Mobile versionCode: Select all
df1 = merged_df.groupby('Dates').apply(
lambda group: group.set_index('InterpOn')['Values 1'].interpolate(
method='index',limit_direction='both', limit_area='inside'))
df2 = merged_df.groupby('Dates').apply(
lambda group: group.set_index('InterpOn')['Values 2'].interpolate(
method='index',limit_direction='both', limit_area='inside'))
< /code>
großes df: < /p>
print(merged_df)
print(merged_df.index)
PyDev console: starting.
InterpOn Date Values 1 Values2
0 0.02367 2025-05-02 NaN 5.138635
1 0.02370 2025-05-02 5.915301 NaN
2 0.04735 2025-05-02 NaN 4.630094
3 0.07102 2025-05-02 NaN 4.304858
4 0.07109 2025-05-02 4.953734 NaN
... ... ... ... ...
8606 2.13260 2035-04-30 0.290885 NaN
8607 2.22667 2035-04-30 0.287620 NaN
8608 2.47405 2035-04-30 0.276654 NaN
8609 2.72641 2035-04-30 0.268110 NaN
8610 2.96886 2035-04-30 0.264625 NaN
[8611 rows x 4 columns]
RangeIndex(start=0, stop=8611, step=1)
< /code>
Kleine df: < /p>
print(merged_df2)
print(merged_df2.index)
InterpOn Date Values 1 Values2
0 0.07102 2025-05-02 NaN 4.304858
1 0.07107 2025-05-02 4.839552 NaN
2 0.09469 2025-05-02 NaN 4.058323
3 0.09893 2025-05-02 4.519238 NaN
4 0.10000 2025-05-02 NaN 4.009879
... ... ... ... ...
1139 0.72500 2025-10-17 NaN 0.408334
1140 0.73387 2025-10-17 NaN 0.404007
1141 0.74206 2025-10-17 0.405744 NaN
1142 0.74570 2025-10-17 NaN 0.398373
1143 0.75000 2025-10-17 NaN 0.396369
[1144 rows x 4 columns]
RangeIndex(start=0, stop=1144, step=1)
< /code>
Ausgabe von df1 + df2 beim Ausführen von Funktion auf großer DF: < /p>
print(df1)
print(df2)
print(df1.reset_index())
print(df2.reset_index())
PyDev console: starting.
Dates InterOn
2025-05-02 0.02367 NaN
0.02374 5.795639
0.04735 5.327642
0.07102 4.858456
0.07122 4.854492
...
2035-04-30 2.13660 0.236831
2.23085 0.234302
2.47869 0.227912
2.73152 0.224016
2.97443 0.223111
Name: Values 1, Length: 8611, dtype: float64
Dates InterOn
2025-05-02 0.02367 5.138635
0.02374 5.137131
0.04735 4.630094
0.07102 4.304858
0.07122 4.302775
...
2035-04-30 2.13660 NaN
2.23085 NaN
2.47869 NaN
2.73152 NaN
2.97443 NaN
Name: Values 2, Length: 8611, dtype: float64
Dates InterpOn Values 1
0 2025-05-02 0.02367 NaN
1 2025-05-02 0.02374 5.795639
2 2025-05-02 0.04735 5.327642
3 2025-05-02 0.07102 4.858456
4 2025-05-02 0.07122 4.854492
... ... ... ...
8606 2035-04-30 2.13660 0.236831
8607 2035-04-30 2.23085 0.234302
8608 2035-04-30 2.47869 0.227912
8609 2035-04-30 2.73152 0.224016
8610 2035-04-30 2.97443 0.223111
[8611 rows x 3 columns]
Dates InterpOn Values 2
0 2025-05-02 0.02367 5.138635
1 2025-05-02 0.02374 5.137131
2 2025-05-02 0.04735 4.630094
3 2025-05-02 0.07102 4.304858
4 2025-05-02 0.07122 4.302775
... ... ... ...
8606 2035-04-30 2.13660 NaN
8607 2035-04-30 2.23085 NaN
8608 2035-04-30 2.47869 NaN
8609 2035-04-30 2.73152 NaN
8610 2035-04-30 2.97443 NaN
[8611 rows x 3 columns]
< /code>
Ausgabe von df1 + df2 beim Ausführen von Funktion auf kleinem df: < /p>
print(df1)
print(df2)
print(df1.reset_index())
print(df2.reset_index())
InterpOn 0.07102 0.07107 0.09469 ... 0.74206 0.74570 0.75000
Dates ...
