Pandas DataFrame | radd method
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Pandas DataFrame.radd(~) method computes and returns the sum of a scalar, sequence, Series or DataFrame and the values in the source DataFrame, that is:
other + DataFrame
Note that this is the reverse of DataFrame.add(~), which does the following:
DataFrame + other
Unless you use the parameters axis, level and fill_value, radd(~) is equivalent to performing addition using the + operator.
Parameters
1. otherlink | scalar or sequence or Series or DataFrame
The resulting DataFrame will be the sum of other and the source DataFrame.
2. axislink | int or string | optional
Whether to broadcast other for each column or row of the source DataFrame:
Axis | Description |
|---|---|
|
|
|
|
This is relevant only when the shape of the source DataFrame and other does not align. By default, axis=1.
3. level | int or string | optional
The name or the integer index of the level to consider. This is relevant only if your DataFrame is Multi-index.
4. fill_valuelink | float or None | optional
The value to replace NaN before the computing the sum. If both the pair-wise entries in the source DataFrame and other are NaN, then the resulting sum will still be NaN. By default, fill_value=None.
Return Value
A new DataFrame computed by the sum of the source DataFrame and other.
Examples
Basic usage
Consider the following DataFrames:
df = pd.DataFrame({"A":[2,3], "B":["a","b"]})df_other = pd.DataFrame({"A":[6,7], "B":["c","d"]})
A B | A B0 2 a | 0 6 c1 3 b | 1 7 d
Taking the sum yields:
df.radd(df_other)
A B0 8 ca1 10 db
Broadcasting
Consider the following DataFrame:
df = pd.DataFrame({"A":[2,3], "B":[4,5]})df
A B0 2 41 3 5
Row-wise addition
By default, axis=1, which means that other will be broadcasted for each row in df:
df.radd([10,20]) # axis=1
A B0 12 241 13 25
Here, we're doing the following element-wise addition:
10+2 20+410+3 20+5
Column-wise addition
To broadcast other for each column in df, set axis=0 like so:
df.radd([10,20], axis=0)
A B0 12 141 23 25
Here, we're doing the following element-wise addition:
10+2 10+420+3 20+5
Specifying fill_value
Consider the following DataFrames:
df = pd.DataFrame({"A":[2,np.NaN], "B":[np.NaN,5]})df_other = pd.DataFrame({"A":[10, 20],"B":[np.NaN,np.NaN]})
A B | A B0 2 NaN | 0 10 NaN1 NaN 5 | 1 20 NaN
By default, when we take the sum using radd(~), any operation with NaN results in NaN:
df.radd(df_other)
A B0 2.0 NaN1 NaN NaN
We can fill the NaN values before we compute the sum by using the fill_value parameter:
df.radd(df_other, fill_value=100)
A B0 12.0 NaN1 120.0 105.0
Notice when the addition is between two NaN, the resulting sum would still be a NaN regardless of fill_value.
