Pandas – How to Concatenate Dataframe Columns

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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(data=bd.data, columns=bd.feature_names)
df_y = pd.DataFrame(data=bd.target, 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

Ajitesh Kumar

Ajitesh has been recently working in the area of AI and machine learning. Currently, his research area includes Safe & Quality AI. In addition, he is also passionate about various different technologies including programming languages such as Java/JEE, Javascript and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc.

He has also authored the book, Building Web Apps with Spring 5 and Angular.
Ajitesh Kumar

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