Mastering Pandas: How to Get First N Rows DataFrames

How to Get the First N Rows in Pandas DataFrame

Pandas is a powerful data analysis library that provides a range of functionalities to manipulate data. One of the essential operations when working with data frames is selecting specific rows from a data frame.

In this article, we will focus on the different ways to extract the first N rows of a data frame and exclude the last N rows of a data frame.

Using df.head() to Get the First N Rows

One of the simplest ways to extract the first N rows of a data frame is to use the “df.head()” method.

This method returns the first N rows of a data frame. For example, suppose we have a data frame that contains information about sales of different products.

We can use the following code to extract the first 5 rows of the data frame.

import pandas as pd
data = pd.read_csv('sales.csv')
first_5_rows = data.head(5)

The “head()” method takes an integer argument which indicates the number of rows to return. In this case, we passed 5, which means we want to return the first five rows of the data frame.

Using Negative Number in df.head() to Get All Rows Excluding Last N Rows

Sometimes, we may need to extract all the rows from a data frame, except for the last N rows. In this case, we can make use of negative numbers in the “head()” method.

For instance, suppose we have a data frame that contains sales data for 12 months. We can use the following code to extract all the rows except the last three rows.

last_3_months_excluded = data.head(-3)

In this code, we passed “-3” as an argument to the “head()” method, which indicates that we want to exclude the last three rows of the data frame.

Complete Example to Get the First N Rows

Now let’s look at a complete example of how to create a data frame and extract the first N rows. For this example, let’s create a data frame that contains information about fruit sales.

Creating a DataFrame

import pandas as pd
data = {'Fruit': ['Apple', 'Orange', 'Banana', 'Mango', 'Pineapple', 'Strawberry', 'Kiwi'],
        'Sales': [100, 200, 150, 300, 400, 250, 350]}
df = pd.DataFrame(data)

The above code creates a data frame with two columns, “Fruit” and “Sales.” The “Fruit” column contains the names of fruits, and the “Sales” column contains the corresponding sales figures.

Getting the First N Rows and Excluding Last N Rows

Now that we have created the data frame, let’s extract the first three rows and exclude the last two rows using the “head()” method.

first_3_rows = df.head(3)
last_2_rows_excluded = df.head(-2)

The “first_3_rows” variable will contain the first three rows of the data frame, while the “last_2_rows_excluded” variable will contain all the rows except the last two rows.

Conclusion

In conclusion, extracting specific rows from a data frame is an essential operation in data analysis. In this article, we discussed two ways to extract the first N rows and exclude the last N rows of a data frame using the “head()” method in Pandas.

By understanding these techniques, you can manipulate data frames effectively and extract the information you need for your analysis.

Getting the First N Rows in Pandas DataFrame – A Step-by-Step Guide

Pandas is a Python library used for data manipulation and analysis, and it provides many useful methods for extracting and manipulating data. This article covers a step-by-step guide on how to get the first N rows in a pandas DataFrame.

We will also cover a technique to get all the rows except the last N rows.

Step 1: Creating DataFrame

The first step in getting the first N rows in pandas DataFrame is to create a DataFrame.

This can be done in many ways, but the most common way is to read data from a file like CSV or Excel. For this example, we will create a DataFrame manually.

In this DataFrame, we will store information about different cars, such as make, model, year, and price.

import pandas as pd

data = {'Make': ['Toyota', 'Ford', 'Chevrolet', 'Honda', 'Nissan'],
          'Model': ['Camry', 'F-150', 'Silverado', 'Civic', 'Altima'],
          'Year': [2018, 2017, 2015, 2019, 2016],
          'Price': [22000, 28000, 25000, 19000, 21000]}

df = pd.DataFrame(data)

The above code creates a DataFrame called “df” with four columns: “Make,” “Model,” “Year,” and “Price.”

Step 2: Getting the First N Rows

The second step is to get the first N rows in the DataFrame. In pandas, this can be done using the “head()” method.

Let’s say we want to get the first three rows of the car DataFrame. We can do this by calling the “head()” method with an argument of 3, like this:

first_three_rows = df.head(3)

The “head()” method returns the first N rows of the DataFrame, where N is the argument passed to the method.

In this case, it returns the first three rows of the car DataFrame to the variable “first_three_rows.”

Step 3 (Optional): Getting All Rows Excluding Last N Rows

If we want to get all the rows in the DataFrame except for the last N rows, we can use the same “head()” method with a negative argument. Let’s say we want to exclude the last two rows of the car DataFrame.

We can do this by calling the “head()” method with an argument of -2.

exclude_last_two_rows = df.head(-2)

The “head()” method with a negative argument returns all the rows in the DataFrame except for the last N rows, where N is the absolute value of the negative argument passed to the method.

In this case, it returns all the rows except the last two rows to the variable “exclude_last_two_rows.”

Complete Example

Let’s look at a complete example that demonstrates how to create a DataFrame, get the first four rows, and exclude the last four rows.

Creating DataFrame

import pandas as pd
data = {'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', 'Philadelphia'],
        'Population': [8398748, 3990456, 2705994, 2325502, 1660272, 1584064],
        'State': ['NY', 'CA', 'IL', 'TX', 'AZ', 'PA']}

  
df = pd.DataFrame(data)
  

first_four_rows = df.head(4)

exclude_last_four_rows = df.head(-4)