Menu
I got the output but trying to find a more efficient way to do this:
(df['budget'] == 0).sum(), (df['revenue'] == 0).sum(),(df['budget_adj'] == 0).sum(), (df['revenue_adj'] == 0).sum()
Output is
(5674, 5993, 5676, 5993)
930
Using Loc to Filter With Multiple Conditions The loc function in pandas can be used to access groups of rows or columns by label. Add each condition you want to be included in the filtered result and concatenate them with the & operator. You'll see our code sample will return a pd. dataframe of our filtered rows.
Here are a few things to keep in mind when comparing two columns in a pandas DataFrame: The number of conditions and choices should be equal. The default value specifies the value to display in the new column if none of the conditions are met. Both NumPy and Pandas are required to make this code work.
The reason is dataframe may be having multiple columns and multiple rows. Selective display of columns with limited rows is always the expected view of users. To fulfill the user’s expectations and also help in machine deep learning scenarios, filtering of Pandas dataframe with multiple conditions is much necessary.
We can use the following code to compare the number of goals by row and output the winner of the match in a third column: The results of the comparison are shown in the new column called winner. Here are a few things to keep in mind when comparing two columns in a pandas DataFrame: The number of conditions and choices should be equal.
How to check if two columns are equal (identical) with pandas ? To check if two columns are equal a solution is to use pandas.DataFrame.equals, example: here we added a column called diff (for difference) where 1 means same value in " Score A " and " Score B" else 0.
You can compare the columns in bulk and sum these up column-wise:
(df[['budget', 'revenue', 'budget_adj', 'revenue_adj']] == 0).sum(axis=0)If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
