You can use df.head() to get the first N rows in Pandas DataFrame.
For example, if you need the first 4 rows, then use:
df.head(4)
Alternatively, you can specify a negative number within the brackets to get all the rows, excluding the last N rows.
For example, you can use the following syntax to get all the rows excluding the last 4 rows:
df.head(-4)
Complete example to get the first N rows in Pandas DataFrame
Step 1: Create a DataFrame
Let’s create a simple DataFrame with 10 rows:
import pandas as pd data = {'Fruits': ['Banana', 'Blueberry', 'Apple', 'Cherry', 'Mango', 'Pineapple', 'Watermelon', 'Papaya', 'Pear', 'Coconut'], 'Price': [2, 1.5, 3, 2.5, 3, 4, 5.5, 3.5, 1.5, 2] } df = pd.DataFrame(data) print(df)
As you can see, there are 10 rows in the DataFrame:
Fruits Price
0 Banana 2.0
1 Blueberry 1.5
2 Apple 3.0
3 Cherry 2.5
4 Mango 3.0
5 Pineapple 4.0
6 Watermelon 5.5
7 Papaya 3.5
8 Pear 1.5
9 Coconut 2.0
Step 2: Get the first N Rows in Pandas DataFrame
You can use the following syntax to get the first 4 rows in the DataFrame:
df.head(4)
Here is the complete code to get the first 4 rows for our example:
import pandas as pd data = {'Fruits': ['Banana', 'Blueberry', 'Apple', 'Cherry', 'Mango', 'Pineapple', 'Watermelon', 'Papaya', 'Pear', 'Coconut'], 'Price': [2, 1.5, 3, 2.5, 3, 4, 5.5, 3.5, 1.5, 2] } df = pd.DataFrame(data) get_rows = df.head(4) print(get_rows)
You’ll now get the first 4 rows:
Fruits Price
0 Banana 2.0
1 Blueberry 1.5
2 Apple 3.0
3 Cherry 2.5
Step 3 (Optional): Get all the rows, excluding the last N rows
Let’s suppose that you’d like to get all the rows, excluding the last N rows.
For example, you can use the code below in order to get all the rows excluding the last 4 rows:
import pandas as pd data = {'Fruits': ['Banana', 'Blueberry', 'Apple', 'Cherry', 'Mango', 'Pineapple', 'Watermelon', 'Papaya', 'Pear', 'Coconut'], 'Price': [2, 1.5, 3, 2.5, 3, 4, 5.5, 3.5, 1.5, 2] } df = pd.DataFrame(data) get_rows = df.head(-4) print(get_rows)
You’ll now see all the rows excluding the last 4 rows:
Fruits Price
0 Banana 2.0
1 Blueberry 1.5
2 Apple 3.0
3 Cherry 2.5
4 Mango 3.0
5 Pineapple 4.0
You can check the Pandas Documentation to learn more about df.head().