16. Matplotlib For Data Visualization

400KB

Adidas US Sales Datasets.xlsx

Open

24KB

Sleep_health_and_lifestyle_dataset.csv

Open

Matplotib is a library that we use for data visualization. We are going to cover following visualization in Matplotlib :

1. Scatter Plot

2. line chart - plot

3. bar chart

4. pie chart

5. histogram

6. stackplot

7. boxplot

Install Matplotlib

pip install matplotlib

Let's Understand Matplotlib first , with an example :

import pandas as pd
import matplotlib.pyplot as plt
month = ['jan','feb','mar','apr']
sale = [100,200,50,400]

plt.plot(month,sale)  # a line chart
plt.show()

Note : From this graph we can see the lowest dip in sales was in march and the highest was in apr

1. Scatter Plot

A scatter plot is a type of graph that shows the relationship between two numerical variables.

import matplotlib.pyplot as plt
month = ['jan','feb','mar','apr']
sale = [100,200,50,400]

plt.scatter(month,sale,color='red')
plt.title('Monthly Sales') # title of the graph
plt.xlabel('Month')  # x axis name
plt.ylabel('Sales')  # y axis name
plt.grid()   # for grid
plt.show()

Multiple Values in Scatter Plot

month = ['jan','feb','mar','apr']
isale = [100,200,50,400]
ssale = [200,400,100,700]

plt.scatter(month,isale,label='Iphone')
plt.scatter(month,ssale,label='Samsung')
plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.grid()
plt.legend()   # legend of the graph
plt.show()

Scatter Plot is also used to find relationship between two variables / data

temp = [10,20,30,40,50,60]
tea = [100,80,70,40,20,10]
plt.scatter(temp,tea)
plt.title('Tea Sales')
plt.xlabel('Temperature')
plt.ylabel('Sales')
plt.grid()
plt.show()

Note : Here we can see , If temperature increase , Tea Sales decreases. It means they have negative relationship.

temp = [10,20,30,40,50,60]
tea = [100,80,70,40,20,10]
ice = [20,50,80,90,100,300]
plt.scatter(temp,tea,label='Tea')
plt.scatter(temp,ice,label='Ice')
plt.title('Tea Sales')
plt.xlabel('Temperature')
plt.ylabel('Sales')
plt.grid()
plt.legend()
plt.show()

Note : Here we can see , If temperature increase , Tea Sales decreases. It means they have negative relationship. But In case of Ice cream , Ice cream and Temp have positive relationship.

Let's See an example of Scatter in Data:

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

	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

Finding Relationship between Units Sold and total

Obviously the more units you sell, the more sale will happen. So Units Sold and total must have positive relationship. Let's check it through scatter plot

plt.scatter(df['Units Sold'],df['total'])
plt.title('Units vs Sales')
plt.xlabel('Units Sold')
plt.ylabel('Sales')
plt.grid()
plt.show()

Conclusion

The scatter plot shows a strong positive relationship between Units Sold and Sales.

This means:

1. As units sold increase, sales also increase.

2. No major outliers are visible that would break the pattern.

In your scatter plot, you can see that many points are clustered (crowded) together, especially between:

1. Units Sold: 100 to 600

2. Sales: 5,000 to 40,000

What this crowd tells us:

Most of your data falls in this range. This means the majority of your products or days have sales in this normal band.

Another Example

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

	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

plt.scatter(df['Sleep Duration'],df['Quality of Sleep'])
plt.title('Sleep Duration vs Quality of Sleep')
plt.xlabel('Sleep Duration')
plt.ylabel('Quality of Sleep')
plt.grid()
plt.show()

Conclusion

When sleep duration is low (around 6 hours), the sleep quality is also lower (around 4–6).

As sleep duration increases to 7–8 hours, the sleep quality becomes better (7–8).

People who sleep 8+ hours generally show the highest sleep quality (8–9).

