pandas.DataFrame.iloc#

propertyDataFrame.iloc[source]#

Purely integer-location based indexing for selection by position.

Deprecated since version 2.2.0:Returning a tuple from a callable is deprecated.

.iloc[] is primarily integer position based (from0 tolength-1 of the axis), but may also be used with a booleanarray.

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.

• Acallable function with one argument (the calling Series orDataFrame) 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 thecalling object, but would like to base your selection onsome value.

• A tuple of row and column indexes. The tuple elements consist of one of theabove inputs, e.g.(0,1).

.iloc will raiseIndexError if a requested indexer isout-of-bounds, exceptslice indexers which allow out-of-boundsindexing (this conforms with python/numpyslice semantics).

See more atSelection by Position.

See also

DataFrame.iat

Fast integer location scalar accessor.

DataFrame.loc

Purely label-location based indexer for selection by label.

Series.iloc

Purely integer-location based indexing for selection by position.

Examples

>>>mydict=[{'a':1,'b':2,'c':3,'d':4},...{'a':100,'b':200,'c':300,'d':400},...{'a':1000,'b':2000,'c':3000,'d':4000}]>>>df=pd.DataFrame(mydict)>>>df      a     b     c     d0     1     2     3     41   100   200   300   4002  1000  2000  3000  4000

Indexing just the rows

With a scalar integer.

>>>type(df.iloc[0])<class 'pandas.core.series.Series'>>>>df.iloc[0]a    1b    2c    3d    4Name: 0, dtype: int64

With a list of integers.

>>>df.iloc[[0]]   a  b  c  d0  1  2  3  4>>>type(df.iloc[[0]])<class 'pandas.core.frame.DataFrame'>

>>>df.iloc[[0,1]]     a    b    c    d0    1    2    3    41  100  200  300  400

With aslice object.

>>>df.iloc[:3]      a     b     c     d0     1     2     3     41   100   200   300   4002  1000  2000  3000  4000

With a boolean mask the same length as the index.

>>>df.iloc[[True,False,True]]      a     b     c     d0     1     2     3     42  1000  2000  3000  4000

With a callable, useful in method chains. Thex passedto thelambda is the DataFrame being sliced. This selectsthe rows whose index label even.

>>>df.iloc[lambdax:x.index%2==0]      a     b     c     d0     1     2     3     42  1000  2000  3000  4000

Indexing both axes

You can mix the indexer types for the index and columns. Use: toselect the entire axis.

With scalar integers.

>>>df.iloc[0,1]2

With lists of integers.

>>>df.iloc[[0,2],[1,3]]      b     d0     2     42  2000  4000

Withslice objects.

>>>df.iloc[1:3,0:3]      a     b     c1   100   200   3002  1000  2000  3000

With a boolean array whose length matches the columns.

>>>df.iloc[:,[True,False,True,False]]      a     c0     1     31   100   3002  1000  3000

With a callable function that expects the Series or DataFrame.

>>>df.iloc[:,lambdadf:[0,2]]      a     c0     1     31   100   3002  1000  3000