Spaces:
Runtime error
Runtime error
File size: 6,226 Bytes
0c76799 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | """
Analysis functions for sales data.
"""
import pandas as pd
import numpy as np
def get_top_models(sales_df, metric='Profit', n=10):
"""
Get top N models by profit or quantity.
"""
if sales_df.empty:
return pd.DataFrame()
if metric == 'Profit':
result = sales_df.groupby('Model')['Profit'].sum().sort_values(ascending=False).head(n)
elif metric == 'Quantity':
result = sales_df.groupby('Model')['Quantity Sold'].sum().sort_values(ascending=False).head(n)
else:
result = sales_df.groupby('Model')[metric].sum().sort_values(ascending=False).head(n)
return result.reset_index()
def get_top_dealers(sales_df, metric='Profit', n=10):
"""
Get top N dealers by profit or quantity.
"""
if sales_df.empty:
return pd.DataFrame()
if metric == 'Profit':
result = sales_df.groupby('Dealer ID')['Profit'].sum().sort_values(ascending=False).head(n)
elif metric == 'Quantity':
result = sales_df.groupby('Dealer ID')['Quantity Sold'].sum().sort_values(ascending=False).head(n)
else:
result = sales_df.groupby('Dealer ID')[metric].sum().sort_values(ascending=False).head(n)
return result.reset_index()
def get_monthly_trends(sales_df):
"""
Calculate monthly sales trends.
"""
if sales_df.empty:
return pd.DataFrame()
monthly = sales_df.groupby(['Year', 'Month_Num', 'Month_Name']).agg({
'Profit': 'sum',
'Quantity Sold': 'sum'
}).reset_index()
monthly['Date'] = pd.to_datetime(monthly['Year'].astype(str) + '-' + monthly['Month_Num'].astype(str) + '-01')
monthly = monthly.sort_values('Date')
return monthly
def get_yearly_summary(sales_df):
"""
Get yearly summary statistics.
"""
if sales_df.empty:
return pd.DataFrame()
yearly = sales_df.groupby('Year').agg({
'Profit': 'sum',
'Quantity Sold': 'sum',
'Avg_Profit_Per_Unit': 'mean'
}).reset_index()
yearly['Profit_Growth'] = yearly['Profit'].pct_change() * 100
yearly['Units_Growth'] = yearly['Quantity Sold'].pct_change() * 100
return yearly
def get_model_performance(sales_df):
"""
Analyze performance by model.
"""
if sales_df.empty:
return pd.DataFrame()
performance = sales_df.groupby('Model').agg({
'Profit': 'sum',
'Quantity Sold': 'sum',
'Avg_Profit_Per_Unit': 'mean',
'Dealer ID': 'nunique'
}).rename(columns={'Dealer ID': 'Num_Dealers'}).reset_index()
performance['Profit_Per_Dealer'] = performance['Profit'] / performance['Num_Dealers']
performance['Units_Per_Dealer'] = performance['Quantity Sold'] / performance['Num_Dealers']
return performance.sort_values('Profit', ascending=False)
def get_seasonal_analysis(sales_df):
"""
Analyze seasonal patterns by month.
"""
if sales_df.empty:
return pd.DataFrame()
seasonal = sales_df.groupby('Month_Name').agg({
'Profit': 'mean',
'Quantity Sold': 'mean'
}).reset_index()
# Order months correctly
month_order = ['January', 'February', 'March', 'April', 'May', 'June',
'July', 'August', 'September', 'October', 'November', 'December']
seasonal['Month_Order'] = seasonal['Month_Name'].apply(lambda x: month_order.index(x) if x in month_order else 0)
seasonal = seasonal.sort_values('Month_Order')
return seasonal
def get_recalls_impact(recalls_df, sales_df):
"""
Analyze potential impact of recalls on sales.
"""
if recalls_df.empty or sales_df.empty:
return pd.DataFrame()
recall_by_model = recalls_df.groupby('Model').agg({
'Units': 'sum',
'System_Affected': lambda x: list(x)
}).reset_index()
# Merge with sales performance
model_performance = get_model_performance(sales_df)
impact_analysis = model_performance.merge(
recall_by_model,
on='Model',
how='left'
)
impact_analysis['Recall_Units'] = impact_analysis['Units'].fillna(0)
impact_analysis['Recall_Ratio'] = impact_analysis['Recall_Units'] / impact_analysis['Quantity Sold']
impact_analysis['Recall_Ratio'] = impact_analysis['Recall_Ratio'].fillna(0)
return impact_analysis.sort_values('Recall_Ratio', ascending=False)
def get_dealer_performance_with_details(sales_df, dealers_df):
"""
Get detailed dealer performance including location data.
"""
if sales_df.empty or dealers_df.empty:
return pd.DataFrame()
sales_by_dealer = sales_df.groupby('Dealer ID').agg({
'Profit': 'sum',
'Quantity Sold': 'sum'
}).reset_index()
result = sales_by_dealer.merge(
dealers_df[['Dealer ID', 'Dealer Name', 'City', 'State', 'Country', 'Latitude', 'Longitude']],
on='Dealer ID',
how='left'
)
return result
def get_model_ranking_over_time(sales_df, n_models=5):
"""
Track top models' performance over time.
"""
if sales_df.empty:
return pd.DataFrame()
# Get top N models overall
top_models = sales_df.groupby('Model')['Profit'].sum().nlargest(n_models).index.tolist()
# Filter and aggregate monthly
filtered = sales_df[sales_df['Model'].isin(top_models)]
monthly = filtered.groupby(['Year', 'Month_Num', 'Model'])['Profit'].sum().reset_index()
monthly['Date'] = pd.to_datetime(monthly['Year'].astype(str) + '-' + monthly['Month_Num'].astype(str) + '-01')
return monthly
def get_quarterly_breakdown(sales_df):
"""
Get quarterly breakdown of sales.
"""
if sales_df.empty:
return pd.DataFrame()
quarterly = sales_df.groupby('Year_Quarter').agg({
'Profit': 'sum',
'Quantity Sold': 'sum'
}).reset_index()
# Extract year and quarter for sorting
quarterly[['Year', 'Quarter']] = quarterly['Year_Quarter'].str.split('-Q', expand=True)
quarterly['Year'] = quarterly['Year'].astype(int)
quarterly['Quarter'] = quarterly['Quarter'].astype(int)
quarterly = quarterly.sort_values(['Year', 'Quarter'])
return quarterly
|