The content presented in this article is intended solely for academic purposes. The opinions expressed are based on my personal understanding and research. It’s important to note that the field of big data and the programming languages discussed, such as Python, R, Power BI, Tableau, and SQL, are dynamic and constantly evolving. This article aims to foster learning, exploration, and discussion within the field rather than provide definitive answers. Reader discretion is advised.
Ifood Kaggle Dataset
Importing Libraries that we need -Loading the Dataset -Running Code
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
marketing1 = pd.read_csv(r'D:\helen\Documents\PythonScripts\datasets\kaggle\ifood_df.csv')
marketing1.head()
OUTPUT JUPYTER NOTEBOOK
marketing1.shape
OUTPUT
(2205, 39)
marketing1.dtypes
OUTPUT JUPYTER NOTEBOOK
marketing1.columns
OUTPUT JUPYTER NOTEBOOK
#missing values
marketing1.isna()
#which columns
marketing1.isna().any
OUTPUT JUPYTER NOTEBOOK
marketing1.Marital.value_counts()
OUTPUT JUPYTER NOTEBOOK
marketing1.describe()
OUTPUT JUPYTER NOTEBOOK
