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I have a dataframe (output here: ; can be read with pd.DataFrame.from_dict(orient='tight')) with two columns that I want the total of. They look like:
Tiered Rates Prices
lower higher
year month
2022 11 41.36 0.00
12 74.42 0.00
2023 1 72.31 0.00
2 71.01 0.00
However, when I tried to add them using the + symbol, I got this error:
monthly_pivot[('Tiered Rates Prices', 'higher')] + monthly_pivot[('Tiered Rates Prices', 'lower')]
Expected a one-dimensional object, got a DataFrame with 2 columns instead.
Printing out the expression gave me this table of all NaN. This was a little strange, as I had expected the result to be a single column. At this point I think the most likely culprit is the fact that this dataframe has the 'year/month' multiindex, and I'm not calling the columns accurately to sum them up with the + operator. So I'm wondering where did I go wrong?
Tiered Rates Prices
higher lower
year month
2022 11 NaN NaN
12 NaN NaN
2023 1 NaN NaN
2 NaN NaN
I was able to total the two columns together, and the resulting column looks like:
monthly_pivot[(('Tiered Rates Prices'))].sum(axis=1)
year month
2022 11 82.72
12 148.84
2023 1 144.62
2 142.02
I have a dataframe (output here: https://pastebin/7RCPsHet; can be read with pd.DataFrame.from_dict(orient='tight')) with two columns that I want the total of. They look like:
Tiered Rates Prices
lower higher
year month
2022 11 41.36 0.00
12 74.42 0.00
2023 1 72.31 0.00
2 71.01 0.00
However, when I tried to add them using the + symbol, I got this error:
monthly_pivot[('Tiered Rates Prices', 'higher')] + monthly_pivot[('Tiered Rates Prices', 'lower')]
Expected a one-dimensional object, got a DataFrame with 2 columns instead.
Printing out the expression gave me this table of all NaN. This was a little strange, as I had expected the result to be a single column. At this point I think the most likely culprit is the fact that this dataframe has the 'year/month' multiindex, and I'm not calling the columns accurately to sum them up with the + operator. So I'm wondering where did I go wrong?
Tiered Rates Prices
higher lower
year month
2022 11 NaN NaN
12 NaN NaN
2023 1 NaN NaN
2 NaN NaN
I was able to total the two columns together, and the resulting column looks like:
monthly_pivot[(('Tiered Rates Prices'))].sum(axis=1)
year month
2022 11 82.72
12 148.84
2023 1 144.62
2 142.02
1 Answer
Reset to default 1Assuming this reproducible input:
monthly_pivot = pd.DataFrame.from_dict(
{
'index': [
(2022, 11),
(2022, 12),
(2023, 1),
(2023, 2),
(2023, 3),
(2023, 4),
(2023, 5),
],
'columns': [
('Tiered Rates Prices', 'lower'),
('Tiered Rates Prices', 'higher'),
],
'data': [
[41.36, 0.0],
[74.42, 0.0],
[72.31, 0.0],
[71.01, 0.0],
[65.05, 0.0],
[37.8, 0.0],
[38.36, 0.0],
],
'index_names': ['year', 'month'],
'column_names': [None, None],
},
orient='tight',
)
Your code should work as expected:
monthly_pivot[('Tiered Rates Prices', 'higher')] + monthly_pivot[('Tiered Rates Prices', 'lower')]
year month
2022 11 41.36
12 74.42
2023 1 72.31
2 71.01
3 65.05
4 37.80
5 38.36
dtype: float64
To yield your shown output, you would need to select the columns as 2D (DataFrames), not 1D (Series):
monthly_pivot[[('Tiered Rates Prices', 'higher')]] + monthly_pivot[[('Tiered Rates Prices', 'lower')]]
Tiered Rates Prices
higher lower
year month
2022 11 NaN NaN
12 NaN NaN
2023 1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
5 NaN NaN
In this case, the columns wouldn't be aligned and the addition would indeed yield NaNs.
Make sure that the output of monthly_pivot[('Tiered Rates Prices', 'higher')] (and the other column) are Series:
type(monthly_pivot[('Tiered Rates Prices', 'higher')])
pandas.core.series.Series
I have a dataframe (output here: ; can be read with pd.DataFrame.from_dict(orient='tight')) with two columns that I want the total of. They look like:
Tiered Rates Prices
lower higher
year month
2022 11 41.36 0.00
12 74.42 0.00
2023 1 72.31 0.00
2 71.01 0.00
However, when I tried to add them using the + symbol, I got this error:
monthly_pivot[('Tiered Rates Prices', 'higher')] + monthly_pivot[('Tiered Rates Prices', 'lower')]
Expected a one-dimensional object, got a DataFrame with 2 columns instead.
