
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import os


import warnings
warnings.filterwarnings('ignore')


data = ("./Sales_Data/")


os.listdir(data)

['.DS_Store',
 'all_data.csv',
 'Sales_April_2019.csv',
 'Sales_August_2019.csv',
 'Sales_December_2019.csv',
 'Sales_February_2019.csv',
 'Sales_January_2019.csv',
 'Sales_July_2019.csv',
 'Sales_June_2019.csv',
 'Sales_March_2019.csv',
 'Sales_May_2019.csv',
 'Sales_November_2019.csv',
 'Sales_October_2019.csv',
 'Sales_September_2019.csv']


# Collecter les noms des fichiers (Datasets)
files = [fichier for fichier in os.listdir(data) if fichier.endswith('.csv')]
files

['all_data.csv',
 'Sales_April_2019.csv',
 'Sales_August_2019.csv',
 'Sales_December_2019.csv',
 'Sales_February_2019.csv',
 'Sales_January_2019.csv',
 'Sales_July_2019.csv',
 'Sales_June_2019.csv',
 'Sales_March_2019.csv',
 'Sales_May_2019.csv',
 'Sales_November_2019.csv',
 'Sales_October_2019.csv',
 'Sales_September_2019.csv']


april = pd.read_csv("./Sales_Data/Sales_April_2019.csv")


april.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18383 entries, 0 to 18382
Data columns (total 6 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   Order ID          18324 non-null  object
 1   Product           18324 non-null  object
 2   Quantity Ordered  18324 non-null  object
 3   Price Each        18324 non-null  object
 4   Order Date        18324 non-null  object
 5   Purchase Address  18324 non-null  object
dtypes: object(6)
memory usage: 861.8+ KB


janvier = pd.read_csv(data + 'Sales_January_2019.csv')


janvier.head()


fevrier = pd.read_csv(data + 'Sales_February_2019.csv')


fevrier.head()


all_data = pd.DataFrame()

for file in files:
  data_interm = pd.read_csv(data + file)
  all_data = pd.concat([all_data, data_interm])


all_data.head()


all_data.shape

(373700, 6)


all_data.to_csv(data+'/all_data.csv',index=False)


all_data.dtypes

Order ID            object
Product             object
Quantity Ordered    object
Price Each          object
Order Date          object
Purchase Address    object
dtype: object


all_data.isnull().sum()

Order ID            1090
Product             1090
Quantity Ordered    1090
Price Each          1090
Order Date          1090
Purchase Address    1090
dtype: int64


all_data = all_data.dropna(how='all')
all_data.shape

(372610, 6)


# def day(x):
#     return x.split('/')[1]
# day('12/30/19 00:01')


def month(x):
    return x.split('/')[0]
#month('12/30/19 00:01')


all_data['Month'] = all_data['Order Date'].apply(month)
all_data


all_data['Month'].unique()

array(['04', '05', 'Order Date', '08', '09', '12', '01', '02', '03', '07',
       '06', '11', '10'], dtype=object)


all_data = all_data[all_data['Month']!='Order Date']
all_data['Month'].unique()

array(['04', '05', '08', '09', '12', '01', '02', '03', '07', '06', '11',
       '10'], dtype=object)


all_data['Month'] = all_data['Month'].astype(int)
all_data['Price Each'] = all_data['Price Each'].astype(float)
all_data['Quantity Ordered'] = all_data['Quantity Ordered'].astype(float)
all_data.dtypes

Order ID             object
Product              object
Quantity Ordered    float64
Price Each          float64
Order Date           object
Purchase Address     object
Month                 int32
dtype: object


all_data["chifre_d'affaire"] = all_data["Quantity Ordered"]*all_data["Price Each"]
all_data


all_data.groupby('Month')["chifre_d'affaire"].sum()

Month
1     3.644513e+06
2     4.404045e+06
3     5.614201e+06
4     6.781340e+06
5     6.305214e+06
6     5.155605e+06
7     5.295552e+06
8     4.488936e+06
9     4.195120e+06
10    7.473454e+06
11    6.399206e+06
12    9.226887e+06
Name: chifre_d'affaire, dtype: float64


from matplotlib.pyplot import figure

figure(figsize=(12, 8), dpi=80)
months = range(1,13)
plt.bar(months, all_data.groupby('Month')["chifre_d'affaire"].sum())
plt.xticks(months)
plt.ylabel("chifre_d'affaire en UDS")
plt.xlabel('Month number');


all_data['Purchase Address']

0                   917 1st St, Dallas, TX 75001
2              682 Chestnut St, Boston, MA 02215
3           669 Spruce St, Los Angeles, CA 90001
4           669 Spruce St, Los Angeles, CA 90001
5              333 8th St, Los Angeles, CA 90001
                          ...                   
11681     840 Highland St, Los Angeles, CA 90001
11682    216 Dogwood St, San Francisco, CA 94016
11683       220 12th St, San Francisco, CA 94016
11684     511 Forest St, San Francisco, CA 94016
11685     250 Meadow St, San Francisco, CA 94016
Name: Purchase Address, Length: 371900, dtype: object


