instruction stringlengths 16 55 | input dict | output stringlengths 573 7.08k |
|---|---|---|
Analyze loyalty score city | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
62.13,
111.4,
87.11,
154.16,
107.24,
146.92,
101.84,
175.28,
118.98,
196.76,
62.21,
133.22,
51.67,
129.18,
53.01,
85.66,
193.85,
114.23,
160.13,
162.11,
69.25,
96.35,
115.85,
149.45,
107.2,
165.05,
63.38,
184.09,
161.6,
174.27
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.022,
0.0869,
0.028800000000000003,
0.0658,
0.0323,
0.0151,
0.0472,
0.0443,
0.025500000000000002,
0.07,
0.0932,
0.0847,
0.0128,
0.0175,
0.0826,
0.0898,
0.0345,
0.051300000000000005,
0.020200000000000003,
0.0613,
0.0733,
0.0979,
0.0845,
0.046,
0.0143,
0.0877,
0.08650000000000001,
0.0257,
0.049100000000000005,
0.014100000000000001
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1929.56,
2117.12,
2109.38,
1986.4,
1992.98,
1972.83,
1987.38,
1867.88,
1737.17,
1630.69,
1612.29,
1619.72,
1596.98,
1547.03,
1559.29,
1737.04,
1906.76,
1920.3,
1791.25,
1914.52,
1786.25,
1734.57,
1653.68,
1486.43,
1521.8,
1345.34,
1437.81,
1495.01,
1476.22,
1532
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
211,
108,
129,
213,
283,
288,
150,
103,
112,
74,
251,
54,
290,
121,
71,
80,
126,
302,
109,
244,
220,
155,
142,
251,
83,
97,
221,
169,
167,
198
]
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1929.56, 2117.12, 2109.38, 1986.4, 1992.98, 1972.83, 1987.38, 1867.88, 1737.17, 1630.69, 1612.29, 1619.72, 1596.98, 1547.03, 1559.29, 1737.04, 1906.76, 1920.3, 1791.25, 1914.52, 1786.25, 1734.57, 1653.68, 1486.43, 1521.8, 1345.34, 1437.81, 1495.01, 1476.22, 1532.0], 'Visitors/Pengunjung': [211, 108, 129, 213, 283, 288, 150, 103, 112, 74, 251, 54, 290, 121, 71, 80, 126, 302, 109, 244, 220, 155, 142, 251, 83, 97, 221, 169, 167, 198], 'Conversion Rate/Rasio Konversi': [0.022, 0.0869, 0.0288, 0.0658, 0.0323, 0.0151, 0.0472, 0.0443, 0.0255, 0.07, 0.0932, 0.0847, 0.0128, 0.0175, 0.0826, 0.0898, 0.0345, 0.0513, 0.0202, 0.0613, 0.0733, 0.0979, 0.0845, 0.046, 0.0143, 0.0877, 0.0865, 0.0257, 0.0491, 0.0141], 'Avg. Order Value/Nilai Pesanan Rata2': [62.13, 111.4, 87.11, 154.16, 107.24, 146.92, 101.84, 175.28, 118.98, 196.76, 62.21, 133.22, 51.67, 129.18, 53.01, 85.66, 193.85, 114.23, 160.13, 162.11, 69.25, 96.35, 115.85, 149.45, 107.2, 165.05, 63.38, 184.09, 161.6, 174.27]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Kelompokkan pendapatan wilayah | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
198.79,
196.59,
57.68,
123.24,
134.05,
137.3,
191.65,
179,
141.71,
55.59,
190.14,
142.75,
112.47,
85.63,
144.38,
175.87,
100.44,
126.06,
108.54,
63.68,
130.18,
67.88,
183.94,
191.18,
180.88,
170.04,
176.09,
51.36,
166.22,
156.79
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0455,
0.0344,
0.0475,
0.057600000000000005,
0.0253,
0.0784,
0.025500000000000002,
0.0553,
0.0548,
0.0502,
0.0458,
0.024300000000000002,
0.0879,
0.0789,
0.0723,
0.037700000000000004,
0.0173,
0.058800000000000005,
0.0327,
0.033,
0.0262,
0.0155,
0.051300000000000005,
0.040400000000000005,
0.08660000000000001,
0.0949,
0.0145,
0.061500000000000006,
0.0993,
0.049800000000000004
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
4861.23,
4947.73,
5082.19,
5032.59,
5131.35,
5023.4,
4956.69,
4921.76,
4809.65,
4737.92,
4684.5,
4636.31,
4754.67,
4764.03,
4837.01,
4864.16,
4899.43,
5073.01,
5064.28,
4960.93,
4908.85,
4863.06,
4707.71,
4729.92,
4602.12,
4604.99,
4561.8,
4605.6,
4698.84,
4778.39
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
153,
259,
62,
250,
193,
259,
114,
122,
141,
149,
168,
64,
255,
282,
233,
77,
239,
253,
137,
70,
62,
105,
188,
46,
79,
289,
244,
214,
173,
180
]
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [4861.23, 4947.73, 5082.19, 5032.59, 5131.35, 5023.4, 4956.69, 4921.76, 4809.65, 4737.92, 4684.5, 4636.31, 4754.67, 4764.03, 4837.01, 4864.16, 4899.43, 5073.01, 5064.28, 4960.93, 4908.85, 4863.06, 4707.71, 4729.92, 4602.12, 4604.99, 4561.8, 4605.6, 4698.84, 4778.39], 'Visitors/Pengunjung': [153, 259, 62, 250, 193, 259, 114, 122, 141, 149, 168, 64, 255, 282, 233, 77, 239, 253, 137, 70, 62, 105, 188, 46, 79, 289, 244, 214, 173, 180], 'Conversion Rate/Rasio Konversi': [0.0455, 0.0344, 0.0475, 0.0576, 0.0253, 0.0784, 0.0255, 0.0553, 0.0548, 0.0502, 0.0458, 0.0243, 0.0879, 0.0789, 0.0723, 0.0377, 0.0173, 0.0588, 0.0327, 0.033, 0.0262, 0.0155, 0.0513, 0.0404, 0.0866, 0.0949, 0.0145, 0.0615, 0.0993, 0.0498], 'Avg. Order Value/Nilai Pesanan Rata2': [198.79, 196.59, 57.68, 123.24, 134.05, 137.3, 191.65, 179.0, 141.71, 55.59, 190.14, 142.75, 112.47, 85.63, 144.38, 175.87, 100.44, 126.06, 108.54, 63.68, 130.18, 67.88, 183.94, 191.18, 180.88, 170.04, 176.09, 51.36, 166.22, 156.79]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Analisis diskon periode waktu | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Paris",
"Mexico City",
"Berlin",
"São Paulo",
"Tokyo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"France",
"Mexico",
"Germany",
"Brazil",
"Japan"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.19,
0.19,
0.04,
0.11,
0.15
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-5.2544,
34.6242,
52.8123,
78.8455,
71.0469
],
"Longitude/Bujur": [
64.3982,
137.1681,
-85.9355,
122.9545,
147.7382
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.38,
0.15,
0.41000000000000003,
0.4,
0.32
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"North America",
"Europe",
"South America",
"Asia"
],
"Revenue/Pendapatan": [
1165,
1522,
1802,
3046,
3632
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Paris', 'Mexico City', 'Berlin', 'São Paulo', 'Tokyo'], 'Country/Negara': ['France', 'Mexico', 'Germany', 'Brazil', 'Japan'], 'Region/Wilayah': ['Europe', 'North America', 'Europe', 'South America', 'Asia'], 'Latitude/Lintang': [-5.2544, 34.6242, 52.8123, 78.8455, 71.0469], 'Longitude/Bujur': [64.3982, 137.1681, -85.9355, 122.9545, 147.7382], 'Revenue/Pendapatan': [1165, 1522, 1802, 3046, 3632], 'Market Share/Pangsa Pasar': [0.38, 0.15, 0.41, 0.4, 0.32], 'Growth Rate/Pertumbuhan': [0.19, 0.19, 0.04, 0.11, 0.15]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Calculate loyalty score time period | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.07,
0.28,
0.2
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
29.16,
46.31,
144.04
],
"Product/Produk": [
"Oil_1",
"Tires_2",
"Accessories_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
297,
929,
378
],
"Stock/Stok": [
181,
131,
25
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Tires_2', 'Accessories_3'], 'Sales/Penjualan': [297, 929, 378], 'Price/Harga': [29.16, 46.31, 144.04], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.07, 0.28, 0.2], 'Stock/Stok': [181, 131, 25]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# profit margin calculation
result = df['Discount/Diskon'].(df['Revenue'] - df['Cost'])/df['Revenue']
print(result)
|
Compare growth rate gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.12,
0.29,
0.12
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
107.6,
161.95,
78.78
],
"Product/Produk": [
"Oil_1",
"Tools_2",
"Tires_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
803,
723,
775
],
"Stock/Stok": [
28,
147,
127
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Tools_2', 'Tires_3'], 'Sales/Penjualan': [803, 723, 775], 'Price/Harga': [107.6, 161.95, 78.78], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.12, 0.29, 0.12], 'Stock/Stok': [28, 147, 127]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Analisis pangsa pasar negara | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
113.75,
144.75,
121.19,
74.37,
91.78,
60.88,
64.61,
96.77,
99.36,
143.42,
83.28,
168.7,
113.59,
98.14,
54.31,
199.02,
108.99,
89.33,
126.97,
138.8,
70.74,
191.31,
150.77,
85.52,
155.72,
172.99,
67.02,
198.59,
122.05,
163.97
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0167,
0.0504,
0.0618,
0.0115,
0.0162,
0.029500000000000002,
0.0648,
0.0143,
0.0864,
0.0913,
0.0844,
0.0734,
0.0358,
0.0937,
0.0526,
0.0618,
0.016,
0.0227,
0.058,
0.08750000000000001,
0.07010000000000001,
0.02,
0.0415,
0.08850000000000001,
0.0472,
0.06810000000000001,
0.08,
0.07,
0.0823,
0.0114
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
3010.64,
3045.43,
3060.29,
3128.93,
3143.19,
3084.98,
2989.56,
2915.09,
2812.27,
2721.09,
2585.29,
2678.03,
2725.86,
2804.14,
2747.48,
2949.53,
2996.84,
3050.78,
3038.66,
3075.41,
2915.4,
2991.14,
2928.31,
2690.9,
2736.94,
2610,
2587.68,
2620.29,
2688.18,
2785.11
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
290,
294,
212,
305,
175,
55,
258,
76,
243,
262,
251,
187,
113,
90,
293,
232,
283,
240,
191,
75,
270,
291,
215,
68,
198,
265,
62,
154,
285,
211
]
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [3010.64, 3045.43, 3060.29, 3128.93, 3143.19, 3084.98, 2989.56, 2915.09, 2812.27, 2721.09, 2585.29, 2678.03, 2725.86, 2804.14, 2747.48, 2949.53, 2996.84, 3050.78, 3038.66, 3075.41, 2915.4, 2991.14, 2928.31, 2690.9, 2736.94, 2610.0, 2587.68, 2620.29, 2688.18, 2785.11], 'Visitors/Pengunjung': [290, 294, 212, 305, 175, 55, 258, 76, 243, 262, 251, 187, 113, 90, 293, 232, 283, 240, 191, 75, 270, 291, 215, 68, 198, 265, 62, 154, 285, 211], 'Conversion Rate/Rasio Konversi': [0.0167, 0.0504, 0.0618, 0.0115, 0.0162, 0.0295, 0.0648, 0.0143, 0.0864, 0.0913, 0.0844, 0.0734, 0.0358, 0.0937, 0.0526, 0.0618, 0.016, 0.0227, 0.058, 0.0875, 0.0701, 0.02, 0.0415, 0.0885, 0.0472, 0.0681, 0.08, 0.07, 0.0823, 0.0114], 'Avg. Order Value/Nilai Pesanan Rata2': [113.75, 144.75, 121.19, 74.37, 91.78, 60.88, 64.61, 96.77, 99.36, 143.42, 83.28, 168.7, 113.59, 98.14, 54.31, 199.02, 108.99, 89.33, 126.97, 138.8, 70.74, 191.31, 150.77, 85.52, 155.72, 172.99, 67.02, 198.59, 122.05, 163.97]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Segment discount gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"New York",
"Paris",
"Tokyo",
"London",
"Sydney"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"US",
"France",
"Japan",
"UK",
"Australia"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.02,
-0.02,
0.13,
-0.05,
0.07
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
6.7207,
69.9411,
23.6835,
-35.1688,
13.9049
],
"Longitude/Bujur": [
134.9165,
10.2526,
17.9766,
-55.5542,
49.4581
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.26,
0.1,
0.25,
0.32,
0.45
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"North America",
"Europe",
"Asia",
"Europe",
"Oceania"
],
"Revenue/Pendapatan": [
4230,
1060,
4837,
1094,
4029
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['New York', 'Paris', 'Tokyo', 'London', 'Sydney'], 'Country/Negara': ['US', 'France', 'Japan', 'UK', 'Australia'], 'Region/Wilayah': ['North America', 'Europe', 'Asia', 'Europe', 'Oceania'], 'Latitude/Lintang': [6.7207, 69.9411, 23.6835, -35.1688, 13.9049], 'Longitude/Bujur': [134.9165, 10.2526, 17.9766, -55.5542, 49.4581], 'Revenue/Pendapatan': [4230, 1060, 4837, 1094, 4029], 'Market Share/Pangsa Pasar': [0.26, 0.1, 0.25, 0.32, 0.45], 'Growth Rate/Pertumbuhan': [-0.02, -0.02, 0.13, -0.05, 0.07]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Visualisasikan penjualan jenis kelamin | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Singapore",
"Berlin",
"Jakarta",
"Dubai",
"Tokyo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Singapore",
"Germany",
"Indonesia",
"UAE",
"Japan"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.07,
-0.03,
0.09,
0,
-0.05
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-11.3435,
76.4062,
36.7018,
63.4598,
20.6415
],
"Longitude/Bujur": [
28.9065,
-158.1406,
-11.0484,
-79.435,
34.5191
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.49,
0.46,
0.24,
0.27,
0.17
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Europe",
"Asia",
"Middle East",
"Asia"
],
"Revenue/Pendapatan": [
3918,
2918,
4028,
3395,
1926
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Singapore', 'Berlin', 'Jakarta', 'Dubai', 'Tokyo'], 'Country/Negara': ['Singapore', 'Germany', 'Indonesia', 'UAE', 'Japan'], 'Region/Wilayah': ['Asia', 'Europe', 'Asia', 'Middle East', 'Asia'], 'Latitude/Lintang': [-11.3435, 76.4062, 36.7018, 63.4598, 20.6415], 'Longitude/Bujur': [28.9065, -158.1406, -11.0484, -79.435, 34.5191], 'Revenue/Pendapatan': [3918, 2918, 4028, 3395, 1926], 'Market Share/Pangsa Pasar': [0.49, 0.46, 0.24, 0.27, 0.17], 'Growth Rate/Pertumbuhan': [-0.07, -0.03, 0.09, 0.0, -0.05]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Geospatial visualization
fig = px.choropleth(df, locations='Country/Negara',
locationmode='country names',
color='Revenue/Pendapatan',
hover_name='City/Kota',
scope='world',
title='Revenue by Country')
fig.update_geos(showcountries=True)
fig.show()
|
Identifikasi skor loyalitas jenis kelamin | {
"Age/Usia": [
32,
46,
39,
67,
21,
34,
42,
53,
47,
34,
29,
29,
56,
44,
38,
53,
45,
22,
63,
26,
27,
29,
67,
63,
26,
27,
42,
44,
37,
57,
66,
37,
48,
68,
64,
48,
31,
70,
69,
38,
30,
39,
30,
37,
23,
35,
18,
61,
20,
67,
62,
53,
48,
32,
48,
41,
52,
28,
22,
35,
24,
62,
55,
23,
24,
51,
19,
40,
66,
70,
63,
53,
31,
40,
52,
29,
45,
42,
48,
57,
31,
64,
54,
48,
41,
62,
52,
52,
31,
51,
55,
64,
65,
51,
20,
56,
53,
25,
24
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082",