2025-05-02 NaN 4.839552 4.567986 ... 1.471894 NaN NaN
2025-05-09 NaN 2.899251 2.735597 ... 0.863719 NaN NaN
2025-05-16 NaN 2.525711 2.380305 ... 0.706109 NaN NaN
2025-05-23 NaN 2.128388 2.007021 ... 0.629245 NaN NaN
2025-05-30 NaN 1.844957 1.740870 ... 0.572882 NaN NaN
2025-06-06 NaN 1.717018 1.620191 ... 0.543621 NaN NaN
2025-06-20 NaN 1.545027 1.458648 ... 0.506780 NaN NaN
2025-07-18 NaN 1.367541 1.290628 ... 0.461288 NaN NaN
2025-08-15 NaN 1.259102 1.188406 ... 0.442713 NaN NaN
2025-09-19 NaN 1.160901 1.095712 ... 0.419598 NaN NaN
2025-10-17 NaN 1.110341 1.046494 ... 0.405744 NaN NaN
[11 rows x 104 columns]
InterpOn 0.07102 0.07107 0.09469 ... 0.74206 0.74570 0.75000
Dates ...
2025-05-02 4.304858 4.304337 4.058323 ... 1.311007 1.297876 1.282278
2025-05-09 2.724090 2.723761 2.568512 ... 0.839582 0.831265 0.821344
2025-05-16 2.350192 2.349904 2.214085 ... 0.690618 0.683802 0.675786
2025-05-23 2.070852 2.070599 1.950801 ... 0.616408 0.610850 0.604349
2025-05-30 1.861961 1.861733 1.754223 ... 0.563564 0.558743 0.553115
2025-06-06 1.683924 1.683721 1.587589 ... 0.533107 0.528862 0.523914
2025-06-20 1.537651 1.537465 1.449704 ... 0.496104 0.492512 0.488336
2025-07-18 1.345252 1.345090 1.268564 ... 0.452838 0.450054 0.446827
2025-08-15 1.256296 1.256143 1.184143 ... 0.435486 0.433208 0.430574
2025-09-19 1.155226 1.155086 1.089053 ... 0.413487 0.411552 0.409319
2025-10-17 1.087805 1.087674 1.025609 ... 0.400107 0.398373 0.396369
[11 rows x 104 columns]
InterpOn Dates 0.07102 0.07107 ... 0.74206 0.7457 0.75
0 2025-05-02 NaN 4.839552 ... 1.471894 NaN NaN
1 2025-05-09 NaN 2.899251 ... 0.863719 NaN NaN
2 2025-05-16 NaN 2.525711 ... 0.706109 NaN NaN
3 2025-05-23 NaN 2.128388 ... 0.629245 NaN NaN
4 2025-05-30 NaN 1.844957 ... 0.572882 NaN NaN
5 2025-06-06 NaN 1.717018 ... 0.543621 NaN NaN
6 2025-06-20 NaN 1.545027 ... 0.506780 NaN NaN
7 2025-07-18 NaN 1.367541 ... 0.461288 NaN NaN
8 2025-08-15 NaN 1.259102 ... 0.442713 NaN NaN
9 2025-09-19 NaN 1.160901 ... 0.419598 NaN NaN
10 2025-10-17 NaN 1.110341 ... 0.405744 NaN NaN
[11 rows x 105 columns]
InterpOn Dates 0.07102 0.07107 ... 0.74206 0.7457 0.75
0 2025-05-02 4.304858 4.304337 ... 1.311007 1.297876 1.282278
1 2025-05-09 2.724090 2.723761 ... 0.839582 0.831265 0.821344
2 2025-05-16 2.350192 2.349904 ... 0.690618 0.683802 0.675786
3 2025-05-23 2.070852 2.070599 ... 0.616408 0.610850 0.604349
4 2025-05-30 1.861961 1.861733 ... 0.563564 0.558743 0.553115
5 2025-06-06 1.683924 1.683721 ... 0.533107 0.528862 0.523914
6 2025-06-20 1.537651 1.537465 ... 0.496104 0.492512 0.488336
7 2025-07-18 1.345252 1.345090 ... 0.452838 0.450054 0.446827
8 2025-08-15 1.256296 1.256143 ... 0.435486 0.433208 0.430574
9 2025-09-19 1.155226 1.155086 ... 0.413487 0.411552 0.409319
10 2025-10-17 1.087805 1.087674 ... 0.400107 0.398373 0.396369
[11 rows x 105 columns]