如何将这些日期附加到我的数据框中？

Question

I have the dates seperated from the original creation of the df due to the fact that it enumerates the entire date list to each row. The number dates I have match the number of rows I have in the enumerated df. Hope you can help, thx!

In:

daily_shares_df = pd.DataFrame(columns = ['Date', 'price', 'capital stock', 'capitalstock/price'])
for i, p in enumerate(average_price, start=0):
    # print("i {}: price {} cs: {} os: {}, cashshares {}".format(i, p, gcs[i], gos[i], (gcs[i]/p)))
    daily_shares_df =daily_shares_df.append({'price': p, 'capital stock':gcs[i], 'capitalstock/price': (gcs[i]/p)}, ignore_index=True)
daily_shares_df.append({'Date':gcsd}, ignore_index=True)

# daily_shares_df = daily_shares_df.round(decimals=2)
# daily_shares_df = daily_shares_df.append({'Date':gcsd}, ignore_index=True, axis=1)
print(daily_shares_df)

Out:

    Date      price  capital stock  capitalstock/price
0    NaN   9.863333        7251.39        7.351865e+02
1    NaN   9.903333       47200.86        4.766159e+03
2    NaN   9.883333      119020.28        1.204252e+04
3    NaN   9.883333    11751250.39        1.188997e+06
4    NaN   9.883333     4790267.25        4.846813e+05
5    NaN   9.913333      -54597.18       -5.507449e+03
6    NaN   9.933333      -46410.80       -4.672228e+03
7    NaN   9.923333       78669.05        7.927684e+03
8    NaN   9.963333      150819.02        1.513741e+04
9    NaN   9.953333      -23295.45       -2.340467e+03
10   NaN   9.970000       87836.67        8.810097e+03
11   NaN  10.003333        6346.19        6.344075e+02
12   NaN  10.023334       10304.31        1.028032e+03
13   NaN  10.023334     -335114.92       -3.343348e+04
14   NaN  10.023334       94276.75        9.405728e+03
15   NaN  10.020000      -38526.78       -3.844988e+03
16   NaN   9.973333        9998.97        1.002571e+03
17   NaN   9.880000      357659.16        3.620032e+04
18   NaN   9.940000        5487.23        5.520352e+02
19   NaN   9.940000      143213.17        1.440776e+04
20   NaN   9.943334      -25900.72       -2.604833e+03
(pystuff) anthonyloupos@anthonys-MBP pystuff % 

Answer 1

If you append a dataframe without a column, it'll create a null value and you can't overwrite null value through append . append is used to add rows to the dataframe, not to fillup the null space.

Instead of doing this:

daily_shares_df = pd.DataFrame(columns = ['Date', 'price', 'capital stock', 'capitalstock/price'])
for i, p in enumerate(average_price, start=0):
    # print("i {}: price {} cs: {} os: {}, cashshares {}".format(i, p, gcs[i], gos[i], (gcs[i]/p)))
    daily_shares_df =daily_shares_df.append({'price': p, 'capital stock':gcs[i], 'capitalstock/price': (gcs[i]/p)}, ignore_index=True)
daily_shares_df.append({'Date':gcsd}, ignore_index=True)

Just simply create a dataframe without the date and then add the date column.

daily_shares_df = pd.DataFrame(columns = ['price', 'capital stock', 'capitalstock/price'])
for i, p in enumerate(average_price, start=0):
    daily_shares_df =daily_shares_df.append({'price': p, 'capital stock':gcs[i], 'capitalstock/price': (gcs[i]/p)}, ignore_index=True)

daily_shares_df["date"]=gcsd
# make sure that gcsd will be of list type