seab

Seaborn ¶

Seaborn is an advanced data visualization tool and it is used for advanced visualization. We are going to Cover these Plots :

1. relplot(relational plot) - scatter line

2. heatmap

3. regression - lmplot

4. catplot(categorical plot) - strip swarm box violin boxen point bar count

5. displot - histplot kdeplot

Install Seaborn - ¶

pip install seaborn

Import Libraries ¶

In [1]:

importpandasaspd importmatplotlib.pyplotasplt importseabornassns

1. relplot ¶

relplot is a high-level plotting function in Seaborn used to visualize relationships between numerical variables.

It shows how one numeric variable changes with another numeric variable.

relplot offers two plots.

1. scatter plot (default)

2. line plot

In [4]:

df = pd.read_csv('relplot data.csv') df.head()

Out[4]:

	Order_ID	Year	Month	City	Customer_Type	Order_Value_K	Delivery_Time_Min	Customer_Rating	Distance_KM	Day_Type	Weather	Festival_Period	Time_Slot
0	1001	2023	Jan	Delhi	Premium	1.2	18	4.8	3	Weekday	Clear	No	Lunch
1	1002	2023	Jan	Delhi	Regular	0.5	25	4.0	5	Weekday	Clear	No	Dinner
2	1003	2023	Jan	Delhi	Premium	1.5	22	4.5	4	Weekend	Rainy	Yes	Dinner
3	1004	2023	Jan	Pune	Regular	0.4	28	3.8	6	Weekday	Rainy	No	Lunch
4	1005	2023	Jan	Pune	Premium	1.0	20	4.6	4	Weekend	Clear	Yes	Dinner

Finding Relationship Between Delivery Time and Customer Rating

In [3]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating') plt.show()

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Delivery time and Customer rating has negative relationship

Segmentation - hue

It uses different colors to separate categories in the data.

In [6]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', hue='Customer_Type') plt.show()

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Regular Customers often get late deliveries rather than premium customer , which is the reason , regular customers give very less rating rather than premium customers.

hue - palette

In [7]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', hue='Customer_Type', palette = { 'Premium':'Green' , 'Regular':'red' }) plt.show()

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In [10]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', hue='Customer_Type', palette = 'Reds') plt.show()

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size ¶

It uses different sizes to separate categories in the data.

In [12]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', hue='Customer_Type', palette = { 'Premium':'Green' , 'Regular':'red' }, size='Customer_Rating') plt.show()

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size - sizes

In [13]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', hue='Customer_Type', palette = { 'Premium':'Green' , 'Regular':'red' }, size='Customer_Rating', sizes=(50,100)) plt.show()

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Segmentation - style ¶

It uses different sizes to separate categories in the data.

In [20]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', hue='Customer_Type', palette = { 'Premium':'Green' , 'Regular':'red' }, size='Customer_Rating', sizes=(50,100), style='Customer_Type') # plt.legend( # bbox_to_anchor=(1, 1), # move legend outside # loc='upper left' # ) plt.show()

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Segmentation - row ¶

It uses different rows to separate categories in the data.

In [23]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', hue='Customer_Type', palette = { 'Premium':'Green' , 'Regular':'red' }, size='Customer_Rating', sizes=(50,100), style='Customer_Type', row='Time_Slot') plt.show()

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Segmentation - col ¶

It uses different cols to separate categories in the data.

In [24]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', hue='Customer_Type', palette = { 'Premium':'Green' , 'Regular':'red' }, size='Customer_Rating', sizes=(50,100), style='Customer_Type', col='Day_Type') plt.show()

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In [25]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', hue='Customer_Type', palette = { 'Premium':'Green' , 'Regular':'red' }, size='Customer_Rating', sizes=(50,100), style='Customer_Type', col='Day_Type', row='Time_Slot') plt.show()

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2. relplot - line ¶

line plot is used to find pattern over time.

by default line plot uses average to show the line.

