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Select pandas rows excluding index number

Not quite sure why I can't figure it out. I want to slice the Pandas framework using index numbers. I have a list / kernel index with index numbers that I DO NOT need, as 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 to create a new data frame excluding these index numbers. I tried

 df.iloc[combined_index]

and obviously this just shows the rows with that index number (opposite to what I want). any help would be greatly appreciated

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python pandas
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4 answers

Not sure if this is what you are looking for, posting this as an answer is 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]: ab 0 1 1 1 2 2 2 3 3 4 5 5 In [35]:
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You can use pd.Int64Index(np.arange(len(df))).difference(index) to form a new ordinal index. For example, if we want to remove rows associated with an ordinal index [1,3,5], then

 import numpy as np import pandas as pd index = pd.Int64Index([1,3,5], dtype=np.int64) df = pd.DataFrame(np.arange(6*2).reshape((6,2)), index=list('ABCDEF')) # 0 1 # A 0 1 # B 2 3 # C 4 5 # D 6 7 # E 8 9 # F 10 11 new_index = pd.Int64Index(np.arange(len(df))).difference(index) print(df.iloc[new_index])

gives

  0 1 A 0 1 C 4 5 E 8 9
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Probably an easier way is to just use a boolean index, and slice usually does something like this:

 df[~df.index.isin(list_to_exclude)]
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Just use .drop and pass it a list of indexes for exception.

 import pandas as pd df = pd.DataFrame({"a": [10, 11, 12, 13, 14, 15]}) df.drop([1, 2, 3], axis=0)

Which deduces this.

  a 0 10 4 14 5 15
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