Pandas DataFrame | mod method
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Pandas DataFrame.mod(~) method computes the modulo of the values in the source DataFrame and another scalar, sequence, Series or DataFrame, that is:
DataFrame % other
Unless you use the parameters axis, level and fill_value, mod(~) is equivalent to computing the modulo using the % operator.
Parameters
1. otherlink | scalar or sequence or Series or DataFrame
The resulting DataFrame will be the modulo of the source DataFrame and other.
2. axislink | int or string | optional
Whether to broadcast other for each column or row of the source DataFrame:
Axis | Description |
|---|---|
|
|
|
|
This is only relevant if the shape of the source DataFrame and that of 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 computation of the modulo. If both the entries in the source DataFrame and the other are NaN, then the result for that entry will still be NaN. By default, fill_value=None.
Return Value
A new DataFrame computed by the modulo of the source DataFrame and other.
Examples
Basic usage
Consider the following DataFrames:
df = pd.DataFrame({"A":[12,13], "B":[24,25]})df_other = pd.DataFrame({"A":[4,5], "B":[7,8]})
A B | A B0 12 24 | 0 4 71 13 25 | 1 5 8
Computing the modulo:
df.mod(df_other)
A B0 0 31 3 1
Note that this is equivalent to:
df % df_other
A B0 0 31 3 1
Broadcasting
Consider the following DataFrame:
df = pd.DataFrame({"A":[12,13], "B":[24,25]})df
A B0 12 241 13 25
Row-wise addition
By default, axis=1, which means that other will be broadcasted for each row in df:
df.mod([10,20]) # axis=1
A B0 2 41 3 5
Here, we're computing the following element-wise modulo:
12%10 24%2013%10 25%20
Column-wise addition
To broadcast other for each column in df, set axis=0 like so:
df.mod([10,20], axis=0)
A B0 2 41 13 5
Here, we're computing the following element-wise modulo:
12%10 24%1013%20 25%20
Specifying fill_value
Consider the following DataFrames:
df = pd.DataFrame({"A":[12,np.NaN], "B":[np.NaN,25]})df_other = pd.DataFrame({"A":[10,20],"B":[np.NaN,np.NaN]})
A B | A B0 12.0 NaN | 0 10 NaN1 NaN 25.0 | 1 20 NaN
By default, when we compute the modulo using mod(~), any operation with NaN results in NaN:
df.mod(df_other)
A B0 2.0 NaN1 NaN NaN
We can fill the NaN values before we compute the modulo by using the fill_value parameter:
df.mod(df_other, fill_value=5)
A B0 2.0 NaN1 5.0 0.0
Notice how the operation between two NaN still results in a NaN, regardless of fill_value.
