How to select specific rows?

To select specific rows from a pandas DataFrame, you can use various methods, including:

1. Using .loc[]: This method allows you to select rows by label (index).
2. Using .iloc[]: This method allows you to select rows by integer position.
3. Using boolean indexing: This method allows you to select rows based on conditions.

1. Using .loc[]

You can select rows by their index labels:

import pandas as pd

# Create a sample DataFrame
data = {
    'column1': [1, 2, 3, 4, 5],
    'column2': ['A', 'B', 'C', 'D', 'E']
}
df = pd.DataFrame(data)

# Select specific rows by index labels
selected_rows = df.loc[1:3]  # Selects rows with index 1, 2, and 3
print(selected_rows)

Output:

   column1 column2
1        2       B
2        3       C
3        4       D

2. Using .iloc[]

You can select rows by their integer position:

# Select specific rows by integer position
selected_rows = df.iloc[1:4]  # Selects rows at positions 1, 2, and 3
print(selected_rows)

Output:

   column1 column2
1        2       B
2        3       C
3        4       D

3. Using Boolean Indexing

You can select rows based on conditions:

# Select rows where column1 is greater than 2
selected_rows = df[df['column1'] > 2]
print(selected_rows)

Output:

   column1 column2
2        3       C
3        4       D
4        5       E

Summary

• Use .loc[] for label-based indexing.
• Use .iloc[] for position-based indexing.
• Use boolean indexing for conditional selection.

These methods provide flexibility in selecting specific rows from a DataFrame based on your needs.