pandas.DataFrame.iloc() function with example in python | 2019

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pandas.DataFrame.iloc(): This function used for purely integer-location based indexing for selection by position.

.iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.

Allowed inputs are:

An integer, e.g. 5.
A list or array of integers, e.g. [4, 3, 0].
A slice object with ints, e.g. 1:7.
A boolean array.
A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference to the calling object, but would like to base your selection on some value.
.iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).

pandas.DataFrame.iloc() function example

import pandas as pd df = pd.DataFrame([[1, 2], [4, 5], [7, 8]], index=['raju', 'ravi', 'dani'], columns=['exp', 'salary']) print(df.iloc[0]) print(df.iloc[[0]]) print(df.iloc[:1]) print(df.iloc[1:2]) print(df.iloc[0:1])


exp       1
salary    2
Name: raju, dtype: int64
      exp  salary
raju    1       2
      exp  salary
raju    1       2
      exp  salary
ravi    4       5
      exp  salary
raju    1       2



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