Access Single Value in Pandas DataFrame Using at Attribute

The pandas DataFrame.at attribute is used to access a single value using the row and column labels. The “at” attribute takes a row and column labels data to get an element from a specified label position of the given DataFrame object.

It will return a single value based on the row and column label, and we can also upload a value in that particular position.

The .at attribute will raise a KeyError if the specified label is not available in the DataFrame.

Example 1

In this following example, we have created a Pandas DataFrame using a python dictionary. The column name is labeled by using the keys in the dictionary and the indexes are auto-generated values from 0 to n-1.

# importing pandas package
import pandas as pd

# create a Pandas DataFrame
df = pd.DataFrame({'A':[1, 2, 3],'B':[7, 8, 6],"C":[5, 6, 2]})

print("DataFrame:")
print(df)

# Access a single value from the DataFrame
result = df.at[0, 'B']
print("Output:",result)

Output

The output is given below −

DataFrame:
   A  B  C
0  1  7  5
1  2  8  6
2  3  6  2

Output: 7

We can see both initialized series object and output for at attribute in the above block. The .at attribute returns 7 for the following row/column pair df.at[0, 'B'].

Example 2

Now let’s update the value “100” in the position [2, 'B'] of the DataFrame object using the at attribute, 2 means the row index and “B” means column name.

# importing pandas package
import pandas as pd

# create a Pandas DataFrame
df = pd.DataFrame({'A':[1, 2, 3],'B':[7, 8, 6],"C":[5, 6, 2]})

print("DataFrame:")
print(df)

# by using .at attribute update a value
df.at[2, 'B'] = 100

print("Value 100 updated:")
print(df)

Output

The output is as follows −

DataFrame:
  A B C
0 1 7 5
1 2 8 6
2 3 6 2

Value 100 updated:
  A   B C
0 1   7 5
1 2   8 6
2 3 100 2

We have successfully updated the value “100” in the last row of the middle column (2, B), we can see the updated DataFrame object in the above output block.