Personal Consumption Expenditure

#dataset obtained from dataworld import os import matplotlib.pyplot as plt %matplotlib inline import seaborn as sns import numpy as np import pandas as pd

df = pd.read_excel('Personal Consumption Expenditures.xlsx')

pip install openpyxl

Requirement already satisfied: openpyxl in c:\programdata\anaconda3\lib\site-packages (3.0.7)Note: you may need to restart the kernel to use updated packages.
Requirement already satisfied: et-xmlfile in c:\programdata\anaconda3\lib\site-packages (from openpyxl) (1.0.1)

df.head(10)

df.tail(10)

df.isnull().sum()

df.columns

df['Month'] = pd.to_datetime(df['Month'])

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 14900 entries, 0 to 14899
Data columns (total 4 columns):
 #   Column               Non-Null Count  Dtype         
---  ------               --------------  -----         
 0   Category             14900 non-null  object        
 1   Sub-Category         14900 non-null  object        
 2   Month                14900 non-null  datetime64[ns]
 3   Millions of Dollars  14900 non-null  int64         
dtypes: datetime64[ns](1), int64(1), object(2)
memory usage: 465.8+ KB

df.shape

x = df['Category'].unique() y = df['Category'].nunique() print(x) print('Number Of Unique:', y)

['Alcoholic beverages' 'Cereals and bakery products' 'Fats and oils'
 'Fish and seafood' 'Fresh fruits and vegetables' 'Meats and poultry'
 'Milk, dairy products, and eggs' 'Processed fruits and vegetables'
 'Sugar and sweets' 'Coffee, tea, and other beverage materials'
 'Mineral waters, soft drinks, and vegetable juices']
Number Of Unique: 11

x= df['Sub-Category'].unique() y = df['Sub-Category'].nunique() print(x) print('Number Of Unique:', y)

['Beer' 'Spirits' 'Wine' 'Bakery products' 'Cereals' 'Fats and oils'
 'Fish and seafood' 'Fruit (fresh)' 'Vegetables (fresh)' 'Beef and veal'
 'Other meats' 'Pork' 'Poultry' 'Eggs' 'Fresh milk'
 'Processed dairy products' 'Processed fruits and vegetables'
 'Sugar and sweets' 'Coffee, tea, and other beverage materials'
 'Mineral waters, soft drinks, and vegetable juices']
Number Of Unique: 20

df.sample(10)

df.describe()

df.isnull().any()

ax = sns.barplot(x='Sub-Category', y='Millions of Dollars', data=df) ax.tick_params(axis='x', rotation=90)

ax = sns.barplot(x='Category', y='Millions of Dollars', data=df) ax.tick_params(axis='x', rotation=90)

import datetime df['Weekday_num'] = pd.DatetimeIndex(df['Month']).weekday df['Weekday'] = pd.DatetimeIndex(df['Month']).day_name() df['Year'] = pd.DatetimeIndex(df['Month']).year df['Month name'] = pd.DatetimeIndex(df['Month']).month_name() #df.drop('Weekday_name', axis='columns', inplace=True)

df.head(10)

sns.countplot(y='Category', data=df)

#df.groupby(['Year'])['Millions of Dollars'].sum().plot(kind='bar') sns.set(rc={'figure.figsize':(20,5)}) ax = sns.barplot(x='Year', y='Millions of Dollars', data=df) ax.tick_params(axis='x', rotation=90)

sns.set(rc={'figure.figsize':(20,5)}) ax = sns.barplot(x='Sub-Category', y='Millions of Dollars', data=df) ax.tick_params(axis='x', rotation=90)

df.loc[df['Sub-Category']== 'Beer']

df['Month name'].unique()

x= df.loc[df['Year']== 2021].nunique()