"CUST_0083",
"CUST_0084",
"CUST_0085",
"CUST_0086",
"CUST_0087",
"CUST_0088",
"CUST_0089",
"CUST_0090",
"CUST_0091",
"CUST_0092",
"CUST_0093",
"CUST_0094",
"CUST_0095",
"CUST_0096",
"CUST_0097",
"CUST_0098",
"CUST_0099"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
4000,
2000,
2000,
7000,
7000,
2000,
3000,
8000,
8000,
4000,
1000,
5000,
7000,
1000,
8000,
10000,
6000,
5000,
9000,
9000,
2000,
9000,
9000,
4000,
10000,
5000,
2000,
3000,
10000,
3000,
3000,
2000,
7000,
9000,
5000,
3000,
6000,
5000,
8000,
5000,
6000,
7000,
5000,
6000,
9000,
8000,
5000,
5000,
6000,
10000,
5000,
7000,
1000,
9000,
7000,
7000,
1000,
8000,
6000,
4000,
4000,
1000,
5000,
1000,
2000,
10000,
2000,
3000,
2000,
8000,
10000,
5000,
8000,
7000,
4000,
3000,
10000,
4000,
6000,
6000,
4000,
1000,
2000,
6000,
10000,
2000,
5000,
6000,
4000,
2000,
4000,
4000,
3000,
7000,
3000,
3000,
2000,
5000,
6000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-11-13",
"2025-03-27",
"2025-02-17",
"2025-01-28",
"2024-07-31",
"2024-09-27",
"2024-05-21",
"2025-01-23",
"2025-01-22",
"2024-05-17",
"2024-09-03",
"2025-04-18",
"2025-03-20",
"2024-11-25",
"2024-09-17",
"2024-06-27",
"2025-02-23",
"2024-10-04",
"2024-09-12",
"2025-03-12",
"2024-12-09",
"2024-05-19",
"2024-05-31",
"2024-11-16",
"2024-10-30",
"2025-01-11",
"2024-07-02",
"2024-07-26",
"2025-03-19",
"2025-02-21",
"2024-06-28",
"2024-05-25",
"2024-07-19",
"2025-03-10",
"2024-06-08",
"2025-02-15",
"2024-05-05",
"2025-02-04",
"2024-11-05",
"2024-06-21",
"2025-03-29",
"2025-02-26",
"2025-01-02",
"2024-12-26",
"2024-10-04",
"2025-03-14",
"2024-11-24",
"2025-03-14",
"2024-06-10",
"2025-01-10",
"2024-12-11",
"2024-09-13",
"2025-04-07",
"2024-07-20",
"2025-01-28",
"2024-05-14",
"2024-10-05",
"2025-03-29",
"2025-01-02",
"2024-10-09",
"2024-11-17",
"2024-08-26",
"2024-10-11",
"2025-04-05",
"2024-12-28",
"2024-10-03",
"2024-06-12",
"2024-10-15",
"2025-01-08",
"2024-09-04",
"2024-11-01",
"2024-10-08",
"2025-02-12",
"2025-04-16",
"2024-11-28",
"2024-12-02",
"2024-09-03",
"2024-12-11",
"2024-10-13",
"2025-02-14",
"2024-12-10",
"2024-10-04",
"2025-01-06",
"2025-03-05",
"2024-08-12",
"2024-05-10",
"2024-09-01",
"2024-12-13",
"2024-05-14",
"2024-11-07",
"2024-09-07",
"2024-08-01",
"2024-11-10",
"2025-03-04",
"2024-08-11",
"2025-01-16",
"2024-09-08",
"2025-01-11",
"2024-11-09"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
20,
89,
8,
39,
24,
37,
42,
19,
83,
3,
77,
85,
28,
4,
70,
31,
89,
63,
22,
6,
91,
21,
53,
89,
11,
40,
85,
6,
45,
27,
5,
50,
91,
15,
71,
67,
29,
65,
23,
32,
17,
80,
75,
33,
88,
39,
57,
30,
8,
9,
11,
96,
7,
18,
83,
95,
52,
81,
82,
5,
45,
99,
50,
65,
3,
72,
40,
96,
94,
5,
76,
51,
47,
43,
29,
96,
57,
65,
28,
7,
93,
89,
53,
34,
22,
87,
25,
62,
24,
50,
17,
40,
59,
8,
12,
54,
21,
91,
31
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082', 'CUST_0083', 'CUST_0084', 'CUST_0085', 'CUST_0086', 'CUST_0087', 'CUST_0088', 'CUST_0089', 'CUST_0090', 'CUST_0091', 'CUST_0092', 'CUST_0093', 'CUST_0094', 'CUST_0095', 'CUST_0096', 'CUST_0097', 'CUST_0098', 'CUST_0099'], 'Age/Usia': [32, 46, 39, 67, 21, 34, 42, 53, 47, 34, 29, 29, 56, 44, 38, 53, 45, 22, 63, 26, 27, 29, 67, 63, 26, 27, 42, 44, 37, 57, 66, 37, 48, 68, 64, 48, 31, 70, 69, 38, 30, 39, 30, 37, 23, 35, 18, 61, 20, 67, 62, 53, 48, 32, 48, 41, 52, 28, 22, 35, 24, 62, 55, 23, 24, 51, 19, 40, 66, 70, 63, 53, 31, 40, 52, 29, 45, 42, 48, 57, 31, 64, 54, 48, 41, 62, 52, 52, 31, 51, 55, 64, 65, 51, 20, 56, 53, 25, 24], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [4000, 2000, 2000, 7000, 7000, 2000, 3000, 8000, 8000, 4000, 1000, 5000, 7000, 1000, 8000, 10000, 6000, 5000, 9000, 9000, 2000, 9000, 9000, 4000, 10000, 5000, 2000, 3000, 10000, 3000, 3000, 2000, 7000, 9000, 5000, 3000, 6000, 5000, 8000, 5000, 6000, 7000, 5000, 6000, 9000, 8000, 5000, 5000, 6000, 10000, 5000, 7000, 1000, 9000, 7000, 7000, 1000, 8000, 6000, 4000, 4000, 1000, 5000, 1000, 2000, 10000, 2000, 3000, 2000, 8000, 10000, 5000, 8000, 7000, 4000, 3000, 10000, 4000, 6000, 6000, 4000, 1000, 2000, 6000, 10000, 2000, 5000, 6000, 4000, 2000, 4000, 4000, 3000, 7000, 3000, 3000, 2000, 5000, 6000], 'Loyalty Score/Skor Loyalitas': [20, 89, 8, 39, 24, 37, 42, 19, 83, 3, 77, 85, 28, 4, 70, 31, 89, 63, 22, 6, 91, 21, 53, 89, 11, 40, 85, 6, 45, 27, 5, 50, 91, 15, 71, 67, 29, 65, 23, 32, 17, 80, 75, 33, 88, 39, 57, 30, 8, 9, 11, 96, 7, 18, 83, 95, 52, 81, 82, 5, 45, 99, 50, 65, 3, 72, 40, 96, 94, 5, 76, 51, 47, 43, 29, 96, 57, 65, 28, 7, 93, 89, 53, 34, 22, 87, 25, 62, 24, 50, 17, 40, 59, 8, 12, 54, 21, 91, 31], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-11-13', '2025-03-27', '2025-02-17', '2025-01-28', '2024-07-31', '2024-09-27', '2024-05-21', '2025-01-23', '2025-01-22', '2024-05-17', '2024-09-03', '2025-04-18', '2025-03-20', '2024-11-25', '2024-09-17', '2024-06-27', '2025-02-23', '2024-10-04', '2024-09-12', '2025-03-12', '2024-12-09', '2024-05-19', '2024-05-31', '2024-11-16', '2024-10-30', '2025-01-11', '2024-07-02', '2024-07-26', '2025-03-19', '2025-02-21', '2024-06-28', '2024-05-25', '2024-07-19', '2025-03-10', '2024-06-08', '2025-02-15', '2024-05-05', '2025-02-04', '2024-11-05', '2024-06-21', '2025-03-29', '2025-02-26', '2025-01-02', '2024-12-26', '2024-10-04', '2025-03-14', '2024-11-24', '2025-03-14', '2024-06-10', '2025-01-10', '2024-12-11', '2024-09-13', '2025-04-07', '2024-07-20', '2025-01-28', '2024-05-14', '2024-10-05', '2025-03-29', '2025-01-02', '2024-10-09', '2024-11-17', '2024-08-26', '2024-10-11', '2025-04-05', '2024-12-28', '2024-10-03', '2024-06-12', '2024-10-15', '2025-01-08', '2024-09-04', '2024-11-01', '2024-10-08', '2025-02-12', '2025-04-16', '2024-11-28', '2024-12-02', '2024-09-03', '2024-12-11', '2024-10-13', '2025-02-14', '2024-12-10', '2024-10-04', '2025-01-06', '2025-03-05', '2024-08-12', '2024-05-10', '2024-09-01', '2024-12-13', '2024-05-14', '2024-11-07', '2024-09-07', '2024-08-01', '2024-11-10', '2025-03-04', '2024-08-11', '2025-01-16', '2024-09-08', '2025-01-11', '2024-11-09']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Segmentasi pertumbuhan grup usia | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Tokyo",
"Mumbai",
"Mexico City",
"London",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Japan",
"India",
"Mexico",
"UK",
"US"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.05,
0.07,
-0.07,
0.01,
0.09
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
41.5197,
21.7984,
66.6411,
-86.7752,
63.1073
],
"Longitude/Bujur": [
45.0023,
-10.0756,
36.7255,
174.7464,
-99.6719
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.49,
0.39,
0.23,
0.29,
0.19
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Asia",
"North America",
"Europe",
"North America"
],
"Revenue/Pendapatan": [
4688,
3787,
2954,
2237,
4598
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Tokyo', 'Mumbai', 'Mexico City', 'London', 'New York'], 'Country/Negara': ['Japan', 'India', 'Mexico', 'UK', 'US'], 'Region/Wilayah': ['Asia', 'Asia', 'North America', 'Europe', 'North America'], 'Latitude/Lintang': [41.5197, 21.7984, 66.6411, -86.7752, 63.1073], 'Longitude/Bujur': [45.0023, -10.0756, 36.7255, 174.7464, -99.6719], 'Revenue/Pendapatan': [4688, 3787, 2954, 2237, 4598], 'Market Share/Pangsa Pasar': [0.49, 0.39, 0.23, 0.29, 0.19], 'Growth Rate/Pertumbuhan': [-0.05, 0.07, -0.07, 0.01, 0.09]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Optimize income gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
98.75,
122.42,
167.55,
170.74,
187.8,
181.51,
123.15,
136.72,
96.95,
162.23,
60.25,
87.53,
54.87,
129.71,
162.76,
146.62,
104.05,
105.14,
78.33,
114.94,
74.9,
67.73,
127.26,
61.33,
106.71,
168.17,
101.91,
104.36,
184.17,
101.27
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0728,
0.0712,
0.0538,
0.07250000000000001,
0.0898,
0.0275,
0.020300000000000002,
0.0111,
0.016900000000000002,
0.0761,
0.0204,
0.034,
0.0177,
0.0945,
0.0256,
0.059300000000000005,
0.0853,
0.0625,
0.0471,
0.08560000000000001,
0.046200000000000005,
0.06770000000000001,
0.079,
0.0152,
0.0985,
0.0978,
0.0762,
0.016300000000000002,
0.0884,
0.0244
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1346.18,
1452.59,
1492.83,
1510.15,
1517.06,
1594.15,
1514.21,
1518.82,
1350.76,
1263.83,
1223.37,
1198.38,
1248.63,
1377.75,
1443.75,
1627.11,
1692.7,
1695.31,
1765.33,
1744.86,
1808.01,
1700.14,
1578.81,
1533,
1470.04,
1399.48,
1476.33,
1448.49,
1614.95,
1631.57
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
164,
144,
74,
201,
157,
292,
110,
107,
251,
155,
182,
59,
260,
299,
174,
199,
103,
307,
127,
213,
205,
210,
230,
90,
254,
255,
61,
141,
168,
184
]
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1346.18, 1452.59, 1492.83, 1510.15, 1517.06, 1594.15, 1514.21, 1518.82, 1350.76, 1263.83, 1223.37, 1198.38, 1248.63, 1377.75, 1443.75, 1627.11, 1692.7, 1695.31, 1765.33, 1744.86, 1808.01, 1700.14, 1578.81, 1533.0, 1470.04, 1399.48, 1476.33, 1448.49, 1614.95, 1631.57], 'Visitors/Pengunjung': [164, 144, 74, 201, 157, 292, 110, 107, 251, 155, 182, 59, 260, 299, 174, 199, 103, 307, 127, 213, 205, 210, 230, 90, 254, 255, 61, 141, 168, 184], 'Conversion Rate/Rasio Konversi': [0.0728, 0.0712, 0.0538, 0.0725, 0.0898, 0.0275, 0.0203, 0.0111, 0.0169, 0.0761, 0.0204, 0.034, 0.0177, 0.0945, 0.0256, 0.0593, 0.0853, 0.0625, 0.0471, 0.0856, 0.0462, 0.0677, 0.079, 0.0152, 0.0985, 0.0978, 0.0762, 0.0163, 0.0884, 0.0244], 'Avg. Order Value/Nilai Pesanan Rata2': [98.75, 122.42, 167.55, 170.74, 187.8, 181.51, 123.15, 136.72, 96.95, 162.23, 60.25, 87.53, 54.87, 129.71, 162.76, 146.62, 104.05, 105.14, 78.33, 114.94, 74.9, 67.73, 127.26, 61.33, 106.71, 168.17, 101.91, 104.36, 184.17, 101.27]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Calculate conversion rate age group | {
"Age/Usia": [
19,
40,
63,
52,
68,
24,
53,
37,
48,
65,
39,
26,
46,
50,
61,
69,
20,
67,
46,
62,
28,
62,
45,
26,
59,
65,
21,
33,
57,
20,
25,
58,
47,
32,
27,
19,
62,
33,
43,
31,
44,
65,
26,
57,
38,
36,
31,
29,
62,
65,
37,
43,
29,
39,
36,
50,
44,
22,
22,
39,
48,
50,
54
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
8000,
6000,
9000,
9000,
7000,
3000,
9000,
1000,
6000,
7000,
3000,
4000,
2000,
3000,
7000,
8000,
8000,
2000,
9000,
4000,
9000,
2000,
5000,
2000,
1000,
7000,
6000,
4000,
8000,
6000,
7000,
1000,
3000,
5000,
8000,
4000,
2000,
3000,
7000,
6000,
6000,
9000,
4000,
2000,
8000,
1000,
7000,
8000,
8000,
4000,
6000,
3000,
4000,
8000,
7000,
8000,
4000,
7000,
8000,
4000,
3000,
2000,
3000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-12-05",
"2025-01-25",
"2025-03-24",
"2024-08-14",
"2024-06-10",
"2024-09-09",
"2024-10-10",
"2024-07-03",
"2024-05-10",
"2024-05-25",
"2025-01-17",
"2025-01-04",
"2024-10-28",
"2024-04-30",
"2024-07-03",
"2025-01-19",
"2024-12-07",
"2024-04-29",
"2025-03-19",
"2024-10-22",
"2024-12-15",
"2024-09-28",
"2024-07-28",
"2025-01-25",
"2024-08-29",
"2025-03-29",
"2024-05-02",
"2024-08-22",
"2024-10-01",
"2024-06-29",
"2025-02-12",
"2024-12-08",
"2025-01-03",
"2025-01-06",
"2025-03-14",
"2024-09-17",
"2024-10-14",
"2024-12-26",
"2024-07-05",
"2024-06-22",
"2024-05-16",
"2024-11-16",
"2024-09-25",
"2024-05-12",
"2024-10-01",
"2025-01-24",
"2024-07-23",
"2024-08-30",
"2024-07-31",
"2024-06-08",
"2025-01-04",
"2024-08-20",
"2024-06-08",
"2025-04-12",
"2024-09-30",
"2024-12-24",
"2024-12-16",
"2024-11-25",
"2024-09-21",
"2024-11-05",
"2025-03-27",
"2025-01-09",
"2024-12-04"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
25,
91,
93,
79,
27,
95,
69,
32,
20,
31,
29,
55,
69,
8,
40,
43,
14,
54,
77,
67,
40,
43,
4,
12,
77,
70,
31,
29,
54,
12,
29,
22,
90,
66,
60,
71,
78,
92,
97,
60,
13,
82,
81,
17,
66,
98,
63,
61,
69,
1,
40,
53,
13,
6,
97,
35,
98,
77,
52,
74,
70,
49,
15
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063'], 'Age/Usia': [19, 40, 63, 52, 68, 24, 53, 37, 48, 65, 39, 26, 46, 50, 61, 69, 20, 67, 46, 62, 28, 62, 45, 26, 59, 65, 21, 33, 57, 20, 25, 58, 47, 32, 27, 19, 62, 33, 43, 31, 44, 65, 26, 57, 38, 36, 31, 29, 62, 65, 37, 43, 29, 39, 36, 50, 44, 22, 22, 39, 48, 50, 54], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki'], 'Income/Pendapatan': [8000, 6000, 9000, 9000, 7000, 3000, 9000, 1000, 6000, 7000, 3000, 4000, 2000, 3000, 7000, 8000, 8000, 2000, 9000, 4000, 9000, 2000, 5000, 2000, 1000, 7000, 6000, 4000, 8000, 6000, 7000, 1000, 3000, 5000, 8000, 4000, 2000, 3000, 7000, 6000, 6000, 9000, 4000, 2000, 8000, 1000, 7000, 8000, 8000, 4000, 6000, 3000, 4000, 8000, 7000, 8000, 4000, 7000, 8000, 4000, 3000, 2000, 3000], 'Loyalty Score/Skor Loyalitas': [25, 91, 93, 79, 27, 95, 69, 32, 20, 31, 29, 55, 69, 8, 40, 43, 14, 54, 77, 67, 40, 43, 4, 12, 77, 70, 31, 29, 54, 12, 29, 22, 90, 66, 60, 71, 78, 92, 97, 60, 13, 82, 81, 17, 66, 98, 63, 61, 69, 1, 40, 53, 13, 6, 97, 35, 98, 77, 52, 74, 70, 49, 15], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-12-05', '2025-01-25', '2025-03-24', '2024-08-14', '2024-06-10', '2024-09-09', '2024-10-10', '2024-07-03', '2024-05-10', '2024-05-25', '2025-01-17', '2025-01-04', '2024-10-28', '2024-04-30', '2024-07-03', '2025-01-19', '2024-12-07', '2024-04-29', '2025-03-19', '2024-10-22', '2024-12-15', '2024-09-28', '2024-07-28', '2025-01-25', '2024-08-29', '2025-03-29', '2024-05-02', '2024-08-22', '2024-10-01', '2024-06-29', '2025-02-12', '2024-12-08', '2025-01-03', '2025-01-06', '2025-03-14', '2024-09-17', '2024-10-14', '2024-12-26', '2024-07-05', '2024-06-22', '2024-05-16', '2024-11-16', '2024-09-25', '2024-05-12', '2024-10-01', '2025-01-24', '2024-07-23', '2024-08-30', '2024-07-31', '2024-06-08', '2025-01-04', '2024-08-20', '2024-06-08', '2025-04-12', '2024-09-30', '2024-12-24', '2024-12-16', '2024-11-25', '2024-09-21', '2024-11-05', '2025-03-27', '2025-01-09', '2024-12-04']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# growth rate calculation
