How Can I Resample Pandas Dataframe By Day On Period Time?

i have a dataframe like this: df.head() Out[2]: price sale_date 0 477,000,000 1396/10/30 1 608,700,000 1396/10/30 2 580,000,000 1396/10/03 3 350,000,000 139

Solution 1:

It seems here not working resample and Grouper with Periods for me in pandas 1.1.3 (I guess bug):

df['sale_date']=df['sale_date'].str.replace('/','').astype(int)
df['price'] = df['price'].str.replace(',','').astype(int)

def conv(x):
    return pd.Period(year=x // 10000,
                     month=x // 100 % 100,
                     day=x % 100, freq='D')
 
df['sale_date'] = df['sale_date'].apply(conv)

# df = df.set_index('sale_date').resample('D')['price'].sum()
#OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1396-03-18 00:00:00

# df = df.set_index('sale_date').groupby(pd.Grouper(freq='D'))['price'].sum()
#OutOfBoundsDatetime: Out of bounds nanosecond timestamp: 1396-03-18 00:00:00

Possible solution is aggregate by sum, so if duplicated sale_date then price values are summed:

df = df.groupby('sale_date')['price'].sum().reset_index()
print (df)
    sale_date      price
0  1396-03-18  328000000
1  1396-10-03  580000000
2  1396-10-30  477000000
3  1396-11-25  608700000
4  1396-12-05  350000000

EDIT: It is possible by Series.reindex with period_range:

s = df.groupby('sale_date')['price'].sum()
rng = pd.period_range(s.index.min(), s.index.max(), name='sale_date')
df = s.reindex(rng, fill_value=0).reset_index()
print (df)
      sale_date      price
0    1396-03-18  328000000
1    1396-03-19          0
2    1396-03-20          0
3    1396-03-21          0
4    1396-03-22          0
..          ...        ...
258  1396-12-01          0
259  1396-12-02          0
260  1396-12-03          0
261  1396-12-04          0
262  1396-12-05  350000000

[263 rows x 2 columns]