How can i classify and handle pandas drop duplicate columns
I want to find & remove duplicate column names in the data frames. I have a dataframe with various data types such as integers, objects, floats etc.
there are two conditions that we have for a dataframe to be found and removed:
1. the column name of dataframe is same
2. values present in the columns are same
I have tried doing df.T.duplicated() but it is too slow for big dataframes, also while browsing i got to know about pivot, pivot_table or corr to list duplicate column names.
Please make me understand when we should use these three things and if there is any other thing we can do?
1. Duplicate columns by name in a Pandas:
with using df.columns.duplicated() you can find duplicate columns by name in a Pandas DataFrame. It is fine to use this way if the data is not huge or gigantic, and the speed of this technique is fast enough.
2. Duplicate columns by values in a Pandas:
The Implementation of finding duplicate columns by values is time consuming and complex at times as well. Generally, I don’t recommend using any pivot, pivot_table or corr for any large datasets. but for detecting two duplicate columns having equivalent correlation we can use corr, by this way you can easily find what you’re booking for.
Answers (2)
Thanks for sharing this very interesting question! Finding and removing duplicate column names can be a challenge with large data. As you said, pivot, pivot_table, Snow Rider, and corr can help in certain cases, but to identify duplicate columns in both name and value, I think you should try a more efficient approach by using df.columns.duplicated() to detect duplicate columns in name, then compare the values of these columns to remove them. Optimizing with these methods will help to process large data faster.
3 Weeks
In some cases, you may have columns fall guys with the same name but with an additional prefix (like _duplicate or _1), and you want to treat them as a group. To handle this, you can normalize the column names and then work with these columns.
1 Month
Thanks for sharing this very interesting question! Finding and removing duplicate column names can be a challenge with large data. As you said, pivot, pivot_table, Snow Rider, and corr can help in certain cases, but to identify duplicate columns in both name and value, I think you should try a more efficient approach by using df.columns.duplicated() to detect duplicate columns in name, then compare the values of these columns to remove them. Optimizing with these methods will help to process large data faster.
In some cases, you may have columns fall guys with the same name but with an additional prefix (like _duplicate or _1), and you want to treat them as a group. To handle this, you can normalize the column names and then work with these columns.