Histogram report and binning on Sales data

In [2]:

import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt

In [3]:

df1 = pd.read_csv('sales_data_sample.csv')

In [4]:

df1.shape

Out[4]:

(2823, 25)

In [5]:

df1.columns

Out[5]:

Index(['ORDERNUMBER', 'QUANTITYORDERED', 'PRICEEACH', 'ORDERLINENUMBER',
       'SALES', 'ORDERDATE', 'STATUS', 'QTR_ID', 'MONTH_ID', 'YEAR_ID',
       'PRODUCTLINE', 'MSRP', 'PRODUCTCODE', 'CUSTOMERNAME', 'PHONE',
       'ADDRESSLINE1', 'ADDRESSLINE2', 'CITY', 'STATE', 'POSTALCODE',
       'COUNTRY', 'TERRITORY', 'CONTACTLASTNAME', 'CONTACTFIRSTNAME',
       'DEALSIZE'],
      dtype='object')

In [6]:

df1.head()

Out[6]:

	ORDERNUMBER	QUANTITYORDERED	PRICEEACH	ORDERLINENUMBER	SALES	ORDERDATE	STATUS	QTR_ID	MONTH_ID	YEAR_ID	...	ADDRESSLINE1	ADDRESSLINE2	CITY	STATE	POSTALCODE	COUNTRY	TERRITORY	CONTACTLASTNAME	CONTACTFIRSTNAME	DEALSIZE
0	10107	30	95.70	2	2871.00	2/24/2003 0:00	Shipped	1	2	2003	...	897 Long Airport Avenue	NaN	NYC	NY	10022	USA	NaN	Yu	Kwai	Small
1	10121	34	81.35	5	2765.90	5/7/2003 0:00	Shipped	2	5	2003	...	59 rue de l'Abbaye	NaN	Reims	NaN	51100	France	EMEA	Henriot	Paul	Small
2	10134	41	94.74	2	3884.34	7/1/2003 0:00	Shipped	3	7	2003	...	27 rue du Colonel Pierre Avia	NaN	Paris	NaN	75508	France	EMEA	Da Cunha	Daniel	Medium
3	10145	45	83.26	6	3746.70	8/25/2003 0:00	Shipped	3	8	2003	...	78934 Hillside Dr.	NaN	Pasadena	CA	90003	USA	NaN	Young	Julie	Medium
4	10159	49	100.00	14	5205.27	10/10/2003 0:00	Shipped	4	10	2003	...	7734 Strong St.	NaN	San Francisco	CA	NaN	USA	NaN	Brown	Julie	Medium

5 rows × 25 columns

In [7]:

bin_columns = ['PRICEEACH', 'SALES']

In [8]:

for i, col in enumerate(bin_columns):
    plt.figure(i)
    sns.histplot(data=df1, x = col)

In [9]:

sns.histplot(data=df1, x = 'SALES', binwidth = 2000)

Out[9]:

<AxesSubplot:xlabel='SALES', ylabel='Count'>

In [10]:

bins = [0, 2000, 4000, 6000, 8000, 10000, 12000, 14000]
df1['bins'] = pd.cut(df1['SALES'], bins)

In [11]:

df1['bins'].value_counts()

Out[11]:

(2000, 4000]      1340
(4000, 6000]       624
(0, 2000]          565
(6000, 8000]       215
(8000, 10000]       63
(10000, 12000]      13
(12000, 14000]       2
Name: bins, dtype: int64

In [ ]: