import numpy as np
import pandas as pd
data = pd.read_csv("Mall_Customers.csv");
data.head()
#On an average, who earns more Male or female?
data[data["Gender"]=="Male"]["Annual Income (k$)"].mean()
62.22727272727273
data[data["Gender"]=="Female"]["Annual Income (k$)"].mean()
59.25
#Male
#On an average, who spends more male or female?
data[data["Gender"]=="Male"]["Spending Score (1-100)"].mean()
48.51136363636363
data[data["Gender"]=="Female"]["Spending Score (1-100)"].mean()
51.526785714285715
#Female
#our dataset consists of how many males or females?
data[data["Gender"]=="Male"].count()
CustomerID 88
Gender 88
Age 88
Annual Income (k$) 88
Spending Score (1-100) 88
dtype: int64
data[data["Gender"]=="Female"].count()
CustomerID 112
Gender 112
Age 112
Annual Income (k$) 112
Spending Score (1-100) 112
dtype: int64
data["Gender"].count()
200
#Person with highest spending index is male or female?
max_spend = data["Spending Score (1-100)"].max()
data[data["Spending Score (1-100)"]==max_spend]["Gender"]
11 Female
Name: Gender, dtype: object
#What is the average age of male and female
data[data["Gender"]=="Male"]["Age"].mean()
39.80681818181818
data[data["Gender"]=="Female"]["Age"].mean()
38.098214285714285
#How many people below the average annual income spend more than the average?
average_annual_income = data["Annual Income (k$)"].mean()
average_spend = data["Spending Score (1-100)"].mean()
len(data[(data["Annual Income (k$)"]<average_annual_income) & (data["Spending Score (1-100)"]>average_spend)])
49
#Do people with annual income higher than the average spend more than the average?
len(data[(data["Annual Income (k$)"]>average_annual_income) & (data["Spending Score (1-100)"]>average_spend)])
48
import jovian
jovian.commit()
[jovian] Saving notebook..