result = df['Customer ID/ID Pelanggan'].pct_change()
print(result)
|
Bandingkan pendapatan kota | {
"Age/Usia": [
54,
53,
68,
42,
53,
56,
22,
42,
19,
40,
66,
68,
18,
18,
39,
64,
70,
53,
56,
23,
64,
45,
47,
32,
33,
61,
23,
22,
21,
50,
19,
19,
64,
35,
32,
66,
59,
68,
21,
55,
24,
35,
38,
29,
54,
43,
28,
67,
41,
27,
42,
62,
34,
49,
61,
26,
35,
54,
70
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
9000,
2000,
7000,
9000,
6000,
9000,
9000,
3000,
9000,
1000,
4000,
3000,
6000,
9000,
8000,
4000,
8000,
1000,
7000,
5000,
5000,
7000,
9000,
5000,
9000,
3000,
3000,
8000,
6000,
3000,
6000,
6000,
6000,
7000,
5000,
1000,
3000,
3000,
2000,
8000,
5000,
3000,
6000,
3000,
9000,
2000,
4000,
9000,
9000,
3000,
5000,
7000,
4000,
4000,
1000,
6000,
6000,
4000,
2000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-03-19",
"2025-01-29",
"2024-12-12",
"2024-10-20",
"2025-01-11",
"2025-01-27",
"2025-01-19",
"2024-11-24",
"2024-12-01",
"2024-12-30",
"2024-07-24",
"2024-07-30",
"2025-02-17",
"2024-11-10",
"2024-05-14",
"2024-12-31",
"2025-01-15",
"2024-05-01",
"2024-07-24",
"2024-05-26",
"2024-05-25",
"2024-06-24",
"2025-01-16",
"2024-08-25",
"2024-05-09",
"2024-10-07",
"2024-12-23",
"2025-03-13",
"2024-12-11",
"2025-01-23",
"2024-05-14",
"2024-10-28",
"2025-01-29",
"2024-12-30",
"2025-01-30",
"2024-09-04",
"2024-07-04",
"2025-03-18",
"2024-09-28",
"2025-01-27",
"2025-01-24",
"2024-07-02",
"2024-11-15",
"2025-02-09",
"2025-01-09",
"2024-10-18",
"2024-09-20",
"2025-02-21",
"2025-03-29",
"2025-01-14",
"2024-05-06",
"2024-12-27",
"2025-01-17",
"2024-09-14",
"2024-08-04",
"2024-12-13",
"2024-04-25",
"2025-04-09",
"2025-04-08"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
22,
92,
19,
30,
74,
63,
52,
22,
42,
3,
82,
90,
70,
42,
5,
15,
51,
12,
98,
39,
10,
89,
40,
59,
36,
33,
96,
27,
79,
99,
71,
68,
100,
25,
62,
50,
68,
37,
3,
24,
8,
78,
98,
64,
13,
53,
48,
4,
21,
25,
50,
61,
52,
77,
38,
61,
55,
28,
40
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059'], 'Age/Usia': [54, 53, 68, 42, 53, 56, 22, 42, 19, 40, 66, 68, 18, 18, 39, 64, 70, 53, 56, 23, 64, 45, 47, 32, 33, 61, 23, 22, 21, 50, 19, 19, 64, 35, 32, 66, 59, 68, 21, 55, 24, 35, 38, 29, 54, 43, 28, 67, 41, 27, 42, 62, 34, 49, 61, 26, 35, 54, 70], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan'], 'Income/Pendapatan': [9000, 2000, 7000, 9000, 6000, 9000, 9000, 3000, 9000, 1000, 4000, 3000, 6000, 9000, 8000, 4000, 8000, 1000, 7000, 5000, 5000, 7000, 9000, 5000, 9000, 3000, 3000, 8000, 6000, 3000, 6000, 6000, 6000, 7000, 5000, 1000, 3000, 3000, 2000, 8000, 5000, 3000, 6000, 3000, 9000, 2000, 4000, 9000, 9000, 3000, 5000, 7000, 4000, 4000, 1000, 6000, 6000, 4000, 2000], 'Loyalty Score/Skor Loyalitas': [22, 92, 19, 30, 74, 63, 52, 22, 42, 3, 82, 90, 70, 42, 5, 15, 51, 12, 98, 39, 10, 89, 40, 59, 36, 33, 96, 27, 79, 99, 71, 68, 100, 25, 62, 50, 68, 37, 3, 24, 8, 78, 98, 64, 13, 53, 48, 4, 21, 25, 50, 61, 52, 77, 38, 61, 55, 28, 40], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-03-19', '2025-01-29', '2024-12-12', '2024-10-20', '2025-01-11', '2025-01-27', '2025-01-19', '2024-11-24', '2024-12-01', '2024-12-30', '2024-07-24', '2024-07-30', '2025-02-17', '2024-11-10', '2024-05-14', '2024-12-31', '2025-01-15', '2024-05-01', '2024-07-24', '2024-05-26', '2024-05-25', '2024-06-24', '2025-01-16', '2024-08-25', '2024-05-09', '2024-10-07', '2024-12-23', '2025-03-13', '2024-12-11', '2025-01-23', '2024-05-14', '2024-10-28', '2025-01-29', '2024-12-30', '2025-01-30', '2024-09-04', '2024-07-04', '2025-03-18', '2024-09-28', '2025-01-27', '2025-01-24', '2024-07-02', '2024-11-15', '2025-02-09', '2025-01-09', '2024-10-18', '2024-09-20', '2025-02-21', '2025-03-29', '2025-01-14', '2024-05-06', '2024-12-27', '2025-01-17', '2024-09-14', '2024-08-04', '2024-12-13', '2024-04-25', '2025-04-09', '2025-04-08']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Segmentasi pangsa pasar grup usia | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.01,
0.16,
0.16
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
79.74,
69.58,
51.78
],
"Product/Produk": [
"Accessories_1",
"Oil_2",
"Tools_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
271,
507,
383
],
"Stock/Stok": [
71,
23,
141
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Accessories_1', 'Oil_2', 'Tools_3'], 'Sales/Penjualan': [271, 507, 383], 'Price/Harga': [79.74, 69.58, 51.78], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.01, 0.16, 0.16], 'Stock/Stok': [71, 23, 141]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Bandingkan pangsa pasar kota | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
172.48,
85.7,
188.02,
97.63,
185.68,
75.64,
130.46,
130.7,
126.33,
145,
96.98,
129.09,
54.07,
145.62,
191.73,
51.63,
119.15,
59.05,
125.72,
124.68,
128.19,
133.96,
55.92,
130,
98.77,
61.24,
63.03,
110.15,
81.04,
161.63
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0969,
0.0173,
0.028200000000000003,
0.0356,
0.0908,
0.0478,
0.0584,
0.0772,
0.07930000000000001,
0.026000000000000002,
0.0782,
0.028800000000000003,
0.0864,
0.030100000000000002,
0.0727,
0.0818,
0.0782,
0.0614,
0.0937,
0.0567,
0.0714,
0.046,
0.0592,
0.0857,
0.0694,
0.0732,
0.07,
0.042100000000000005,
0.036500000000000005,
0.09280000000000001
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1375.32,
1477.47,
1475.19,
1528.11,
1517.37,
1474.4,
1425.08,
1363.82,
1258.41,
1096.3,
1188.2,
1169.2,
1080.2,
1110.14,
1194.24,
1205.47,
1378.74,
1340.1,
1404.61,
1393.52,
1311.56,
1231.01,
1139.49,
1000.63,
964.85,
968.06,
962.57,
954.1,
1024.57,
1180.15
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
262,
297,
294,
200,
136,
110,
72,
296,
184,
114,
288,
271,
95,
242,
62,
263,
152,
261,
76,
257,
46,
226,
76,
243,
277,
229,
72,
81,
217,
242
]
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1375.32, 1477.47, 1475.19, 1528.11, 1517.37, 1474.4, 1425.08, 1363.82, 1258.41, 1096.3, 1188.2, 1169.2, 1080.2, 1110.14, 1194.24, 1205.47, 1378.74, 1340.1, 1404.61, 1393.52, 1311.56, 1231.01, 1139.49, 1000.63, 964.85, 968.06, 962.57, 954.1, 1024.57, 1180.15], 'Visitors/Pengunjung': [262, 297, 294, 200, 136, 110, 72, 296, 184, 114, 288, 271, 95, 242, 62, 263, 152, 261, 76, 257, 46, 226, 76, 243, 277, 229, 72, 81, 217, 242], 'Conversion Rate/Rasio Konversi': [0.0969, 0.0173, 0.0282, 0.0356, 0.0908, 0.0478, 0.0584, 0.0772, 0.0793, 0.026, 0.0782, 0.0288, 0.0864, 0.0301, 0.0727, 0.0818, 0.0782, 0.0614, 0.0937, 0.0567, 0.0714, 0.046, 0.0592, 0.0857, 0.0694, 0.0732, 0.07, 0.0421, 0.0365, 0.0928], 'Avg. Order Value/Nilai Pesanan Rata2': [172.48, 85.7, 188.02, 97.63, 185.68, 75.64, 130.46, 130.7, 126.33, 145.0, 96.98, 129.09, 54.07, 145.62, 191.73, 51.63, 119.15, 59.05, 125.72, 124.68, 128.19, 133.96, 55.92, 130.0, 98.77, 61.24, 63.03, 110.15, 81.04, 161.63]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Forecast market share for/untuk month | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"London",
"Jakarta",
"Singapore",
"São Paulo",
"Mumbai"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"UK",
"Indonesia",
"Singapore",
"Brazil",
"India"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.15,
0.19,
-0.03,
0.24,
0.08
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
11.884,
77.0865,
-35.2855,
3.0397,
-54.4591
],
"Longitude/Bujur": [
-151.3358,
-120.2734,
-62.8963,
20.5467,
-34.682900000000004
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.12,
0.15,
0.31,
0.29,
0.44
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"Asia",
"Asia",
"South America",
"Asia"
],
"Revenue/Pendapatan": [
4302,
4025,
4238,
1680,
4559
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['London', 'Jakarta', 'Singapore', 'São Paulo', 'Mumbai'], 'Country/Negara': ['UK', 'Indonesia', 'Singapore', 'Brazil', 'India'], 'Region/Wilayah': ['Europe', 'Asia', 'Asia', 'South America', 'Asia'], 'Latitude/Lintang': [11.884, 77.0865, -35.2855, 3.0397, -54.4591], 'Longitude/Bujur': [-151.3358, -120.2734, -62.8963, 20.5467, -34.6829], 'Revenue/Pendapatan': [4302, 4025, 4238, 1680, 4559], 'Market Share/Pangsa Pasar': [0.12, 0.15, 0.31, 0.29, 0.44], 'Growth Rate/Pertumbuhan': [0.15, 0.19, -0.03, 0.24, 0.08]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Visualisasikan pengunjung grup usia | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"London",
"Mumbai",
"Sydney",
"Berlin",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"UK",
"India",
"Australia",
"Germany",
"US"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.05,
0.01,
-0.07,
0.18,
-0.04
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
72.3728,
-58.5947,
89.7596,
22.3944,
0.8445
],
"Longitude/Bujur": [
140.0561,
-161.8252,
93.5911,
97.8351,
-170.5593
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.46,
0.36,
0.48,
0.33,
0.28
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"Asia",
"Oceania",
"Europe",
"North America"
],
"Revenue/Pendapatan": [
1130,
1401,
3581,
1703,
3700
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['London', 'Mumbai', 'Sydney', 'Berlin', 'New York'], 'Country/Negara': ['UK', 'India', 'Australia', 'Germany', 'US'], 'Region/Wilayah': ['Europe', 'Asia', 'Oceania', 'Europe', 'North America'], 'Latitude/Lintang': [72.3728, -58.5947, 89.7596, 22.3944, 0.8445], 'Longitude/Bujur': [140.0561, -161.8252, 93.5911, 97.8351, -170.5593], 'Revenue/Pendapatan': [1130, 1401, 3581, 1703, 3700], 'Market Share/Pangsa Pasar': [0.46, 0.36, 0.48, 0.33, 0.28], 'Growth Rate/Pertumbuhan': [-0.05, 0.01, -0.07, 0.18, -0.04]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Geospatial visualization
fig = px.choropleth(df, locations='Country/Negara',
locationmode='country names',
color='Revenue/Pendapatan',
hover_name='City/Kota',
scope='world',
title='Revenue by Country')
fig.update_geos(showcountries=True)
fig.show()
|
Perkiraan pendapatan for/untuk minggu | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Paris",
"New York",
"Dubai",
"Sydney",
"London"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"France",
"US",
"UAE",
"Australia",
"UK"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.24,
0.28,
0.05,
-0.04,
-0.08
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
17.3867,
36.0032,
28.9952,
22.4,
88.1219
],
"Longitude/Bujur": [
-169.4641,
0.12250000000000001,
99.2327,
-4.8377,
150.5256
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.21,
0.49,
0.30000000000000004,
0.38,
0.46
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"North America",
"Middle East",
"Oceania",
"Europe"
],
"Revenue/Pendapatan": [
3692,
1823,
2049,
2757,
4673
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Paris', 'New York', 'Dubai', 'Sydney', 'London'], 'Country/Negara': ['France', 'US', 'UAE', 'Australia', 'UK'], 'Region/Wilayah': ['Europe', 'North America', 'Middle East', 'Oceania', 'Europe'], 'Latitude/Lintang': [17.3867, 36.0032, 28.9952, 22.4, 88.1219], 'Longitude/Bujur': [-169.4641, 0.1225, 99.2327, -4.8377, 150.5256], 'Revenue/Pendapatan': [3692, 1823, 2049, 2757, 4673], 'Market Share/Pangsa Pasar': [0.21, 0.49, 0.3, 0.38, 0.46], 'Growth Rate/Pertumbuhan': [0.24, 0.28, 0.05, -0.04, -0.08]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Correlate loyalty score and/dan income | {
"Age/Usia": [
54,
19,
42,
56,
40,
70,
49,
23,
38,
68,
68,
38,
30,
44,
19,
69,
48,
39,
59,
59,
70,
54,
42,
51,
54,
25,
37,
41,
58,
18,
36,
55,
48,
33,
40,
53,
41,
59,
69,
34,
42,
39,
30,
25,
21,
50,
64,
45,
32,
61,
58,
40,
67
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
2000,
6000,
2000,
4000,
3000,
10000,
1000,
9000,
9000,
1000,
7000,
3000,
2000,
2000,
4000,
7000,
3000,
7000,
10000,
6000,
5000,
8000,
8000,
10000,
7000,
8000,
6000,
7000,
8000,
2000,
6000,
4000,
4000,
3000,
5000,
6000,
2000,
9000,
4000,
3000,
4000,
1000,
3000,
6000,
5000,
7000,
4000,
7000,
5000,
7000,
9000,
9000,
6000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-01-30",
"2024-05-05",
"2024-05-02",
"2024-10-19",
"2025-01-24",
"2024-12-17",
"2025-02-08",
"2024-10-12",
"2025-02-18",
"2024-10-28",
"2025-04-20",
"2025-02-14",
"2025-03-05",
"2025-03-19",
"2024-06-07",
"2025-02-13",
"2025-01-16",
"2025-04-09",
"2025-04-17",
"2024-10-08",
"2025-02-01",
"2024-07-01",
"2024-11-27",
"2024-11-21",
"2024-09-28",
"2025-02-05",
"2024-05-03",
"2024-11-13",
"2025-01-13",
"2025-03-15",
"2025-03-03",
"2024-12-02",
"2024-06-18",
"2024-07-06",
"2024-10-31",
"2024-10-22",
"2025-04-10",
"2025-01-31",
"2024-07-08",
"2025-03-19",
"2025-03-18",
"2024-08-27",
"2024-10-07",
"2024-10-05",
"2024-12-26",
"2024-11-06",
"2024-08-13",
"2024-05-02",
"2024-10-24",
"2024-07-24",
"2024-09-10",
"2024-07-24",
"2024-06-20"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
90,
99,
85,
