plot_gasoline_prices.py

import pandas as pd
import matplotlib.pyplot as plt
import eia

def retrieve_time_series(api, series_ID):
    """
    Return the time series dataframe, based on API and unique Series ID
    Arguments:
        api: API that we're connected to
        series_ID: string. Name of the series that we want to pull from the EIA API
    Outputs:
        df: Pandas dataframe of time series
    """
    #Retrieve Data By Series ID 
    series_search = api.data_by_series(series=series_ID)
    ##Create a pandas dataframe from the retrieved time series
    df = pd.DataFrame(series_search)
    return df

def scatterplot(x_data, y_data, x_label, y_label, title):
    """
    Arguments:
        x_data: Series. Desired x-axis for scatterplot.
        y_data: Series. Desired y-axis for scatterplot.
        x_label: String. Label for x-axis.
        y_label: String. Label for y-axis.
        title: String. Title of plot
    Outputs:
        Scatterplot in console.
    """
    fig, ax = plt.subplots()
    ax.scatter(x_data, y_data, s = 30, color = '#539caf', alpha = 0.75)
    ax.set_title(title)
    ax.set_xlabel(x_label)
    ax.set_ylabel(y_label)
    fig.autofmt_xdate()
    
#####EXECUTE IN MAIN BLOCK

#Create EIA API using your specific API key
api_key = 'YOUR API KEY HERE'
api = eia.API(api_key)
    
#Pull the oil WTI price data
series_ID='PET.EER_EPMRU_PF4_RGC_DPG.D'
gasoline_price_df=retrieve_time_series(api, series_ID)
gasoline_price_df.reset_index(level=0, inplace=True)
#Rename the columns for easer analysis
gasoline_price_df.rename(columns={'index':'Date',
            gasoline_price_df.columns[1]:'Gasoline_Price'}, 
            inplace=True)
     
#Visualize anomalies using matplotlib function
scatterplot(gasoline_price_df['Date'],
                gasoline_price_df['Gasoline_Price'], 
                'Date', 
                'Gasoline Price (Dollars Per Gallon)', 
                'US Gulf Coast Gasoline Price Time Series: 2014-Present')