Including a function to extract FRED data filtering by a range of years

Money_Supply_FRED.py

#!python3

import sys ,os , requests
import time, json
import matplotlib.pyplot as plt


# This library retrieves and relies on data coming from FRED API
# Attributes

api_key="api_key=2665c3dd755e8d7912000c862a60c365"

def category_search_by():

    entry = input("Please introduce keywords: ")
    req1 = "https://api.stlouisfed.org/fred/series/search?search_text="+entry+"&"+api_key+"&file_type=json"
    req = requests.get(req1)
    print(json.loads(req.text)['seriess'])

def data_series_filtered(id, start_yr, end_yr):

    # This function is intended to help the user to filter by start/end yr i.e. of certain series

    req1 = "https://api.stlouisfed.org/fred/series/observations?series_id=" + id + "&" + api_key + "&file_type=json"

    print(req1 + "\n")
    req = requests.get(req1)
    observations = json.loads(req.text)['observations']
    date_s= list()
    value_s = list ()
    index = 0

    while index < len(observations):

        year = int(observations[index]["date"][0:4])

        if year >= int(start_yr)  and year <= int(end_yr):

            date = observations[index]["date"]
            date_s.append(date)
            value = float(observations[index]["value"])
            value_s.append(value)
            index = index + 1

        else:
            index = index + 1

    print(date_s,value_s)

    return date_s, value_s

def data_series_extraction(id):

    req1 = "https://api.stlouisfed.org/fred/series/observations?series_id="+id+"&"+api_key+"&file_type=json"

    print(req1+"\n")
    req = requests.get(req1)
    observations =json.loads(req.text)['observations']
    date_s = list()
    value_s = list()

    for i in observations:

        month = "-04-"
        #Selecting only April months' of each year

        if month in i['date']:
            date_s.append(i['date'])
            value_s.append(float(i['value']))

    date_s_s = [*range(0,len(date_s))]

    # if data on data_s_s returned instead of date_s (yearly data) then it is intended to avoid date overlapping in the x axis
    # when using matplotlib

    return date_s_s,value_s



def plot_single(date_s,value_s,x_label,y_label,title_s):


     plt.plot(date_s, value_s)
     plt.ylabel(y_label)
     plt.xlabel(x_label)
     plt.title(title_s)
     plt.show()

def plot_multiple(d1,v1,d2,v2,title_s,x_label,y_label):
    plt.plot(d1, v1)
    plt.plot(d2, v2)
    plt.ylabel(y_label)
    plt.xlabel(x_label)
    plt.title(title_s)
    plt.show()

def main():

    # Example to plot filtered data from FRED repositories

    dm1, vm1 = data_series_filtered("MANMM101USA657S", "2000", "2020")
    dm2, vm2 = data_series_filtered("MABMM301USA657S", "2000", "2020")
    plot_multiple(dm1, vm1, dm2, vm2, "M1 vs. M2 Growth Rate (Previous Period, Not Seasonally Adjusted)","2011-2020","% Growth Rate")

    #data_series_extraction("WM2NS","From 1980 t0 2021 (May)","Billions of Dollars","M2 United States (Money Stock)")

    #dm2,vm2 = data_series_extraction("M2SL")
    #dm1,vm1 = data_series_extraction("M1SL")
    #plot_multiple(dm1, vm1, dm2, vm2,"M1 vs. M2 United States (Money Stock)","From 1959 to 2021 (May)", "Billions of Dollars")

    #plot_single(dm2,vm2,"From 1959 t0 2021 (May)", "Billions of Dollars", "M2 United States (Money Stock)")
    #data_series_extraction("M1SL", "From 1959 t0 2021 (May)", "Billions of Dollars", "M1 United States (Money Stock)")

if __name__ == "__main__":
    main()

New Function that aims to offer the ability to filter and extract date from a year range