64,
6,
40,
61,
19,
69,
78,
26,
42,
90,
17,
82,
84,
96,
40,
83,
41,
25,
21,
75,
18,
3,
50,
8,
29,
13,
85,
87,
6,
59,
30,
96,
92,
93,
83,
1,
92,
53,
10,
52,
68,
13,
66,
76,
98,
94,
91,
15,
69,
37
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053'], 'Age/Usia': [54, 19, 42, 56, 40, 70, 49, 23, 38, 68, 68, 38, 30, 44, 19, 69, 48, 39, 59, 59, 70, 54, 42, 51, 54, 25, 37, 41, 58, 18, 36, 55, 48, 33, 40, 53, 41, 59, 69, 34, 42, 39, 30, 25, 21, 50, 64, 45, 32, 61, 58, 40, 67], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki'], 'Income/Pendapatan': [2000, 6000, 2000, 4000, 3000, 10000, 1000, 9000, 9000, 1000, 7000, 3000, 2000, 2000, 4000, 7000, 3000, 7000, 10000, 6000, 5000, 8000, 8000, 10000, 7000, 8000, 6000, 7000, 8000, 2000, 6000, 4000, 4000, 3000, 5000, 6000, 2000, 9000, 4000, 3000, 4000, 1000, 3000, 6000, 5000, 7000, 4000, 7000, 5000, 7000, 9000, 9000, 6000], 'Loyalty Score/Skor Loyalitas': [90, 99, 85, 64, 6, 40, 61, 19, 69, 78, 26, 42, 90, 17, 82, 84, 96, 40, 83, 41, 25, 21, 75, 18, 3, 50, 8, 29, 13, 85, 87, 6, 59, 30, 96, 92, 93, 83, 1, 92, 53, 10, 52, 68, 13, 66, 76, 98, 94, 91, 15, 69, 37], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-01-30', '2024-05-05', '2024-05-02', '2024-10-19', '2025-01-24', '2024-12-17', '2025-02-08', '2024-10-12', '2025-02-18', '2024-10-28', '2025-04-20', '2025-02-14', '2025-03-05', '2025-03-19', '2024-06-07', '2025-02-13', '2025-01-16', '2025-04-09', '2025-04-17', '2024-10-08', '2025-02-01', '2024-07-01', '2024-11-27', '2024-11-21', '2024-09-28', '2025-02-05', '2024-05-03', '2024-11-13', '2025-01-13', '2025-03-15', '2025-03-03', '2024-12-02', '2024-06-18', '2024-07-06', '2024-10-31', '2024-10-22', '2025-04-10', '2025-01-31', '2024-07-08', '2025-03-19', '2025-03-18', '2024-08-27', '2024-10-07', '2024-10-05', '2024-12-26', '2024-11-06', '2024-08-13', '2024-05-02', '2024-10-24', '2024-07-24', '2024-09-10', '2024-07-24', '2024-06-20']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Hitung pangsa pasar produk | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Mumbai",
"Paris",
"São Paulo",
"Tokyo",
"New York"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"India",
"France",
"Brazil",
"Japan",
"US"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.12,
0.24,
0.02,
-0.05,
-0.1
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
59.244,
82.4541,
2.4613,
61.4826,
-24.3944
],
"Longitude/Bujur": [
52.2886,
176.4716,
-138.0176,
-98.3051,
71.9768
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.13,
0.19,
0.1,
0.46,
0.26
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Europe",
"South America",
"Asia",
"North America"
],
"Revenue/Pendapatan": [
4680,
3152,
3672,
4987,
4793
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Mumbai', 'Paris', 'São Paulo', 'Tokyo', 'New York'], 'Country/Negara': ['India', 'France', 'Brazil', 'Japan', 'US'], 'Region/Wilayah': ['Asia', 'Europe', 'South America', 'Asia', 'North America'], 'Latitude/Lintang': [59.244, 82.4541, 2.4613, 61.4826, -24.3944], 'Longitude/Bujur': [52.2886, 176.4716, -138.0176, -98.3051, 71.9768], 'Revenue/Pendapatan': [4680, 3152, 3672, 4987, 4793], 'Market Share/Pangsa Pasar': [0.13, 0.19, 0.1, 0.46, 0.26], 'Growth Rate/Pertumbuhan': [0.12, 0.24, 0.02, -0.05, -0.1]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# moving average calculation
result = df['Longitude/Bujur'].rolling(7).mean()
print(result)
|
Analisis pendapatan periode waktu | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.2,
0.07,
0.16
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
20.56,
11.1,
138.72
],
"Product/Produk": [
"Smartphone_1",
"TV_2",
"Laptop_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
497,
276,
658
],
"Stock/Stok": [
115,
115,
20
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Smartphone_1', 'TV_2', 'Laptop_3'], 'Sales/Penjualan': [497, 276, 658], 'Price/Harga': [20.56, 11.1, 138.72], 'Category/Kategori': ['Electronics/Elektronik', 'Electronics/Elektronik', 'Electronics/Elektronik'], 'Discount/Diskon': [0.2, 0.07, 0.16], 'Stock/Stok': [115, 115, 20]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Forecast revenue for/untuk year | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Paris",
"Mexico City",
"Mumbai",
"New York",
"Sydney"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"France",
"Mexico",
"India",
"US",
"Australia"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.29,
0.05,
-0.04,
0.26,
-0.07
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-54.8075,
18.1095,
73.0632,
-75.4714,
59.7417
],
"Longitude/Bujur": [
2.6261,
103.6082,
-145.1563,
83.3641,
-125.4302
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.41000000000000003,
0.29,
0.43,
0.34,
0.14
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"North America",
"Asia",
"North America",
"Oceania"
],
"Revenue/Pendapatan": [
1564,
4896,
2098,
4459,
2281
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Paris', 'Mexico City', 'Mumbai', 'New York', 'Sydney'], 'Country/Negara': ['France', 'Mexico', 'India', 'US', 'Australia'], 'Region/Wilayah': ['Europe', 'North America', 'Asia', 'North America', 'Oceania'], 'Latitude/Lintang': [-54.8075, 18.1095, 73.0632, -75.4714, 59.7417], 'Longitude/Bujur': [2.6261, 103.6082, -145.1563, 83.3641, -125.4302], 'Revenue/Pendapatan': [1564, 4896, 2098, 4459, 2281], 'Market Share/Pangsa Pasar': [0.41, 0.29, 0.43, 0.34, 0.14], 'Growth Rate/Pertumbuhan': [0.29, 0.05, -0.04, 0.26, -0.07]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Segment revenue time period | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.12,
0.09,
0.29
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
197.94,
177.55,
155.37
],
"Product/Produk": [
"Oil_1",
"Tires_2",
"Accessories_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
177,
820,
234
],
"Stock/Stok": [
21,
34,
151
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Oil_1', 'Tires_2', 'Accessories_3'], 'Sales/Penjualan': [177, 820, 234], 'Price/Harga': [197.94, 177.55, 155.37], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.12, 0.09, 0.29], 'Stock/Stok': [21, 34, 151]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Segmentasi nilai pesanan rata-rata produk | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.14,
0.22,
0.2
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
90,
56,
73.15
],
"Product/Produk": [
"Dairy_1",
"Fruits_2",
"Snacks_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
107,
103,
397
],
"Stock/Stok": [
87,
143,
53
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Dairy_1', 'Fruits_2', 'Snacks_3'], 'Sales/Penjualan': [107, 103, 397], 'Price/Harga': [90.0, 56.0, 73.15], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.14, 0.22, 0.2], 'Stock/Stok': [87, 143, 53]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Segmentasi pertumbuhan segmen pelanggan | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
58.28,
97.08,
188.67,
191.48,
137.45,
50.38,
147.9,
161.82,
97.03,
102.29,
108.59,
192.72,
75.99,
115.55,
185.04,
92.28,
152.81,
132.72,
144.53,
168.4,
63.73,
67.32,
53.66,
165.26,
61.51,
180.79,
56.46,
184.8,
112.93,
88.08
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0644,
0.0361,
0.08510000000000001,
0.010700000000000001,
0.018500000000000003,
0.0746,
0.049800000000000004,
0.031100000000000003,
0.098,
0.0487,
0.0757,
0.0772,
0.0495,
0.09630000000000001,
0.0426,
0.09910000000000001,
0.09330000000000001,
0.0985,
0.08,
0.0131,
0.028800000000000003,
0.055,
0.032,
0.0918,
0.0227,
0.0126,
0.0815,
0.0649,
0.0194,
0.060700000000000004
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1274.72,
1416.61,
1413.81,
1412.13,
1459.62,
1370.37,
1318.63,
1279.75,
1115.48,
1115.38,
969.3,
995.12,
994.94,
1137.95,
1179.37,
1319.15,
1345.13,
1353.37,
1435.74,
1354.5,
1356.67,
1164.41,
1179.29,
1083.02,
1015.72,
1025.43,
953.98,
1020.91,
1112.51,
1193.95
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
242,
86,
134,
290,
173,
299,
116,
84,
183,
200,
229,
131,
236,
122,
288,
147,
188,
210,
58,
143,
194,
140,
224,
182,
214,
168,
83,
83,
216,
194
]
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1274.72, 1416.61, 1413.81, 1412.13, 1459.62, 1370.37, 1318.63, 1279.75, 1115.48, 1115.38, 969.3, 995.12, 994.94, 1137.95, 1179.37, 1319.15, 1345.13, 1353.37, 1435.74, 1354.5, 1356.67, 1164.41, 1179.29, 1083.02, 1015.72, 1025.43, 953.98, 1020.91, 1112.51, 1193.95], 'Visitors/Pengunjung': [242, 86, 134, 290, 173, 299, 116, 84, 183, 200, 229, 131, 236, 122, 288, 147, 188, 210, 58, 143, 194, 140, 224, 182, 214, 168, 83, 83, 216, 194], 'Conversion Rate/Rasio Konversi': [0.0644, 0.0361, 0.0851, 0.0107, 0.0185, 0.0746, 0.0498, 0.0311, 0.098, 0.0487, 0.0757, 0.0772, 0.0495, 0.0963, 0.0426, 0.0991, 0.0933, 0.0985, 0.08, 0.0131, 0.0288, 0.055, 0.032, 0.0918, 0.0227, 0.0126, 0.0815, 0.0649, 0.0194, 0.0607], 'Avg. Order Value/Nilai Pesanan Rata2': [58.28, 97.08, 188.67, 191.48, 137.45, 50.38, 147.9, 161.82, 97.03, 102.29, 108.59, 192.72, 75.99, 115.55, 185.04, 92.28, 152.81, 132.72, 144.53, 168.4, 63.73, 67.32, 53.66, 165.26, 61.51, 180.79, 56.46, 184.8, 112.93, 88.08]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Optimalkan nilai pesanan rata-rata negara | {
"Age/Usia": [
64,
68,
69,
66,
62,
51,
38,
20,
33,
70,
42,
21,
36,
25,
28,
43,
63,
51,
29,
19,
67,
43,
37,
49,
55,
41,
36,
33,
60,
60,
48,
25,
40,
36,
63,
56,
53,
68,
59,
70,
27,
69,
64,
24,
46,
29,
51,
42,
36,
29,
41,
19,
26,
36,
42,
21,
61,
70,
24,
40,
42,
44,
18,
39,
54,
21,
70,
60,
52,
33,
61,
62,
31,
47,
49,
64,
22,
22,
61,
51,
67,
27,
26,
48,
56,
23,
18,
55,
46,
45,
31,
30,
21,
19,
24,
40,
27,
65,
56,
24
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082",
"CUST_0083",
"CUST_0084",
"CUST_0085",
"CUST_0086",
"CUST_0087",
"CUST_0088",
"CUST_0089",
"CUST_0090",
"CUST_0091",
"CUST_0092",
"CUST_0093",
"CUST_0094",
"CUST_0095",
"CUST_0096",
"CUST_0097",
"CUST_0098",
"CUST_0099",
"CUST_0100"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
3000,
7000,
8000,
5000,
10000,
1000,
6000,
4000,
4000,
6000,
6000,
2000,
7000,
4000,
9000,
1000,
4000,
5000,
1000,
4000,
9000,
8000,
5000,
7000,
9000,
10000,
7000,
5000,
9000,
3000,
9000,
9000,
3000,
10000,
3000,
4000,
4000,
1000,
2000,
8000,
6000,
5000,
4000,
5000,
9000,
1000,
2000,
4000,
7000,
10000,
7000,
3000,
8000,
10000,
6000,
8000,
4000,
7000,
1000,
1000,
7000,
6000,
4000,
4000,
3000,
3000,
6000,
6000,
2000,
5000,
6000,
10000,
5000,
6000,
6000,
5000,
2000,
5000,
6000,
2000,
10000,
3000,
4000,
3000,
4000,
3000,
8000,
7000,
8000,
4000,
5000,
2000,
2000,
8000,
2000,
8000,
6000,
2000,
9000,
6000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-01-03",
"2024-09-22",
"2025-04-15",
"2024-05-05",
"2025-03-16",
"2024-07-14",
"2024-09-30",
"2024-04-24",
"2025-03-10",
"2025-04-20",
"2024-10-23",
"2024-11-15",
"2024-09-20",
"2024-10-10",
"2024-12-14",
"2024-05-23",
"2024-04-24",
"2025-02-27",
"2024-09-30",
"2024-08-12",
"2024-09-16",
"2024-05-16",
"2025-04-13",
"2024-11-07",
"2024-06-20",
"2024-07-06",
"2024-09-27",
"2024-10-12",
"2025-02-06",
"2024-05-14",
"2024-09-15",
"2024-10-18",
"2024-12-12",
"2024-05-12",
"2024-12-27",
"2024-09-11",
"2024-05-15",
"2024-06-04",
"2024-12-07",
"2024-08-11",
"2024-09-20",
"2024-10-17",
"2024-11-26",
"2025-01-14",
"2025-01-10",
"2024-07-26",
"2024-05-16",
"2025-03-31",
"2025-01-18",
"2024-10-31",
"2024-10-26",
"2024-05-26",
"2024-10-10",
"2024-07-26",
"2025-01-10",
"2024-12-20",
"2024-10-27",
"2025-01-02",
"2024-12-14",
"2025-01-26",
"2024-10-17",
"2024-11-15",
"2024-05-18",
"2024-08-20",
"2024-10-20",
"2024-10-12",
"2025-03-29",
"2025-04-11",
"2024-11-12",
"2024-05-13",
"2025-04-11",
"2024-08-14",
"2024-08-16",
"2025-02-13",
"2024-11-18",
"2024-05-27",
"2025-02-27",
"2024-05-28",
"2024-11-16",
"2024-06-20",
"2024-12-24",
"2024-06-02",
"2024-12-16",
"2024-05-21",
"2024-06-27",
"2024-08-13",
"2025-03-31",
"2024-10-03",
"2024-06-06",
"2025-03-25",
"2024-12-25",
"2024-08-18",
"2024-11-13",
"2024-09-17",
"2024-05-28",
"2024-12-21",
"2025-01-05",
"2024-06-04",
"2025-01-19",
"2024-07-10"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
89,
4,
65,
19,
31,
95,
35,
68,
47,
98,
60,
58,
93,
83,
34,
75,
84,
90,
30,
18,
46,
35,
19,
35,
63,
16,
26,
95,
4,
86,
21,
93,
100,
18,
53,
20,
82,
31,
46,
18,
19,
48,
42,
51,
83,
54,
55,
80,
39,
51,
4,
76,
41,
94,
55,
23,
33,
5,
41,
74,
82,
2,
44,
1,
61,
28,
91,
43,
61,
39,
33,
99,
43,
79,
53,
91,
70,
55,
38,
47,
62,
57,
1,
2,
91,
23,
47,
80,
67,
91,
3,
8,
34,
100,
91,
46,
67,
68,
38,
96
