Pandas - 根据逻辑语句定位值

Question

I am using the this dataset for a project. I am trying to find the total yield for each inverter for the 34 day duration of the dataset (basically use the final and initial value available for each inverter). I have been able to get the list of inverters using pd.unique() (there are 22 inverters for each solar power plant.

I am having trouble querying the total_yield data for each inverter. Here is what I have tried:

def get_yields(arr: np.ndarray, df:pd.core.frame.DataFrame) -> np.ndarray:
    delta = np.zeros(len(arr))
    index =0
    for i in arr:
        initial = df.loc[df["DATE_TIME"]=="15-05-2020 02:00"]
        initial = initial.loc[initial["INVERTER_ID"]==i]
        initial.reset_index(inplace=True,drop=True)
        initial = initial.at[0,"TOTAL_YIELD"]
        final = df.loc[(df["DATE_TIME"]=="17-06-2020 23:45")]
        final = final.loc[final["INVERTER_ID"]==i]
        final.reset_index(inplace=True, drop=True)
        final = final.at[0,"TOTAL_YIELD"]

        delta[index] = final - initial
        index = index + 1
    return delta

Reference: arr is the array of inverters, listed below. df is the generation dataframe for each plant.
The problem is that not every inverter has a data point for each interval. This makes this function only work for the inverters at the first plant, not the second one.

My second approach was to filter by the inverter first, then take the first and last data points. But I get an error- 'Series' objects are mutable, thus they cannot be hashed Here is the code for that so far:

def get_yields2(arr: np.ndarray, df: pd.core.frame.DataFrame) -> np.ndarry:
    delta = np.zeros(len(arr))
    index = 0
    for i in arr:
        initial = df.loc(df["INVERTER_ID"] == i)
        index += 1
        break
    return delta

List of inverters at plant 1 for reference(labeled as SOURCE_KEY ):

['1BY6WEcLGh8j5v7' '1IF53ai7Xc0U56Y' '3PZuoBAID5Wc2HD' '7JYdWkrLSPkdwr4'
 'McdE0feGgRqW7Ca' 'VHMLBKoKgIrUVDU' 'WRmjgnKYAwPKWDb' 'ZnxXDlPa8U1GXgE'
 'ZoEaEvLYb1n2sOq' 'adLQvlD726eNBSB' 'bvBOhCH3iADSZry' 'iCRJl6heRkivqQ3'
 'ih0vzX44oOqAx2f' 'pkci93gMrogZuBj' 'rGa61gmuvPhdLxV' 'sjndEbLyjtCKgGv'
 'uHbuxQJl8lW7ozc' 'wCURE6d3bPkepu2' 'z9Y9gH1T5YWrNuG' 'zBIq5rxdHJRwDNY'
 'zVJPv84UY57bAof' 'YxYtjZvoooNbGkE']

List of inverters at plant 2:

['4UPUqMRk7TRMgml' '81aHJ1q11NBPMrL' '9kRcWv60rDACzjR' 'Et9kgGMDl729KT4'
 'IQ2d7wF4YD8zU1Q' 'LYwnQax7tkwH5Cb' 'LlT2YUhhzqhg5Sw' 'Mx2yZCDsyf6DPfv'
 'NgDl19wMapZy17u' 'PeE6FRyGXUgsRhN' 'Qf4GUc1pJu5T6c6' 'Quc1TzYxW2pYoWX'
 'V94E5Ben1TlhnDV' 'WcxssY2VbP4hApt' 'mqwcsP2rE7J0TFp' 'oZ35aAeoifZaQzV'
 'oZZkBaNadn6DNKz' 'q49J1IKaHRwDQnt' 'rrq4fwE8jgrTyWY' 'vOuJvMaM2sgwLmb'
 'xMbIugepa2P7lBB' 'xoJJ8DcxJEcupym']

Thank you very much.

Answer 1

I can't download the dataset to test this. Getting "To May Requests" Error.

However, you should be able to do this with a groupby .

import pandas as pd
result = df.groupby('INVERTER_ID')['TOTAL_YIELD'].agg(['max','min'])
result['delta'] = result['max']-result['min']
print(result[['delta']])  

Answer 2

So if I'm understanding this right, what you want is the TOTAL_YIELD for each inverter for the beginning of the time period starting 5-05-2020 02:00 and ending 17-06-2020 23:45 . Try this:

# enumerate lets you have an index value along with iterating through the array
for i, code in enumerate(arr): 
    # to filter the info to between the two dates, but not necessarily assuming that
    # each inverter's data starts and ends at each date
    inverter_df = df.loc[df['DATE_TIME'] >= pd.to_datetime('15-05-2020 02:00:00')]
    inverter_df = inverter_df.loc[inverter_df['DATE_TIME'] <= pd.to_datetime('17-06-2020      
    23:45:00')]
    inverter_df = inverter_df.loc[inverter_df["INVERTER_ID"]==code]]

    # sort by date
    inverter_df.sort_values(by='DATE_TIME', inplace= True)
    # grab TOTAL_YIELD at the first available date
    initial = inverter_df['TOTAL_YIELD'].iloc[0]
    # grab TOTAL_YIELD at the last available date
    final = inverter_df['TOTAL_YIELD'].iloc[-1]
    delta[index] = final - initial