In [27]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', kind='line') plt.show()

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In [29]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', kind='line', estimator='sum') plt.show()

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An Example : ¶

In [31]:

df = pd.read_excel('Adidas US Sales Datasets.xlsx') df['total'] = df['Price per Unit'] * df['Units Sold'] df

Out[31]:

	Retailer	Invoice Date	Region	State	City	Product	Price per Unit	Units Sold	Sales Method	total
0	Foot Locker	2020-01-01	Northeast	New York	New York	Men's Street Footwear	50.0	1200	In-store	60000.0
1	Foot Locker	2020-01-02	Northeast	New York	New York	Men's Athletic Footwear	50.0	1000	In-store	50000.0
2	Foot Locker	2020-01-03	Northeast	New York	New York	Women's Street Footwear	40.0	1000	In-store	40000.0
3	Foot Locker	2020-01-04	Northeast	New York	New York	Women's Athletic Footwear	45.0	850	In-store	38250.0
4	Foot Locker	2020-01-05	Northeast	New York	New York	Men's Apparel	60.0	900	In-store	54000.0
...	...	...	...	...	...	...	...	...	...	...
9643	Foot Locker	2021-01-24	Northeast	New Hampshire	Manchester	Men's Apparel	50.0	64	Outlet	3200.0
9644	Foot Locker	2021-01-24	Northeast	New Hampshire	Manchester	Women's Apparel	41.0	105	Outlet	4305.0
9645	Foot Locker	2021-02-22	Northeast	New Hampshire	Manchester	Men's Street Footwear	41.0	184	Outlet	7544.0
9646	Foot Locker	2021-02-22	Northeast	New Hampshire	Manchester	Men's Athletic Footwear	42.0	70	Outlet	2940.0
9647	Foot Locker	2021-02-22	Northeast	New Hampshire	Manchester	Women's Street Footwear	29.0	83	Outlet	2407.0

9648 rows × 10 columns

In [32]:

sns.relplot(data=df, x='Units Sold', y='total') plt.show()

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In [34]:

sns.relplot(data=df, x='Units Sold', y='total', kind='line', marker='o') plt.show()

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In [35]:

sns.relplot(data=df, x='Units Sold', y='total', kind='line', marker='o', estimator='sum') plt.show()

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In [36]:

df = pd.read_csv('relplot data.csv') df.head()

Out[36]:

	Order_ID	Year	Month	City	Customer_Type	Order_Value_K	Delivery_Time_Min	Customer_Rating	Distance_KM	Day_Type	Weather	Festival_Period	Time_Slot
0	1001	2023	Jan	Delhi	Premium	1.2	18	4.8	3	Weekday	Clear	No	Lunch
1	1002	2023	Jan	Delhi	Regular	0.5	25	4.0	5	Weekday	Clear	No	Dinner
2	1003	2023	Jan	Delhi	Premium	1.5	22	4.5	4	Weekend	Rainy	Yes	Dinner
3	1004	2023	Jan	Pune	Regular	0.4	28	3.8	6	Weekday	Rainy	No	Lunch
4	1005	2023	Jan	Pune	Premium	1.0	20	4.6	4	Weekend	Clear	Yes	Dinner

In [38]:

sns.relplot(data=df, x='Delivery_Time_Min', y='Customer_Rating', kind='line', hue='Customer_Type') plt.show()

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Let's Explore With Examples ¶

In [42]:

df = pd.read_excel('Adidas US Sales Datasets.xlsx') df['total'] = df['Price per Unit'] * df['Units Sold'] df['Invoice Date'] = pd.to_datetime(df['Invoice Date'],format='%Y-%m-%d') df['Year'] = df['Invoice Date'].dt.year df.head()

Out[42]:

	Retailer	Invoice Date	Region	State	City	Product	Price per Unit	Units Sold	Sales Method	total	Year
0	Foot Locker	2020-01-01	Northeast	New York	New York	Men's Street Footwear	50.0	1200	In-store	60000.0	2020
1	Foot Locker	2020-01-02	Northeast	New York	New York	Men's Athletic Footwear	50.0	1000	In-store	50000.0	2020
2	Foot Locker	2020-01-03	Northeast	New York	New York	Women's Street Footwear	40.0	1000	In-store	40000.0	2020
3	Foot Locker	2020-01-04	Northeast	New York	New York	Women's Athletic Footwear	45.0	850	In-store	38250.0	2020
4	Foot Locker	2020-01-05	Northeast	New York	New York	Men's Apparel	60.0	900	In-store	54000.0	2020

In [45]:

sns.relplot(data=df, x='Units Sold', y='total', ) plt.show()

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Region Wise

In [46]:

sns.relplot(data=df, x='Units Sold', y='total', hue='Region') plt.show()

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In [52]:

sns.relplot(data=df, x='Units Sold', y='total', col='Region', col_wrap=2, hue='Region') plt.show()

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Yearly Average Sales ¶

In [50]:

sns.relplot(data=df, x='Year', y='total', kind='line') plt.show()

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In [51]:

sns.relplot(data=df, x='Year', y='total', kind='line', hue='Region') plt.show()

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Total Sales ¶

In [53]:

sns.relplot(data=df, x='Year', y='total', kind='line', hue='Region', estimator='sum') plt.show()

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📊 Correlation Heatmap — Notebook Explanation ¶

📌 What is Correlation? ¶

Correlation measures how strongly two numerical variables are related.