👉 In simple words:

The more you sleep (up to about 8 hours), the better your sleep quality becomes. There is a clear positive relationship between sleep duration and sleep quality.

plt.scatter(df['Sleep Duration'],df['Quality of Sleep'],label='Sleep Duration')
plt.scatter(df['Sleep Duration'],df['Stress Level'],label='Stress Level')
plt.title('Sleep Duration vs Quality of Sleep')
plt.xlabel('Sleep Duration')
plt.ylabel('Quality of Sleep')
plt.grid()
plt.legend()
plt.show()

Conclusion

Sleep Duration vs Quality of Sleep (Blue Dots):

1. When people sleep more (7–8+ hours) → their sleep quality is higher (7–9).

2. When sleep duration is less (5.8–6.5 hours) → sleep quality is lower (4–6).

3. More sleep = better sleep quality.

Sleep Duration vs Stress Level (Orange Dots)

1. When people sleep less (around 6 hours) → their stress level is higher (7–8).

2. As sleep increases to 7–8 hours, stress level drops to 4–5.

3. With 8+ hours of sleep, stress becomes lowest (3–4).

4. More sleep = lower stress.

Overall Meaning

1. Sleep quality increases as sleep duration increases.

2. Stress decreases as sleep duration increases.

In simple words:

Sleeping more makes your sleep better and your stress lower.

2. Line chart

A line chart connects data points with straight lines to show how something changes over time. In simple words , we use line charts to show how something changes over time, so we can understand patterns, trends, and comparisons easily.

month = ['jan','feb','mar','apr']
isale = [100,200,50,400]

plt.plot(month,isale,marker='o')  # a line chart
plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.grid()
plt.show()

month = ['jan','feb','mar','apr']
isale = [100,200,50,400]
ssale = [200,400,100,700]

plt.plot(month,isale,marker='o',label='Iphone')  # a line chart
plt.plot(month,ssale,marker='o',label='Samsung')
plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.grid()
plt.legend()
plt.show()

Conclusion

1. Both brands rise in February, fall in March, and grow strongly in April.

2. Samsung consistently sells more than iPhone in all four months.

Let's Use Line Chart on Data

Find Monthly Sales in Adidas Sales

Your manager has asked you to find Monthly Sales trend for adidas sales. Here is how you will do it.

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['Month'] = df['Invoice Date'].dt.month
df

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

9648 rows × 11 columns

sdf = df.groupby('Month').agg(
    revenue = ('total','sum')
)
sdf = sdf.reset_index()
sdf

	Month	revenue
0	1	9744767.0
1	2	8263853.0
2	3	7694984.0
3	4	9691420.0
4	5	10741720.0
5	6	9803147.0
6	7	12550419.0
7	8	12293226.0
8	9	10405584.0
9	10	8538758.0
10	11	9023440.0
11	12	11415332.0

import numpy as np

plt.figure(figsize=(10,4))
plt.plot(sdf['Month'],sdf['revenue'],marker='o')
plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Revenue In Crores')
plt.grid()
plt.xticks(np.arange(1,13),['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec'])
plt.show()

png

Conclusion

The monthly revenue trend shows clear seasonal fluctuations throughout the year. Revenue declines during the first quarter, reaching its lowest point in March. This is followed by a strong upward trajectory from April to July, with July recording the highest revenue of the year. Although revenues remain relatively high in August, a noticeable decline occurs from September to October. The final quarter shows recovery, with December ending the year on a strong note.

Overall, the data indicates two major growth periods—April to July and November to December—suggesting potential seasonal demand cycles or successful mid-year and year-end business strategies.

3. Bar Chart

A bar chart is used when you want to compare categories.

city = ['delhi','pune','agra','bangalore']
isale = [100,200,50,400]

plt.bar(city,isale,color='red')
plt.show()

png

Let's Use this on Data

Give me State wise sales on Adidas

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

	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

sdf = df.groupby('State').agg(
    revenue = ('total','sum')
)
sdf = sdf.reset_index()
sdf =sdf.sort_values('revenue',ascending=False)
sdf

