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

data = pd.read_csv("Invoice.csv")

data.head()

data.info()

data.describe()

df=pd.DataFrame(data)

df.drop(['Amount 1'],axis=1,inplace=True)

df =df.iloc[:, ~df.columns.str.contains('Unnamed')]

df.info()

import datetime
df['year']=pd.DatetimeIndex(df['AccountingDate']).year
df['month']=pd.DatetimeIndex(df['AccountingDate']).month
df.info()

df['AccountingDate'].min()
df['month']
df["month"]=df["month"]. astype(str)
df["year"]=df["year"]. astype(str)
df["month_year"]=df[["month","year"]].agg('-'.join,axis=1)
df.isnull().sum()
print(df.columns.tolist())

df.groupby("month_year").sum()["Amount "].reset_index()
df["Amount "]=df["Amount "].astype(int)
df["Amount "]
df_sales=df.groupby("month_year").sum()["Amount "].reset_index()
plt.figure(figsize=(15,6))
sns.barplot(x="month_year",y="Amount ",data=df_sales)
plt.xlabel("Date")
plt.ylabel("Amount")
plt.title("analysis of sales")
plt.show()
df_sales
plt.figure(figsize=(15,6))
sns.countplot(x="AmountCurrency",data=df)
plt.xlabel("AmountCurrency")
plt.ylabel("higest currency used")
plt.title("currency analysis")
plt.show()
df_supplier=df.groupby("SupplierId").sum()["Amount "].reset_index()
plt.figure(figsize=(120,20))
sns.barplot(x="SupplierId",y="Amount ",data=df_supplier)
plt.xlabel("Supplier")
plt.ylabel("Amount")
plt.title("analysis of supplier sales")
plt.xticks(rotation='vertical',size=10)
plt.ylim(1000,100000)
plt.show()

df_location=df.groupby("SupplierLocationId").sum()["Amount "].reset_index()

plt.figure(figsize=(40,10))
sns.barplot(x="SupplierLocationId",y="Amount ",data=df_location)
plt.xlabel("SupplierLocationId")
plt.ylabel("Amount")
plt.title("analysis of supplierlocation sales")
plt.xticks(rotation='vertical',size=10)
plt.show()
58499617f385bd3aa532d655fe99293ee40e65a7
<mask token>

<mask token>
data.head()
data.info()
data.describe()
<mask token>
df.drop(['Amount 1'], axis=1, inplace=True)
<mask token>
df.info()
<mask token>
df.info()
df['AccountingDate'].min()
df['month']
<mask token>
df.isnull().sum()
print(df.columns.tolist())
df.groupby('month_year').sum()['Amount '].reset_index()
<mask token>
df['Amount ']
<mask token>
plt.figure(figsize=(15, 6))
sns.barplot(x='month_year', y='Amount ', data=df_sales)
plt.xlabel('Date')
plt.ylabel('Amount')
plt.title('analysis of sales')
plt.show()
df_sales
plt.figure(figsize=(15, 6))
sns.countplot(x='AmountCurrency', data=df)
plt.xlabel('AmountCurrency')
plt.ylabel('higest currency used')
plt.title('currency analysis')
plt.show()
<mask token>
plt.figure(figsize=(120, 20))
sns.barplot(x='SupplierId', y='Amount ', data=df_supplier)
plt.xlabel('Supplier')
plt.ylabel('Amount')
plt.title('analysis of supplier sales')
plt.xticks(rotation='vertical', size=10)
plt.ylim(1000, 100000)
plt.show()
<mask token>
plt.figure(figsize=(40, 10))
sns.barplot(x='SupplierLocationId', y='Amount ', data=df_location)
plt.xlabel('SupplierLocationId')
plt.ylabel('Amount')
plt.title('analysis of supplierlocation sales')
plt.xticks(rotation='vertical', size=10)
plt.show()