• Value range: -1 to +1

• +1 → strong positive relationship

• -1 → strong negative relationship

• 0 → no relationship

---

📌 What is a Correlation Heatmap? ¶

A correlation heatmap is a visual way to represent correlation values using colors.

Instead of reading numbers, we interpret color intensity.

---

🎨 How to Read Colors in a Correlation Heatmap ¶

Color Meaning	Interpretation
Dark positive color	Strong positive correlation
Dark negative color	Strong negative correlation
Light / neutral color	Weak or no correlation

👉 Darker the color = stronger the relationship

---

📌 Why Use a Correlation Heatmap? ¶

• To quickly identify relationships

• To detect multicollinearity

• To find important features in data analysis

---

📌 Example Interpretation ¶

If a heatmap shows:

• Hours_Studied vs Marks → dark positive color → More study hours leads to higher marks

• Speed vs Travel_Time → dark negative color → Higher speed reduces travel time

---

🧠 Key Takeaway ¶

A correlation heatmap visually shows how strongly and in which direction numerical variables are related using color intensity.

---

In [54]:

df = pd.read_csv('Sleep_health_and_lifestyle_dataset.csv') df

Out[54]:

	Person ID	Gender	Age	Occupation	Sleep Duration	Quality of Sleep	Physical Activity Level	Stress Level	BMI Category	Blood Pressure	Heart Rate	Daily Steps	Sleep Disorder
0	1	Male	27	Software Engineer	6.1	6	42	6	Overweight	126/83	77	4200	NaN
1	2	Male	28	Doctor	6.2	6	60	8	Normal	125/80	75	10000	NaN
2	3	Male	28	Doctor	6.2	6	60	8	Normal	125/80	75	10000	NaN
3	4	Male	28	Sales Representative	5.9	4	30	8	Obese	140/90	85	3000	Sleep Apnea
4	5	Male	28	Sales Representative	5.9	4	30	8	Obese	140/90	85	3000	Sleep Apnea
...	...	...	...	...	...	...	...	...	...	...	...	...	...
369	370	Female	59	Nurse	8.1	9	75	3	Overweight	140/95	68	7000	Sleep Apnea
370	371	Female	59	Nurse	8.0	9	75	3	Overweight	140/95	68	7000	Sleep Apnea
371	372	Female	59	Nurse	8.1	9	75	3	Overweight	140/95	68	7000	Sleep Apnea
372	373	Female	59	Nurse	8.1	9	75	3	Overweight	140/95	68	7000	Sleep Apnea
373	374	Female	59	Nurse	8.1	9	75	3	Overweight	140/95	68	7000	Sleep Apnea

374 rows × 13 columns

In [60]:

cdf = df[ ['Sleep Duration','Quality of Sleep','Stress Level','Age'] ].corr() cdf

Out[60]:

	Sleep Duration	Quality of Sleep	Stress Level	Age
Sleep Duration	1.000000	0.883213	-0.811023	0.344709
Quality of Sleep	0.883213	1.000000	-0.898752	0.473734
Stress Level	-0.811023	-0.898752	1.000000	-0.422344
Age	0.344709	0.473734	-0.422344	1.000000

In [61]:

sns.heatmap(data=cdf,annot=True) plt.show()

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regression - lmplot ¶

In [62]:

df = pd.read_excel('Adidas US Sales Datasets.xlsx') df['total'] = df['Price per Unit'] * df['Units Sold'] df.head()

Out[62]:

	Retailer	Invoice Date	Region	State	City	Product	Price per Unit	Units Sold	Sales Method	total
0	Foot Locker	2020-01-01	Northeast	New York	New York	Men's Street Footwear	50.0	1200	In-store	60000.0
1	Foot Locker	2020-01-02	Northeast	New York	New York	Men's Athletic Footwear	50.0	1000	In-store	50000.0
2	Foot Locker	2020-01-03	Northeast	New York	New York	Women's Street Footwear	40.0	1000	In-store	40000.0
3	Foot Locker	2020-01-04	Northeast	New York	New York	Women's Athletic Footwear	45.0	850	In-store	38250.0
4	Foot Locker	2020-01-05	Northeast	New York	New York	Men's Apparel	60.0	900	In-store	54000.0

In [69]:

sns.lmplot(data=df, x='Units Sold', y='total', scatter_kws={'color': 'blue'}, line_kws={'color': 'red'}, col='Region', col_wrap=2 ) plt.show()

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