#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

df.head(10)

df.tail(10)

df.isnull().sum()

df.columns

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

df.info()

df.shape

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

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

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()