Pandas – How to Concatenate Dataframe Columns

data frame concatenation by columns

Quick code sample on how to concatenate the data frames columns. We will work with example of Boston dataset found with sklearn.datasets. One should note that data frames could be concatenated by rows and columns. In this post, you will learn about how to concatenate data frames by columns.

Here is the code for working with Boston datasets. First and foremost, the Boston dataset will be loaded.

from sklearn.datasets import load_boston
bd = load_boston()

Once loaded, let’s create different different data frames comprising of data and target variable.

df_x = pd.DataFrame(, columns=bd.feature_names)
df_y = pd.DataFrame(, columns=["MEDV"])

This above creates two data frames comprising of data (features) and the values of target variable. Here are the snapshots.

Fig 1. Data frame representing dataset (with features)
Fig 2. Data frame representing dataset (target variable)

Use the following command to concatenate the data frames.

df = pd.concat([df_x, df_y], axis=1)

Here is the resulting data frame from concatenation of two data frames by columns.

dataframes concatenated by columns
Fig 2. Data frame created by concatenating data frame by columns

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog,
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