<mask token>
data = pd.read_csv('Invoice.csv')
data.head()
data.info()
data.describe()
df = pd.DataFrame(data)
df.drop(['Amount 1'], axis=1, inplace=True)
df = df.iloc[:, ~df.columns.str.contains('Unnamed')]
df.info()
<mask token>
df['year'] = pd.DatetimeIndex(df['AccountingDate']).year
df['month'] = pd.DatetimeIndex(df['AccountingDate']).month
df.info()
df['AccountingDate'].min()
df['month']
df['month'] = df['month'].astype(str)
df['year'] = df['year'].astype(str)
df['month_year'] = df[['month', 'year']].agg('-'.join, axis=1)
df.isnull().sum()
print(df.columns.tolist())
df.groupby('month_year').sum()['Amount '].reset_index()
df['Amount '] = df['Amount '].astype(int)
df['Amount ']
df_sales = df.groupby('month_year').sum()['Amount '].reset_index()
plt.figure(figsize=(15, 6))
sns.barplot(x='month_year', y='Amount ', data=df_sales)
plt.xlabel('Date')
plt.ylabel('Amount')
plt.title('analysis of sales')
plt.show()
df_sales
plt.figure(figsize=(15, 6))
sns.countplot(x='AmountCurrency', data=df)
plt.xlabel('AmountCurrency')
plt.ylabel('higest currency used')
plt.title('currency analysis')
plt.show()
df_supplier = df.groupby('SupplierId').sum()['Amount '].reset_index()
plt.figure(figsize=(120, 20))
sns.barplot(x='SupplierId', y='Amount ', data=df_supplier)
plt.xlabel('Supplier')
plt.ylabel('Amount')
plt.title('analysis of supplier sales')
plt.xticks(rotation='vertical', size=10)
plt.ylim(1000, 100000)
plt.show()
df_location = df.groupby('SupplierLocationId').sum()['Amount '].reset_index()
plt.figure(figsize=(40, 10))
sns.barplot(x='SupplierLocationId', y='Amount ', data=df_location)
plt.xlabel('SupplierLocationId')
plt.ylabel('Amount')
plt.title('analysis of supplierlocation sales')
plt.xticks(rotation='vertical', size=10)
plt.show()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
data = pd.read_csv('Invoice.csv')
data.head()
data.info()
data.describe()
df = pd.DataFrame(data)
df.drop(['Amount 1'], axis=1, inplace=True)
df = df.iloc[:, ~df.columns.str.contains('Unnamed')]
df.info()
import datetime
df['year'] = pd.DatetimeIndex(df['AccountingDate']).year
df['month'] = pd.DatetimeIndex(df['AccountingDate']).month
df.info()
df['AccountingDate'].min()
df['month']
df['month'] = df['month'].astype(str)
df['year'] = df['year'].astype(str)
df['month_year'] = df[['month', 'year']].agg('-'.join, axis=1)
df.isnull().sum()
print(df.columns.tolist())
df.groupby('month_year').sum()['Amount '].reset_index()
df['Amount '] = df['Amount '].astype(int)
df['Amount ']
df_sales = df.groupby('month_year').sum()['Amount '].reset_index()
plt.figure(figsize=(15, 6))
sns.barplot(x='month_year', y='Amount ', data=df_sales)
plt.xlabel('Date')
plt.ylabel('Amount')
plt.title('analysis of sales')
plt.show()
df_sales
plt.figure(figsize=(15, 6))
sns.countplot(x='AmountCurrency', data=df)
plt.xlabel('AmountCurrency')
plt.ylabel('higest currency used')
plt.title('currency analysis')
plt.show()
df_supplier = df.groupby('SupplierId').sum()['Amount '].reset_index()
plt.figure(figsize=(120, 20))
sns.barplot(x='SupplierId', y='Amount ', data=df_supplier)
plt.xlabel('Supplier')
plt.ylabel('Amount')
plt.title('analysis of supplier sales')
plt.xticks(rotation='vertical', size=10)
plt.ylim(1000, 100000)
plt.show()
df_location = df.groupby('SupplierLocationId').sum()['Amount '].reset_index()
plt.figure(figsize=(40, 10))
sns.barplot(x='SupplierLocationId', y='Amount ', data=df_location)
plt.xlabel('SupplierLocationId')
plt.ylabel('Amount')
plt.title('analysis of supplierlocation sales')
plt.xticks(rotation='vertical', size=10)
plt.show()

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

data = pd.read_csv("Invoice.csv")

data.head()

data.info()

data.describe()

df=pd.DataFrame(data)

df.drop(['Amount 1'],axis=1,inplace=True)

df =df.iloc[:, ~df.columns.str.contains('Unnamed')]

df.info()

import datetime
df['year']=pd.DatetimeIndex(df['AccountingDate']).year
df['month']=pd.DatetimeIndex(df['AccountingDate']).month
df.info()

df['AccountingDate'].min()
df['month']
df["month"]=df["month"]. astype(str)
df["year"]=df["year"]. astype(str)
df["month_year"]=df[["month","year"]].agg('-'.join,axis=1)
df.isnull().sum()
print(df.columns.tolist())

df.groupby("month_year").sum()["Amount "].reset_index()
df["Amount "]=df["Amount "].astype(int)
df["Amount "]
df_sales=df.groupby("month_year").sum()["Amount "].reset_index()
plt.figure(figsize=(15,6))
sns.barplot(x="month_year",y="Amount ",data=df_sales)
plt.xlabel("Date")
plt.ylabel("Amount")
plt.title("analysis of sales")
plt.show()
df_sales
plt.figure(figsize=(15,6))
sns.countplot(x="AmountCurrency",data=df)
plt.xlabel("AmountCurrency")
plt.ylabel("higest currency used")
plt.title("currency analysis")
plt.show()
df_supplier=df.groupby("SupplierId").sum()["Amount "].reset_index()
plt.figure(figsize=(120,20))
sns.barplot(x="SupplierId",y="Amount ",data=df_supplier)
plt.xlabel("Supplier")
plt.ylabel("Amount")
plt.title("analysis of supplier sales")
plt.xticks(rotation='vertical',size=10)
plt.ylim(1000,100000)
plt.show()

df_location=df.groupby("SupplierLocationId").sum()["Amount "].reset_index()

plt.figure(figsize=(40,10))
sns.barplot(x="SupplierLocationId",y="Amount ",data=df_location)
plt.xlabel("SupplierLocationId")
plt.ylabel("Amount")
plt.title("analysis of supplierlocation sales")
plt.xticks(rotation='vertical',size=10)
plt.show()