# Visualizing Data — Women's Fashion Catalog

## An Introduction to the dataset

The data set can be found [here](https://www.kaggle.com/code/mohamedaminesoltani/eda-e-commerce-women-fashion/data) on Kaggle. It consists of 7 columns and 30758 rows. The data type of all columns are strings and contains no NULL values. The dataset does contain strings labeled as `Nan`, which are placeholders for `np.nan`.

Make sure you download the `.csv` file and move it to the working directory of your Jupyter Notebook.

# Filtering and Pre-processing the data

Preprocessing data is always the first step in building any usable dataset. It helps eliminate all the unnecessary data that we won't be using. By preprocessing the data, we are essentially helping the program only look at relevant data.

### Importing the Required Libraries

```py
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
from wordcloud import WordCloud
```

### Loading data as a Pandas DataFrame

```py
dataset = pd.read_csv('FashionDataset.csv')  # Importing CSV file
print(dataset.head())
print(f"Size: {dataset.shape[0]}")  # Number of Rows
```

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660380397264/oWHQwXAu4.png align="left")

### Dropping Unnecessary Columns

```py
data_set_trim_1 = dataset.drop(['Deatils', "Sizes", 'Unnamed: 0'], axis=1)  # Since we only care about numeric data, for now, we can remove all the data we don't need (also, yes details is spelled like that in the dataset)
data_set_trim_1.head()
```

Output:

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660282673318/rlB-hu3fJ.png align="left")

### Pre-Processing the data

```py
data_set_trim_1["MRP"] = data_set_trim_1["MRP"].str.replace("Rs\n", "")  # 'Rs\n420' -> '420'
data_set_trim_1["Category"] = data_set_trim_1["Category"].str.replace("-Women", "")  # 'Watch-Women' -> 'Watch'
data_set_trim_1["Discount"] = data_set_trim_1["Discount"].str.replace("% off", "")  # '50% off' -> '50'
data_set_trim_1.head()
```

Output:

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660282864929/XFvy_qkvz.png align="left")

At this point, I thought the data would be converted to integers, but I was wrong all the data were still strings. To verify this, I ran the following one-liner

```py
type(data_set_trim_1.iloc[22]["SellPrice"])  #Noticing that SellPrice as well as MRP and Discount are Strings and not text
```

Output:

```python
str
```

Now I would convert all the strings into integers using the `.apply()` attribute and replace the string `Nan` values with actual `np.NaN` values

```py
data_set_trim_2 = data_set_trim_1.replace("Nan",np.nan)  # Replacing all "Nan" strings with np.NAN
data_set_trim_2.dropna(inplace=True, subset=['MRP', "BrandName", "SellPrice"])
print(data_set_trim_2.dtypes)  # All Columns are String DataType
data_set_trim_2[['MRP', 'SellPrice', "Discount"]] = data_set_trim_2[['MRP', 'SellPrice', "Discount"]].apply(pd.to_numeric)  # Changing Required Columns to integer
print(data_set_trim_2.dtypes)
```

Output

```python
BrandName    object
MRP          object
SellPrice    object
Discount     object
Category     object
dtype: object
BrandName    object
MRP           int64
SellPrice     int64
Discount      int64
Category     object
dtype: object
```

### Finalising the data

```py
f_data = data_set_trim_2  # Setting Final Data
print(f"Size: {f_data.shape[0]}")
```

we return with `22550` rows worth of complete data. Now we can start visualizing it ;)

## Accessing Basic Information

Let's start with something Simple. How many unique brands do we have in our dataset?

```py
f_data.nunique()["BrandName"]  # Number of Brands in Dataset
```

Output:

```python
177
```

That's a lot of brands! Let's get a deeper look at the data using the `.describe()` command

```py
f_data.describe().T
```

Output:

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660284465754/8pJbpuu_d.png align="left")

What about the most expensive Items?

```py
f_data[f_data.SellPrice == f_data.SellPrice.max()]  # Most Expensive Item(s)
```

Output:

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660284132392/IB33YWtrH.png align="left")

(Yikes! Those are some expensive watches)

What about the most expensive brands? Let's figure out the average price of any article from a brand

```py
f_data.groupby('BrandName')['SellPrice'].mean().sort_values(ascending=False).head()  # Mean Price of every Brand
```