Printing out the expression gave me this table of all NaN. This was a little strange, as I had expected the result to be a single column. At this point I think the most likely culprit is the fact that this dataframe has the 'year/month' multiindex, and I'm not calling the columns accurately to sum them up with the + operator. So I'm wondering where did I go wrong?
Tiered Rates Prices
higher lower
year month
2022 11 NaN NaN
12 NaN NaN
2023 1 NaN NaN
2 NaN NaN
I was able to total the two columns together, and the resulting column looks like:
monthly_pivot[(('Tiered Rates Prices'))].sum(axis=1)
year month
2022 11 82.72
12 148.84
2023 1 144.62
2 142.02
I have a dataframe (output here: https://pastebin/7RCPsHet; can be read with pd.DataFrame.from_dict(orient='tight')) with two columns that I want the total of. They look like:
Tiered Rates Prices
lower higher
year month
2022 11 41.36 0.00
12 74.42 0.00
2023 1 72.31 0.00
2 71.01 0.00
However, when I tried to add them using the + symbol, I got this error:
monthly_pivot[('Tiered Rates Prices', 'higher')] + monthly_pivot[('Tiered Rates Prices', 'lower')]
Expected a one-dimensional object, got a DataFrame with 2 columns instead.
Printing out the expression gave me this table of all NaN. This was a little strange, as I had expected the result to be a single column. At this point I think the most likely culprit is the fact that this dataframe has the 'year/month' multiindex, and I'm not calling the columns accurately to sum them up with the + operator. So I'm wondering where did I go wrong?
Tiered Rates Prices
higher lower
year month
2022 11 NaN NaN
12 NaN NaN
2023 1 NaN NaN
2 NaN NaN
I was able to total the two columns together, and the resulting column looks like:
monthly_pivot[(('Tiered Rates Prices'))].sum(axis=1)
year month
2022 11 82.72
12 148.84
2023 1 144.62
2 142.02
-
Please provide a reproducible input for
monthy_pivot(e.g. the output ofmonthy_pivot.to_dict('tight')). I cannot reproduce your issue. – mozway Commented Nov 19, 2024 at 5:29 - @mozway thank you for the method. I've pasted the output to here: pastebin/7RCPsHet. The crazy thing is if I just isolate the output to the two columns in question then the issue isn't reproducible, and they only happen with the full table output. – Long Vuong Commented Nov 19, 2024 at 16:08
1 Answer
Reset to default 1Assuming this reproducible input:
monthly_pivot = pd.DataFrame.from_dict(
{
'index': [
(2022, 11),
(2022, 12),
(2023, 1),
(2023, 2),
(2023, 3),
(2023, 4),
(2023, 5),
],
'columns': [
('Tiered Rates Prices', 'lower'),
('Tiered Rates Prices', 'higher'),
],
'data': [
[41.36, 0.0],
[74.42, 0.0],
[72.31, 0.0],
[71.01, 0.0],
[65.05, 0.0],
[37.8, 0.0],
[38.36, 0.0],
],
'index_names': ['year', 'month'],
'column_names': [None, None],
},
orient='tight',
)
Your code should work as expected:
monthly_pivot[('Tiered Rates Prices', 'higher')] + monthly_pivot[('Tiered Rates Prices', 'lower')]
year month
2022 11 41.36
12 74.42
2023 1 72.31
2 71.01
3 65.05
4 37.80
5 38.36
dtype: float64
To yield your shown output, you would need to select the columns as 2D (DataFrames), not 1D (Series):
monthly_pivot[[('Tiered Rates Prices', 'higher')]] + monthly_pivot[[('Tiered Rates Prices', 'lower')]]
Tiered Rates Prices
higher lower
year month
2022 11 NaN NaN
12 NaN NaN
2023 1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
5 NaN NaN
In this case, the columns wouldn't be aligned and the addition would indeed yield NaNs.
Make sure that the output of monthly_pivot[('Tiered Rates Prices', 'higher')] (and the other column) are Series:
type(monthly_pivot[('Tiered Rates Prices', 'higher')])
pandas.core.series.Series
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monthy_pivot(e.g. the output ofmonthy_pivot.to_dict('tight')). I cannot reproduce your issue. – mozway Commented Nov 19, 2024 at 5:29