How to Set Value for Particular Cell in Pandas DataFrame Using Index

  1. Set Value for Particular Cell in Pandas DataFrame Using pandas.dataframe.at Method
  2. Set Value for Particular Cell in Pandas DataFrame Using Dataframe.set_value() Method
  3. Set Value for Particular Cell in Pandas DataFrame Using Dataframe.loc Method

Pandas is a data-centric python package that makes data analysis in python easy and coherent. In this article, we will look into different methods of accessing and setting values for a particular cell in pandas DataFrame data structure using an index.

Set Value for Particular Cell in Pandas DataFrame Using pandas.dataframe.at Method

pandas.dataframe.at method is primarily used when we need to set a single value in a DataFrame.

import pandas as pd

sample_df = pd.DataFrame([
                [10, 20, 30],
                [11, 21, 31],
                [15, 25, 35]],
                index=[0, 1, 2],
                columns=['Col1', 'Col2', 'Col3'])
                  
print"\nOriginal DataFrame"
print(pd.DataFrame(sample_df))
sample_df.at[0, 'Col1'] = 99
sample_df.at[1, 'Col2'] = 99
sample_df.at[2, 'Col3'] = 99

print"\nModified DataFrame"
print(pd.DataFrame(sample_df))

Output:

Original DataFrame
   Col1  Col2  Col3
0    10    20    30
1    11    21    31
2    15    25    35

Modified DataFrame
   Col1  Col2  Col3
0    99    20    30
1    11    99    31
2    15    25    99

As you might notice, while accessing the cell we have specified index and column as .at[0, 'Col1'] among which the first parameter is the index, and the second is the column.

If you leave the column and only specify the index, all values for that index will be modified.

Set Value for Particular Cell in Pandas DataFrame Using Dataframe.set_value() Method

Another alternative is the Dataframe.set_value() method. This is much similar to the previous method and accesses one value at a time, but with a slight difference in syntax.

import pandas as pd

sample_df = pd.DataFrame([
                [10, 20, 30],
                [11, 21, 31],
                [15, 25, 35]],
                index=[0, 1, 2],
                columns=['Col1', 'Col2', 'Col3'])
                  
print"\nOriginal DataFrame"
print(pd.DataFrame(sample_df))

sample_df.set_value(0, 'Col1',99)
sample_df.set_value(1, 'Col2',99)
sample_df.set_value(2, 'Col3',99)

print"\nModified DataFrame"
print(pd.DataFrame(sample_df))

Output:

Original DataFrame
   Col1  Col2  Col3
0    10    20    30
1    11    21    31
2    15    25    35

Modified DataFrame
   Col1  Col2  Col3
0    99    20    30
1    11    99    31
2    15    25    99

Set Value for Particular Cell in Pandas DataFrame Using Dataframe.loc Method

Another viable method to set a particular cell with a slight difference in syntax is the dataframe.loc method.

import pandas as pd

sample_df = pd.DataFrame([[10, 20, 30],
                [11, 21, 31],
                [15, 25, 35]],
                index=[0, 1, 2],
                columns=['Col1', 'Col2', 'Col3'])
                  
print"\nOriginal DataFrame"
print(pd.DataFrame(sample_df))

sample_df.loc[0, 'Col3'] = 99
sample_df.loc[1, 'Col2'] = 99
sample_df.loc[2, 'Col1'] = 99

print"\nModified DataFrame"
print(pd.DataFrame(sample_df))

Output:

Original DataFrame
   Col1  Col2  Col3
0    10    20    30
1    11    21    31
2    15    25    35

Modified DataFrame
   Col1  Col2  Col3
0    10    20    99
1    11    99    31
2    99    25    35

All the above-mentioned methods in the article are convenient ways to modify or set a particular cell in pandas DataFrame, with minute differences in syntax and specification.

Related Article - Pandas DataFrame

  • How to Convert Index of a Pandas Dataframe Into a Column
  • How to Change the Order of Pandas DataFrame Columns
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