![]() Text_length = pd.Series()ĭf = df.assign(text_length = text_length. Now we can apply condition on the new column such as: df = dfĭef pass_filter(df, label, length, pass_type): Now add one extra column with the above series in the data frame: df = df.assign(text_length = text_length. ![]() In my case I was just trying to get the number of tokens: text_length = pd.Series() Now apply some function on the every element of the list and put that in a panda series: text_length = pd.Series() Let us assume that you want to drop the column with 'header' so get that column in a list first. I have the following simpler solution which always works. If you want to drop rows of data frame on the basis of some complicated condition on the column value then writing that in the way shown above can be complicated. For folks interested in how the underlying memory organization plays into execution speed here is a great Link on Speeding up Pandas: Something like: comparedf df 'ID', 'CLASS A', 'CLASS B', 'CLASS C' rowislikepreviousone (comparedf comparedf.shift (1)). of 7 runs, 1000 loops each)Ī column is basically a Series i.e a NumPy array, it can be indexed without any cost. 1 Answer Sorted by: 3 In your case, I wouldn't use dropduplicates but get the indices to keep using the shift method. %timeit dft.drop(dft.index, inplace=True)Ĩ90 µs ± 94.9 µs per loop (mean ± std. I would like to end with some profiling stats on why drop solution is slower than raw column based filtration:- %timeit df_new = dfģ45 µs ± 10.5 µs per loop (mean ± std. This can also be simplified for cases like: Delete all rows where column E is negative df = df This can easily be extended to filter out rows containing NaN s (non numeric entries):. You can assign it back to df to actually delete vs filter ing done above ![]() A boolean df satisfying the condition:- df > 0Ī boolean series for all rows satisfying the condition Note if any element in the row fails the condition the row is marked false (df > 0).all(axis=1)įinally filter out rows from data frame based on the condition df Say you want to delete all rows with negative values. This is for folks directed here based on the question's title (not OP 's problem) I will expand on generic solution to provide a drop free alternative.
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