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Python | Extracting rows using Pandas .iloc[]

The .iloc[] indexer in pandas is used to select rows and columns by their integer index. It's purely integer-location based indexing, meaning you'll be using integers to specify the location.

Here's a tutorial on how to extract rows using .iloc[]:

1. Setup:

Make sure you have the necessary library:

pip install pandas

2. Import the necessary library:

import pandas as pd

3. Create a Sample DataFrame:

For this tutorial, let's use a sample DataFrame:

data = {
    'A': [1, 2, 3, 4, 5],
    'B': [10, 20, 30, 40, 50],
    'C': ['p', 'q', 'r', 's', 't']
}

df = pd.DataFrame(data)
print(df)

4. Extracting Rows Using `.iloc[]`:

a. Single Row:

To extract the row with index 2:

row = df.iloc[2]
print(row)

b. Multiple Rows:

To extract rows with index 1 and 3:

rows = df.iloc[[1, 3]]
print(rows)

c. Row Range:

To extract rows from index 1 up to (and excluding) 4:

rows_range = df.iloc[1:4]
print(rows_range)

5. Extracting Rows and Columns Simultaneously:

With .iloc[], you can also specify both row and column indices to extract specific rows and columns.

a. Single Row, Multiple Columns:

Extract the second row and the first two columns:

subset = df.iloc[1, 0:2]
print(subset)

b. Multiple Rows, Multiple Columns:

Extract the first three rows and the first two columns:

subset = df.iloc[0:3, 0:2]
print(subset)

6. Caveats and Tips:

Remember that Python uses 0-based indexing. So, the first row is indexed as 0, the second as 1, and so on.
When specifying a range, the ending index is exclusive. For instance, df.iloc[1:4] will extract rows with indices 1, 2, and 3, but not 4.
Be cautious while using .iloc[] since it relies on integer indices. If the DataFrame has been modified, shuffled, or rows have been removed, the integer index might not correspond to the original row order.

Summary:

The .iloc[] indexer in pandas is a powerful tool for extracting rows and columns based on their integer location. It provides a variety of ways to extract data, from single rows/columns to ranges and specific lists of indices. However, always remember it's purely integer-location based, so make sure to account for the actual order of the DataFrame.

Index-based row extraction in Pandas:
- Rows can be extracted by providing the integer index of the desired row.
```
# Extract row at index 2
selected_row = df.iloc[2]
```
Extracting single row with iloc in Pandas:
- Use iloc to extract a single row based on its integer index.
```
# Extract single row at index 2
single_row = df.iloc[2]
```
Pandas iloc for extracting multiple rows:
- Use a list of integer indices to extract multiple rows.
```
# Extract multiple rows at indices 1, 3, and 5
multiple_rows = df.iloc[[1, 3, 5]]
```
Slicing rows with iloc in Pandas DataFrame:
- Slice rows using integer indices to select a range.
```
# Slice rows from index 2 to 5
sliced_rows = df.iloc[2:6]
```
Selecting rows by position using iloc:
- Use iloc for selecting rows based on their positional index.
```
# Select rows at positions 0, 2, and 4
selected_rows = df.iloc[[0, 2, 4]]
```
Accessing rows and columns using iloc in Pandas:
- Combine iloc for both rows and columns by providing integer indices for both.
```
# Accessing specific row and column
value = df.iloc[2, 1]  # Row at index 2, column at index 1
```