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082', 'CUST_0083', 'CUST_0084', 'CUST_0085', 'CUST_0086', 'CUST_0087', 'CUST_0088', 'CUST_0089', 'CUST_0090', 'CUST_0091', 'CUST_0092', 'CUST_0093', 'CUST_0094', 'CUST_0095', 'CUST_0096', 'CUST_0097', 'CUST_0098', 'CUST_0099', 'CUST_0100'], 'Age/Usia': [64, 68, 69, 66, 62, 51, 38, 20, 33, 70, 42, 21, 36, 25, 28, 43, 63, 51, 29, 19, 67, 43, 37, 49, 55, 41, 36, 33, 60, 60, 48, 25, 40, 36, 63, 56, 53, 68, 59, 70, 27, 69, 64, 24, 46, 29, 51, 42, 36, 29, 41, 19, 26, 36, 42, 21, 61, 70, 24, 40, 42, 44, 18, 39, 54, 21, 70, 60, 52, 33, 61, 62, 31, 47, 49, 64, 22, 22, 61, 51, 67, 27, 26, 48, 56, 23, 18, 55, 46, 45, 31, 30, 21, 19, 24, 40, 27, 65, 56, 24], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan'], 'Income/Pendapatan': [3000, 7000, 8000, 5000, 10000, 1000, 6000, 4000, 4000, 6000, 6000, 2000, 7000, 4000, 9000, 1000, 4000, 5000, 1000, 4000, 9000, 8000, 5000, 7000, 9000, 10000, 7000, 5000, 9000, 3000, 9000, 9000, 3000, 10000, 3000, 4000, 4000, 1000, 2000, 8000, 6000, 5000, 4000, 5000, 9000, 1000, 2000, 4000, 7000, 10000, 7000, 3000, 8000, 10000, 6000, 8000, 4000, 7000, 1000, 1000, 7000, 6000, 4000, 4000, 3000, 3000, 6000, 6000, 2000, 5000, 6000, 10000, 5000, 6000, 6000, 5000, 2000, 5000, 6000, 2000, 10000, 3000, 4000, 3000, 4000, 3000, 8000, 7000, 8000, 4000, 5000, 2000, 2000, 8000, 2000, 8000, 6000, 2000, 9000, 6000], 'Loyalty Score/Skor Loyalitas': [89, 4, 65, 19, 31, 95, 35, 68, 47, 98, 60, 58, 93, 83, 34, 75, 84, 90, 30, 18, 46, 35, 19, 35, 63, 16, 26, 95, 4, 86, 21, 93, 100, 18, 53, 20, 82, 31, 46, 18, 19, 48, 42, 51, 83, 54, 55, 80, 39, 51, 4, 76, 41, 94, 55, 23, 33, 5, 41, 74, 82, 2, 44, 1, 61, 28, 91, 43, 61, 39, 33, 99, 43, 79, 53, 91, 70, 55, 38, 47, 62, 57, 1, 2, 91, 23, 47, 80, 67, 91, 3, 8, 34, 100, 91, 46, 67, 68, 38, 96], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-01-03', '2024-09-22', '2025-04-15', '2024-05-05', '2025-03-16', '2024-07-14', '2024-09-30', '2024-04-24', '2025-03-10', '2025-04-20', '2024-10-23', '2024-11-15', '2024-09-20', '2024-10-10', '2024-12-14', '2024-05-23', '2024-04-24', '2025-02-27', '2024-09-30', '2024-08-12', '2024-09-16', '2024-05-16', '2025-04-13', '2024-11-07', '2024-06-20', '2024-07-06', '2024-09-27', '2024-10-12', '2025-02-06', '2024-05-14', '2024-09-15', '2024-10-18', '2024-12-12', '2024-05-12', '2024-12-27', '2024-09-11', '2024-05-15', '2024-06-04', '2024-12-07', '2024-08-11', '2024-09-20', '2024-10-17', '2024-11-26', '2025-01-14', '2025-01-10', '2024-07-26', '2024-05-16', '2025-03-31', '2025-01-18', '2024-10-31', '2024-10-26', '2024-05-26', '2024-10-10', '2024-07-26', '2025-01-10', '2024-12-20', '2024-10-27', '2025-01-02', '2024-12-14', '2025-01-26', '2024-10-17', '2024-11-15', '2024-05-18', '2024-08-20', '2024-10-20', '2024-10-12', '2025-03-29', '2025-04-11', '2024-11-12', '2024-05-13', '2025-04-11', '2024-08-14', '2024-08-16', '2025-02-13', '2024-11-18', '2024-05-27', '2025-02-27', '2024-05-28', '2024-11-16', '2024-06-20', '2024-12-24', '2024-06-02', '2024-12-16', '2024-05-21', '2024-06-27', '2024-08-13', '2025-03-31', '2024-10-03', '2024-06-06', '2025-03-25', '2024-12-25', '2024-08-18', '2024-11-13', '2024-09-17', '2024-05-28', '2024-12-21', '2025-01-05', '2024-06-04', '2025-01-19', '2024-07-10']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Compare average order value city | {
"Age/Usia": [
48,
62,
64,
20,
56,
18,
27,
66,
27,
48,
38,
44,
41,
25,
28,
47,
40,
42,
30,
57,
67,
29,
53,
61,
42,
35,
24,
44,
53,
58,
19,
58,
53,
25,
23,
55,
58,
21,
59,
50,
29,
55,
65,
18,
28,
45,
51,
47,
62,
26,
28,
22,
18,
38,
36,
53,
39,
25,
51,
39,
70,
19,
31,
50,
66,
59,
28,
37,
59,
21,
29,
33,
54,
32,
18,
50,
55,
28,
64,
20,
53,
47,
41,
23,
19,
29,
68,
69,
33,
53,
48,
27,
58,
50,
65
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082",
"CUST_0083",
"CUST_0084",
"CUST_0085",
"CUST_0086",
"CUST_0087",
"CUST_0088",
"CUST_0089",
"CUST_0090",
"CUST_0091",
"CUST_0092",
"CUST_0093",
"CUST_0094",
"CUST_0095"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
10000,
9000,
7000,
3000,
1000,
4000,
2000,
6000,
1000,
2000,
2000,
9000,
3000,
2000,
4000,
1000,
3000,
5000,
1000,
2000,
8000,
9000,
4000,
5000,
3000,
3000,
4000,
9000,
9000,
4000,
3000,
4000,
7000,
6000,
7000,
10000,
6000,
10000,
7000,
3000,
4000,
3000,
6000,
9000,
7000,
3000,
5000,
9000,
4000,
3000,
2000,
6000,
9000,
5000,
5000,
6000,
1000,
4000,
1000,
8000,
8000,
4000,
8000,
10000,
10000,
7000,
5000,
9000,
5000,
7000,
8000,
6000,
6000,
2000,
3000,
4000,
2000,
5000,
4000,
7000,
1000,
8000,
7000,
6000,
4000,
3000,
2000,
1000,
3000,
2000,
4000,
2000,
1000,
5000,
8000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2025-01-05",
"2024-06-30",
"2024-10-06",
"2024-07-10",
"2025-01-09",
"2025-02-17",
"2025-03-24",
"2024-10-04",
"2024-05-28",
"2024-12-17",
"2025-01-13",
"2024-07-20",
"2025-03-17",
"2024-06-01",
"2025-03-16",
"2025-01-22",
"2024-09-28",
"2024-07-12",
"2025-03-16",
"2024-04-21",
"2024-10-12",
"2025-01-21",
"2024-12-29",
"2024-09-27",
"2025-04-10",
"2024-05-27",
"2024-07-12",
"2025-03-10",
"2024-12-02",
"2024-12-30",
"2024-11-17",
"2025-01-05",
"2025-02-27",
"2024-07-17",
"2024-12-25",
"2024-10-05",
"2024-06-30",
"2024-08-26",
"2025-02-14",
"2024-10-19",
"2025-03-07",
"2025-03-04",
"2024-11-04",
"2024-10-23",
"2024-05-19",
"2025-01-28",
"2025-04-13",
"2024-06-06",
"2024-06-23",
"2024-05-27",
"2025-02-07",
"2025-01-06",
"2024-10-06",
"2025-03-03",
"2024-09-26",
"2025-01-27",
"2024-11-30",
"2024-09-13",
"2025-03-17",
"2024-10-10",
"2025-01-31",
"2025-03-15",
"2024-10-07",
"2024-04-25",
"2024-12-05",
"2024-06-04",
"2024-12-29",
"2025-01-09",
"2024-05-27",
"2024-07-19",
"2024-11-25",
"2025-01-14",
"2024-09-12",
"2024-07-11",
"2024-10-13",
"2024-08-06",
"2024-05-17",
"2024-06-11",
"2024-10-01",
"2024-08-06",
"2024-10-09",
"2024-11-05",
"2025-03-24",
"2024-11-01",
"2025-03-01",
"2024-12-24",
"2024-06-15",
"2024-05-10",
"2024-07-08",
"2024-12-21",
"2024-08-23",
"2024-09-05",
"2024-11-05",
"2024-06-04",
"2024-05-17"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
96,
40,
41,
15,
54,
78,
15,
23,
63,
17,
51,
77,
90,
88,
87,
26,
22,
10,
88,
96,
19,
99,
85,
31,
83,
58,
12,
42,
92,
28,
73,
98,
63,
45,
36,
90,
98,
40,
5,
26,
99,
5,
6,
24,
81,
43,
54,
15,
19,
65,
7,
49,
48,
25,
2,
43,
15,
13,
23,
60,
3,
55,
44,
92,
1,
89,
48,
18,
15,
43,
40,
93,
93,
50,
29,
99,
51,
93,
1,
11,
31,
26,
76,
55,
93,
38,
40,
22,
83,
91,
99,
40,
36,
2,
26
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082', 'CUST_0083', 'CUST_0084', 'CUST_0085', 'CUST_0086', 'CUST_0087', 'CUST_0088', 'CUST_0089', 'CUST_0090', 'CUST_0091', 'CUST_0092', 'CUST_0093', 'CUST_0094', 'CUST_0095'], 'Age/Usia': [48, 62, 64, 20, 56, 18, 27, 66, 27, 48, 38, 44, 41, 25, 28, 47, 40, 42, 30, 57, 67, 29, 53, 61, 42, 35, 24, 44, 53, 58, 19, 58, 53, 25, 23, 55, 58, 21, 59, 50, 29, 55, 65, 18, 28, 45, 51, 47, 62, 26, 28, 22, 18, 38, 36, 53, 39, 25, 51, 39, 70, 19, 31, 50, 66, 59, 28, 37, 59, 21, 29, 33, 54, 32, 18, 50, 55, 28, 64, 20, 53, 47, 41, 23, 19, 29, 68, 69, 33, 53, 48, 27, 58, 50, 65], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [10000, 9000, 7000, 3000, 1000, 4000, 2000, 6000, 1000, 2000, 2000, 9000, 3000, 2000, 4000, 1000, 3000, 5000, 1000, 2000, 8000, 9000, 4000, 5000, 3000, 3000, 4000, 9000, 9000, 4000, 3000, 4000, 7000, 6000, 7000, 10000, 6000, 10000, 7000, 3000, 4000, 3000, 6000, 9000, 7000, 3000, 5000, 9000, 4000, 3000, 2000, 6000, 9000, 5000, 5000, 6000, 1000, 4000, 1000, 8000, 8000, 4000, 8000, 10000, 10000, 7000, 5000, 9000, 5000, 7000, 8000, 6000, 6000, 2000, 3000, 4000, 2000, 5000, 4000, 7000, 1000, 8000, 7000, 6000, 4000, 3000, 2000, 1000, 3000, 2000, 4000, 2000, 1000, 5000, 8000], 'Loyalty Score/Skor Loyalitas': [96, 40, 41, 15, 54, 78, 15, 23, 63, 17, 51, 77, 90, 88, 87, 26, 22, 10, 88, 96, 19, 99, 85, 31, 83, 58, 12, 42, 92, 28, 73, 98, 63, 45, 36, 90, 98, 40, 5, 26, 99, 5, 6, 24, 81, 43, 54, 15, 19, 65, 7, 49, 48, 25, 2, 43, 15, 13, 23, 60, 3, 55, 44, 92, 1, 89, 48, 18, 15, 43, 40, 93, 93, 50, 29, 99, 51, 93, 1, 11, 31, 26, 76, 55, 93, 38, 40, 22, 83, 91, 99, 40, 36, 2, 26], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2025-01-05', '2024-06-30', '2024-10-06', '2024-07-10', '2025-01-09', '2025-02-17', '2025-03-24', '2024-10-04', '2024-05-28', '2024-12-17', '2025-01-13', '2024-07-20', '2025-03-17', '2024-06-01', '2025-03-16', '2025-01-22', '2024-09-28', '2024-07-12', '2025-03-16', '2024-04-21', '2024-10-12', '2025-01-21', '2024-12-29', '2024-09-27', '2025-04-10', '2024-05-27', '2024-07-12', '2025-03-10', '2024-12-02', '2024-12-30', '2024-11-17', '2025-01-05', '2025-02-27', '2024-07-17', '2024-12-25', '2024-10-05', '2024-06-30', '2024-08-26', '2025-02-14', '2024-10-19', '2025-03-07', '2025-03-04', '2024-11-04', '2024-10-23', '2024-05-19', '2025-01-28', '2025-04-13', '2024-06-06', '2024-06-23', '2024-05-27', '2025-02-07', '2025-01-06', '2024-10-06', '2025-03-03', '2024-09-26', '2025-01-27', '2024-11-30', '2024-09-13', '2025-03-17', '2024-10-10', '2025-01-31', '2025-03-15', '2024-10-07', '2024-04-25', '2024-12-05', '2024-06-04', '2024-12-29', '2025-01-09', '2024-05-27', '2024-07-19', '2024-11-25', '2025-01-14', '2024-09-12', '2024-07-11', '2024-10-13', '2024-08-06', '2024-05-17', '2024-06-11', '2024-10-01', '2024-08-06', '2024-10-09', '2024-11-05', '2025-03-24', '2024-11-01', '2025-03-01', '2024-12-24', '2024-06-15', '2024-05-10', '2024-07-08', '2024-12-21', '2024-08-23', '2024-09-05', '2024-11-05', '2024-06-04', '2024-05-17']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Identifikasi skor loyalitas jenis kelamin | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.2,
0.25,
0.15
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
93.43,
126.36,
183.55
],
"Product/Produk": [
"Accessories_1",
"Tires_2",
"Oil_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
868,
428,
790
],
"Stock/Stok": [
184,
130,
198
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Accessories_1', 'Tires_2', 'Oil_3'], 'Sales/Penjualan': [868, 428, 790], 'Price/Harga': [93.43, 126.36, 183.55], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.2, 0.25, 0.15], 'Stock/Stok': [184, 130, 198]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Segment growth rate region | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Berlin",
"Dubai",
"Sydney",
"Singapore",
"São Paulo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Germany",
"UAE",
"Australia",
"Singapore",
"Brazil"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
0.17,
0.17,
-0.03,
-0.01,
-0.04
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-40.1641,
47.8796,
39.1437,
-58.8714,
57.1894
],
"Longitude/Bujur": [
175.8081,
-136.9615,
51.1702,
166.526,
38.7624
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.46,
0.33,
0.15,
0.33,
0.2
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Europe",
"Middle East",
"Oceania",
"Asia",
"South America"
],
"Revenue/Pendapatan": [
4876,
3122,
3611,
2876,
1584
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Berlin', 'Dubai', 'Sydney', 'Singapore', 'São Paulo'], 'Country/Negara': ['Germany', 'UAE', 'Australia', 'Singapore', 'Brazil'], 'Region/Wilayah': ['Europe', 'Middle East', 'Oceania', 'Asia', 'South America'], 'Latitude/Lintang': [-40.1641, 47.8796, 39.1437, -58.8714, 57.1894], 'Longitude/Bujur': [175.8081, -136.9615, 51.1702, 166.526, 38.7624], 'Revenue/Pendapatan': [4876, 3122, 3611, 2876, 1584], 'Market Share/Pangsa Pasar': [0.46, 0.33, 0.15, 0.33, 0.2], 'Growth Rate/Pertumbuhan': [0.17, 0.17, -0.03, -0.01, -0.04]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Perkiraan nilai pesanan rata-rata for/untuk 3 bulan | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.16,
0.23,
0.21
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
39.42,
72.33,
52.47
],
"Product/Produk": [
"Tools_1",
"Tires_2",
"Oil_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
476,
233,
993
],
"Stock/Stok": [
181,
70,
109
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Tools_1', 'Tires_2', 'Oil_3'], 'Sales/Penjualan': [476, 233, 993], 'Price/Harga': [39.42, 72.33, 52.47], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.16, 0.23, 0.21], 'Stock/Stok': [181, 70, 109]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Optimalkan rasio konversi kategori | {
"Age/Usia": [
67,
39,
58,
64,
69,
69,
70,
53,
61,
26,
57,
58,
41,
52,
26,
38,
21,
19,
54,
47,
23,
23,
60,
52,
45,
49,
66,
34,
59,
36,
25,
62,
57,
55,
21,
22,
62,
47,
45,
66,
33,
30,
32,
53,
50,
22,
51,
66,
68,
47,
23,
29,
64,
24,
37,
28,
21,
58
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
7000,
10000,
7000,
9000,
10000,
7000,
9000,
2000,
8000,
8000,
7000,
2000,
8000,
8000,
3000,
3000,
5000,
6000,
6000,
8000,
8000,
6000,
2000,
9000,
7000,
10000,
9000,
7000,
5000,
4000,
8000,
3000,
5000,
10000,
3000,
3000,
3000,
5000,
3000,
4000,
3000,
5000,
10000,
3000,
2000,
5000,
3000,
9000,
10000,
1000,
7000,
10000,
5000,
4000,
5000,
10000,
2000,
9000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-11-01",
"2024-08-11",
"2024-07-31",
"2025-02-09",
"2024-10-21",
"2024-11-05",
"2024-07-28",
"2024-07-21",
"2025-02-04",
"2025-02-03",
"2024-12-23",
"2024-08-05",
"2025-04-15",
"2025-01-20",
"2025-03-15",
"2025-03-26",
"2024-12-06",
"2024-05-26",
"2024-08-08",
"2024-07-13",
"2024-12-08",
"2024-08-03",
"2024-08-27",
"2025-01-07",
"2024-11-18",
"2024-08-30",
"2025-02-16",
"2024-10-07",
"2025-03-04",
"2024-04-22",
"2025-02-19",
"2024-09-25",
"2025-03-26",
"2025-03-16",
"2024-06-01",
"2025-02-25",
"2024-06-08",
"2024-08-29",
"2024-12-10",
"2024-05-25",
"2024-11-02",
"2024-06-03",
"2025-03-10",
"2024-12-16",
"2024-12-01",
"2024-07-11",
"2025-02-25",
"2025-02-15",
"2024-11-20",
"2024-06-13",
"2025-03-31",
"2024-09-01",
"2024-10-25",
"2025-04-02",
"2024-12-22",
"2024-08-06",
"2024-09-23",
"2024-10-15"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
96,
66,
55,
68,
35,
96,
13,
85,
26,
48,
48,
43,
30,
56,
96,
