import numpy as np import pandas as pd import matplotlib

file = "U.S._natural_gas_production.csv" gas_df = pd.read_csv(file, decimal=',') gas_df.head(3)

gas_df.shape

gas_df.columns

gas_df.info()

gas_df["Month"] = pd.to_datetime(gas_df["Month"])

month_type = gas_df['Month'].dtype

gas_df.info()

gas_df['Month'].dtype

gas_df['Month'].dt.year.head()

gas_df['Month'].dt.month.tail()

yearly_gas_df = gas_df.groupby(gas_df["Month"].dt.year).sum() yearly_gas_df

plot = yearly_gas_df.filter(items=['U.S.']).plot(kind="bar") plot.set_xlabel("Year");

filtered_yearly_gas_df = yearly_gas_df[1:-1] filtered_yearly_gas_df

plot = filtered_yearly_gas_df.filter(items=['U.S.']).plot(kind="bar") plot.set_xlabel("Year");

filtered_yearly_gas_df.columns[1:].sort_values()

plot = filtered_yearly_gas_df.filter(items=['Colorado', 'Louisiana', 'Ohio', 'Utah']).plot() plot.set_xlabel("Year");

file = "U.S._crude_oil_production.csv" oil_df = pd.read_csv(file, decimal=',') oil_df.head(3)

oil_df["Month"] = pd.to_datetime(oil_df["Month"])

month_type_oil = oil_df['Month'].dtype

yearly_oil_df = oil_df.groupby(oil_df["Month"].dt.year).sum() yearly_oil_df

yearly_oil_df.columns = yearly_oil_df.columns.str.strip() yearly_oil_df

yearly_oil_df.filter(items=['U.S. Crude Oil']).plot(kind='bar');

filtered_yearly_oil_df = yearly_oil_df[1:-1]

total_gas = filtered_yearly_gas_df.filter(items=['U.S.']) total_gas.columns = [ 'Gas' ] display(total_gas.head(3)) total_oil = filtered_yearly_oil_df.filter(items=['U.S. Crude Oil']) total_oil.columns = [ 'Crude Oil' ] total_oil.head(3)

merged_df = pd.concat([total_oil, total_gas], axis='columns') merged_df

plot = merged_df.plot(kind="bar") plot.set_xlabel("Year") plot.legend(['Gas (Millions of Cubic feet)', 'Crude Oil (Thousands of barrels)']);