How to convert a pandas DataFrame subset of columns AND rows into a numpy array?

Given a pandas dataframe, we have to convert a pandas DataFrame subset of columns AND rows into a numpy array. By Pranit Sharma Last updated : October 03, 2023

Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and data.

Columns are the different fields that contain their particular values when we create a DataFrame. We can perform certain operations on both row & column values.

Problem statement

Suppose we are given a DataFrame containing some columns like a, b, c, and d and we need to select only those rows in which the value for column c is greater than 1, but we only need columns of a and b for these rows.

Converting a pandas DataFrame subset of columns AND rows into a numpy array

For this purpose, we are simply going to filter out the columns of DataFrame with the traditional square bracket fashion inside which we will write a specific condition according to our requirements. Consider the below-given code statement to achieve this task,

res = df[df.c > 1][['a', 'b']].values

Let us understand with the help of an example,

Python program to convert a pandas DataFrame subset of columns AND rows into a numpy array

# Importing pandas package
import pandas as pd

# Importing numpy package
import numpy as np

# Creating two dictionary
d = {
    'a':[0.9,0.8,1,1.1,0],
    'b':[0.3,0.5,2.1,1,1.2],
    'c':[0,0,1.1,1.9,0.1],
    'd':[9,8,0,0,0]
}

# Creating a DataFrame
df = pd.DataFrame(d)

# Display DataFrame
print("DataFrame1:\n",df,"\n")

# Filtering out data
res = df[df.c > 1][['a', 'b']].values

# display result
print("Result:\n",res)