Limited rows selection with given column in Pandas

Pandas is the go-to library for data manipulation in Python. One common task is selecting a limited number of rows from specific columns in a DataFrame. This article demonstrates various methods to accomplish this with practical examples.

What is Row and Column Selection?

Row and column selection allows you to extract subsets of your DataFrame based on position, labels, or conditions. This is essential for data analysis, preprocessing, and creating focused views of your data.

Method 1: Using iloc for Position-Based Selection

The iloc method selects rows and columns by their integer positions. It's useful when you know exactly which positions you want ?

import pandas as pd

# Create a sample DataFrame
data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda', 'Mike'],
    'Age': [28, 24, 35, 32, 30],
    'City': ['New York', 'Paris', 'Berlin', 'London', 'Sydney'],
    'Salary': [50000, 45000, 60000, 55000, 52000]
}

df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

Original DataFrame:
    Name  Age      City  Salary
0   John   28  New York   50000
1   Anna   24     Paris   45000
2  Peter   35    Berlin   60000
3  Linda   32    London   55000
4   Mike   30    Sydney   52000

Now let's select the first 3 rows from specific columns ?

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda', 'Mike'],
    'Age': [28, 24, 35, 32, 30],
    'City': ['New York', 'Paris', 'Berlin', 'London', 'Sydney'],
    'Salary': [50000, 45000, 60000, 55000, 52000]
}

df = pd.DataFrame(data)

# Select first 3 rows, columns 0 and 1 (Name and Age)
subset = df.iloc[:3, [0, 1]]
print(subset)

    Name  Age
0   John   28
1   Anna   24
2  Peter   35

Method 2: Using loc for Label-Based Selection

The loc method selects rows and columns by their labels or names, making your code more readable ?

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda', 'Mike'],
    'Age': [28, 24, 35, 32, 30],
    'City': ['New York', 'Paris', 'Berlin', 'London', 'Sydney'],
    'Salary': [50000, 45000, 60000, 55000, 52000]
}

df = pd.DataFrame(data)

# Select first 3 rows by index (0 to 2) and specific columns by name
subset = df.loc[:2, ['Name', 'Age', 'City']]
print(subset)

    Name  Age      City
0   John   28  New York
1   Anna   24     Paris
2  Peter   35    Berlin

Method 3: Using Boolean Indexing with Limited Results

Boolean indexing filters rows based on conditions. You can combine it with head() to limit the results ?

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda', 'Mike'],
    'Age': [28, 24, 35, 32, 30],
    'City': ['New York', 'Paris', 'Berlin', 'London', 'Sydney'],
    'Salary': [50000, 45000, 60000, 55000, 52000]
}

df = pd.DataFrame(data)

# Select rows where Age > 30, show only Name and City, limit to 2 results
subset = df[df['Age'] > 30][['Name', 'City']].head(2)
print(subset)

    Name    City
2  Peter  Berlin
3  Linda  London

Method 4: Using head() and tail() with Column Selection

Combine head() or tail() with column selection for simple cases ?

import pandas as pd

data = {
    'Name': ['John', 'Anna', 'Peter', 'Linda', 'Mike'],
    'Age': [28, 24, 35, 32, 30],
    'City': ['New York', 'Paris', 'Berlin', 'London', 'Sydney'],
    'Salary': [50000, 45000, 60000, 55000, 52000]
}

df = pd.DataFrame(data)

# Get first 3 rows with selected columns
first_three = df[['Name', 'Salary']].head(3)
print("First 3 rows:")
print(first_three)

print("\nLast 2 rows:")
last_two = df[['Name', 'Age']].tail(2)
print(last_two)

First 3 rows:
    Name  Salary
0   John   50000
1   Anna   45000
2  Peter   60000

Last 2 rows:
   Name  Age
3  Linda   32
4   Mike   30

Comparison of Methods

Conclusion

Pandas offers multiple methods for selecting limited rows with specific columns. Use iloc for position-based selection, loc for label-based selection, and combine boolean indexing with head() for conditional filtering. Choose the method that best fits your data analysis needs.