Pandas DataFrame | truediv method
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Pandas DataFrame.truediv(~) method divides the values in the source DataFrame by a scalar, sequence, Series or DataFrame, that is:
DataFrame / other
Unless you use the parameters axis, level and fill_value, the truediv(~) is equivalent to performing division using the / operator. Also, truediv(~) is equivalent to div(~).
Parameters
1. otherlink | scalar or sequence or Series or DataFrame
The resulting DataFrame will be the source DataFrame divided by 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 when 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. Note that division between two NaN still results in NaN. By default, fill_value=None.
Return Value
A new DataFrame resulting from the division.
Examples
Basic usage
Consider the following DataFrames:
df = pd.DataFrame({"A":[2,3], "B":[4,5]})df_other = pd.DataFrame({"A":[1,10], "B":[100,1000]})
A B | A B0 2 4 | 0 1 1001 3 5 | 1 10 1000
Performing true division yields:
df.truediv(df_other)
A B0 2.0 0.0401 0.3 0.005
Note that this is equivalent to:
df/ df_other
A B0 2.0 0.0401 0.3 0.005
Broadcasting
Consider the following DataFrame:
df = pd.DataFrame({"A":[2,3], "B":[4,5]})df
A B0 20 401 30 50
Row-wise division
By default, axis=1, which means that other will be broadcasted for each row in df:
df.truediv([1,10]) # axis=1
A B0 2.0 0.41 3.0 0.5
Here, we're doing the following element-wise float division:
2/1 4/103/1 5/10
Column-wise division
To broadcast other for each column in df, set axis=0 like so:
df.truediv([1,10], axis=0)
A B0 2.0 4.01 0.3 0.5
Here, we're doing the following element-wise float division:
2/1 4/13/10 5/10
Specifying fill_value
Consider the following DataFrames:
df = pd.DataFrame({"A":[12,20],"B":[np.NaN,np.NaN]})df_other = pd.DataFrame({"A":[3,np.NaN], "B":[np.NaN,4]})
A B | A B0 3.0 NaN | 0 12 NaN1 NaN 4.0 | 1 20 NaN
By default, when we compute the division using truediv(~), any operation with NaN results in NaN:
df.truediv(df_other)
A B0 4.0 NaN1 NaN NaN
We can fill the NaN values before we perform division by using the fill_value parameter:
df.truediv(df_other, fill_value=2)
A B0 4.0 NaN1 10.0 0.5
Here, notice how the division between two NaN still results in NaN regardless of fill_value.
