows by Series.ge (greater or equal, >= ) with boolean indexing and then get first values by Series.dt.date and GroupBy.first :

df['Date'] = pd.to_datetime(df['Date'])
m = df['A'].ge(df['B'])

df1 = df[m].groupby(df['Date'].dt.date).first()
print (df1)
                          Date  A  B
Date                                
2019-09-13 2019-09-13 21:55:00  3  2
2019-09-16 2019-09-16 06:10:00  4  2

Or create helper column by dates and then use DataFrame.drop_duplicates :

df['Date'] = pd.to_datetime(df['Date'])
df['d'] = df['Date'].dt.date

m = df['A'].ge(df['B'])

df1 = df[m].drop_duplicates('d')
print (df1)
                   Date  A  B           d
191 2019-09-13 21:55:00  3  2  2019-09-13
197 2019-09-16 06:10:00  4  2  2019-09-16

---

for d in df1.Date:
    print ('A is larger than B' + f" on {d}")
A is larger than B on 2019-09-13 21:55:00
A is larger than B on 2019-09-16 06:10:00