# Importing the pandas and plotly modules import pandas as pd import plotly.express as px # Reading in the sales data df = pd.read_csv('sales_data.csv', parse_dates=['date']) # Take a look at the first datapoints df.head()

df.info()

df.describe(include='all', datetime_is_numeric=True)

# What are the total sales for each payment method? fig = px.histogram(df, x='payment', y='total', color='client_type', # text_auto=True, title='Total sales by payment mothod', labels={ 'client_type': 'Client', 'payment': 'Payment method' }) fig.show()

# What is the average unit price for each product line? avg_unit = df.groupby(['product_line', 'client_type'], as_index=False)[['unit_price']].mean().round(2) fig = px.bar(avg_unit, x='product_line', y='unit_price', color='client_type', barmode='group', # text_auto=True, title='Average unit price by product line', labels={ 'client_type': 'Client', 'product_line': 'Product line', 'unit_price': 'Avg. unit price' }) fig.show()

# Grouping by client_type and obtaining the total sum of sales client_sales = df.groupby('client_type', as_index=False)[['total']].sum() # Adding an 'perc_sales' column client_sales['perc_sales'] = (client_sales.total / client_sales.total.sum()) * 100 # Separating percentages by client_type and rounding retail_sales_per = client_sales['perc_sales'][0].round(2) wholesale_sales_per = client_sales['perc_sales'][1].round(2) # Obtaining the normalized count percentages of client_type client_orders = df.client_type.value_counts(normalize=True) # Printing the results print('Retail accounts for: {}% of total sales and {}% of total orders'.format( retail_sales_per, (client_orders[0]*100).round(2))) print('Wholesale accounts for: {}% of total sales and {}% of total orders'.format( wholesale_sales_per, (client_orders[1]*100).round(2)))

fig = px.histogram(df, x='client_type', color='client_type') fig.update_layout(showlegend=False) fig.show()

fig = px.pie(df, values='total', names='client_type') fig.update_traces(textposition='inside', textinfo='percent+label', showlegend=False) fig.show()

fig = px.histogram(df, x='total', y='product_line', color='client_type', barmode='group', # text_auto=True, title='Total sales by product line', labels={ 'client_type': 'Client', 'product_line': 'product line' }) fig.show()

fig = px.histogram(df, x='quantity', y='product_line', color='client_type', barmode='group', # text_auto=True, title='Sales volume by product line', labels={ 'client_type': 'Client', 'product_line': 'product' }) fig.show()

fig = px.histogram(df, y='total', x='product_line', facet_col='warehouse', color='client_type', barmode='group', title='Warehouse total sales by product line') fig.show()

fig = px.scatter(df, x='date', y='total', color='client_type', opacity=0.5, marginal_x='histogram', title='Client sales by date', labels={'client_type': 'client'}) fig.show()