Open In Colab

Time Resampling

import pandas as pd
pd.date_range(start="07-1-2025" , end="7-30-2025", freq='W')
DatetimeIndex(['2025-07-06', '2025-07-13', '2025-07-20', '2025-07-27'], dtype='datetime64[ns]', freq='W-SUN')
TIME SERIES OFFSET ALIASES
ALIAS DESCRIPTION
B business day frequency
C custom business day frequency (experimental)
D calendar day frequency
W weekly frequency
M month end frequency
SM semi-month end frequency (15th and end of month)
BM business month end frequency
CBM custom business month end frequency
MS month start frequency
SMS semi-month start frequency (1st and 15th)
BMS business month start frequency
CBMS custom business month start frequency
Q quarter end frequency
intentionally left blank
ALIAS DESCRIPTION
BQ business quarter endfrequency
QS quarter start frequency
BQS business quarter start frequency
A year end frequency
BA business year end frequency
AS year start frequency
BAS business year start frequency
BH business hour frequency
H hourly frequency
T, min minutely frequency
S secondly frequency
L, ms milliseconds
U, us microseconds
N nanoseconds 
df = pd.read_csv("/content/starbucks.csv")#, parse_dates=True, index_col= "Date")
df.head()
Date Close Volume
0 2015-01-02 38.0061 6906098
1 2015-01-05 37.2781 11623796
2 2015-01-06 36.9748 7664340
3 2015-01-07 37.8848 9732554
4 2015-01-08 38.4961 13170548 
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1006 entries, 0 to 1005
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Date    1006 non-null   object 
 1   Close   1006 non-null   float64
 2   Volume  1006 non-null   int64  
dtypes: float64(1), int64(1), object(1)
memory usage: 23.7+ KB
df.index = pd.to_datetime(df["Date"])
df.drop("Date", axis=1)
Close Volume
Date
2015-01-02 38.0061 6906098
2015-01-05 37.2781 11623796
2015-01-06 36.9748 7664340
2015-01-07 37.8848 9732554
2015-01-08 38.4961 13170548
... ... ...
2018-12-24 60.5600 6323252
2018-12-26 63.0800 16646238
2018-12-27 63.2000 11308081
2018-12-28 63.3900 7712127
2018-12-31 64.4000 7690183

1006 rows × 2 columns

df.shape
(1006, 2)
df = pd.read_csv("/content/starbucks.csv", parse_dates=True, index_col= "Date")
df.head()
Close Volume
Date
2015-01-02 38.0061 6906098
2015-01-05 37.2781 11623796
2015-01-06 36.9748 7664340
2015-01-07 37.8848 9732554
2015-01-08 38.4961 13170548 
df.iloc[0]
2015-01-02
Close 3.800610e+01
Volume 6.906098e+06 

df.loc["2015-01-02"]
2015-01-02
Close 3.800610e+01
Volume 6.906098e+06 

df.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1006 entries, 2015-01-02 to 2018-12-31
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Close   1006 non-null   float64
 1   Volume  1006 non-null   int64  
dtypes: float64(1), int64(1)
memory usage: 23.6 KB
full_date_range = pd.date_range(start=df.index.min(), end=df.index.max(), freq='B')
print("Full date range with business days:")
print(full_date_range)
Full date range with business days:
DatetimeIndex(['2015-01-02', '2015-01-05', '2015-01-06', '2015-01-07',
               '2015-01-08', '2015-01-09', '2015-01-12', '2015-01-13',
               '2015-01-14', '2015-01-15',
               ...
               '2018-12-18', '2018-12-19', '2018-12-20', '2018-12-21',
               '2018-12-24', '2018-12-25', '2018-12-26', '2018-12-27',
               '2018-12-28', '2018-12-31'],
              dtype='datetime64[ns]', length=1042, freq='B')
df.index.min()
Timestamp('2015-01-02 00:00:00')
df.index.max()
Timestamp('2018-12-31 00:00:00')
df.resample(rule= "M").mean()
Close Volume
Date
2015-01-31 38.729470 1.336800e+07
2015-02-28 42.862432 7.908719e+06
2015-03-31 44.321836 8.905969e+06
2015-04-30 45.508914 8.540996e+06
2015-05-31 47.488650 6.723716e+06
2015-06-30 49.761627 6.856079e+06
2015-07-31 52.437959 8.087759e+06
2015-08-31 52.797976 1.064881e+07
2015-09-30 53.006186 8.935548e+06
2015-10-31 57.051373 8.893461e+06
2015-11-30 58.296355 7.077431e+06
2015-12-31 57.219514 7.952858e+06
2016-01-31 55.138963 1.383605e+07
2016-02-29 54.446810 1.119357e+07
2016-03-31 55.858968 8.203693e+06
2016-04-30 56.531200 9.407233e+06
2016-05-31 53.016571 7.849410e+06
2016-06-30 52.669300 8.495649e+06
2016-07-31 54.551905 1.018464e+07
2016-08-31 53.518291 7.797496e+06
2016-09-30 52.022771 9.178403e+06
2016-10-31 50.945319 7.073672e+06
2016-11-30 52.845452 1.128694e+07
2016-12-31 55.331981 7.898740e+06
2017-01-31 55.057330 9.438392e+06
2017-02-28 54.157374 1.032388e+07
2017-03-31 54.155570 9.806376e+06
2017-04-30 56.968300 9.546442e+06
2017-05-31 59.081968 7.295628e+06
2017-06-30 59.031950 7.745303e+06
2017-07-31 55.933905 1.105518e+07
2017-08-31 52.567952 1.024694e+07
2017-09-30 52.995195 9.225082e+06
2017-10-31 53.310614 8.129790e+06
2017-11-30 55.359462 1.025010e+07
2017-12-31 57.006955 8.758599e+06
2018-01-31 58.205433 1.155463e+07
2018-02-28 54.729784 1.255456e+07
2018-03-31 56.913367 8.852274e+06
2018-04-30 57.635371 8.503481e+06
2018-05-31 56.482395 6.841657e+06
2018-06-30 53.663433 1.612197e+07
2018-07-31 50.105995 1.165892e+07
2018-08-31 52.279430 8.617108e+06
2018-09-30 55.234000 9.342414e+06
2018-10-31 57.035104 1.243318e+07
2018-11-30 66.363652 1.557092e+07
2018-12-31 64.609474 1.316915e+07 
print(df.index.is_monotonic_increasing)
True
1/7/2025 ,3/ 7/ 20225   1/7/20
df.sort_index()
Close Volume
Date
2015-01-02 38.0061 6906098
2015-01-05 37.2781 11623796
2015-01-06 36.9748 7664340
2015-01-07 37.8848 9732554
2015-01-08 38.4961 13170548
... ... ...
2018-12-24 60.5600 6323252
2018-12-26 63.0800 16646238
2018-12-27 63.2000 11308081
2018-12-28 63.3900 7712127
2018-12-31 64.4000 7690183

1006 rows × 2 columns

df["Close"].resample(rule= "W").max().plot(kind = "bar", figsize=(12,5))

df["Close"].resample("Y").mean().plot(kind="bar", figsize = (12,5))

df["Close"].resample("M").mean().plot(kind="bar",figsize=(12,5))