ANALISIS PENJUALAN TOKO RETAIL#
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('retail_sales_dataset.csv')
df.head()
| Transaction ID | Date | Customer ID | Gender | Age | Product Category | Quantity | Price per Unit | Total Amount | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 2023-11-24 | CUST001 | Male | 34 | Beauty | 3 | 50 | 150 |
| 1 | 2 | 2023-02-27 | CUST002 | Female | 26 | Clothing | 2 | 500 | 1000 |
| 2 | 3 | 2023-01-13 | CUST003 | Male | 50 | Electronics | 1 | 30 | 30 |
| 3 | 4 | 2023-05-21 | CUST004 | Male | 37 | Clothing | 1 | 500 | 500 |
| 4 | 5 | 2023-05-06 | CUST005 | Male | 30 | Beauty | 2 | 50 | 100 |
df.describe()
| Transaction ID | Age | Quantity | Price per Unit | Total Amount | |
|---|---|---|---|---|---|
| count | 1000.000000 | 1000.00000 | 1000.000000 | 1000.000000 | 1000.000000 |
| mean | 500.500000 | 41.39200 | 2.514000 | 179.890000 | 456.000000 |
| std | 288.819436 | 13.68143 | 1.132734 | 189.681356 | 559.997632 |
| min | 1.000000 | 18.00000 | 1.000000 | 25.000000 | 25.000000 |
| 25% | 250.750000 | 29.00000 | 1.000000 | 30.000000 | 60.000000 |
| 50% | 500.500000 | 42.00000 | 3.000000 | 50.000000 | 135.000000 |
| 75% | 750.250000 | 53.00000 | 4.000000 | 300.000000 | 900.000000 |
| max | 1000.000000 | 64.00000 | 4.000000 | 500.000000 | 2000.000000 |
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 9 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Transaction ID 1000 non-null int64
1 Date 1000 non-null object
2 Customer ID 1000 non-null object
3 Gender 1000 non-null object
4 Age 1000 non-null int64
5 Product Category 1000 non-null object
6 Quantity 1000 non-null int64
7 Price per Unit 1000 non-null int64
8 Total Amount 1000 non-null int64
dtypes: int64(5), object(4)
memory usage: 70.4+ KB
dari sini kita bisa mengetahui rentan usia terbanyak membeli adalah usia 40-55
bins = [0, 19, 29, 39, 55, 65, float('inf')]
labels = ['0-19', '20-29', '30-39', '40-55', '56-65', '65++']
# Add a new column for age range
df['Age Range'] = pd.cut(df['Age'], bins=bins, labels=labels, right=False)
# Count the number of transactions for each age range
age_range_counts = df['Age Range'].value_counts().sort_index()
# Plotting the data
plt.figure(figsize=(10, 6))
age_range_counts.plot(kind='bar', color='skyblue')
plt.title('Number of Transactions by Age Range')
plt.xlabel('Age Range')
plt.ylabel('Number of Transactions')
plt.xticks(rotation=0)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()
dapat kita ketahui kategori terlaris adalah clothing dengan jumlah pembelian 350 barang
# Menghitung jumlah transaksi untuk setiap gender
category_counts = df['Product Category'].value_counts()
# Plotting the data
plt.figure(figsize=(10, 6))
category_counts.plot(kind='bar', color='skyblue')
plt.title('Number of Transactions by category')
plt.xlabel(' cstegory')
plt.ylabel('Number of Transactions')
plt.xticks(rotation=0)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()
#
# Menghitung jumlah transaksi untuk setiap gender
gender_counts = df['Gender'].value_counts()
# Plotting the data
plt.figure(figsize=(10, 6))
gender_counts.plot(kind='bar', color='skyblue')
plt.title('Number of Transactions by Gender')
plt.xlabel('Gender')
plt.ylabel('Number of Transactions')
plt.xticks(rotation=0)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()
# Menghitung jumlah transaksi untuk setiap kombinasi gender dan kategori produk
product_counts_by_gender = df.groupby(['Gender', 'Product Category']).size().unstack().fillna(0)
# Menampilkan hasil
print(product_counts_by_gender)
# Plotting the product counts by gender
product_counts_by_gender.plot(kind='bar', stacked=True, figsize=(12, 7), color=['skyblue', 'lightgreen', 'lightcoral', 'orange', 'purple'])
plt.title('Number of Transactions for Each Product Category by Gender')
plt.xlabel('Gender')
plt.ylabel('Number of Transactions')
plt.xticks(rotation=0)
plt.legend(title='Product Category')
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()
Product Category Beauty Clothing Electronics
Gender
Female 166 174 170
Male 141 177 172
berikut adalah grafik pendapatan stiap bulan pendapatan tertinggiterdapat pada bulan mei dengan pendapatan total 53.150$
# Mengubah kolom 'Date' menjadi tipe data datetime
df['Date'] = pd.to_datetime(df['Date'])
# Menambahkan kolom 'Month' untuk bulan dan tahun
df['Month'] = df['Date'].dt.to_period('M')
# Menghitung total pendapatan untuk setiap bulan
monthly_revenue = df.groupby('Month')['Total Amount'].sum()
# Menampilkan hasil
print(monthly_revenue)
# Plotting the monthly revenue using a line plot
plt.figure(figsize=(10, 6))
monthly_revenue.plot(kind='line', marker='o', color='skyblue')
plt.title('Monthly Revenue')
plt.xlabel('Month')
plt.ylabel('Total Revenue')