	State	revenue
31	New York	8670464.0
4	California	8580508.0
8	Florida	7820589.0
42	Texas	6612371.0
39	South Carolina	3593112.0
17	Louisiana	3377031.0
46	Washington	3222093.0
45	Virginia	3074415.0
36	Oregon	3047049.0
27	Nevada	2981134.0
32	North Carolina	2936581.0
30	New Mexico	2824641.0
11	Idaho	2742753.0
10	Hawaii	2734457.0
9	Georgia	2708591.0
5	Colorado	2569036.0
41	Tennessee	2567190.0
0	Alabama	2513424.0
28	New Hampshire	2339267.0
21	Michigan	2287283.0
49	Wyoming	2282342.0
34	Ohio	2269283.0
2	Arizona	2254096.0
23	Mississippi	2218609.0
44	Vermont	2041598.0
25	Montana	1930761.0
1	Alaska	1810428.0
3	Arkansas	1802672.0
6	Connecticut	1646448.0
20	Massachusetts	1578435.0
35	Oklahoma	1512059.0
7	Delaware	1508537.0
37	Pennsylvania	1478794.0
43	Utah	1387620.0
47	West Virginia	1311160.0
16	Kentucky	1241148.0
15	Kansas	1225314.0
29	New Jersey	1220446.0
12	Illinois	1204063.0
38	Rhode Island	1202256.0
24	Missouri	1189515.0
18	Maine	1129728.0
13	Indiana	1084723.0
40	South Dakota	1041101.0
19	Maryland	951134.0
33	North Dakota	950930.0
48	Wisconsin	948894.0
14	Iowa	909811.0
22	Minnesota	903918.0
26	Nebraska	728838.0

plt.figure(figsize=(20,4))
plt.bar(sdf['State'],sdf['revenue'],width=0.4)
plt.xticks(rotation=90)
plt.grid()
plt.show()

png

plt.figure(figsize=(10,10))
plt.barh(sdf['State'],sdf['revenue'],color='green',height=0.5)
plt.show()

png

Conclusion

States like Nebraska and Minnesota show the lowest sales, while California, Florida, and New York generate the highest revenue. This clearly highlights which states are underperforming and which are the strongest markets.

4. Pie Chart

Pie chart is used to show percentage or proportion of the whole part. Pie charts are best when you want to display:

1. Market share

2. Budget distribution

3. Population split

4. Sales contribution of each product

It shows what portion each category contributes to the total.

quantity = [13.3,2.2,8.7,5.6]
ing = ['sugar','protein','total fat','saturated fat']

plt.pie(quantity,labels=ing,autopct='%1.1f%%',colors=['red','blue','green','yellow'])
plt.show()
plt.show()

png

quantity = [13.3,2.2,8.7,5.6]
ing = ['sugar','protein','total fat','saturated fat']

plt.pie(quantity,
        labels=ing,
        autopct='%1.1f%%',
        colors=['red','blue','green','yellow'],
        explode=[0.1,0,0,0],
        shadow=True)
plt.show()
plt.show()

png

Let's understand this on data

percentage contribution of Sales Method in Adidas

df.head()

	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

rdf = df['Sales Method'].value_counts().reset_index()
rdf

	Sales Method	count
0	Online	4889
1	Outlet	3019
2	In-store	1740

plt.pie(rdf['count'],
        labels=rdf['Sales Method'],
        autopct='%1.1f%%',
        explode=[0.1,0,0],
        shadow=True)
plt.show()

png

5. Histogram

A histogram is a graph that shows the distribution of numerical data. It splits the data into ranges (called bins) and tells how many values fall into each bin.

1. Normal Distribution: mean = median = mode

2. Right-Skewed Distribution: mean > median > mode

3. Left-Skewed Distribution: mean < median < mode

office_employee_ages = [
# very low frequency (tails)
30,30,70,70,

31,31,69,69,
32,32,68,68,
33,33,67,67,

# low frequency
34,34,34,66,66,66,
35,35,35,65,65,65,

# medium frequency
36,36,36,36,64,64,64,64,
37,37,37,37,63,63,63,63,

38,38,38,38,38,62,62,62,62,62,
39,39,39,39,39,61,61,61,61,61,

# high frequency (center region)
40,40,40,40,40,40,60,60,60,60,60,60,
41,41,41,41,41,41,59,59,59,59,59,59,

42,42,42,42,42,42,42,58,58,58,58,58,58,58,
43,43,43,43,43,43,43,57,57,57,57,57,57,57,

