How to create a function to convert monthly data to daily, weekly in pandas dataframe? - pandas

Question

I have the below monthly data in the dataframe and I need to convert the data to weekly, daily, biweekly.
date chair_price vol_chair
01-09-2018 23 30
01-10-2018 53 20
daily: price as same and vol_chair divided by days of the month
weekly: price as same and vol_chair divided by number of weeks in a month
expected output:
daily:
date chair_price vol_chair
01-09-2018 23 1
02-09-2018 23 1
03-09-2018 23 1
..
30-09-2018 23 1
01-10-2018 53 0.64
..
31-10-2018 53 0.64
weekly:
date chair_price vol_chair
02-09-2018 23 6
09-09-2018 23 6
16-09-2018 23 6
23-09-2018 23 6
30-09-2018 23 6
07-10-2018 53 5
14-10-2018 53 5
..
I am using below code as for column vol, any quick way to do it together i.e. keep price same and vol - take action and find number of weeks in a month
df.resample('W').ffill().agg(lambda x: x/4)
df.resample('D').ffill().agg(lambda x: x/30)
and need to use calendar.monthrange(2012,1)[1] to identify days
def func_count_number_of_weeks(df):
return len(calendar.monthcalendar(df['DateRange'].year, df['DateRange'].month))
def func_convert_from_monthly(df, col, category, columns):
if category == "Daily":
df['number_of_days'] = df['DateRange'].dt.daysinmonth
for column in columns:
df[column] = df[column] / df['number_of_days']
df.drop('number_of_days', axis=1, inplace=True)
elif category == "Weekly":
df['number_of_weeks'] = df.apply(func_count_number_of_weeks, axis=1)
for column in columns:
df[column] = df[column] / df['number_of_weeks']
df.drop('number_of_weeks', axis=1, inplace=True)
return df
def func_resample_from_monthly(df,col, category):
df.set_index(col, inplace=True)
df.index = pd.to_datetime(df.index, dayfirst=True)
if category == "Monthly":
df = df.resample('MS').ffill()
elif category == "Weekly":
df = df.resample('W').ffill()
return df

Answer 1

Use:
#convert to datetimeindex
df.index = pd.to_datetime(df.index, dayfirst=True)
#add new next month for correct resample
idx = df.index[-1] + pd.offsets.MonthBegin(1)
df = df.append(df.iloc[[-1]].rename({df.index[-1]: idx}))
#resample with forward filling values, remove last helper row
#df1 = df.resample('D').ffill().iloc[:-1]
df1 = df.resample('W').ffill().iloc[:-1]
#divide by size of months
df1['vol_chair'] /= df1.resample('MS')['vol_chair'].transform('size')
print (df1)
chair_price vol_chair
date
2018-09-02 23 6.0
2018-09-09 23 6.0
2018-09-16 23 6.0
2018-09-23 23 6.0
2018-09-30 23 6.0
2018-10-07 53 5.0
2018-10-14 53 5.0
2018-10-21 53 5.0
2018-10-28 53 5.0