Pandas is a powerful Python library for data analysis and manipulation, widely used in various domains, including the world of fashion. Utilizing Pandas, fashion experts and developers can spot trends, patterns, and insights by analyzing datasets related to the fashion industry. In this article, we’ll delve into the powerful Pandas functions, **mean** and **sum**, and their applications in the analysis of fashion data.

These functions can be very helpful in discovering important information about fashion items like sales, price trends, product rating, and more. By calculating the mean and sum of various attributes, we can draw valuable insights to make informed decisions about styling and fashion trends.

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## The solution to the problem

To demonstrate the usage of pandas **mean** and **sum** functions, let’s assume we have a dataset containing details about different fashion items like their style, colors, price, and rating. We’ll import this dataset into a pandas DataFrame and begin our analysis using the mean and sum functions.

import pandas as pd # Read data from a CSV file and load it into a DataFrame data = pd.read_csv('fashion_items.csv') # Calculate mean and sum of the price column mean_price = data['price'].mean() sum_price = data['price'].sum() print('Mean price:', mean_price) print('Total price:', sum_price)

## Step-by-step explanation of the code

- First, we import the pandas library with the alias ‘pd’.
- Next, we read the data from a CSV file named ‘fashion_items.csv’ and load it into a DataFrame named ‘data’ using the pd.read_csv function. The dataset contains information about various fashion items.
- Then, we calculate the mean price of all the fashion items using the mean() function applied to the ‘price’ column of the DataFrame. This value is stored in a variable named ‘mean_price’.
- Similarly, we calculate the total price of all fashion items by calling the sum() function on the ‘price’ column. This value is stored in a variable named ‘sum_price’.
- Finally, we print the calculated mean and total prices of the fashion items.

## Related libraries and functions in Pandas

There are a plethora of libraries and functions that complement the use of pandas for data analysis in the fashion industry. Some of these useful functions besides **mean** and **sum** include:

## Pandas groupby function

The **groupby** function is particularly helpful for aggregating data based on specific columns. For example, if we want to analyze the mean and total price of fashion items for each style present in our dataset.

# Group data by style and calculate mean and sum of the price grouped_data = data.groupby('style')['price'].agg(['mean', 'sum']) print(grouped_data)

## Pandas merge function

The **merge** function allows us to combine two DataFrames based on a common column. For instance, suppose we have a separate dataset containing information about the popularity of each style. By merging both DataFrames, we can transform this information into valuable insights.

# Import data related to style popularity style_popularity_data = pd.read_csv('style_popularity.csv') # Merge the original data and style_popularity_data based on the 'style' column merged_data = pd.merge(data, style_popularity_data, on='style') print(merged_data.head())

By understanding and implementing these powerful functions within the Pandas library, fashion experts and developers can make informed decisions and analyze the latest trends and styles with ease.