24,
31,
18,
5,
71,
58,
29,
70,
15,
52,
89,
18,
96,
83,
84,
50,
63,
29,
4,
13,
88,
29,
89,
41,
23,
99,
53,
58,
59,
38,
14,
42,
46,
31,
14,
42,
58,
44,
57,
19,
33,
22,
88
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058'], 'Age/Usia': [67, 39, 58, 64, 69, 69, 70, 53, 61, 26, 57, 58, 41, 52, 26, 38, 21, 19, 54, 47, 23, 23, 60, 52, 45, 49, 66, 34, 59, 36, 25, 62, 57, 55, 21, 22, 62, 47, 45, 66, 33, 30, 32, 53, 50, 22, 51, 66, 68, 47, 23, 29, 64, 24, 37, 28, 21, 58], 'Gender/Jenis Kelamin': ['Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [7000, 10000, 7000, 9000, 10000, 7000, 9000, 2000, 8000, 8000, 7000, 2000, 8000, 8000, 3000, 3000, 5000, 6000, 6000, 8000, 8000, 6000, 2000, 9000, 7000, 10000, 9000, 7000, 5000, 4000, 8000, 3000, 5000, 10000, 3000, 3000, 3000, 5000, 3000, 4000, 3000, 5000, 10000, 3000, 2000, 5000, 3000, 9000, 10000, 1000, 7000, 10000, 5000, 4000, 5000, 10000, 2000, 9000], 'Loyalty Score/Skor Loyalitas': [96, 66, 55, 68, 35, 96, 13, 85, 26, 48, 48, 43, 30, 56, 96, 24, 31, 18, 5, 71, 58, 29, 70, 15, 52, 89, 18, 96, 83, 84, 50, 63, 29, 4, 13, 88, 29, 89, 41, 23, 99, 53, 58, 59, 38, 14, 42, 46, 31, 14, 42, 58, 44, 57, 19, 33, 22, 88], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-11-01', '2024-08-11', '2024-07-31', '2025-02-09', '2024-10-21', '2024-11-05', '2024-07-28', '2024-07-21', '2025-02-04', '2025-02-03', '2024-12-23', '2024-08-05', '2025-04-15', '2025-01-20', '2025-03-15', '2025-03-26', '2024-12-06', '2024-05-26', '2024-08-08', '2024-07-13', '2024-12-08', '2024-08-03', '2024-08-27', '2025-01-07', '2024-11-18', '2024-08-30', '2025-02-16', '2024-10-07', '2025-03-04', '2024-04-22', '2025-02-19', '2024-09-25', '2025-03-26', '2025-03-16', '2024-06-01', '2025-02-25', '2024-06-08', '2024-08-29', '2024-12-10', '2024-05-25', '2024-11-02', '2024-06-03', '2025-03-10', '2024-12-16', '2024-12-01', '2024-07-11', '2025-02-25', '2025-02-15', '2024-11-20', '2024-06-13', '2025-03-31', '2024-09-01', '2024-10-25', '2025-04-02', '2024-12-22', '2024-08-06', '2024-09-23', '2024-10-15']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Korelasi stok and/dan pengunjung | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
53.35,
104.13,
82.59,
82.67,
82.7,
67.42,
170.61,
166.08,
93.25,
185.35,
197.22,
184.58,
165.21,
70.55,
81.46,
184.71,
155.57,
90.51,
52.58,
178.41,
151.51,
170.83,
132.97,
78.73,
98.18,
199.2,
176.3,
79.95,
116.52,
156.41
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.060200000000000004,
0.061900000000000004,
0.066,
0.0217,
0.0776,
0.0763,
0.0931,
0.0945,
0.0722,
0.0412,
0.039400000000000004,
0.0176,
0.0587,
0.0419,
0.014100000000000001,
0.0316,
0.0494,
0.0443,
0.0703,
0.051000000000000004,
0.0123,
0.0567,
0.045700000000000005,
0.074,
0.07350000000000001,
0.06430000000000001,
0.0424,
0.0413,
0.0189,
0.0437
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
4514.44,
4610.49,
4569.13,
4686.26,
4626.8,
4655.8,
4491.58,
4522.54,
4275.25,
4205.12,
4135.88,
4033.26,
4131.85,
4112.06,
4254.95,
4308.77,
4380.46,
4408.72,
4437.6,
4386.69,
4342.46,
4343.31,
4194.86,
4039.69,
3948.55,
3848.28,
3864.8,
3855.2,
3914,
3971.34
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
224,
249,
137,
227,
255,
244,
101,
97,
88,
228,
69,
50,
160,
176,
161,
137,
210,
100,
278,
92,
180,
87,
97,
201,
236,
253,
253,
208,
210,
173
]
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [4514.44, 4610.49, 4569.13, 4686.26, 4626.8, 4655.8, 4491.58, 4522.54, 4275.25, 4205.12, 4135.88, 4033.26, 4131.85, 4112.06, 4254.95, 4308.77, 4380.46, 4408.72, 4437.6, 4386.69, 4342.46, 4343.31, 4194.86, 4039.69, 3948.55, 3848.28, 3864.8, 3855.2, 3914.0, 3971.34], 'Visitors/Pengunjung': [224, 249, 137, 227, 255, 244, 101, 97, 88, 228, 69, 50, 160, 176, 161, 137, 210, 100, 278, 92, 180, 87, 97, 201, 236, 253, 253, 208, 210, 173], 'Conversion Rate/Rasio Konversi': [0.0602, 0.0619, 0.066, 0.0217, 0.0776, 0.0763, 0.0931, 0.0945, 0.0722, 0.0412, 0.0394, 0.0176, 0.0587, 0.0419, 0.0141, 0.0316, 0.0494, 0.0443, 0.0703, 0.051, 0.0123, 0.0567, 0.0457, 0.074, 0.0735, 0.0643, 0.0424, 0.0413, 0.0189, 0.0437], 'Avg. Order Value/Nilai Pesanan Rata2': [53.35, 104.13, 82.59, 82.67, 82.7, 67.42, 170.61, 166.08, 93.25, 185.35, 197.22, 184.58, 165.21, 70.55, 81.46, 184.71, 155.57, 90.51, 52.58, 178.41, 151.51, 170.83, 132.97, 78.73, 98.18, 199.2, 176.3, 79.95, 116.52, 156.41]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Correlation analysis
corr_matrix = df.corr(numeric_only=True)
fig = px.imshow(corr_matrix, text_auto=True,
title='Correlation Matrix')
fig.show()
|
Optimalkan skor loyalitas jenis kelamin | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
134.42,
121.18,
108.88,
97.22,
102.79,
184.62,
109.87,
101.36,
172.34,
167.19,
155.34,
84.45,
183.35,
140.92,
92.77,
156.45,
107.36,
55.67,
59.39,
78.26,
138.42,
190.19,
144.13,
122.89,
79.76,
79.39,
133.72,
84.41,
81.43,
98.14
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0709,
0.0892,
0.020800000000000003,
0.0403,
0.0206,
0.0852,
0.029900000000000003,
0.0531,
0.0603,
0.07100000000000001,
0.052000000000000005,
0.0782,
0.066,
0.057100000000000005,
0.018500000000000003,
0.0671,
0.06720000000000001,
0.0741,
0.099,
0.08660000000000001,
0.091,
0.07440000000000001,
0.0596,
0.027600000000000003,
0.030500000000000003,
0.0545,
0.0351,
0.0801,
0.075,
0.08610000000000001
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
4885.67,
4923.74,
5045.85,
5030.3,
5131.55,
5005.91,
5051.45,
5039.22,
4913.45,
4851.07,
4773.86,
4869.94,
4811.08,
4854.35,
4966.83,
5119.77,
5118.75,
5295.45,
5197.78,
5242.79,
5266.27,
5248.47,
5196.57,
5155.52,
4903.49,
4999.3,
5091.51,
5046.53,
5152.16,
5160.01
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
73,
80,
226,
180,
186,
208,
112,
280,
62,
192,
158,
133,
185,
248,
145,
282,
292,
76,
303,
247,
214,
256,
49,
56,
288,
278,
238,
291,
159,
275
]
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [4885.67, 4923.74, 5045.85, 5030.3, 5131.55, 5005.91, 5051.45, 5039.22, 4913.45, 4851.07, 4773.86, 4869.94, 4811.08, 4854.35, 4966.83, 5119.77, 5118.75, 5295.45, 5197.78, 5242.79, 5266.27, 5248.47, 5196.57, 5155.52, 4903.49, 4999.3, 5091.51, 5046.53, 5152.16, 5160.01], 'Visitors/Pengunjung': [73, 80, 226, 180, 186, 208, 112, 280, 62, 192, 158, 133, 185, 248, 145, 282, 292, 76, 303, 247, 214, 256, 49, 56, 288, 278, 238, 291, 159, 275], 'Conversion Rate/Rasio Konversi': [0.0709, 0.0892, 0.0208, 0.0403, 0.0206, 0.0852, 0.0299, 0.0531, 0.0603, 0.071, 0.052, 0.0782, 0.066, 0.0571, 0.0185, 0.0671, 0.0672, 0.0741, 0.099, 0.0866, 0.091, 0.0744, 0.0596, 0.0276, 0.0305, 0.0545, 0.0351, 0.0801, 0.075, 0.0861], 'Avg. Order Value/Nilai Pesanan Rata2': [134.42, 121.18, 108.88, 97.22, 102.79, 184.62, 109.87, 101.36, 172.34, 167.19, 155.34, 84.45, 183.35, 140.92, 92.77, 156.45, 107.36, 55.67, 59.39, 78.26, 138.42, 190.19, 144.13, 122.89, 79.76, 79.39, 133.72, 84.41, 81.43, 98.14]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Analyze growth rate customer segment | {
"Age/Usia": [
66,
34,
56,
69,
24,
60,
34,
21,
25,
57,
44,
24,
45,
24,
19,
60,
38,
26,
21,
39,
33,
18,
59,
25,
54,
36,
68,
63,
28,
63,
56,
22,
65,
22,
43,
67,
29,
47,
32,
42,
54,
52,
36,
46,
49,
66,
25,
57,
65,
30,
44,
45,
32,
28,
41,
49,
48,
31,
34,
44,
19,
48,
37,
37,
42,
32,
45,
63,
48
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
6000,
6000,
4000,
10000,
7000,
8000,
5000,
7000,
4000,
2000,
7000,
7000,
6000,
10000,
9000,
4000,
6000,
8000,
7000,
5000,
2000,
5000,
4000,
7000,
4000,
10000,
10000,
4000,
3000,
2000,
8000,
5000,
7000,
6000,
6000,
5000,
3000,
2000,
2000,
3000,
8000,
10000,
4000,
9000,
8000,
6000,
4000,
6000,
5000,
5000,
4000,
3000,
4000,
5000,
7000,
8000,
9000,
3000,
2000,
1000,
5000,
5000,
5000,
9000,
6000,
5000,
3000,
3000,
9000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-10-21",
"2024-10-31",
"2024-07-11",
"2024-10-27",
"2024-08-07",
"2024-10-16",
"2024-12-11",
"2024-09-14",
"2024-06-12",
"2024-08-23",
"2024-08-24",
"2024-07-22",
"2024-09-29",
"2024-12-02",
"2024-08-20",
"2024-11-29",
"2024-12-09",
"2024-05-19",
"2024-11-19",
"2025-04-20",
"2025-02-02",
"2024-07-09",
"2024-05-26",
"2024-08-25",
"2024-10-31",
"2024-08-01",
"2025-01-19",
"2025-04-15",
"2024-06-05",
"2024-11-14",
"2025-01-10",
"2024-12-19",
"2025-03-15",
"2025-02-10",
"2025-04-06",
"2024-10-24",
"2024-09-26",
"2024-05-02",
"2024-10-11",
"2024-04-24",
"2024-05-03",
"2024-05-21",
"2025-04-03",
"2025-01-11",
"2025-01-04",
"2024-08-26",
"2024-08-26",
"2024-10-03",
"2025-01-10",
"2024-10-09",
"2024-07-20",
"2025-02-01",
"2024-05-31",
"2024-12-28",
"2024-11-05",
"2024-09-13",
"2024-09-12",
"2024-11-24",
"2025-02-10",
"2024-12-14",
"2024-08-07",
"2024-10-14",
"2024-10-03",
"2024-11-27",
"2024-11-15",
"2024-04-22",
"2024-10-25",
"2024-08-10",
"2024-12-22"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
23,
88,
22,
24,
66,
60,
91,
73,
55,
57,
16,
27,
78,
33,
76,
7,
38,
28,
89,
70,
15,
76,
65,
67,
77,
61,
53,
30,
96,
89,
66,
39,
98,
68,
83,
31,
89,
18,
71,
13,
22,
96,
71,
23,
56,
18,
66,
43,
52,
63,
24,
55,
71,
92,
96,
63,
66,
99,
44,
98,
71,
23,
87,
6,
5,
5,
50,
77,
23
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069'], 'Age/Usia': [66, 34, 56, 69, 24, 60, 34, 21, 25, 57, 44, 24, 45, 24, 19, 60, 38, 26, 21, 39, 33, 18, 59, 25, 54, 36, 68, 63, 28, 63, 56, 22, 65, 22, 43, 67, 29, 47, 32, 42, 54, 52, 36, 46, 49, 66, 25, 57, 65, 30, 44, 45, 32, 28, 41, 49, 48, 31, 34, 44, 19, 48, 37, 37, 42, 32, 45, 63, 48], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki'], 'Income/Pendapatan': [6000, 6000, 4000, 10000, 7000, 8000, 5000, 7000, 4000, 2000, 7000, 7000, 6000, 10000, 9000, 4000, 6000, 8000, 7000, 5000, 2000, 5000, 4000, 7000, 4000, 10000, 10000, 4000, 3000, 2000, 8000, 5000, 7000, 6000, 6000, 5000, 3000, 2000, 2000, 3000, 8000, 10000, 4000, 9000, 8000, 6000, 4000, 6000, 5000, 5000, 4000, 3000, 4000, 5000, 7000, 8000, 9000, 3000, 2000, 1000, 5000, 5000, 5000, 9000, 6000, 5000, 3000, 3000, 9000], 'Loyalty Score/Skor Loyalitas': [23, 88, 22, 24, 66, 60, 91, 73, 55, 57, 16, 27, 78, 33, 76, 7, 38, 28, 89, 70, 15, 76, 65, 67, 77, 61, 53, 30, 96, 89, 66, 39, 98, 68, 83, 31, 89, 18, 71, 13, 22, 96, 71, 23, 56, 18, 66, 43, 52, 63, 24, 55, 71, 92, 96, 63, 66, 99, 44, 98, 71, 23, 87, 6, 5, 5, 50, 77, 23], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-10-21', '2024-10-31', '2024-07-11', '2024-10-27', '2024-08-07', '2024-10-16', '2024-12-11', '2024-09-14', '2024-06-12', '2024-08-23', '2024-08-24', '2024-07-22', '2024-09-29', '2024-12-02', '2024-08-20', '2024-11-29', '2024-12-09', '2024-05-19', '2024-11-19', '2025-04-20', '2025-02-02', '2024-07-09', '2024-05-26', '2024-08-25', '2024-10-31', '2024-08-01', '2025-01-19', '2025-04-15', '2024-06-05', '2024-11-14', '2025-01-10', '2024-12-19', '2025-03-15', '2025-02-10', '2025-04-06', '2024-10-24', '2024-09-26', '2024-05-02', '2024-10-11', '2024-04-24', '2024-05-03', '2024-05-21', '2025-04-03', '2025-01-11', '2025-01-04', '2024-08-26', '2024-08-26', '2024-10-03', '2025-01-10', '2024-10-09', '2024-07-20', '2025-02-01', '2024-05-31', '2024-12-28', '2024-11-05', '2024-09-13', '2024-09-12', '2024-11-24', '2025-02-10', '2024-12-14', '2024-08-07', '2024-10-14', '2024-10-03', '2024-11-27', '2024-11-15', '2024-04-22', '2024-10-25', '2024-08-10', '2024-12-22']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
Forecast price for/untuk year | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
177.15,
179.1,
89.85,
128.85,
51.62,
66.95,
169.7,
175.1,
130.86,
189.79,
158.87,
131.49,
91.56,
86.42,
169.2,
190.49,
76.62,
169.76,
187.07,
104.54,
188.65,
162.4,
173.82,
83.12,
56.45,
151.11,
75.32,
54.1,
104.02,
185.2
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0309,
0.0692,
0.085,
0.08700000000000001,
0.012400000000000001,
0.07440000000000001,
0.09870000000000001,
0.0171,
0.045700000000000005,
0.032600000000000004,
0.0497,
0.0881,
0.0993,
0.0951,
0.037700000000000004,
0.0844,
0.0494,
0.028300000000000002,
0.0971,
0.0325,
0.042800000000000005,
0.0625,
0.0844,
0.058,
0.044,
0.0599,
0.0142,
0.0426,
0.09430000000000001,
0.0488
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
2911.36,
3103.26,
3179.19,
3202.28,
3175.56,
3093.76,
3129.49,
2993.54,
2938.08,
2806.02,
2762.95,
2706,
2713.89,
2864.16,
2856.41,
3005.64,
3003.82,
3067.74,
3101.77,
3039.46,
2889.51,
2871.83,
2837.44,
2657.35,
2622.05,
2626.15,
2584.07,
2683.03,
2719.88,
2885.58
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
122,
268,
223,
190,
228,
100,
148,
190,
141,
121,
51,
157,
225,
118,
247,
139,
230,
183,
74,
225,
247,
231,
191,
223,
205,
155,
239,
277,
265,
271
]
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [2911.36, 3103.26, 3179.19, 3202.28, 3175.56, 3093.76, 3129.49, 2993.54, 2938.08, 2806.02, 2762.95, 2706.0, 2713.89, 2864.16, 2856.41, 3005.64, 3003.82, 3067.74, 3101.77, 3039.46, 2889.51, 2871.83, 2837.44, 2657.35, 2622.05, 2626.15, 2584.07, 2683.03, 2719.88, 2885.58], 'Visitors/Pengunjung': [122, 268, 223, 190, 228, 100, 148, 190, 141, 121, 51, 157, 225, 118, 247, 139, 230, 183, 74, 225, 247, 231, 191, 223, 205, 155, 239, 277, 265, 271], 'Conversion Rate/Rasio Konversi': [0.0309, 0.0692, 0.085, 0.087, 0.0124, 0.0744, 0.0987, 0.0171, 0.0457, 0.0326, 0.0497, 0.0881, 0.0993, 0.0951, 0.0377, 0.0844, 0.0494, 0.0283, 0.0971, 0.0325, 0.0428, 0.0625, 0.0844, 0.058, 0.044, 0.0599, 0.0142, 0.0426, 0.0943, 0.0488], 'Avg. Order Value/Nilai Pesanan Rata2': [177.15, 179.1, 89.85, 128.85, 51.62, 66.95, 169.7, 175.1, 130.86, 189.79, 158.87, 131.49, 91.56, 86.42, 169.2, 190.49, 76.62, 169.76, 187.07, 104.54, 188.65, 162.4, 173.82, 83.12, 56.45, 151.11, 75.32, 54.1, 104.02, 185.2]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Time series forecasting
