Python: Creating New Column in Dataframe based on Start and End Dates in Another Dataframe

Question

I have the following data frame denoting start and end dates for a specific quarter:

My function below takes an existing data set (not shown) and creates a new column called "Quarter". If the date in my existing data set falls within the start and end dates in the above dataframe, then the new "Quarter" column gets a label (i.e. Q1 or Q2). Otherwise I want it to be blank.

# dynamic function for quarterly cuts

    def quarters(df, df_quarters):
        for i, m in df.iterrows():
            for j, (Quarter, Start_Date, End_Date) in df_quarters.iterrows():
                if (m['date'] >= Start_Date) & (m['date'] <= End_Date):
                    df.set_value(i, 'Quarter', Quarter)
                    break 

    quarters(WSI_Hourly, df_quarters)

The function I wrote above works but with a hitch. It still labels any date prior to 2016, 1, 1 in my dataset as Q1. For example, if I have a date such as 2015, 12, 3 the Quarter column should be blank since it falls out of range. But it still labels it as Q1.

*Any help is greatly appreciated.

Shijo · Accepted Answer · 2017-01-10 19:49:06Z

try import pandas as pd import numpy as np

df1 = pd.DataFrame({'StartDate': pd.date_range('2016-01-01', periods=9, freq='5D'), 'EndDate': pd.date_range('2016-01-04', periods=9, freq='5D'), 'Quarter': np.arange(1, 10, 1)})

df2 = pd.DataFrame(dict(values=np.random.randn(10), date_time=pd.date_range('2016-01-01', periods=10, freq='D')))

df2['Quarter'] = np.piecewise(np.zeros(len(df2)), [(df2.date_time.values >= start_date)&(df2.date_time.values <= end_date) for start_date, end_date in zip(df1.StartDate.values, df1.EndDate.values)], df1.Quarter.values)

print df1

print df2

input data

        EndDate  Quarter  StartDate
0 2016-01-04        1 2016-01-01
1 2016-01-09        2 2016-01-06
2 2016-01-14        3 2016-01-11
3 2016-01-19        4 2016-01-16
4 2016-01-24        5 2016-01-21
5 2016-01-29        6 2016-01-26
6 2016-02-03        7 2016-01-31
7 2016-02-08        8 2016-02-05
8 2016-02-13        9 2016-02-10

output

   date_time    values  Quarter
0 2016-01-01  0.074264      1.0
1 2016-01-02  0.621282      1.0
2 2016-01-03  0.398398      1.0
3 2016-01-04 -3.435242      1.0
4 2016-01-05 -1.613446      0.0
5 2016-01-06  1.256619      2.0
6 2016-01-07  0.835417      2.0
7 2016-01-08 -0.532238      2.0
8 2016-01-09  0.047838      2.0
9 2016-01-10  0.598660      0.0

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