Pandas：使用iterrows（）和pd.Series将值附加到系列

Question

My input data looks like this:

   cat  start               target
0   1   2016-09-01 00:00:00 4.370279
1   1   2016-09-01 00:00:00 1.367778
2   1   2016-09-01 00:00:00 0.385834

I want to build out a series using "start" for the Start Date and "target" for the series values. The iterrows() is pulling the correct values for "imp", but when appending to the time_series, only the first value is carried through to all series points. What's the reason for "data=imp" pulling the 0th row every time?

t0 = model_input_test['start'][0] # t0 = 2016-09-01 00:00:00
num_ts = len(model_input_test.index) # num_ts = 1348
time_series = []
for i, row in model_input_test.iterrows():
    imp = row.loc['target']
    print(imp)
    index = pd.DatetimeIndex(start=t0, freq='H', periods=num_ts)
    time_series.append(pd.Series(data=imp, index=index))

A screenshot can be seen here .

Series "time_series" should look like this:

2016-09-01 00:00:00    4.370279
2016-09-01 01:00:00    1.367778
2016-09-01 02:00:00    0.385834

But ends up looking like this:

2016-09-01 00:00:00    4.370279
2016-09-01 01:00:00    4.370279
2016-09-01 02:00:00    4.370279

I'm using Jupyter conda_python3 on Sagemaker.

Answer 1

When using dataframes, there are usually better ways to go about tasks then iterating through the dataframe. For example, in your case, you can create your series like this:

time_series = (df.set_index(pd.date_range(pd.to_datetime(df.start).iloc[0],
                                        periods = len(df), freq='H')))['target']


>>> time_series
2016-09-01 00:00:00    4.370279
2016-09-01 01:00:00    1.367778
2016-09-01 02:00:00    0.385834
Freq: H, Name: target, dtype: float64
>>> type(time_series)
<class 'pandas.core.series.Series'>

Essentially, this says: "set the index to be a date range incremented hourly from your first date, then take the target column"

Answer 2

给定数据帧df和系列start和target ，您只需使用set_index ：

time_series = df.set_index('start')['target']