How to set value for particular cell in pandas DataFrame using index ?

To set a value for a specific cell in a pandas DataFrame using the index label, you can use the .loc or .at accessors. Below is a detailed explanation with examples:

1. Using loc (Label-Based Indexing)

The .loc accessor is used to access or modify values by row index label and column name.

Syntax

df.loc[row_index_label, column_name] = new_value

Example

import pandas as pd

# Create a DataFrame with a custom index
data = {
    'A': [10, 20, 30],
    'B': [40, 50, 60],
    'C': [70, 80, 90]
}
df = pd.DataFrame(data, index=['X', 'Y', 'Z'])
print("Original DataFrame:")
print(df)

Output:

   A   B   C
X 10  40  70
Y 20  50  80
Z 30  60  90

Set Value for Index ‘Y’ and Column ‘B’

df.loc['Y', 'B'] = 99
print("\nDataFrame after modification:")
print(df)

Output:

   A   B   C
X 10  40  70
Y 20  99  80
Z 30  60  90

2. Using at (Faster Scalar Access)

The .at accessor is optimized for scalar (single-cell) operations and is faster than .loc for this purpose.

Syntax

df.at[row_index_label, column_name] = new_value

Example

# Set value for index 'Z' and column 'A'
df.at['Z', 'A'] = 55
print(df)

Output:

    A   B   C
X  10  40  70
Y  20  99  80
Z  55  60  90

3. Handling Integer-Based Indexes

If your DataFrame uses the default integer index (e.g., 0, 1, 2), you can still use .loc or .at:

# Create a DataFrame with a default integer index
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Set value for row index 1 and column 'B'
df.loc[1, 'B'] = 99
print(df)

Output:

   A   B
0  1   4
1  2  99
2  3   6

4. Set Values Conditionally Based on Index

You can also use conditions on the index to modify cells.

Example: Set values for rows where index > 1

# Create a DataFrame with a numeric index
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
}, index=[1, 2, 3])

# Set column 'B' to 0 for rows where index > 1
df.loc[df.index > 1, 'B'] = 0
print(df)

Output:

   A  B
1  1  4
2  2  0
3  3  0

5. Handling MultiIndex DataFrames

For DataFrames with a MultiIndex (hierarchical index), use tuples to specify the index labels.

Example

# Create a MultiIndex DataFrame
index = pd.MultiIndex.from_tuples(
    [('Group1', 'Item1'), ('Group1', 'Item2'), ('Group2', 'Item1')],
    names=['Group', 'Item']
)
df = pd.DataFrame({'Price': [100, 200, 300]}, index=index)
print("Original DataFrame:")
print(df)

Output:

               Price
Group  Item        
Group1 Item1     100
       Item2     200
Group2 Item1     300

Modify Value for MultiIndex (‘Group1’, ‘Item2’)

df.loc[('Group1', 'Item2'), 'Price'] = 250
print("\nDataFrame after modification:")
print(df)

Output:

               Price
Group  Item        
Group1 Item1     100
       Item2     250
Group2 Item1     300

6. Handling Duplicate Index Labels

If the DataFrame has duplicate index labels, .loc will modify all rows with that index. Be cautious!

Example

# Create a DataFrame with duplicate index labels
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
}, index=['X', 'X', 'Y'])

# Set column 'A' to 99 for all rows with index 'X'
df.loc['X', 'A'] = 99
print(df)

Output:

    A  B
X  99  4
X  99  5
Y   3  6

7. Using iloc (Position-Based Indexing)

If you want to use the integer position (not the index label), use .iloc. This is useful when the index is non-integer.

Example

# Set value for the 2nd row (position 1) and 1st column (position 0)
df.iloc[1, 0] = 999
print(df)

Common Pitfalls

1. KeyError: Occurs if the specified index or column does not exist.
2. SettingWithCopyWarning: Triggered when modifying a slice of a DataFrame. Use .copy() to avoid this.

Summary Table

Method	Use Case	Syntax	Performance
loc	Label-based indexing for single or multiple cells	df.loc[row, col] = value	Slower for scalars
at	Optimized for scalar (single-cell) access	df.at[row, col] = value	Faster for scalars
iloc	Position-based indexing	df.iloc[row_pos, col_pos] = value	Position-dependent

Examples Recap

1. Basic .loc/.at for single cells.
2. Conditional updates based on index.
3. MultiIndex DataFrames.
4. Integer-based indexing with .iloc.

By understanding these methods, you can efficiently modify values in a DataFrame using index labels!