Reputation: 389
I have a sort of FIZZ-BUZZ problem. I have a calendar with weekdays. In the next column I put 'FIZZ' at certain rows. If there is an empty gap between two 'FIZZ' I put 'BUZZ' in between except if the weekday is 'SUN'. See the code below (I use 0.15.2 pandas version):
import datetime
import pandas as pd
dict_weekday = {1: 'MON', 2: 'TUE', 3: 'WED', 4: 'THU', 5: 'FRI', 6: 'SAT', 7: 'SUN'}
df = pd.DataFrame(pd.date_range(datetime.date(2014, 1, 1), datetime.date(2014, 1, 10), freq='D'), columns=['Date'])
df['Weekday'] = df['Date'].apply(lambda x: dict_weekday[x.isoweekday()])
df['A'] = df['Weekday']
idx_lst = [0, 2, 3, 5, 9]
df.loc[idx_lst, 'A'] = 'FIZZ'
previous_idx = idx_lst[0]
for idx in idx_lst:
print idx
try:
print df.loc[idx - 1, 'Weekday'], df.loc[idx, 'Weekday']
if idx - previous_idx == 2 and df.loc[idx - 1, 'Weekday'] != 'SUN':
df.loc[idx-1, 'A'] = 'BUZZ'
except KeyError:
continue
previous_idx = idx
print df
The output is:
0
2
2014-12-18 00:00:00 FRI
3
FRI SAT
5
2014-12-21 00:00:00 MON
9
2014-12-18 00:00:00 FRI
Date Weekday A
0 2014-01-01 WED FIZZ
1 2014-01-02 THU BUZZ
2 2014-01-03 FRI FIZZ
3 2014-01-04 SAT FIZZ
4 2014-01-05 SUN BUZZ
5 2014-01-06 MON FIZZ
6 2014-01-07 TUE TUE
7 2014-01-08 WED WED
8 2014-01-09 THU THU
9 2014-01-10 FRI FIZZ
Notice line 4, there should be SUN in column A instead of BUZZ. Notice also that if idx-1 is not in idx_lst the .loc[idx-1] gives a time stamp. if I use .ix instead of .loc I get the correct answer:
0
2
THU FRI
3
FRI SAT
5
SUN MON
9
THU FRI
Date Weekday A
0 2014-01-01 WED FIZZ
1 2014-01-02 THU BUZZ
2 2014-01-03 FRI FIZZ
3 2014-01-04 SAT FIZZ
4 2014-01-05 SUN SUN
5 2014-01-06 MON FIZZ
6 2014-01-07 TUE TUE
7 2014-01-08 WED WED
8 2014-01-09 THU THU
9 2014-01-10 FRI FIZZ
Any explanations ? Thanks in advance.
Upvotes: 4
Views: 897
Reputation: 881037
The surprising behavior is due to pd.Series
attempting to coerce datetime-like values to pd.Timestamps
.
df.loc[1]
returns pd.Series([pd.Timestamp('2014-01-02'), 'THU', 'THU'])
which unfortunately gets coerced to Timestamps since all three values are datetime-like:
In [154]: pd.Series([pd.Timestamp('2014-01-02'), 'THU', 'THU'])
Out[154]:
0 2014-01-02
1 2014-12-18
2 2014-12-18
dtype: datetime64[ns]
In contrast, df.loc[2]
does not coerce the values to Timestamps since 'FIZZ'
is not datelike:
In [155]: pd.Series([pd.Timestamp('2014-01-03'), 'FRI', 'FIZZ'])
Out[155]:
0 2014-01-03 00:00:00
1 FRI
2 FIZZ
dtype: object
The problem can be avoided by forming the Series, df['Weekday']
first before using .loc
:
In [158]: df['Weekday'].loc[1]
Out[158]: 'THU'
This works because df['Weekday'].dtype
remains dtype('O')
; there is no conversion to Timestamps.
for idx in idx_lst:
try:
# print(idx-1, df.ix[idx - 1, 'Weekday'], df.loc[idx - 1, 'Weekday'])
if idx - previous_idx == 2 and df['Weekday'].loc[idx - 1] != 'SUN':
df.loc[idx-1, 'A'] = 'BUZZ'
except KeyError:
continue
previous_idx = idx
yields
Date Weekday A
0 2014-01-01 WED FIZZ
1 2014-01-02 THU BUZZ
2 2014-01-03 FRI FIZZ
3 2014-01-04 SAT FIZZ
4 2014-01-05 SUN SUN
5 2014-01-06 MON FIZZ
6 2014-01-07 TUE TUE
7 2014-01-08 WED WED
8 2014-01-09 THU THU
9 2014-01-10 FRI FIZZ
Upvotes: 2