💡 Problem Formulation: When using pandas, a popular data manipulation library in Python, a common task is to extract specific rows from a DataFrame. The desired output is a subset of the original DataFrame, containing only the rows of interest. This article provides solutions for this problem using the iloc indexer, which allows integer-based, positional indexing of rows.
Method 1: Extracting a Single Row
The simplest use of iloc is extracting a single row from a DataFrame by its index position. You specify the index of the row you want to retrieve, and pandas returns it as a Series object.
Here’s an example:
import pandas as pd
# Sample DataFrame
df = pd.DataFrame({
'name': ['Alice', 'Bob', 'Charlie'],
'age': [24, 27, 22]
})
# Extract the second row
row = df.iloc[1]
print(row)Output:
name Bob age 27 Name: 1, dtype: object
This code snippet creates a DataFrame with three rows and then extracts the second row (index 1, as Python uses zero-based indexing) using iloc. The result shows the name and age corresponding to that row as a Series.
Method 2: Extracting Multiple Rows
With iloc, you can also slice the DataFrame to extract multiple rows at once. You use a slice object with start and end indices to define the range of rows you want to retrieve.
Here’s an example:
import pandas as pd
# Sample DataFrame
df = pd.DataFrame({
'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
'age': [24, 27, 22, 30, 25]
})
# Extract rows 1 up to 4
rows = df.iloc[1:4]
print(rows)Output:
name age 1 Bob 27 2 Charlie 22 3 David 30
This snippet demonstrates how to use slicing with iloc to extract rows from index 1 to 3 (the stop index is exclusive). The result is a DataFrame containing the specified range of rows.
Method 3: Extracting Rows with Step
Similar to Python lists, you can specify a step in your slicing to extract non-consecutive rows from a DataFrame. This can be particularly useful if you want to sample data at regular intervals.
Here’s an example:
import pandas as pd
# Sample DataFrame
df = pd.DataFrame({
'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
'age': [24, 27, 22, 30, 25]
})
# Extract every other row
alt_rows = df.iloc[::2]
print(alt_rows)Output:
name age 0 Alice 24 2 Charlie 22 4 Eva 25
Here we use a step of 2 to get every other row from the DataFrame. The iloc indexer takes the syntax [start:end:step], and by omitting the start and end, it defaults to the beginning and end of the DataFrame.
Method 4: Using a List of Indices
When you need specific, non-consecutive rows, iloc allows you to pass a list of indices. This returns a DataFrame that includes only the rows specified by the list.
Here’s an example:
import pandas as pd
# Sample DataFrame
df = pd.DataFrame({
'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
'age': [24, 27, 22, 30, 25]
})
# Extract rows with index 0 and 3
specific_rows = df.iloc[[0, 3]]
print(specific_rows)Output:
name age 0 Alice 24 3 David 30
The code here demonstrates how to use a list of indices to retrieve the first and fourth rows from the DataFrame. The indices in the list do not have to be in order.
Bonus One-Liner Method 5: Extracting the Last Row
As a bonus, if you need to retrieve the last row of a DataFrame, you can use the -1 index with iloc to do so in a concise manner.
Here’s an example:
import pandas as pd
# Sample DataFrame
df = pd.DataFrame({
'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
'age': [24, 27, 22, 30, 25]
})
# Extract the last row
last_row = df.iloc[-1]
print(last_row)Output:
name Eva age 25 Name: 4, dtype: object
This one-liner extracts the last row of the DataFrame using Python’s negative indexing. It is clean and quick when you’re only interested in the most recent data point assuming the data is ordered chronologically.
Summary/Discussion
- Method 1: Extracting a Single Row. It’s straightforward and easy to use for extracting a specific row. The downside is that it only retrieves one row at a time.
- Method 2: Extracting Multiple Rows. This method allows the extraction of a range of rows, which is useful for batch operations. However, it does not allow for non-sequential row extraction.
- Method 3: Extracting Rows with Step. It’s useful when you need to sample your data or get rows at regular intervals. It’s not suitable for random row access.
- Method 4: Using a List of Indices. This method provides flexibility by allowing non-consecutive rows to be retrieved. The limitation is the manual specification of all the required indices.
- Method 5: Extracting the Last Row. Quick and easy for getting the latest entry. It’s limited to only retrieving the last row.