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Grouping Sales Data By Month

Let's say you have sales data stored as a list of dictionaries, where each dictionary represents a sale and contains details about the fruit, the quantity sold, and the month of sale. For example:

sales_data = [
    {"fruit": "apple", "quantity": 5, "month": "January"},
    {"fruit": "banana", "quantity": 4, "month": "January"},
    {"fruit": "apple", "quantity": 7, "month": "February"},
    ...
]

To report the sum of sales for each fruit, grouped by month, you can use Python's built-in data types and a for loop. Here's a sample Python code to achieve that:


# Initialize an empty dictionary to store the results
monthly_sales = {}

# Loop over the sales data
for sale in sales_data:
    # Get the month and fruit from the sale
    month = sale["month"]
    fruit = sale["fruit"]
    quantity = sale["quantity"]

    # If the month is not in the monthly sales yet, add it
    if month not in monthly_sales:
        monthly_sales[month] = {}

    # If the fruit is not in the month's sales yet, add it
    if fruit not in monthly_sales[month]:
        monthly_sales[month][fruit] = 0

    # Add the quantity to the appropriate fruit's sales for the month
    monthly_sales[month][fruit] += quantity

The variable monthly_sales is a dictionary where each key is a month, and each value is another dictionary. In the nested dictionary, each key is a fruit, and each value is the sum of the quantities sold in that month.

This will give you the total quantity of each fruit sold in each month. You can then use this data to create a bar chart with month on the X-axis and quantity on the Y-axis for each fruit.

Analyzing Sales Data for Specific Fruits Over a Six Month Period

Assume we're looking at a six month period (from January to June), and we're interested in three specific fruits: "apple", "banana", and "cherry". Here's how we might adapt our Python code to handle this specific case:


# Specify the fruits and months we're interested in
fruits = ["apple", "banana", "cherry"]
months = ["January", "February", "March", "April", "May", "June"]

# Initialize an empty dictionary to store the results
monthly_sales = {month: {fruit: 0 for fruit in fruits} for month in months}

# Loop over the sales data
for sale in sales_data:
    # Get the month and fruit from the sale
    month = sale["month"]
    fruit = sale["fruit"]
    quantity = sale["quantity"]

    # Only add to the count if the month and fruit are in our lists
    if month in months and fruit in fruits:
        monthly_sales[month][fruit] += quantity

In this example, we first initialize monthly_sales with all the months and fruits we're interested in, setting the initial quantity sold to 0 for each fruit in each month. Then, when looping through sales_data, we only add to the quantity sold if the sale's month and fruit are in our specified lists. This will give us the total quantity of each selected fruit sold in each selected month.

Visualizing Sales Data Using a Bar Chart

To visualize the sales data using a bar chart, you can use the pandas and matplotlib libraries in Python. Pandas makes it easy to create a DataFrame from your dictionary, and matplotlib lets you plot that data. Here's how you can do it:


import pandas as pd
import matplotlib.pyplot as plt

# Convert the dictionary to a pandas DataFrame
df = pd.DataFrame(monthly_sales)

# Transpose the DataFrame so that months are on the x-axis and fruits are individual bars
df = df.transpose()

# Plot the DataFrame as a bar chart
df.plot(kind='bar', stacked=False)

# Show the plot
plt.show()

In the first line of this script, we convert the monthly_sales dictionary to a DataFrame. We then transpose the DataFrame so that the months are on the x-axis and the fruits are individual bars. We plot the DataFrame as a bar chart and finally, we display the plot. This will give you a visual representation of the total quantity of each selected fruit sold in each selected month.

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