"917 1st St, Dallas, TX 75001".split(',')[1]

' Dallas'


def city(x):
    return x.split(',')[1]


all_data['ville'] = all_data['Purchase Address'].apply(city)
all_data


all_data['ville'].unique()

array([' Dallas', ' Boston', ' Los Angeles', ' San Francisco', ' Seattle',
       ' Atlanta', ' New York City', ' Portland', ' Austin'], dtype=object)


ca_by_ville = all_data.groupby('ville')['ville'].count()
ca_by_ville

ville
 Atlanta          29762
 Austin           19810
 Boston           39868
 Dallas           29640
 Los Angeles      59210
 New York City    49752
 Portland         24930
 San Francisco    89464
 Seattle          29464
Name: ville, dtype: int64


ca_by_ville.plot(kind='bar', figsize=(12, 8))
plt.ylabel('Chiffre daffaire en Millions de dollars')
plt.title('Chiffre daffaire par Ville')
plt.show()


from matplotlib.pyplot import figure

figure(figsize=(12, 8), dpi=80)
plt.bar(all_data.groupby('ville')['ville'].count().index,all_data.groupby('ville')['ville'].count())
plt.xticks(rotation='vertical')
plt.ylabel("Received orders")
plt.xlabel('Nom de ville');


all_data


all_data['Hour']=pd.to_datetime(all_data['Order Date']).dt.hour


keys=[]
hours=[]
for key,hour in all_data.groupby('Hour'):
    keys.append(key)
    hours.append(len(hour))
hours

[7820,
 4700,
 2486,
 1662,
 1708,
 2642,
 4964,
 8022,
 12512,
 17496,
 21888,
 24822,
 25174,
 24258,
 21968,
 20350,
 20768,
 21798,
 24560,
 25810,
 24456,
 21842,
 17644,
 12550]


from matplotlib.pyplot import figure

figure(figsize=(12, 8), dpi=80)
plt.grid()
plt.plot(keys,hours)
plt.xlabel('Heure de la journée')
plt.ylabel('Nombre des commandes');


all_data.groupby('Product')['Quantity Ordered'].sum().plot(kind='bar',figsize=(12,8));


all_data.groupby('Product')['Price Each'].mean()

Product
20in Monitor                   109.99
27in 4K Gaming Monitor         389.99
27in FHD Monitor               149.99
34in Ultrawide Monitor         379.99
AA Batteries (4-pack)            3.84
AAA Batteries (4-pack)           2.99
Apple Airpods Headphones       150.00
Bose SoundSport Headphones      99.99
Flatscreen TV                  300.00
Google Phone                   600.00
LG Dryer                       600.00
LG Washing Machine             600.00
Lightning Charging Cable        14.95
Macbook Pro Laptop            1700.00
ThinkPad Laptop                999.99
USB-C Charging Cable            11.95
Vareebadd Phone                400.00
Wired Headphones                11.99
iPhone                         700.00
Name: Price Each, dtype: float64


# Vizuation de quantités vendus et le prix
products=all_data.groupby('Product')['Quantity Ordered'].sum().index
quantity=all_data.groupby('Product')['Quantity Ordered'].sum()
prices=all_data.groupby('Product')['Price Each'].mean()


plt.figure(figsize=(40,24))
fig,ax1=plt.subplots()
ax2=ax1.twinx()
ax1.bar(products,quantity,color='g')
ax2.plot(products,prices,'b-')
ax1.set_xticklabels(products,rotation='vertical',size=8)

[Text(0, 0, '20in Monitor'),
 Text(1, 0, '27in 4K Gaming Monitor'),
 Text(2, 0, '27in FHD Monitor'),
 Text(3, 0, '34in Ultrawide Monitor'),
 Text(4, 0, 'AA Batteries (4-pack)'),
 Text(5, 0, 'AAA Batteries (4-pack)'),
 Text(6, 0, 'Apple Airpods Headphones'),
 Text(7, 0, 'Bose SoundSport Headphones'),
 Text(8, 0, 'Flatscreen TV'),
 Text(9, 0, 'Google Phone'),
 Text(10, 0, 'LG Dryer'),
 Text(11, 0, 'LG Washing Machine'),
 Text(12, 0, 'Lightning Charging Cable'),
 Text(13, 0, 'Macbook Pro Laptop'),
 Text(14, 0, 'ThinkPad Laptop'),
 Text(15, 0, 'USB-C Charging Cable'),
 Text(16, 0, 'Vareebadd Phone'),
 Text(17, 0, 'Wired Headphones'),
 Text(18, 0, 'iPhone')]


df=all_data[all_data['Order ID'].duplicated(keep=False)]


df['Grouped']=df.groupby('Order ID')['Product'].transform(lambda x: ','.join(x))

df


df['Grouped']

