import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt 

pd.options.display.max_rows = 10

cars  = pd.read_csv('/home/jovyan/MonDossier/matplolib/files/cars.csv',index_col=0)
cars

	Date	Customer Name	Gender	Annual Income	Dealer_Name	Company	Model	Engine	Transmission	Color	Price ($)	Dealer_No	Body Style	Phone	Dealer_Region
Car_id
C_CND_000001	1/2/2022	Geraldine	Male	13500	Buddy Storbeck's Diesel Service Inc	Ford	Expedition	Double Overhead Camshaft	Auto	Black	26000	06457-3834	SUV	8264678	Middletown
C_CND_000002	1/2/2022	Gia	Male	1480000	C & M Motors Inc	Dodge	Durango	Double Overhead Camshaft	Auto	Black	19000	60504-7114	SUV	6848189	Aurora
C_CND_000003	1/2/2022	Gianna	Male	1035000	Capitol KIA	Cadillac	Eldorado	Overhead Camshaft	Manual	Red	31500	38701-8047	Passenger	7298798	Greenville
C_CND_000004	1/2/2022	Giselle	Male	13500	Chrysler of Tri-Cities	Toyota	Celica	Overhead Camshaft	Manual	Pale White	14000	99301-3882	SUV	6257557	Pasco
C_CND_000005	1/2/2022	Grace	Male	1465000	Chrysler Plymouth	Acura	TL	Double Overhead Camshaft	Auto	Red	24500	53546-9427	Hatchback	7081483	Janesville
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
C_CND_023902	12/31/2023	Martin	Male	13500	C & M Motors Inc	Plymouth	Voyager	Overhead Camshaft	Manual	Red	12000	60504-7114	Passenger	8583598	Pasco
C_CND_023903	12/31/2023	Jimmy	Female	900000	Ryder Truck Rental and Leasing	Chevrolet	Prizm	Double Overhead Camshaft	Auto	Black	16000	06457-3834	Hardtop	7914229	Middletown
C_CND_023904	12/31/2023	Emma	Male	705000	Chrysler of Tri-Cities	BMW	328i	Overhead Camshaft	Manual	Red	21000	99301-3882	Sedan	7659127	Scottsdale
C_CND_023905	12/31/2023	Victoire	Male	13500	Chrysler Plymouth	Chevrolet	Metro	Double Overhead Camshaft	Auto	Black	31000	53546-9427	Passenger	6030764	Austin
C_CND_023906	12/31/2023	Donovan	Male	1225000	Pars Auto Sales	Lexus	ES300	Double Overhead Camshaft	Auto	Pale White	27500	38701-8047	Hardtop	7020564	Middletown

23906 rows × 15 columns

data cleaning

#replace Double Overhead Camshaft with Double Overhead Camshaft
cars["Engine"]
cars["Engine"]=cars["Engine"].replace('Â', '', regex=True)

#Setting regex=True 
#allows the replace() method to interpret the first argument ('Â') as a regular expression pattern.

#change date format from 12/31/2023 to 31/12/2023 using datetime
import datetime as dt
cars["Date"]= pd.to_datetime(cars["Date"],format = "%m/%d/%Y" )

#pandas doesn’t allow implicit conversion from one type to another in the same column:
#create new column to have the month/year as a string 
cars['FormattedDate'] = cars['Date'].dt.strftime('%d-%Y')

#null values control
cars.isnull().sum()

Date             0
Customer Name    0
Gender           0
Annual Income    0
Dealer_Name      0
                ..
Dealer_No        0
Body Style       0
Phone            0
Dealer_Region    0
FormattedDate    0
Length: 16, dtype: int64

cars

	Date	Customer Name	Gender	Annual Income	Dealer_Name	Company	Model	Engine	Transmission	Color	Price ($)	Dealer_No	Body Style	Phone	Dealer_Region	FormattedDate
Car_id
C_CND_000001	2022-01-02	Geraldine	Male	13500	Buddy Storbeck's Diesel Service Inc	Ford	Expedition	Double Overhead Camshaft	Auto	Black	26000	06457-3834	SUV	8264678	Middletown	02-2022
C_CND_000002	2022-01-02	Gia	Male	1480000	C & M Motors Inc	Dodge	Durango	Double Overhead Camshaft	Auto	Black	19000	60504-7114	SUV	6848189	Aurora	02-2022
C_CND_000003	2022-01-02	Gianna	Male	1035000	Capitol KIA	Cadillac	Eldorado	Overhead Camshaft	Manual	Red	31500	38701-8047	Passenger	7298798	Greenville	02-2022
C_CND_000004	2022-01-02	Giselle	Male	13500	Chrysler of Tri-Cities	Toyota	Celica	Overhead Camshaft	Manual	Pale White	14000	99301-3882	SUV	6257557	Pasco	02-2022
C_CND_000005	2022-01-02	Grace	Male	1465000	Chrysler Plymouth	Acura	TL	Double Overhead Camshaft	Auto	Red	24500	53546-9427	Hatchback	7081483	Janesville	02-2022
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
C_CND_023902	2023-12-31	Martin	Male	13500	C & M Motors Inc	Plymouth	Voyager	Overhead Camshaft	Manual	Red	12000	60504-7114	Passenger	8583598	Pasco	31-2023
C_CND_023903	2023-12-31	Jimmy	Female	900000	Ryder Truck Rental and Leasing	Chevrolet	Prizm	Double Overhead Camshaft	Auto	Black	16000	06457-3834	Hardtop	7914229	Middletown	31-2023
C_CND_023904	2023-12-31	Emma	Male	705000	Chrysler of Tri-Cities	BMW	328i	Overhead Camshaft	Manual	Red	21000	99301-3882	Sedan	7659127	Scottsdale	31-2023
C_CND_023905	2023-12-31	Victoire	Male	13500	Chrysler Plymouth	Chevrolet	Metro	Double Overhead Camshaft	Auto	Black	31000	53546-9427	Passenger	6030764	Austin	31-2023
C_CND_023906	2023-12-31	Donovan	Male	1225000	Pars Auto Sales	Lexus	ES300	Double Overhead Camshaft	Auto	Pale White	27500	38701-8047	Hardtop	7020564	Middletown	31-2023

