perform math operation between scalar and pandas dataframe
Question:
In an existing code, I used the following code to perform operation against a dataframe column.
df.loc[:, ['test1']] = m/(df.loc[:, ['rh']]*d1)
Here, both m and d1 are scalar. ‘test1’ and ‘rh’ are the column names.
Is this the right way or the best practice to perform math operation against the dataframe?
Answers:
Yes, what you have there is fine. If you’re looking for ways to improve it, a couple suggestions:
- When accessing entire columns (like you’re doing here), you can be more concise by not using
.loc, and just doing df["test1"] and df["rh"]
- You could alternatively use the
.apply() method, which is useful for a more general case of wanting to perform any arbitrary operation (that you can implement in a function, anyway) on a DataFrame column. You could use it here, and it would look like
df["test1"] = df["rh"].apply(lambda rh: m/(rh*d1)), though it is almost certainly not necessary for this simple case.
In an existing code, I used the following code to perform operation against a dataframe column.
df.loc[:, ['test1']] = m/(df.loc[:, ['rh']]*d1)
Here, both m and d1 are scalar. ‘test1’ and ‘rh’ are the column names.
Is this the right way or the best practice to perform math operation against the dataframe?
Yes, what you have there is fine. If you’re looking for ways to improve it, a couple suggestions:
- When accessing entire columns (like you’re doing here), you can be more concise by not using
.loc, and just doingdf["test1"]anddf["rh"] - You could alternatively use the
.apply()method, which is useful for a more general case of wanting to perform any arbitrary operation (that you can implement in a function, anyway) on a DataFrame column. You could use it here, and it would look like
df["test1"] = df["rh"].apply(lambda rh: m/(rh*d1)), though it is almost certainly not necessary for this simple case.