44,44,44,44,44,44,44,44,56,56,56,56,56,56,56,56,

# peak (very high frequency)
45,45,45,45,45,45,45,45,45,
55,55,55,55,55,55,55,55,55,

46,46,46,46,46,46,46,46,46,46,
54,54,54,54,54,54,54,54,54,54,

47,47,47,47,47,47,47,47,47,47,47,
53,53,53,53,53,53,53,53,53,53,53,

48,48,48,48,48,48,48,48,48,48,48,48,
52,52,52,52,52,52,52,52,52,52,52,52,

# absolute peak //
49,49,49,49,49,49,49,49,49,49,49,49,49,
51,51,51,51,51,51,51,51,51,51,51,51,51,

# center value
50,50,50,50,50,50,50,50,50,50,50,50,50,50,50
]

plt.figure(figsize=(10,6))
plt.hist(office_employee_ages,
         color='skyblue',
         bins=20,   # bin can be caclulated by square root of number of values
         edgecolor='black',
         )
plt.xticks(np.arange(30,72,2))
plt.grid()

png

np.mean(office_employee_ages) , np.median(office_employee_ages)

(np.float64(50.0), np.float64(50.0))

Conclusion

1. The histogram of office_employee_ages is symmetrical and bell-shaped, indicating a normal distribution.

2. Most employees are middle-aged (around 45–50 years).

3. The mean and median are very close, which confirms the symmetry of the data.

4. The few employees at the extremes (30–35 and 65–70) represent the tails of the distribution.

instagram_user_ages = [
# high frequency (left side)
30,30,30,30,30,30,30,30,30,30,
31,31,31,31,31,31,31,31,31,
32,32,32,32,32,32,32,32,
33,33,33,33,33,33,33,

# medium-high frequency
34,34,34,34,34,34,34,
35,35,35,35,35,35,
36,36,36,36,36,36,
37,37,37,37,37,37,

# medium frequency
38,38,38,38,38,
39,39,39,39,39,
40,40,40,40,40,
41,41,41,41,41,

# low-medium
42,42,42,42,
43,43,43,43,
44,44,44,44,
45,45,45,45,

# low frequency
46,46,46,
47,47,47,
48,48,48,
49,49,49,

# very low (tail starts)
50,50,
51,51,
52,52,
53,53,

# extremely low = long right tail
54,
55,
56,
57,
58,
59,
60,
61,
62,
63,
64,
65,
66,
67,
68,
69,
70
]

plt.figure(figsize=(10,6))
plt.hist(instagram_user_ages,
         color='orange',
         bins=20,
         edgecolor='black')
plt.xticks(np.arange(30,72,2))
plt.grid()
plt.title("Right Skewed Distribution")
plt.show()

png

np.mean(instagram_user_ages) , np.median(instagram_user_ages)

(np.float64(41.46969696969697), np.float64(39.0))

Conclusion

1. The age distribution of Instagram users is right-skewed (positively skewed).

2. Most users are young (30–35 years).

3. The long tail on the right shows a few older users.

4. The mean is higher than the median, which is typical for right-skewed data.

5. For skewed distributions like this, the median better represents the “typical” user age than the mean.

facebook_user_ages = [
# very low (left tail)
30,
31,
32,
33,
34,
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,

# low frequency
45,45,
46,46,
47,47,
48,48,

# medium frequency
49,49,49,
50,50,50,
51,51,51,
52,52,52,

# medium-high frequency
53,53,53,53,
54,54,54,54,
55,55,55,55,
56,56,56,56,

# high frequency
57,57,57,57,57,
58,58,58,58,58,
59,59,59,59,59,
60,60,60,60,60,

# very high frequency (peak areas)
61,61,61,61,61,61,
62,62,62,62,62,62,
63,63,63,63,63,63,

64,64,64,64,64,64,64,
65,65,65,65,65,65,65,

66,66,66,66,66,66,66,66,
67,67,67,67,67,67,67,67,

# absolute peak region (end)
68,68,68,68,68,68,68,68,68,
69,69,69,69,69,69,69,69,69,
70,70,70,70,70,70,70,70,70
]


plt.figure(figsize=(10,6))
plt.hist(facebook_user_ages,
         color='green',
         bins=20,
         edgecolor='black')
plt.xticks(np.arange(30,72,2))
plt.grid()
plt.title("Left Skewed Distribution")
plt.show()

png

np.mean(facebook_user_ages) , np.median(facebook_user_ages)