23906 rows × 16 columns

cars.columns

Index(['Date', 'Customer Name', 'Gender', 'Annual Income', 'Dealer_Name',
       'Company', 'Model', 'Engine', 'Transmission', 'Color', 'Price ($)',
       'Dealer_No ', 'Body Style', 'Phone', 'Dealer_Region', 'FormattedDate'],
      dtype='object')

Charts

Bar Chart: Average Price by Model

df2 = cars.groupby('Model')['Price ($)'].aggregate(np.mean).round(2).reset_index()
df2

	Model	Price ($)
0	3-Sep	37986.38
1	3000GT	22282.25
2	300M	21394.89
3	323i	17355.63
4	328i	27821.36
...	...	...
149	Viper	30905.26
150	Voyager	31491.11
151	Windstar	23682.84
152	Wrangler	20134.55
153	Xterra	18901.08

154 rows × 2 columns

df2_sorted = df2.sort_values(by='Price ($)', ascending=False).head(5)
plt.bar (df2_sorted["Model"],df2_sorted["Price ($)"],color="blue", alpha=0.2,width = 0.5)
plt.title('Average Price by Model')
plt.xlabel('body style')
plt.ylabel('average price')
plt.show()

Bar Chart: Average Price by Body style

df = cars.groupby('Body Style')['Price ($)'].aggregate(np.mean).round(2).reset_index()
df

	Body Style	Price ($)
0	Hardtop	29151.10
1	Hatchback	27127.04
2	Passenger	28942.03
3	SUV	26767.67
4	Sedan	29832.80

df_sorted = df.sort_values(by='Price ($)', ascending=False)
plt.bar (df_sorted["Body Style"],df_sorted["Price ($)"],color="y", alpha=0.4,width = 0.5)
plt.title('Average Price by Body Styles')
plt.xlabel('body style')
plt.ylabel('average price')
plt.show()

fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize=(15, 5))


axes[0].bar (df_sorted["Body Style"],df_sorted["Price ($)"],color="green", alpha=0.4,width = 0.5)
axes[0].set_title('Body Style')

axes[1].bar (df2_sorted["Model"],df2_sorted["Price ($)"],color="green",  alpha=0.8,width = 0.5)
axes[1].set_title('Model')

fig.suptitle('Average price by body style and model', fontsize=16)
plt.subplots_adjust(left=0.2, wspace=0.2, top=0.8)
plt.show()

Pie Chart: Distribution of Cars by Transmission Type

cars["Transmission"].value_counts()

Auto      12571
Manual    11335
Name: Transmission, dtype: int64

cars["Transmission"].value_counts().plot(kind="pie",  autopct='%.2f%%', shadow=True, startangle=90)
plt.axis('equal')
plt.title('Distribution of Cars by Transmission', fontsize=14)
plt.show()

area Chart: Trends in Car Prices Over Time

cars.plot(kind="area", x = "Date", y = "Annual Income",color="skyblue", alpha=0.5 )

<AxesSubplot:xlabel='Date'>

Bar Chart: Number of Cars Sold by Dealer Region

number_by_regions = cars["Dealer_Region"].value_counts()
regions = ['Middletown', 'Aurora', 'Greenville', 'Pasco', 'Janesville',
       'Scottsdale', 'Austin']
explode = (0, 0.2, 0, 0, 0.8, 0,0) 
number_by_regions.plot(kind= "pie", autopct='%.2f%%',labels=None,shadow=True, startangle=90, explode= explode)

plt.legend(regions,title_fontsize=20, fontsize=12, 
           fancybox=True, shadow=True, ncol=2,loc='center left', 
          bbox_to_anchor=(2, 0, 1, 1))
plt.title('Numbers of cars sold by region', fontsize=13)
plt.show()

BAR chart: Annual Income Distribution

cars["year"]= cars["Date"].dt.year

annual_income = cars.groupby([ 'year'])['Price ($)'].sum().plot(
    kind='bar', color = ['cyan', 'dodgerblue'])

Box Plot: the distribution of prices by engine type, showing median, quartiles, and outliers.

cars.boxplot(column='Price ($)', by='Engine')

# Show the plot
plt.title('Car Prices by Model')
plt.suptitle('')  # This removes the default title to keep it clean
plt.xlabel('Model')
plt.ylabel('Price ($)')
plt.show()

Engine_groups= cars.groupby("Engine")
Engine_groups.get_group("Overhead Camshaft")["Price ($)"].describe()

count    11335.000000
mean     27914.710631
std      15862.871978
min       1700.000000
25%      17000.000000
50%      22001.000000
75%      34000.000000
max      85800.000000
Name: Price ($), dtype: float64

Scatter : the relationship between annual income and price.

cars.plot(kind = "scatter", x='Annual Income' , y ='Price ($)', color='salmon', s=100, marker='*')
plt.title('Poids en fonction de la robe')
plt.xlabel('annual income')
plt.ylabel('price')
plt.show()

bar chart : Gender Distribution of Car Purchases

cars["Gender"].value_counts()

Male      18798
Female     5108
Name: Gender, dtype: int64

df3= cars["Gender"].value_counts()

df3.plot(kind = "bar" ,color = ['lightsteelblue', 'IndianRed'])

<AxesSubplot:>