Get first N records in Pandas DataFrame

When working with large datasets in Python using the Pandas library, it is often necessary to extract a specific number of records from a column to analyze or process the data, such as the first 10 values from a column. For instance, if you have a DataFrame df with column A, you can quickly get first 10 values using df['A'].head(10).

1. Using head() Method to Get the First n values

head() method is one of the simplest ways to retrieve the first few rows of a DataFrame or a specific column. By default, it returns the first five rows, but you can specify any number by passing it as an argument. This method is significant because it provides a quick and efficient way to preview data without loading the entire dataset into memory.

Let us see how to fetch the first n records of a Pandas DataFrame, we will fetch first 10 rows:

import pandas as pd
dict = {'Name' : ['Sumit Tyagi', 'Sukritin','Akriti Goel', 'Sanskriti','Abhishek Jain'],'Age':[22, 20, 45, 21, 22],'Marks':[90, 84, 33, 87, 82]}
df = pd.DataFrame(dict)
print(df)

# Getting first 3 rows from the DataFrame
df_first_3 = df.head(3)
print(df_first_3)

Output:

2. Using iloc for for Positional Selection

The iloc method allows you to select data by index positions, which is particularly useful when you need precise control over which rows to extract. This method is significant because it supports slicing and indexing similar to Python lists, making it intuitive for users familiar with Python's native data structures.

import pandas as pd
dict = {'Name' : ['Sumit Tyagi', 'Sukritin','Akriti Goel', 'Sanskriti','Abhishek Jain'],'Age':[22, 20, 45, 21, 22],'Marks':[90, 84, 33, 87, 82]}
df = pd.DataFrame(dict)

# Get first 10 values using iloc
first_3_values = df.iloc[:3, df.columns.get_loc('Name')]
print(first_3_values)

0    Sumit Tyagi
1       Sukritin
2    Akriti Goel
Name: Name, dtype: object

3. loc for Label-Based Selection

The loc method is used for selecting rows and columns by labels. Although it's more commonly used for row selection, it can be adapted for columns by specifying the column name and slicing the rows. This method is significant because it allows for more readable and descriptive code, especially when working with labeled indices.

import pandas as pd
dict = {'Name' : ['Sumit Tyagi', 'Sukritin','Akriti Goel', 'Sanskriti','Abhishek Jain'],'Age':[22, 20, 45, 21, 22],'Marks':[90, 84, 33, 87, 82]}
df = pd.DataFrame(dict)

# Get first 10 values using iloc
first_3_values = df.iloc[:3, df.columns.get_loc('Marks')]
print(first_3_values)

0    90
1    84
2    33
Name: Marks, dtype: int64

We can also fetch, first n records of of "specific columns". For example:

import pandas as pd
dict = {'Name' : ['Sumit Tyagi', 'Sukritin','Akriti Goel', 'Sanskriti','Abhishek Jain'],'Age':[22, 20, 45, 21, 22],'Marks':[90, 84, 33, 87, 82]}
df = pd.DataFrame(dict)

# Getting first 2 rows of columns Age and Marks from df
df_first_2 = df[['Age', 'Marks']].head(2)
print(df_first_2)

   Age  Marks
0   22     90
1   20     84

4. Getting first n records with Slice Operator Directly

Using the slice operator ([:]) is one of the simplest ways to retrieve the first n records from a Pandas column or DataFrame. The slice operator allows you to select specific rows or ranges efficiently and intuitively. The slice operator is a Python-native technique, making it highly intuitive for those familiar with basic list slicing.

import pandas as pd
dict = {'Name' : ['Sumit Tyagi', 'Sukritin','Akriti Goel', 'Sanskriti','Abhishek Jain'],'Age':[22, 20, 45, 21, 22],'Marks':[90, 84, 33, 87, 82]}
df = pd.DataFrame(dict)

# Getting first 2 rows of columns Age and Marks from df
df_first_2 = df[:2]
print(df_first_2)

          Name  Age  Marks
0  Sumit Tyagi   22     90
1     Sukritin   20     84

Choosing the Right Method for Extracting Data in Pandas