Python | Pandas dataframe.set_value()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas dataframe.set_value() function put a single value at passed column and index. It takes the axis labels as input and a scalar value to be placed at the specified index in the dataframe. Alternative to this function is .at[] or .iat[].

Syntax:DataFrame.set_value(index, col, value, takeable=False)
Parameters :
index : row label
col : column label
value : scalar value
takeable : interpret the index/col as indexers, default False

Return : frame : DataFrame If label pair is contained, will be reference to calling DataFrame, otherwise a new object

Example #1: Use set_value() function to set the value in the dataframe at a particular index.

# importing pandas as pd
import pandas as pd

# Creating the dataframe
df = pd.DataFrame({"A":[1, 5, 3, 4, 2],
                   "B":[3, 2, 4, 3, 4],
                   "C":[2, 2, 7, 3, 4],
                   "D":[4, 3, 6, 12, 7]})

# Print the dataframe
df

Lets use the dataframe.set_value() function to set value of a particular index.

# set value of a cell which has index label "2" and column label "B"
df.set_value(2, 'B', 100)

Output :


Example #2: Use set_value() function to set value of a non-existent index and column in the dataframe.

# importing pandas as pd
import pandas as pd

# Creating the dataframe
df = pd.DataFrame({"A":[1, 5, 3, 4, 2],
                   "B":[3, 2, 4, 3, 4],
                   "C":[2, 2, 7, 3, 4],
                   "D":[4, 3, 6, 12, 7]})

# Print the dataframe
df

Lets use the dataframe.set_value() function to set value of a particular index.

# set value of a cell which has index label "8" and column label "8"
df.set_value(8, 8, 1000)

Output :

Notice, for the non-existent row and column in the dataframe, a new row and column has been inserted.