from statsmodels.tsa.arima.model import ARIMA
# Prepare data
ts_data = df.set_index('Date/Tanggal')['Revenue/Pendapatan']
model = ARIMA(ts_data, order=(1,1,1))
model_fit = model.fit()
forecast = model_fit.forecast(steps=7)
print('Forecast for next 7 days:', forecast)
|
Analyze average order value product | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.01,
0.05,
0.27
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
41.59,
152.91,
175.27
],
"Product/Produk": [
"Headphones_1",
"Laptop_2",
"TV_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
246,
971,
721
],
"Stock/Stok": [
162,
58,
44
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Headphones_1', 'Laptop_2', 'TV_3'], 'Sales/Penjualan': [246, 971, 721], 'Price/Harga': [41.59, 152.91, 175.27], 'Category/Kategori': ['Electronics/Elektronik', 'Electronics/Elektronik', 'Electronics/Elektronik'], 'Discount/Diskon': [0.01, 0.05, 0.27], 'Stock/Stok': [162, 58, 44]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Kelompokkan pangsa pasar kategori | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
71.25,
111.18,
85.06,
129.36,
63.01,
187.63,
92.03,
189.45,
131.09,
161.71,
134.77,
128.22,
50.27,
74.57,
120.34,
178.23,
164.19,
171.93,
90.67,
142.15,
199.27,
95.58,
133.41,
132.34,
195.91,
77.9,
118.07,
75.04,
65.08,
151.97
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.0546,
0.09860000000000001,
0.0917,
0.0969,
0.09340000000000001,
0.07350000000000001,
0.0606,
0.0114,
0.089,
0.061700000000000005,
0.0442,
0.0891,
0.0859,
0.045700000000000005,
0.0823,
0.035500000000000004,
0.0762,
0.049,
0.0599,
0.0217,
0.0907,
0.0912,
0.0398,
0.07050000000000001,
0.0188,
0.079,
0.0171,
0.0776,
0.0268,
0.031400000000000004
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
2424.36,
2543.69,
2630.15,
2622.91,
2724.41,
2570.61,
2558.62,
2512.34,
2415.35,
2336.93,
2255.42,
2313.75,
2325.81,
2343.68,
2470.08,
2505.31,
2585.32,
2606.36,
2610.37,
2580.67,
2596.07,
2590.04,
2376.33,
2330.5,
2245.88,
2267.9,
2148,
2273.95,
2389.22,
2443.11
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
124,
192,
173,
114,
176,
166,
106,
137,
269,
262,
266,
57,
78,
289,
172,
271,
177,
160,
203,
99,
158,
83,
42,
202,
277,
216,
172,
252,
86,
246
]
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [2424.36, 2543.69, 2630.15, 2622.91, 2724.41, 2570.61, 2558.62, 2512.34, 2415.35, 2336.93, 2255.42, 2313.75, 2325.81, 2343.68, 2470.08, 2505.31, 2585.32, 2606.36, 2610.37, 2580.67, 2596.07, 2590.04, 2376.33, 2330.5, 2245.88, 2267.9, 2148.0, 2273.95, 2389.22, 2443.11], 'Visitors/Pengunjung': [124, 192, 173, 114, 176, 166, 106, 137, 269, 262, 266, 57, 78, 289, 172, 271, 177, 160, 203, 99, 158, 83, 42, 202, 277, 216, 172, 252, 86, 246], 'Conversion Rate/Rasio Konversi': [0.0546, 0.0986, 0.0917, 0.0969, 0.0934, 0.0735, 0.0606, 0.0114, 0.089, 0.0617, 0.0442, 0.0891, 0.0859, 0.0457, 0.0823, 0.0355, 0.0762, 0.049, 0.0599, 0.0217, 0.0907, 0.0912, 0.0398, 0.0705, 0.0188, 0.079, 0.0171, 0.0776, 0.0268, 0.0314], 'Avg. Order Value/Nilai Pesanan Rata2': [71.25, 111.18, 85.06, 129.36, 63.01, 187.63, 92.03, 189.45, 131.09, 161.71, 134.77, 128.22, 50.27, 74.57, 120.34, 178.23, 164.19, 171.93, 90.67, 142.15, 199.27, 95.58, 133.41, 132.34, 195.91, 77.9, 118.07, 75.04, 65.08, 151.97]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Hitung pendapatan segmen pelanggan | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Electronics/Elektronik",
"Electronics/Elektronik",
"Electronics/Elektronik"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.01,
0.1,
0.1
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
154.13,
126.38,
84.91
],
"Product/Produk": [
"Headphones_1",
"Smartphone_2",
"Laptop_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
907,
206,
648
],
"Stock/Stok": [
121,
72,
82
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Headphones_1', 'Smartphone_2', 'Laptop_3'], 'Sales/Penjualan': [907, 206, 648], 'Price/Harga': [154.13, 126.38, 84.91], 'Category/Kategori': ['Electronics/Elektronik', 'Electronics/Elektronik', 'Electronics/Elektronik'], 'Discount/Diskon': [0.01, 0.1, 0.1], 'Stock/Stok': [121, 72, 82]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# z-score calculation
result = df['Category/Kategori'].(df['{col}'] - df['{col}'].mean())/df['{col}'].std()
print(result)
|
Bandingkan diskon kota | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Automotive/Otomotif",
"Automotive/Otomotif",
"Automotive/Otomotif"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.08,
0.21,
0.08
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
193.34,
83.85,
178.17
],
"Product/Produk": [
"Tires_1",
"Accessories_2",
"Tools_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
699,
105,
121
],
"Stock/Stok": [
68,
183,
71
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Tires_1', 'Accessories_2', 'Tools_3'], 'Sales/Penjualan': [699, 105, 121], 'Price/Harga': [193.34, 83.85, 178.17], 'Category/Kategori': ['Automotive/Otomotif', 'Automotive/Otomotif', 'Automotive/Otomotif'], 'Discount/Diskon': [0.08, 0.21, 0.08], 'Stock/Stok': [68, 183, 71]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Compare discount time period | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.05,
0.09,
0.14
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
181.55,
183.82,
137.98
],
"Product/Produk": [
"Fruits_1",
"Snacks_2",
"Dairy_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
586,
879,
682
],
"Stock/Stok": [
75,
161,
174
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Fruits_1', 'Snacks_2', 'Dairy_3'], 'Sales/Penjualan': [586, 879, 682], 'Price/Harga': [181.55, 183.82, 137.98], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.05, 0.09, 0.14], 'Stock/Stok': [75, 161, 174]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Optimalkan pengunjung negara | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": [
147.47,
109.1,
98.65,
106.21,
89.72,
190.1,
198.44,
110.11,
174.24,
157.71,
198.41,
83.41,
131.77,
128.38,
140.52,
62.34,
170.6,
61.26,
52.11,
58.17,
148.89,
155.73,
166.45,
191.49,
62.3,
99.5,
54.25,
88.95,
53.35,
60.28
],
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": [
0.049600000000000005,
0.06620000000000001,
0.0709,
0.0694,
0.020900000000000002,
0.0267,
0.0639,
0.0341,
0.038,
0.08750000000000001,
0.0341,
0.0398,
0.0441,
0.0158,
0.0393,
0.0412,
0.0412,
0.0286,
0.0655,
0.0495,
0.06860000000000001,
0.08990000000000001,
0.0413,
0.0763,
0.024800000000000003,
0.0188,
0.0261,
0.027800000000000002,
0.07680000000000001,
0.036500000000000005
],
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": [
"2025-03-23",
"2025-03-24",
"2025-03-25",
"2025-03-26",
"2025-03-27",
"2025-03-28",
"2025-03-29",
"2025-03-30",
"2025-03-31",
"2025-04-01",
"2025-04-02",
"2025-04-03",
"2025-04-04",
"2025-04-05",
"2025-04-06",
"2025-04-07",
"2025-04-08",
"2025-04-09",
"2025-04-10",
"2025-04-11",
"2025-04-12",
"2025-04-13",
"2025-04-14",
"2025-04-15",
"2025-04-16",
"2025-04-17",
"2025-04-18",
"2025-04-19",
"2025-04-20",
"2025-04-21"
],
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": [
1646.04,
1788.65,
1821.35,
1882.7,
1820.9,
1781.82,
1762.55,
1702.37,
1647.74,
1476.48,
1420.16,
1345.21,
1391.81,
1499.9,
1608.16,
1546.25,
1626.12,
1790.72,
1753.63,
1736.55,
1665.52,
1553.72,
1413.28,
1253.74,
1299.75,
1152.54,
1168.46,
1207.52,
1286.17,
1392.67
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": [
287,
229,
304,
132,
144,
161,
179,
68,
198,
234,
68,
157,
190,
95,
104,
151,
191,
76,
247,
126,
91,
163,
227,
241,
204,
216,
128,
274,
115,
295
]
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Date/Tanggal': ['2025-03-23', '2025-03-24', '2025-03-25', '2025-03-26', '2025-03-27', '2025-03-28', '2025-03-29', '2025-03-30', '2025-03-31', '2025-04-01', '2025-04-02', '2025-04-03', '2025-04-04', '2025-04-05', '2025-04-06', '2025-04-07', '2025-04-08', '2025-04-09', '2025-04-10', '2025-04-11', '2025-04-12', '2025-04-13', '2025-04-14', '2025-04-15', '2025-04-16', '2025-04-17', '2025-04-18', '2025-04-19', '2025-04-20', '2025-04-21'], 'Revenue/Pendapatan': [1646.04, 1788.65, 1821.35, 1882.7, 1820.9, 1781.82, 1762.55, 1702.37, 1647.74, 1476.48, 1420.16, 1345.21, 1391.81, 1499.9, 1608.16, 1546.25, 1626.12, 1790.72, 1753.63, 1736.55, 1665.52, 1553.72, 1413.28, 1253.74, 1299.75, 1152.54, 1168.46, 1207.52, 1286.17, 1392.67], 'Visitors/Pengunjung': [287, 229, 304, 132, 144, 161, 179, 68, 198, 234, 68, 157, 190, 95, 104, 151, 191, 76, 247, 126, 91, 163, 227, 241, 204, 216, 128, 274, 115, 295], 'Conversion Rate/Rasio Konversi': [0.0496, 0.0662, 0.0709, 0.0694, 0.0209, 0.0267, 0.0639, 0.0341, 0.038, 0.0875, 0.0341, 0.0398, 0.0441, 0.0158, 0.0393, 0.0412, 0.0412, 0.0286, 0.0655, 0.0495, 0.0686, 0.0899, 0.0413, 0.0763, 0.0248, 0.0188, 0.0261, 0.0278, 0.0768, 0.0365], 'Avg. Order Value/Nilai Pesanan Rata2': [147.47, 109.1, 98.65, 106.21, 89.72, 190.1, 198.44, 110.11, 174.24, 157.71, 198.41, 83.41, 131.77, 128.38, 140.52, 62.34, 170.6, 61.26, 52.11, 58.17, 148.89, 155.73, 166.45, 191.49, 62.3, 99.5, 54.25, 88.95, 53.35, 60.28]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Analyze income city | {
"Age/Usia": [
70,
60,
54,
28,
39,
24,
54,
30,
30,
18,
53,
46,
34,
32,
40,
61,
34,
26,
67,
24,
18,
48,
49,
68,
48,
26,
39,
28,
50,
61,
44,
18,
54,
32,
70,
35,
23,
57,
66,
21,
45,
51,
50,
64,
36,
38,
23,
56,
34,
59,
69,
40,
24,
51,
47,
56,
20,
62,
70,
29,
30,
24,
52,
26,
24,
19,
38,
55,
67,
42,
58,
40,
43,
36,
23,
69,
57,
32,
36,
53,
36,
46
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060",
"CUST_0061",
"CUST_0062",
"CUST_0063",
"CUST_0064",
"CUST_0065",
"CUST_0066",
"CUST_0067",
"CUST_0068",
"CUST_0069",
"CUST_0070",
"CUST_0071",
"CUST_0072",
"CUST_0073",
"CUST_0074",
"CUST_0075",
"CUST_0076",
"CUST_0077",
"CUST_0078",
"CUST_0079",
"CUST_0080",
"CUST_0081",
"CUST_0082"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
9000,
6000,
7000,
4000,
7000,
5000,
4000,
9000,
4000,
1000,
5000,
6000,
9000,
4000,
10000,
2000,
4000,
3000,
9000,
8000,
1000,
1000,
5000,
7000,
3000,
2000,
6000,
3000,
8000,
2000,
5000,
8000,
9000,
6000,
2000,
2000,
4000,
5000,
8000,
6000,
7000,
4000,
6000,
8000,
8000,
5000,
5000,
9000,
10000,
2000,
6000,
7000,
9000,
7000,
9000,
1000,
5000,
5000,
1000,
5000,
1000,
4000,
3000,
10000,
10000,
2000,
9000,
9000,
8000,
3000,
9000,
10000,
5000,
6000,
9000,
9000,
3000,
5000,
10000,
2000,
4000,
10000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-12-09",
"2025-02-26",
"2025-04-11",
"2024-11-22",
"2024-10-23",
"2025-01-08",
"2024-05-06",
"2024-10-23",
"2025-01-24",
"2024-09-01",
"2025-03-25",
"2024-11-03",
"2024-04-21",
"2024-09-18",
"2025-01-04",
"2024-07-11",
"2024-12-18",
"2024-11-29",
"2025-02-27",
"2024-07-05",
"2024-06-14",
"2024-05-14",
"2025-03-02",
"2025-02-05",
"2024-06-08",
"2024-09-07",
"2025-03-17",
"2025-03-17",
"2025-01-04",
"2024-10-18",
"2024-09-17",
"2024-10-19",
"2024-08-11",
"2024-11-03",
"2024-06-27",
"2025-02-17",
"2024-06-16",
"2024-05-03",
"2024-09-06",
"2024-06-22",
"2024-06-01",
"2024-09-18",
"2025-03-09",
"2024-09-02",
"2025-02-08",
"2025-03-22",
"2024-11-24",
"2024-09-18",
"2025-04-03",
"2025-01-18",
"2024-09-23",
"2024-04-27",
"2025-01-07",
"2024-07-30",
"2025-02-01",
"2024-05-03",
"2024-11-12",
"2024-08-15",
"2024-08-30",
"2024-05-08",
"2024-11-29",
"2024-11-06",
"2024-05-23",
"2024-05-17",
"2024-07-18",
"2024-08-13",
"2024-06-04",
"2024-11-08",
"2024-09-29",
"2025-02-03",
"2024-05-28",
"2024-12-06",
"2024-04-29",
"2024-07-04",
"2024-10-24",
"2024-11-14",
"2024-09-09",
"2024-09-14",
"2024-12-17",
"2025-03-01",
"2024-06-06",
"2024-06-25"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
9,
74,
11,
35,
37,
78,
62,
85,
88,
49,
2,
66,
58,
30,
87,
48,
2,
91,
56,
53,
62,
64,
31,
42,
63,
54,
14,
42,
96,
85,
3,
24,
26,
41,
19,
70,
45,
41,
89,
28,
44,
100,
48,
78,
69,
84,
54,
99,
24,
22,
45,
98,
10,
27,
45,
2,
34,
100,
28,
90,
10,
43,
56,
76,
87,
42,
50,
37,
78,
81,
40,
47,
8,
82,
85,
75,
53,
50,
94,
19,
46,
69
