Data Visualization in a loop using Seaborn and Matplotlib

3 min readMay 8, 2022

In this story I will share how I automated a task of Data Visualization using Python where I was had to create boxplots and histplots for numerical columns present in the dataset. There were total 330 columns and plotting each column one by one was very hectic. So, I did a bit of research on Stack Overflow, read Seaborn and Matplotlib documentations, and finally created 10 lines of code for this task.

I have used Kaggle diabetes.csv dataset to demonstrate this task.

import pandas as pd
df = pd.read_csv('diabetes.csv')
df

Converting all the columns in the dataframe into a list:

columns = df.columns#convert into list----------------
columns= list(columns)columns

Suppose you have a DataFrame where there are also some string columns in that case you can select the integer columns using below code:

#select integer columns from the dataframe------------------
df1.select_dtypes('int64') #convert into list----------------
columns = list(df1.select_dtypes('int64'))#prints a list of all integer columns--------
columns

df.info()

importing seaborn and matplotlib :

import seaborn as sns
import matplotlib.pyplot as plt# a for loop to automate our task--------------
for i in df.columns:
    plt.figure()
    plt.tight_layout()
    sns.set(rc={"figure.figsize":(8, 5)})    f, (ax_box, ax_hist) = plt.subplots(2, sharex=True)
    plt.gca().set(xlabel= i,ylabel='Frequency')
    sns.boxplot(df[i], ax=ax_box , linewidth= 1.0)
    sns.histplot(df[i], ax=ax_hist , bins = 10,kde=True)

Output: