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Not quite sure why I can't figure this out. I'm looking to slice a Pandas dataframe by using index numbers. I have a list/core index with the index numbers that i do NOT need, shown below
pandas.core.index.Int64Index Int64Index([2340, 4840, 3163, 1597, 491 , 5010, 911 , 3085, 5486, 5475, 1417, 2663, 4204, 156 , 5058, 1990, 3200, 1218, 3280, 793 , 824 , 3625, 1726, 1971, 2845, 4668, 2973, 3039, 376 , 4394, 3749, 1610, 3892, 2527, 324 , 5245, 696 , 1239, 4601, 3219, 5138, 4832, 4762, 1256, 4437, 2475, 3732, 4063, 1193], dtype=int64) How can I create a new dataframe excluding these index numbers. I tried
df.iloc[combined_index] and obviously this just shows the rows with those index number (the opposite of what I want). any help will be greatly appreciated
800
Dealing with index and axis If you want the concatenation to ignore existing indices, you can set the argument ignore_index=True . Then, the resulting DataFrame index will be labeled with 0 , …, n-1 . To concatenate DataFrames horizontally along the axis 1 , you can set the argument axis=1 .
Filter Rows by Condition You can use df[df["Courses"] == 'Spark'] to filter rows by a condition in pandas DataFrame. Not that this expression returns a new DataFrame with selected rows.
Not sure if that's what you are looking for, posting this as an answer, because it's too long for a comment:
In [31]: d = {'a':[1,2,3,4,5,6], 'b':[1,2,3,4,5,6]} In [32]: df = pd.DataFrame(d) In [33]: bad_df = df.index.isin([3,5]) In [34]: df[~bad_df] Out[34]: a b 0 1 1 1 2 2 2 3 3 4 5 5 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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