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060', 'CUST_0061', 'CUST_0062', 'CUST_0063', 'CUST_0064', 'CUST_0065', 'CUST_0066', 'CUST_0067', 'CUST_0068', 'CUST_0069', 'CUST_0070', 'CUST_0071', 'CUST_0072', 'CUST_0073', 'CUST_0074', 'CUST_0075', 'CUST_0076', 'CUST_0077', 'CUST_0078', 'CUST_0079', 'CUST_0080', 'CUST_0081', 'CUST_0082'], 'Age/Usia': [70, 60, 54, 28, 39, 24, 54, 30, 30, 18, 53, 46, 34, 32, 40, 61, 34, 26, 67, 24, 18, 48, 49, 68, 48, 26, 39, 28, 50, 61, 44, 18, 54, 32, 70, 35, 23, 57, 66, 21, 45, 51, 50, 64, 36, 38, 23, 56, 34, 59, 69, 40, 24, 51, 47, 56, 20, 62, 70, 29, 30, 24, 52, 26, 24, 19, 38, 55, 67, 42, 58, 40, 43, 36, 23, 69, 57, 32, 36, 53, 36, 46], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan'], 'Income/Pendapatan': [9000, 6000, 7000, 4000, 7000, 5000, 4000, 9000, 4000, 1000, 5000, 6000, 9000, 4000, 10000, 2000, 4000, 3000, 9000, 8000, 1000, 1000, 5000, 7000, 3000, 2000, 6000, 3000, 8000, 2000, 5000, 8000, 9000, 6000, 2000, 2000, 4000, 5000, 8000, 6000, 7000, 4000, 6000, 8000, 8000, 5000, 5000, 9000, 10000, 2000, 6000, 7000, 9000, 7000, 9000, 1000, 5000, 5000, 1000, 5000, 1000, 4000, 3000, 10000, 10000, 2000, 9000, 9000, 8000, 3000, 9000, 10000, 5000, 6000, 9000, 9000, 3000, 5000, 10000, 2000, 4000, 10000], 'Loyalty Score/Skor Loyalitas': [9, 74, 11, 35, 37, 78, 62, 85, 88, 49, 2, 66, 58, 30, 87, 48, 2, 91, 56, 53, 62, 64, 31, 42, 63, 54, 14, 42, 96, 85, 3, 24, 26, 41, 19, 70, 45, 41, 89, 28, 44, 100, 48, 78, 69, 84, 54, 99, 24, 22, 45, 98, 10, 27, 45, 2, 34, 100, 28, 90, 10, 43, 56, 76, 87, 42, 50, 37, 78, 81, 40, 47, 8, 82, 85, 75, 53, 50, 94, 19, 46, 69], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-12-09', '2025-02-26', '2025-04-11', '2024-11-22', '2024-10-23', '2025-01-08', '2024-05-06', '2024-10-23', '2025-01-24', '2024-09-01', '2025-03-25', '2024-11-03', '2024-04-21', '2024-09-18', '2025-01-04', '2024-07-11', '2024-12-18', '2024-11-29', '2025-02-27', '2024-07-05', '2024-06-14', '2024-05-14', '2025-03-02', '2025-02-05', '2024-06-08', '2024-09-07', '2025-03-17', '2025-03-17', '2025-01-04', '2024-10-18', '2024-09-17', '2024-10-19', '2024-08-11', '2024-11-03', '2024-06-27', '2025-02-17', '2024-06-16', '2024-05-03', '2024-09-06', '2024-06-22', '2024-06-01', '2024-09-18', '2025-03-09', '2024-09-02', '2025-02-08', '2025-03-22', '2024-11-24', '2024-09-18', '2025-04-03', '2025-01-18', '2024-09-23', '2024-04-27', '2025-01-07', '2024-07-30', '2025-02-01', '2024-05-03', '2024-11-12', '2024-08-15', '2024-08-30', '2024-05-08', '2024-11-29', '2024-11-06', '2024-05-23', '2024-05-17', '2024-07-18', '2024-08-13', '2024-06-04', '2024-11-08', '2024-09-29', '2025-02-03', '2024-05-28', '2024-12-06', '2024-04-29', '2024-07-04', '2024-10-24', '2024-11-14', '2024-09-09', '2024-09-14', '2024-12-17', '2025-03-01', '2024-06-06', '2024-06-25']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Kelompokkan nilai pesanan rata-rata jenis kelamin | {
"Age/Usia": [
46,
39,
23,
18,
33,
41,
27,
66,
48,
69,
68,
68,
52,
37,
70,
46,
23,
58,
35,
21,
21,
25,
60,
25,
22,
23,
28,
69,
18,
28,
52,
67,
44,
28,
63,
32,
55,
67,
69,
66,
46,
30,
34,
43,
33,
23,
52,
36,
43,
67,
62,
65,
43,
25,
49,
26,
60,
26,
59,
39
],
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": [
"CUST_0001",
"CUST_0002",
"CUST_0003",
"CUST_0004",
"CUST_0005",
"CUST_0006",
"CUST_0007",
"CUST_0008",
"CUST_0009",
"CUST_0010",
"CUST_0011",
"CUST_0012",
"CUST_0013",
"CUST_0014",
"CUST_0015",
"CUST_0016",
"CUST_0017",
"CUST_0018",
"CUST_0019",
"CUST_0020",
"CUST_0021",
"CUST_0022",
"CUST_0023",
"CUST_0024",
"CUST_0025",
"CUST_0026",
"CUST_0027",
"CUST_0028",
"CUST_0029",
"CUST_0030",
"CUST_0031",
"CUST_0032",
"CUST_0033",
"CUST_0034",
"CUST_0035",
"CUST_0036",
"CUST_0037",
"CUST_0038",
"CUST_0039",
"CUST_0040",
"CUST_0041",
"CUST_0042",
"CUST_0043",
"CUST_0044",
"CUST_0045",
"CUST_0046",
"CUST_0047",
"CUST_0048",
"CUST_0049",
"CUST_0050",
"CUST_0051",
"CUST_0052",
"CUST_0053",
"CUST_0054",
"CUST_0055",
"CUST_0056",
"CUST_0057",
"CUST_0058",
"CUST_0059",
"CUST_0060"
],
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": [
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Other/Lainnya",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Other/Lainnya",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Other/Lainnya",
"Female/Perempuan",
"Female/Perempuan",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan",
"Male/Laki-laki",
"Male/Laki-laki",
"Female/Perempuan"
],
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": [
2000,
6000,
5000,
7000,
3000,
8000,
9000,
8000,
3000,
3000,
3000,
6000,
2000,
4000,
2000,
6000,
10000,
8000,
9000,
4000,
2000,
4000,
5000,
4000,
5000,
4000,
9000,
9000,
4000,
7000,
3000,
3000,
1000,
4000,
6000,
10000,
4000,
7000,
9000,
6000,
10000,
6000,
9000,
10000,
2000,
6000,
1000,
9000,
10000,
3000,
9000,
1000,
10000,
2000,
2000,
6000,
7000,
9000,
6000,
3000
],
"Last Purchase Date/Tanggal Pembelian Terakhir": [
"2024-12-11",
"2024-06-21",
"2024-10-11",
"2025-02-26",
"2024-08-03",
"2025-04-06",
"2024-05-07",
"2024-12-31",
"2025-03-26",
"2024-08-17",
"2024-08-11",
"2024-05-14",
"2024-09-12",
"2024-06-04",
"2025-03-20",
"2025-03-16",
"2025-01-11",
"2025-01-22",
"2024-07-04",
"2024-08-05",
"2025-02-05",
"2024-06-26",
"2024-08-17",
"2025-03-10",
"2024-09-02",
"2024-08-27",
"2025-02-05",
"2025-02-27",
"2024-05-08",
"2025-03-30",
"2024-12-21",
"2025-01-12",
"2024-11-09",
"2024-04-24",
"2025-04-12",
"2024-10-09",
"2025-03-30",
"2024-07-05",
"2024-08-08",
"2024-07-08",
"2025-01-30",
"2025-01-25",
"2025-01-26",
"2024-11-04",
"2024-09-09",
"2025-04-13",
"2024-06-17",
"2024-07-21",
"2024-05-08",
"2024-11-16",
"2025-01-31",
"2025-04-02",
"2024-05-16",
"2025-01-11",
"2025-01-17",
"2024-05-17",
"2025-03-24",
"2024-11-16",
"2025-01-28",
"2024-08-31"
],
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": [
93,
12,
79,
96,
14,
41,
14,
46,
15,
66,
71,
10,
43,
92,
91,
25,
30,
4,
60,
92,
29,
77,
79,
49,
35,
1,
9,
41,
56,
65,
61,
83,
18,
66,
19,
86,
33,
52,
51,
3,
64,
17,
68,
81,
33,
97,
53,
20,
72,
85,
20,
95,
27,
92,
25,
15,
95,
36,
50,
90
],
"Market Share/Pangsa Pasar": null,
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Customer ID/ID Pelanggan': ['CUST_0001', 'CUST_0002', 'CUST_0003', 'CUST_0004', 'CUST_0005', 'CUST_0006', 'CUST_0007', 'CUST_0008', 'CUST_0009', 'CUST_0010', 'CUST_0011', 'CUST_0012', 'CUST_0013', 'CUST_0014', 'CUST_0015', 'CUST_0016', 'CUST_0017', 'CUST_0018', 'CUST_0019', 'CUST_0020', 'CUST_0021', 'CUST_0022', 'CUST_0023', 'CUST_0024', 'CUST_0025', 'CUST_0026', 'CUST_0027', 'CUST_0028', 'CUST_0029', 'CUST_0030', 'CUST_0031', 'CUST_0032', 'CUST_0033', 'CUST_0034', 'CUST_0035', 'CUST_0036', 'CUST_0037', 'CUST_0038', 'CUST_0039', 'CUST_0040', 'CUST_0041', 'CUST_0042', 'CUST_0043', 'CUST_0044', 'CUST_0045', 'CUST_0046', 'CUST_0047', 'CUST_0048', 'CUST_0049', 'CUST_0050', 'CUST_0051', 'CUST_0052', 'CUST_0053', 'CUST_0054', 'CUST_0055', 'CUST_0056', 'CUST_0057', 'CUST_0058', 'CUST_0059', 'CUST_0060'], 'Age/Usia': [46, 39, 23, 18, 33, 41, 27, 66, 48, 69, 68, 68, 52, 37, 70, 46, 23, 58, 35, 21, 21, 25, 60, 25, 22, 23, 28, 69, 18, 28, 52, 67, 44, 28, 63, 32, 55, 67, 69, 66, 46, 30, 34, 43, 33, 23, 52, 36, 43, 67, 62, 65, 43, 25, 49, 26, 60, 26, 59, 39], 'Gender/Jenis Kelamin': ['Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Other/Lainnya', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Other/Lainnya', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Other/Lainnya', 'Female/Perempuan', 'Female/Perempuan', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan', 'Male/Laki-laki', 'Male/Laki-laki', 'Female/Perempuan'], 'Income/Pendapatan': [2000, 6000, 5000, 7000, 3000, 8000, 9000, 8000, 3000, 3000, 3000, 6000, 2000, 4000, 2000, 6000, 10000, 8000, 9000, 4000, 2000, 4000, 5000, 4000, 5000, 4000, 9000, 9000, 4000, 7000, 3000, 3000, 1000, 4000, 6000, 10000, 4000, 7000, 9000, 6000, 10000, 6000, 9000, 10000, 2000, 6000, 1000, 9000, 10000, 3000, 9000, 1000, 10000, 2000, 2000, 6000, 7000, 9000, 6000, 3000], 'Loyalty Score/Skor Loyalitas': [93, 12, 79, 96, 14, 41, 14, 46, 15, 66, 71, 10, 43, 92, 91, 25, 30, 4, 60, 92, 29, 77, 79, 49, 35, 1, 9, 41, 56, 65, 61, 83, 18, 66, 19, 86, 33, 52, 51, 3, 64, 17, 68, 81, 33, 97, 53, 20, 72, 85, 20, 95, 27, 92, 25, 15, 95, 36, 50, 90], 'Last Purchase Date/Tanggal Pembelian Terakhir': ['2024-12-11', '2024-06-21', '2024-10-11', '2025-02-26', '2024-08-03', '2025-04-06', '2024-05-07', '2024-12-31', '2025-03-26', '2024-08-17', '2024-08-11', '2024-05-14', '2024-09-12', '2024-06-04', '2025-03-20', '2025-03-16', '2025-01-11', '2025-01-22', '2024-07-04', '2024-08-05', '2025-02-05', '2024-06-26', '2024-08-17', '2025-03-10', '2024-09-02', '2024-08-27', '2025-02-05', '2025-02-27', '2024-05-08', '2025-03-30', '2024-12-21', '2025-01-12', '2024-11-09', '2024-04-24', '2025-04-12', '2024-10-09', '2025-03-30', '2024-07-05', '2024-08-08', '2024-07-08', '2025-01-30', '2025-01-25', '2025-01-26', '2024-11-04', '2024-09-09', '2025-04-13', '2024-06-17', '2024-07-21', '2024-05-08', '2024-11-16', '2025-01-31', '2025-04-02', '2024-05-16', '2025-01-11', '2025-01-17', '2024-05-17', '2025-03-24', '2024-11-16', '2025-01-28', '2024-08-31']})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Compare market share time period | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.28,
0.04,
0.23
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
26.36,
50.69,
172.84
],
"Product/Produk": [
"Dairy_1",
"Beverages_2",
"Fruits_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
941,
715,
129
],
"Stock/Stok": [
78,
96,
110
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Dairy_1', 'Beverages_2', 'Fruits_3'], 'Sales/Penjualan': [941, 715, 129], 'Price/Harga': [26.36, 50.69, 172.84], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.28, 0.04, 0.23], 'Stock/Stok': [78, 96, 110]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Comprehensive data analysis
analysis_result = df.describe(include='all')
print(analysis_result)
# Additional insights
print('\nTop performing items:')
print(df.nlargest(3, df.columns[1]))
print('\nTrend analysis:')
if 'Date/Tanggal' in df.columns:
df['Month/Bulan'] = pd.to_datetime(df['Date/Tanggal']).dt.month
monthly_trend = df.groupby('Month/Bulan').mean(numeric_only=True)
print(monthly_trend)
|
Calculate average order value gender | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": [
"Food/Makanan",
"Food/Makanan",
"Food/Makanan"
],
"City/Kota": null,
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": null,
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": [
0.27,
0.28,
0.08
],
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": null,
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": null,
"Longitude/Bujur": null,
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": null,
"Price/Harga": [
140.03,
118.54,
34.64
],
"Product/Produk": [
"Snacks_1",
"Beverages_2",
"Dairy_3"
],
"Region/Wilayah": null,
"Revenue/Pendapatan": null,
"Sales/Penjualan": [
287,
139,
621
],
"Stock/Stok": [
18,
151,
30
],
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'Product/Produk': ['Snacks_1', 'Beverages_2', 'Dairy_3'], 'Sales/Penjualan': [287, 139, 621], 'Price/Harga': [140.03, 118.54, 34.64], 'Category/Kategori': ['Food/Makanan', 'Food/Makanan', 'Food/Makanan'], 'Discount/Diskon': [0.27, 0.28, 0.08], 'Stock/Stok': [18, 151, 30]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# cumulative sum calculation
result = df['Category/Kategori'].cumsum()
print(result)
|
Segment price time period | {
"Age/Usia": null,
"Avg. Order Value/Nilai Pesanan Rata2": null,
"Category/Kategori": null,
"City/Kota": [
"Tokyo",
"Berlin",
"Sydney",
"London",
"São Paulo"
],
"Conversion Rate/Rasio Konversi": null,
"Country/Negara": [
"Japan",
"Germany",
"Australia",
"UK",
"Brazil"
],
"Customer ID/ID Pelanggan": null,
"Date/Tanggal": null,
"Discount/Diskon": null,
"Gender/Jenis Kelamin": null,
"Growth Rate/Pertumbuhan": [
-0.08,
0.12,
0.02,
0.04,
0.01
],
"Income/Pendapatan": null,
"Last Purchase Date/Tanggal Pembelian Terakhir": null,
"Latitude/Lintang": [
-40.4542,
0.07970000000000001,
-41.4007,
-45.3629,
64.7025
],
"Longitude/Bujur": [
92.6457,
53.7816,
-137.037,
-5.8395,
20.3322
],
"Loyalty Score/Skor Loyalitas": null,
"Market Share/Pangsa Pasar": [
0.14,
0.2,
0.33,
0.26,
0.4
],
"Price/Harga": null,
"Product/Produk": null,
"Region/Wilayah": [
"Asia",
"Europe",
"Oceania",
"Europe",
"South America"
],
"Revenue/Pendapatan": [
3319,
1729,
3170,
1230,
2884
],
"Sales/Penjualan": null,
"Stock/Stok": null,
"Visitors/Pengunjung": null
} | import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
# Load data
df = pd.DataFrame({'City/Kota': ['Tokyo', 'Berlin', 'Sydney', 'London', 'São Paulo'], 'Country/Negara': ['Japan', 'Germany', 'Australia', 'UK', 'Brazil'], 'Region/Wilayah': ['Asia', 'Europe', 'Oceania', 'Europe', 'South America'], 'Latitude/Lintang': [-40.4542, 0.0797, -41.4007, -45.3629, 64.7025], 'Longitude/Bujur': [92.6457, 53.7816, -137.037, -5.8395, 20.3322], 'Revenue/Pendapatan': [3319, 1729, 3170, 1230, 2884], 'Market Share/Pangsa Pasar': [0.14, 0.2, 0.33, 0.26, 0.4], 'Growth Rate/Pertumbuhan': [-0.08, 0.12, 0.02, 0.04, 0.01]})
# Basic data cleaning
df = df.dropna() # Remove missing values
df = df.drop_duplicates() # Remove duplicates
# Customer segmentation using KMeans
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
# Select features and scale
X = df[['Age/Usia', 'Income/Pendapatan', 'Loyalty Score/Skor Loyalitas']]
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# Cluster into 3 segments
kmeans = KMeans(n_clusters=3, random_state=42)
df['Segment/Segmen'] = kmeans.fit_predict(X_scaled)
# Visualize segments
fig = px.scatter_3d(df, x='Age/Usia', y='Income/Pendapatan',
z='Loyalty Score/Skor Loyalitas',
color='Segment/Segmen',
title='Customer Segments')
fig.show()
|
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