How to access a single value in pandas DataFrame using the integer positions?

The pandas.DataFrame.iat attribute is used to access a single value of the DataFrame using the row/column integer positions and It is very similar to the iloc in pandas instead of accessing a group of elements here we will access a single element.

The “iat” attribute takes the integer index values of both rows and columns for getting or setting the element in a particular place.

The attribute will raise an “IndexError” if the given integer position is out of bounds.

Example 1

In this following example, we have created a DataFrame, accessing the 2nd-row 1st column element by using the iat attribute.

# importing pandas package
import pandas as pd

# create a Pandas DataFrame
df = pd.DataFrame({'A':[1, 'u', 60], 'B':[12.2, 17.4, 34.34], 'C':list('XYZ')})

print("DataFrame:")
print(df)

# get a value using integer position
result = df.iat[1, 0]

print("Output:")
print(result)

Output

The output is given below −

DataFrame:
   A     B   C
0  1  12.20  X
1  u  17.40  Y
2 60  34.34  Z

Output:
u

We have successfully accessed the 2nd-row 1st column element by specifying 1, 0 to the iat attribute.

Example 2

Now, let’s update the value “50” at the 2nd-row 1st column using the “iat” attribute.

# importing pandas package
import pandas as pd

# create a Pandas DataFrame
df = pd.DataFrame({'A':[1, 'u', 60], 'B':[12.2, 17.4, 34.34], 'C':list('XYZ')})

print("DataFrame:")
print(df)

# set a value
df.iat[1, 0] = 50

print("Updated DataFrame:")
print(df)

Output

The output is as follows −

DataFrame:
   A      B  C
0  1  12.20  X
1  u  17.40  Y
2 60  34.34  Z

Updated DataFrame:
   A      B  C
0  1  12.20  X
1 50  17.40  Y
2 60  34.34  Z

We successfully updated the value “50” at integer index position “1, 0” by using the “.iat” property of the pandas.DataFrame; we can observe both the DataFrame objects in the above output block.