CODEWITHSUNDEEP
pythonpandasdataframeconditional-statements
Arnold
Arnold

Reputation: 903

"IndexError: positional indexers are out-of-bounds" when they're demonstrably not

Here's an MWE of some code I'm using. I slowly whittle down an initial dataframe via slicing and some conditions until I have only the rows that I need. Each block of five rows actually represents a different object so that, as I whittle things down, if any one row in each block of five meets the criteria, I want to keep it -- this is what the loop over keep.index accomplishes. No matter what, when I'm done I can see that the final indices I want exist, but I get an error message saying "IndexError: positional indexers are out-of-bounds." What is happening here?

import pandas as pd
import numpy as np

temp = np.random.rand(100,5)

df = pd.DataFrame(temp, columns=['First', 'Second', 'Third', 'Fourth', 'Fifth'])

df_cut = df.iloc[10:]

keep = df_cut.loc[(df_cut['First'] < 0.5) & (df_cut['Second'] <= 0.6)]

new_indices_to_use = []
for item in keep.index:
    remainder = (item % 5)
    add = np.arange(0-remainder,5-remainder,1)
    inds_to_use = item + add
    new_indices_to_use.append(inds_to_use)

new_indices_to_use = [ind for sublist in new_indices_to_use for ind in sublist]
final_indices_to_use = []
for item in new_indices_to_use:
    if item not in final_indices_to_use:
        final_indices_to_use.append(item)

final = df_cut.iloc[final_indices_to_use]

Upvotes: 26

Views: 80176

Answers (1)

TemporalWolf
TemporalWolf

Reputation: 7952

From Pandas documentation on .iloc (emphasis mine):

Pandas provides a suite of methods in order to get purely integer based indexing. The semantics follow closely python and numpy slicing. These are 0-based indexing.

You're trying to use it by label, which means you need .loc

From your example:

>>>print df_cut.iloc[89]
...
Name: 99, dtype: float64

>>>print df_cut.loc[89]
...
Name: 89, dtype: float64

Upvotes: 54

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