(np.float64(58.363013698630134), np.float64(61.0))

Conclusion

1. The age distribution of Facebook users is left-skewed (negatively skewed).

2. Most users are older (60–70 years).

3. The long tail on the left indicates a smaller number of younger users.

4. The mean is lower than the median, which is typical for left-skewed data.

5. For skewed distributions like this, the median better represents the “typical” user age than the mean.

Let's use this on Data

import math
import numpy as np
df = pd.read_csv('Sleep_health_and_lifestyle_dataset.csv')
df

	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

plt.hist(df['Age'],
         color='orange',
         bins=30,
         edgecolor='black')

plt.show()

png

df['Age'].mean()  , df['Age'].median() , df['Age'].mode()

(np.float64(42.18449197860963),
 np.float64(43.0),
 0    43
 Name: Age, dtype: int64)

There is not much difference between mean , median and mode , so we can use mean here if asked about average age also.

6. Stackplot

A stack plot is a type of plot in Python (using Matplotlib) that shows how multiple datasets contribute to a total over a period or sequence.

1. Each dataset is stacked on top of the previous one.

2. Useful for showing cumulative contributions.

3. X-axis usually represents time or categories, Y-axis is the value.

# X-axis (Months)
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May']

# Sales of three products
product_a = [100, 120, 150, 170, 200]
product_b = [80, 90, 100, 120, 130]
product_c = [50, 60, 70, 80, 90]

import matplotlib.pyplot as plt

plt.figure(figsize=(10,6))
plt.stackplot(months, product_a, product_b, product_c,
              labels=['Product A','Product B','Product C'],
              colors=['skyblue','orange','green'])
plt.title("Monthly Sales Stack Plot")
plt.xlabel("Months")
plt.ylabel("Sales")
plt.legend(loc='upper left')
plt.show()

png

df  = pd.read_excel('Adidas US Sales Datasets.xlsx')
df['Month'] = df['Invoice Date'].dt.month
mdf = df.pivot_table(index='Month',columns='Product',values='Units Sold').reset_index()
mdf

Product	Month	Men's Apparel	Men's Athletic Footwear	Men's Street Footwear	Women's Apparel	Women's Athletic Footwear	Women's Street Footwear
0	1	195.753521	280.944828	365.337931	271.117241	196.929577	273.577465
1	2	193.833333	249.443609	372.523077	251.832000	184.348837	245.654135
2	3	157.179104	247.606061	366.469697	239.742424	179.598540	241.400000
3	4	184.957447	253.514286	379.085714	280.305556	194.521739	250.543478
4	5	204.264286	264.740741	370.797101	283.751825	203.459259	242.644444
5	6	173.214876	244.524590	375.826446	282.462810	188.770492	225.737705
6	7	222.629921	293.589147	409.037879	300.060606	213.527559	250.661417
7	8	216.288732	348.652778	419.619718	296.514085	238.707143	291.552448
8	9	210.920290	306.503704	374.059259	286.521429	216.057971	272.739130
9	10	172.544776	247.956204	314.925373	245.179104	170.101449	199.868613
10	11	167.053030	230.879699	308.458647	235.369231	177.772727	197.444444
11	12	189.922481	269.848000	363.476562	259.492063	205.093750	228.349593

plt.stackplot(mdf['Month'],
              mdf["Men's Apparel"],
              mdf["Women's Apparel"],
              mdf["Men's Athletic Footwear"],
              labels=['MA','WA','MF'])
plt.legend()
plt.show()

png

7. Box Plot

plt.boxplot(df['Units Sold'],vert=False,showmeans=True,meanline=True)
plt.show()

png

A. Scatter Plot – 10 Questions

1. Plot a scatter plot showing the relationship between Temperature vs Tea Sales.

2. Using the lists temp = [10,20,30,40,50,60] ice = [20,50,80,90,100,300] Plot both in the same scatter plot with a legend.