Output:

```python
BrandName
just cavalli      18309.600000
coach             12616.769231
versus            11555.600000
ted baker         10031.111111
emporio armani     9423.509804
Name: SellPrice, dtype: float64
```

You can similarly find the cheapest brand by changing `ascending=False` to `True`

## Real Visualizations using Matplotlib and Seaborn

Let's start by plotting a heatmap of our data.

```py
sns.heatmap(f_data.corr(),annot=True,cmap='coolwarm',linewidths=0.2)
```

Output:

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660284776400/cDLJfnPVD.png align="left")

What about a plot which shows us the amount of items in each category?

```py
plt.figure(figsize=(20,7))  #setting the plot size
sns.countplot(f_data["Category"])
```

Output:

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660284847338/m6IKw-jwm.png align="left")

Here is a scatter plot of all the prices per category

```py
plt.figure(figsize=(20,7))
sns.scatterplot(f_data['MRP'],f_data['SellPrice'],hue=f_data['Category'])
```

Output:

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660285468095/7anRo55AH.png align="left")

Let's figure out which category is the most discounted

```py
def modify(d):
    if int(d) in range(0,41):
        return '0-40%'
    elif int(d) in range(41,71):
        return '40-70%'
    elif int(d) in range(71,101):
        return '<70%'
f_data['D_range'] = f_data['Discount'].apply(modify)  # adding a new column
f_data.head()
```

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660285572692/Y1UZ5JhBX.png align="left")

```py
plt.figure(figsize=(20,5))
sns.countplot(f_data['Category'],hue=f_data['D_range'])
```

Output:

![image.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660285630734/YcSj7qNRA.png align="left")

# Fun with WordClouds

We've had some interesting plots, but now let's have some fun with '✨word clouds✨

Since the Word Cloud Module takes a string as input we are going to be joining the various strings

```py
textCategory = " ".join(i for i in f_data['Category'])
textCompany = " ".join(i for i in f_data['BrandName'])
```

```py
word_cloud_company = WordCloud(collocations=False, background_color='black', width=1920, height=1080).generate(textCompany)
word_cloud_company.to_file('company.png')

word_cloud_category = WordCloud(collocations=False, background_color='black', width=1920, height=1080).generate(textCategory)
word_cloud_category.to_file('category.png')
```

Output:

![company.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660380939668/MkN9I9ojR.png align="left")

![category.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660380945084/lE5KBHDHE.png align="left")

How about using the sizes column we dropped while preprocessing

Let's make a word cloud of the Sizes and Details column!

#Processing the data

```py
only_size = dataset["Sizes"]
only_size = only_size.replace("Nan",np.nan)  # Replacing all "Nan" strings with np.NAN
only_size.dropna(inplace=True)
only_size = only_size.str.replace("Size:", "")
only_size = only_size.str.replace(",", " ")
only_size.head()
```

```py
textSize = " ".join(i for i in only_size)
word_cloud_size = WordCloud(collocations=False, background_color='black', width=1920, height=1080).generate(textSize)
word_cloud_size.to_file('size.png')
```

![size.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660381595693/C1kfWTJKn.png align="left")

Similarily we can do the same thing with the details column

```py
only_details = dataset["Deatils"]
only_details = only_details.replace("Nan",np.nan)  # Replacing all "Nan" strings with np.NAN
only_details.dropna(inplace=True)
only_details = only_details.str.replace("Size:", "")
only_details = only_details.str.replace(",", " ")
only_details.head()
```

```py
textDetail = " ".join(i for i in only_details)
word_cloud_detail = WordCloud(collocations=False, background_color='black', width=1920, height=1080).generate(textDetail)
word_cloud_detail.to_file('detail.png')
```

![detail.png](https://cdn.hashnode.com/res/hashnode/image/upload/v1660381647839/9sTkhiukM.png align="left")

# Conclusion

Well, that's all, folks! I don't really have a conclusion, but DATA IS SO COOL!

You can find the Google Colab Link [here](https://colab.research.google.com/drive/1GOPw9ZLgMxADKj2D3nDdnxzEf115D6jD?usp=sharing)