plt.xticks(rotation=45)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()
Month
2023-01 35450
2023-02 44060
2023-03 28990
2023-04 33870
2023-05 53150
2023-06 36715
2023-07 35465
2023-08 36960
2023-09 23620
2023-10 46580
2023-11 34920
2023-12 44690
2024-01 1530
Freq: M, Name: Total Amount, dtype: int64
berikut adalah rentang usia dengan pemebelian kategori barang terbanyak ,seperti yang saya jelaskan di atas pembelian terbanyak terdapat pada usia 40-55 dengan rincian total pembelian clothing 337 , beauty 268, electronics 305
df['Age Group'] = pd.cut(df['Age'], bins=bins, labels=labels, right=False)
# Menghitung jumlah total pembelian untuk setiap kelompok kategori produk dan rentang usia
grouped_data = df.groupby(['Product Category', 'Age Group'])['Quantity'].sum().reset_index()
print(grouped_data)
# Membuat plot
fig, ax = plt.subplots(figsize=(10, 6))
grouped_data.pivot(index='Age Group', columns='Product Category', values='Quantity').plot(kind='bar', ax=ax)
plt.title('Total Quantity Purchased by Age Group and Product Category')
plt.xlabel('Age Group')
plt.ylabel('Total Quantity Purchased')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
Product Category Age Group Quantity
0 Beauty 0-19 24
1 Beauty 20-29 177
2 Beauty 30-39 159
3 Beauty 40-55 268
4 Beauty 56-65 143
5 Beauty 65++ 0
6 Clothing 0-19 19
7 Clothing 20-29 178
8 Clothing 30-39 171
9 Clothing 40-55 337
10 Clothing 56-65 189
11 Clothing 65++ 0
12 Electronics 0-19 11
13 Electronics 20-29 174
14 Electronics 30-39 176
15 Electronics 40-55 305
16 Electronics 56-65 183
17 Electronics 65++ 0
berikut adalah rentang usia dengan pengeluaran terbanyak ,seperti yang saya jelaskan di atas pembelian terbanyak terdapat pada usia 40-55 dengan rincian beauty 52.080 dolar, clothing 48015 dolar, electronics 50830
df['Age Group'] = pd.cut(df['Age'], bins=bins, labels=labels, right=False)
# Menghitung jumlah total pembelian untuk setiap kelompok kategori produk dan rentang usia
grouped_data_amount = df.groupby(['Product Category', 'Age Group'])['Total Amount'].sum().reset_index()
print(grouped_data_amount)
# Membuat plot
fig, ax = plt.subplots(figsize=(10, 6))
grouped_data_amount.pivot(index='Age Group', columns='Product Category', values='Total Amount').plot(kind='bar', ax=ax)
plt.title('Total Quantity Purchased by Age Group and Product Category')
plt.xlabel('Age Group')
plt.ylabel('Total Quantity Purchased')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
Product Category Age Group Total Amount
0 Beauty 0-19 4960
1 Beauty 20-29 33250
2 Beauty 30-39 32555
3 Beauty 40-55 52080
4 Beauty 56-65 20670
5 Beauty 65++ 0
6 Clothing 0-19 4085
7 Clothing 20-29 39565
8 Clothing 30-39 32605
9 Clothing 40-55 48015
10 Clothing 56-65 31310
11 Clothing 65++ 0
12 Electronics 0-19 2170
13 Electronics 20-29 32555
14 Electronics 30-39 33140
15 Electronics 40-55 50830
16 Electronics 56-65 38210
17 Electronics 65++ 0
berikut adalah jumlah barang yang di beli olaeh setiap gender dengan rincian Beauty Female 418 Beauty Male 353 Clothing Female 441 Clothing Male 453 Electronics Female 439 Electronics Male 410
# Mengelompokkan data berdasarkan kategori produk dan jenis kelamin
grouped_gender_data = df.groupby(['Product Category', 'Gender'])['Quantity'].sum().reset_index()
print(grouped_gender_data)
# Membuat plot
fig, ax = plt.subplots(figsize=(10, 6))
grouped_gender_data.pivot(index='Gender', columns='Product Category', values='Quantity').plot(kind='bar', ax=ax)
plt.title('Total Quantity Purchased by Gender and Product Category')
plt.xlabel('Gender')
plt.ylabel('Total Quantity Purchased')
plt.xticks(rotation=0)
plt.tight_layout()
plt.show()
Product Category Gender Quantity
0 Beauty Female 418
1 Beauty Male 353
2 Clothing Female 441
3 Clothing Male 453
4 Electronics Female 439
5 Electronics Male 410
berikut adalah jumlah pengeluaran yang dikeluarkan oleh setiap gender dengan rincian Product Category Gender Total Amount Beauty Female 74830 Beauty Male 68685 Clothing Female 81275 Clothing Male 74305 Electronics Female 76735 Electronics Male 80170
# Mengelompokkan data berdasarkan kategori produk dan jenis kelamin
grouped_gender_data_bayar = df.groupby(['Product Category', 'Gender'])['Total Amount'].sum().reset_index()
print(grouped_gender_data_bayar)
# Membuat plot
fig, ax = plt.subplots(figsize=(10, 6))
grouped_gender_data_bayar.pivot(index='Gender', columns='Product Category', values='Total Amount').plot(kind='bar', ax=ax)
plt.title('Total Quantity Purchased by Gender and Product Category')
plt.xlabel('Gender')
plt.ylabel('Total Quantity Purchased')
plt.xticks(rotation=0)
plt.tight_layout()
plt.show()
Product Category Gender Total Amount
0 Beauty Female 74830
1 Beauty Male 68685
2 Clothing Female 81275
3 Clothing Male 74305
4 Electronics Female 76735
5 Electronics Male 80170