3. Create a scatter plot between Units Sold vs Total Sales from the Adidas dataset.

4. In the Adidas scatter plot, highlight the range where most points appear (use annotations).

5. From the Sleep dataset, plot Sleep Duration vs Quality of Sleep.

6. Plot Sleep Duration vs Stress Level using a scatter plot.

7. Plot: month = ['jan','feb','mar','apr'] sale = [100,200,50,400] Add a small written conclusion.

8. Plot a scatter plot showing iPhone vs Samsung monthly sales (two series).

9. Add grid=True to a scatter plot.

10. Create a scatter plot using any two numerical columns from the Adidas dataset and write 1 line about the pattern.

---

B. Line Chart – 10 Questions

1. Plot a line chart for month = ['jan','feb','mar','apr'] isale = [100,200,50,400].

2. Plot line charts for iPhone vs Samsung monthly sales with markers.

3. From Adidas data, create a line chart for Month vs Revenue.

4. Plot the Adidas Month 1 → Month 12 revenue trend using a line graph.

5. Add custom x-ticks (jan–dec) to your line chart.

6. Plot two lines on the same graph: Revenue 2020 vs Revenue 2021.

7. Use markers in a line chart and observe the improvement in readability.

8. Create a line chart with grid=False and compare visually to one with grid.

9. Compare revenue between two states across all 12 months.

10. Create a line chart showing the rolling average (moving trend) of Adidas revenue.

---

C. Bar Chart – 8 Questions

1. Create a bar chart of state-wise revenue in Adidas, sorted descending.

2. Make a horizontal bar chart for the same data.

3. Plot: city = ['delhi','pune','agra','bangalore'] isale = [100,200,50,400].

4. Create a bar chart showing Male vs Female count in Sleep dataset.

5. Create a bar chart of Product-wise total units sold in Adidas.

6. Create a bar chart of Occupation vs Average Sleep Duration.

7. Make a grouped bar chart comparing iPhone vs Samsung sales (4 months).

8. Use a bar chart to compare categories (any categorical variable from Adidas).

---

D. Pie Chart – 5 Questions

1. Pie chart of revenue contribution of top 5 states (Adidas).

2. Pie chart of gender distribution in Sleep dataset.

3. Stylish pie chart using quantity = [13.3, 2.2, 8.7, 5.6] use explode + shadow.

4. Pie chart showing product category distribution.

5. Create any pie chart that represents parts of a whole using your own dataset values.

---

E. Histogram – 7 Questions

1. Histogram for Units Sold in Adidas.

2. Histogram for Sleep Duration.

3. Generate 100 random values (mean=50, std=10) → plot histogram.

4. Histogram for Daily Steps from Sleep dataset.

5. Create two histograms for same data using bins=5 and bins=20.

6. Create 3 lists that produce:

   • normal distribution

   • right skew

   • left skew and plot all 3 histograms.

7. Plot a histogram using 100 random integers (1–500).

---

F. Boxplot – 5 Questions

1. Boxplot of Units Sold to detect outliers.

2. Plot three boxplots together: Units Sold, Price per Unit, Total Sales.

3. Boxplot for Sleep Duration vs Quality of Sleep.

4. Boxplot showing state-wise revenue spread.

5. Boxplot comparing two products from Adidas.

---

G. Stackplot – 3 Questions

1. Create a stackplot using iPhone and Samsung month-wise sales.

2. Add labels + custom colors to a stackplot.

3. Create a stackplot using any two numeric lists from your datasets.

---

H. Python Lists – 2 Questions

1. Generate a list of 100 random integers (1–500) and plot a histogram.

2. Using:

   • temp = [...]

   • tea = [...]

   • ice = [...] Plot three scatter plots in one figure (subplot method).