Loc,iloc using locate rows and columns by in Python | S-Logix

How to Locate Rows and Columns Using loc, iloc in Python

Locate Rows and Columns Using loc, iloc

Condition for Using loc and iloc in Python

Description:
In Pandas, loc and iloc are used to locate or select rows and columns in a DataFrame.

These functions are widely used for indexing and retrieving data based on either labels (loc) or integer positions (iloc).

loc: Used for label-based indexing. You can select rows and columns by their labels (index names or column names).
iloc: Used for integer-based indexing. You select rows and columns by their positions (integer indices).

Step-by-Step Process

Import Pandas:
Import the Pandas library with import pandas as pd.
Create or Load a DataFrame:
Create a DataFrame manually or load it from a file.
Using loc:
Use df.loc[row_label, column_label] to access a specific cell or slice rows/columns by label.
Using iloc:
Use df.iloc[row_position, col_position] to access specific rows and columns using integer positions.

Sample Source Code

# Code for Locating Rows and Columns Using loc and iloc

import pandas as pd

data = {
'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
'Age': [25, 30, 35, 40, 45],
'Salary': [50000, 60000, 70000, 80000, 90000]
}
df = pd.DataFrame(data)

print("Original DataFrame:")
print(df)

# Using loc (Label-based indexing)

# Access the row where Name is 'Alice'
print("\nUsing loc to access the row where Name is 'Alice':")
print(df.loc[df['Name'] == 'Alice'])

# Access specific row and column (row label = 2, column label = 'Age')
print("\nUsing loc to access specific row and column (index 2, 'Age'):")
print(df.loc[2, 'Age'])

# Select a range of rows (from index 1 to 3) and columns (from 'Name' to 'Salary')
print("\nUsing loc to select a range of rows and columns:")
print(df.loc[1:3, 'Name':'Salary'])

# Using iloc (Integer-based indexing)

# Access the row at position 1 (second row)
print("\nUsing iloc to access the row at position 1:")
print(df.iloc[1])

# Access the element at row position 2 and column position 1 ('Age' is the second column)
print("\nUsing iloc to access the element at row position 2 and column position 1:")
print(df.iloc[2, 1])

# Select a range of rows (from position 0 to 3) and columns (from position 0 to 2)
print("\nUsing iloc to select a range of rows and columns:")
print(df.iloc[0:3, 0:2])

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