You can just sum and set axis=1 to sum the rows, which will ignore non-numeric columns; from pandas 2.0+ you also need to specify numeric_only=True.
In [91]:
df = pd.DataFrame({'a': [1,2,3], 'b': [2,3,4], 'c':['dd','ee','ff'], 'd':[5,9,1]})
df['e'] = df.sum(axis=1, numeric_only=True)
df
Out[91]:
a b c d e
0 1 2 dd 5 8
1 2 3 ee 9 14
2 3 4 ff 1 8
If you want to just sum specific columns then you can create a list of the columns and remove the ones you are not interested in:
In [98]:
col_list= list(df)
col_list.remove('d')
col_list
Out[98]:
['a', 'b', 'c']
In [99]:
df['e'] = df[col_list].sum(axis=1)
df
Out[99]:
a b c d e
0 1 2 dd 5 3
1 2 3 ee 9 5
2 3 4 ff 1 7
sum docs
You can just sum and set axis=1 to sum the rows, which will ignore non-numeric columns; from pandas 2.0+ you also need to specify numeric_only=True.
In [91]:
df = pd.DataFrame({'a': [1,2,3], 'b': [2,3,4], 'c':['dd','ee','ff'], 'd':[5,9,1]})
df['e'] = df.sum(axis=1, numeric_only=True)
df
Out[91]:
a b c d e
0 1 2 dd 5 8
1 2 3 ee 9 14
2 3 4 ff 1 8
If you want to just sum specific columns then you can create a list of the columns and remove the ones you are not interested in:
In [98]:
col_list= list(df)
col_list.remove('d')
col_list
Out[98]:
['a', 'b', 'c']
In [99]:
df['e'] = df[col_list].sum(axis=1)
df
Out[99]:
a b c d e
0 1 2 dd 5 3
1 2 3 ee 9 5
2 3 4 ff 1 7
sum docs
If you have just a few columns to sum, you can write:
df['e'] = df['a'] + df['b'] + df['d']
This creates new column e with the values:
a b c d e
0 1 2 dd 5 8
1 2 3 ee 9 14
2 3 4 ff 1 8
For longer lists of columns, EdChum's answer is preferred.
I think the right way to interpret the axis parameter is what axis you sum 'over' (or 'across'), rather than the 'direction' the sum is computed in. Specifying axis = 0 computes the sum over the rows, giving you a total for each column; axis = 1 computes the sum across the columns, giving you a total for each row.
I was a reading the source code in pandas project, and I think that this come from Numpy, in this library is used in that way(0 sum vertically and 1 horizonally), and additionally Pandas use under the hood numpy in order to make this sum.
In this link you could check that pandas use numpy.cumsum function to make the sum.
And this link is for numpy documentation.
If you are looking a way to remember how to use the axis parameter, the 'anant' answer, its a good approach, interpreting the sum over the axis instead across. So when is specified 0 you are computing the sum over the rows(iterating over the index in order to be more pandas doc complaint). When axis is 1 you are iterating over the columns.
Drop the first row of that dataframe, as it just as the column names in it, and convert it to an int. Right now, it is an object because of the mixed data types:
df2 = df.iloc[1:].astype(int).copy()
Then, apply groupby.sum() and specify the column as well:
df3 = df2.groupby('Numero_department')['ALL_PPA'].sum()
I think using .dropna() before summing the DF will help remove any rows or columns (depending on the axis= you choose) with nan values. According to the screenshot provided, please drop the first line of the DF as it is a string.
It specifies the axis along which the means are computed. By default axis=0. This is consistent with the numpy.mean usage when axis is specified explicitly (in numpy.mean, axis==None by default, which computes the mean value over the flattened array) , in which axis=0 along the rows (namely, index in pandas), and axis=1 along the columns. For added clarity, one may choose to specify axis='index' (instead of axis=0) or axis='columns' (instead of axis=1).
A B
0 0.626386 1.52325 → → axis=1 → →
↓ ↓
↓ axis=0 ↓
↓ ↓
These answers do help explain this, but it still isn't perfectly intuitive for a non-programmer (i.e. someone like me who is learning Python for the first time in context of data science coursework). I still find using the terms "along" or "for each" wrt to rows and columns to be confusing.
What makes more sense to me is to say it this way:
- Axis 0 will act on all the ROWS in each COLUMN
- Axis 1 will act on all the COLUMNS in each ROW
So a mean on axis 0 will be the mean of all the rows in each column, and a mean on axis 1 will be a mean of all the columns in each row.
Ultimately this is saying the same thing as @zhangxaochen and @Michael, but in a way that is easier for me to internalize.