0                USB-C Charging Cable,USB-C Charging Cable
2        Bose SoundSport Headphones,Bose SoundSport Hea...
3        Google Phone,Wired Headphones,Google Phone,Wir...
4        Google Phone,Wired Headphones,Google Phone,Wir...
5                        Wired Headphones,Wired Headphones
                               ...                        
11681        AAA Batteries (4-pack),AAA Batteries (4-pack)
11682                                        iPhone,iPhone
11683                                        iPhone,iPhone
11684        34in Ultrawide Monitor,34in Ultrawide Monitor
11685            USB-C Charging Cable,USB-C Charging Cable
Name: Grouped, Length: 371900, dtype: object


# Dropping duplicates (Order ID)
df2=df.drop_duplicates(subset=['Order ID'])
df2


df2['Grouped'].value_counts()[0:5]

Lightning Charging Cable,Lightning Charging Cable    19831
AAA Batteries (4-pack),AAA Batteries (4-pack)        19826
AA Batteries (4-pack),AA Batteries (4-pack)          19809
USB-C Charging Cable,USB-C Charging Cable            19792
Wired Headphones,Wired Headphones                    17208
Name: Grouped, dtype: int64


df2['Grouped'].value_counts()[0:5].plot.pie()

<AxesSubplot:ylabel='Grouped'>

Comment analyser les ventes de votre entreprise¶

Découverte des données¶

Merger la data¶

Merging 12 months of sales data into a single csv file¶

Mettre all_data sous un seul fichier csv¶

Vérifier les valeurs manquantes¶

supprimer les valeurs manquantes¶

Quel est le mois durant lequel nous avons réalisé le meilleur chiffre d'affaire?¶

On doit supprimer `'Order Date'`¶

Convertir Object to numeric¶

Création d'une nouvelle colonne `chifre_d'affaire`¶

Dans quelle ville nous avons enregistré un maximun de commandes?¶

En quel moment doit on faire une compagne publicitaire, pour avoir plus de ventes?¶

Quel produit se vend le plus?¶

Quelles sont les combinaisons de produits qui se vendent le plus?¶

	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
0	141234	iPhone	1	700	01/22/19 21:25	944 Walnut St, Boston, MA 02215
1	141235	Lightning Charging Cable	1	14.95	01/28/19 14:15	185 Maple St, Portland, OR 97035
2	141236	Wired Headphones	2	11.99	01/17/19 13:33	538 Adams St, San Francisco, CA 94016
3	141237	27in FHD Monitor	1	149.99	01/05/19 20:33	738 10th St, Los Angeles, CA 90001
4	141238	Wired Headphones	1	11.99	01/25/19 11:59	387 10th St, Austin, TX 73301

	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
0	150502	iPhone	1	700	02/18/19 01:35	866 Spruce St, Portland, ME 04101
1	150503	AA Batteries (4-pack)	1	3.84	02/13/19 07:24	18 13th St, San Francisco, CA 94016
2	150504	27in 4K Gaming Monitor	1	389.99	02/18/19 09:46	52 6th St, New York City, NY 10001
3	150505	Lightning Charging Cable	1	14.95	02/02/19 16:47	129 Cherry St, Atlanta, GA 30301
4	150506	AA Batteries (4-pack)	2	3.84	02/28/19 20:32	548 Lincoln St, Seattle, WA 98101

	Order ID	Product	Quantity Ordered	Price Each	Order Date	Purchase Address
0	176558	USB-C Charging Cable	2	11.95	04/19/19 08:46	917 1st St, Dallas, TX 75001
1	NaN	NaN	NaN	NaN	NaN	NaN
2	176559	Bose SoundSport Headphones	1	99.99	04/07/19 22:30	682 Chestnut St, Boston, MA 02215
3	176560	Google Phone	1	600	04/12/19 14:38	669 Spruce St, Los Angeles, CA 90001
4	176560	Wired Headphones	1	11.99	04/12/19 14:38	669 Spruce St, Los Angeles, CA 90001

Comment analyser les ventes de votre entreprise¶

Découverte des données¶

Merger la data¶

Merging 12 months of sales data into a single csv file¶

Mettre all_data sous un seul fichier csv¶

Vérifier les valeurs manquantes¶

supprimer les valeurs manquantes¶

Quel est le mois durant lequel nous avons réalisé le meilleur chiffre d'affaire?¶

On doit supprimer 'Order Date'¶

Convertir Object to numeric¶

Création d'une nouvelle colonne chifre_d'affaire¶

Dans quelle ville nous avons enregistré un maximun de commandes?¶

En quel moment doit on faire une compagne publicitaire, pour avoir plus de ventes?¶

Quel produit se vend le plus?¶

Quelles sont les combinaisons de produits qui se vendent le plus?¶

On doit supprimer `'Order Date'`¶

Création d'une nouvelle colonne `chifre_d'affaire`¶