ex-04-eda's Introduction

Ex-04-EDA

AIM:

To perform EDA on the given data set.

EXPLANATION:

The primary aim with exploratory analysis is to examine the data for distribution, outliers and anomalies to direct specific testing of your hypothesis.

ALGORITHM:

STEP 1:

Import the required packages(pandas,numpy,seaborn).

STEP 2:

Read the given .csv file.

STEP 3:

Convert the file into a dataframe and get information of the data.

STEP 4:

Remove the non numerical data columns using drop() method.

STEP 5:

Replace the null values using (.fillna).

STEP 6:

Returns object containing counts of unique values using (value_counts()).

STEP 7:

Plot the counts in the form of Histogram or Bar Graph.

STEP 8:

Find the pairwise correlation of all columns in the dataframe(.corr()).

STEP 9:

Save the final data set into the file.

CODE:

import pandas as pd
import numpy as np
import seaborn as sns
df=pd.read_csv("supermarket.csv")
df.info()
df.head()
df.tail()
df.isnull().sum()
df["City"].value_counts()
df["Gender"].value_counts()
df["Payment"].value_counts()
sns.countplot(x="Invoice ID",data=df)
sns.countplot(x="Total",data=df)
sns.countplot(x='gross income',data=df)
sns.countplot(x='Payment',data=df)
sns.displot(df["cogs"])
sns.countplot(x="Gender",hue="Quantity",data=df)
sns.displot(df[df["Product line"]==0]["Total"])
pd.crosstab(df["Payment"],df["Quantity"])
pd.crosstab(df["Gender"],df["Quantity"])
df.corr()
sns.heatmap(df.corr(),annot=True)