Pandas Series - iloc[] property
The Pandas iloc[] property is used for integer-location based (from 0 to length-1 of the axis) indexing for selection by position. It can 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 or DataFrame) and that returns valid output for indexing (one of the above).
Exceptions
Raises 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).
Example: Indexing rows of a DataFrame
In the example below, a DataFrame df is created. The iloc[] is used to index some rows of this DataFrame.
import pandas as pd
import numpy as np
df = pd.DataFrame({
"Salary": [25, 24, 30, 28],
"Bonus": [10, 8, 9, np.nan],
"Others": [5, 4, 7, 5]},
index= ["2015", "2016", "2017", "2018"]
)
print("The DataFrame is:")
print(df)
#indexing first row using an integer
print("\ndf.iloc[0] returns:")
print(df.iloc[0])
print(type(df.iloc[0]))
#indexing first row using a list
print("\ndf.iloc[[0]] returns:")
print(df.iloc[[0]])
print(type(df.iloc[[0]]))
#indexing first three row using a list
print("\ndf.iloc[[1,2,3]] returns:")
print(df.iloc[[1,2,3]])
#indexing first three row using slice object
print("\ndf.iloc[:3] returns:")
print(df.iloc[:3])
#indexing first and third rows using boolean array
#the boolean array must have same length as the index
print("\ndf.iloc[[True, False, True, False]] returns:")
print(df.iloc[[True, False, True, False]])
The output of the above code will be:
The DataFrame is:
Salary Bonus Others
2015 25 10.0 5
2016 24 8.0 4
2017 30 9.0 7
2018 28 NaN 5
df.iloc[0] returns:
Salary 25.0
Bonus 10.0
Others 5.0
Name: 2015, dtype: float64
<class 'pandas.core.series.Series'>
df.iloc[[0]] returns:
Salary Bonus Others
2015 25 10.0 5
<class 'pandas.core.frame.DataFrame'>
df.iloc[[1,2,3]] returns:
Salary Bonus Others
2016 24 8.0 4
2017 30 9.0 7
2018 28 NaN 5
df.iloc[:3] returns:
Salary Bonus Others
2015 25 10.0 5
2016 24 8.0 4
2017 30 9.0 7
df.iloc[[True, False, True, False]] returns:
Salary Bonus Others
2015 25 10.0 5
2017 30 9.0 7
Example: Indexing both axes of a DataFrame
In the example below, the iloc[] is used to index both axes of given DataFrame.
import pandas as pd
import numpy as np
df = pd.DataFrame({
"Salary": [25, 24, 30, 28],
"Bonus": [10, 8, 9, np.nan],
"Others": [5, 4, 7, 5]},
index= ["2015", "2016", "2017", "2018"]
)
print("The DataFrame is:")
print(df)
#indexing element at (0,0)
print("\ndf.iloc[0, 0] returns:")
print(df.iloc[0, 0])
#indexing rows (first and third)
#and columns (second and third)
print("\ndf.iloc[[0, 2],[1, 2]] returns:")
print(df.iloc[[0, 2],[1, 2]])
#indexing whole second column
print("\ndf.iloc[:,1] returns:")
print(df.iloc[:,1])
#indexing rows (first three rows)
#and columns (first two columns)
print("\ndf.iloc[:3, :2] returns:")
print(df.iloc[:3, :2])
#indexing whole first and third columns
#using boolean array
print("\ndf.iloc[:, [True, False, True]] returns:")
print(df.iloc[:, [True, False, True]])
The output of the above code will be:
The DataFrame is:
Salary Bonus Others
2015 25 10.0 5
2016 24 8.0 4
2017 30 9.0 7
2018 28 NaN 5
df.iloc[0, 0] returns:
25
df.iloc[[0, 2],[1, 2]] returns:
Bonus Others
2015 10.0 5
2017 9.0 7
df.iloc[:,1] returns:
2015 10.0
2016 8.0
2017 9.0
2018 NaN
Name: Bonus, dtype: float64
df.iloc[:3, :2] returns:
Salary Bonus
2015 25 10.0
2016 24 8.0
2017 30 9.0
df.iloc[:, [True, False, True]] returns:
Salary Others
2015 25 5
2016 24 4
2017 30 7
2018 28 5
Example: using callable function
The example below demonstrates on how to use callable function with iloc[].
import pandas as pd
import numpy as np
df = pd.DataFrame({
"Salary": [25, 24, 30, 28, 25],
"Bonus": [10, 8, 9, np.nan, 9],
"Others": [5, 4, 7, 5, 8]},
index= [2015, 2016, 2017, 2018, 2019]
)
print("The DataFrame is:")
print(df)
#indexing rows if index is odd
print("\ndf.iloc[lambda x: x.index % 2 != 0] returns:")
print(df.iloc[lambda x: x.index % 2 != 0])
#indexing first and third columns
print("\ndf.iloc[:, lambda x: [0, 2]] returns:")
print(df.iloc[:, lambda x: [0, 2]])
The output of the above code will be:
The DataFrame is:
Salary Bonus Others
2015 25 10.0 5
2016 24 8.0 4
2017 30 9.0 7
2018 28 NaN 5
2019 25 9.0 8
df.iloc[lambda x: x.index % 2 != 0] returns:
Salary Bonus Others
2015 25 10.0 5
2017 30 9.0 7
2019 25 9.0 8
df.iloc[:, lambda x: [0, 2]] returns:
Salary Others
2015 25 5
2016 24 4
2017 30 7
2018 28 5
2019 25 8
Example: Setting values
The iloc[] can also be used to set the value of elements. Consider the example below:
import pandas as pd
import numpy as np
df = pd.DataFrame({
"Salary": [25, 24, 30, 28, 25],
"Bonus": [10, 8, 9, np.nan, 9],
"Others": [5, 4, 7, 5, 8]},
index= [2015, 2016, 2017, 2018, 2019]
)
print("The DataFrame is:")
print(df)
#setting the value of first column to 50
df.iloc[:,0] = 100
print("\n After modifying first column:")
print(df)
#setting the value of fourth row to 25
df.iloc[3] = 25
print("\n After modifying fourth row:")
print(df)
The output of the above code will be:
The DataFrame is:
Salary Bonus Others
2015 25 10.0 5
2016 24 8.0 4
2017 30 9.0 7
2018 28 NaN 5
2019 25 9.0 8
After modifying first column:
Salary Bonus Others
2015 100 10.0 5
2016 100 8.0 4
2017 100 9.0 7
2018 100 NaN 5
2019 100 9.0 8
After modifying fourth row:
Salary Bonus Others
2015 100 10.0 5
2016 100 8.0 4
2017 100 9.0 7
2018 25 25.0 25
2019 100 9.0 8
❮ Pandas Series - Functions
