Use pandas and matplotlib to produce a chart of natural gas prices

12-30-2021 324 words 2 minutes 0 views

Contents

In this example Python code, we use requests to fetch data from the EIA.gov website and json from the Python standard library to parse the json data. Next, we load the daily prices into a Pandas DataFrame and format the date column and set it as the index. Finally, we use Pandas .plot() to create a chart saving it as a png image.

Additionally, we use date slicing to generate a 1 month chart of February 2021 showing the price spike experienced during winter storm Uri.

import json
from rich import print
import pandas as pd
import requests

API_KEY = "YOUR_API_KEY"
url = f"https://api.eia.gov/series/?api_key={API_KEY}&series_id=NG.RNGWHHD.D"

resp = requests.get(url)
natgasdata= resp.json()
df = pd.DataFrame(natgasdata["series"][0]["data"], columns=["Date", "Price"])
df['Date'] = pd.to_datetime(df['Date'].astype(str), format='%Y%m%d')
df.set_index('Date', inplace=True)

df.plot(
    y = 'Price', figsize=(15,10), title="Natural Gas Prices"
)
    .get_figure()
    .savefig('natural-gas-prices.png')

df['2021-02-01':'2021-03-01'].plot(
    y = 'Price', figsize=(15,10), title="Natural Gas Prices - February 2021"
)
    .get_figure()
    .savefig('natural-gas-prices-feb2021.png')

Module Imports

import json
from rich import print
import pandas as pd
import requests

Fetch Price Data

API_KEY = "YOUR_API_KEY" # Obtain API Key at https://www.eia.gov/opendata/register.php
url = f"https://api.eia.gov/series/?api_key={API_KEY}&series_id=NG.RNGWHHD.D"

resp = requests.get(url)
natgasdata= resp.json()

Create DataFrame

df = pd.DataFrame(natgasdata["series"][0]["data"], columns=["Date", "Price"])
df

	Date	Price
0	20211228	3.32
1	20211227	3.45
2	20211223	3.56
3	20211222	3.95
4	20211221	3.96
...	...	...
6290	19970113	4.00
6291	19970110	3.92
6292	19970109	3.61
6293	19970108	3.80
6294	19970107	3.82

6295 rows × 2 columns

Transform DataFrame

Convert the Data column type from a string into a datetime and then set the Date column as the index.

df['Date'] = pd.to_datetime(df['Date'].astype(str), format='%Y%m%d')
df.set_index('Date', inplace=True)
df

	Price
Date
2021-12-28	3.32
2021-12-27	3.45
2021-12-23	3.56
2021-12-22	3.95
2021-12-21	3.96
...	...
1997-01-13	4.00
1997-01-10	3.92
1997-01-09	3.61
1997-01-08	3.80
1997-01-07	3.82

6295 rows × 1 columns

Plot the chart

df.plot( y = 'Price', figsize=(15,10), title="Natural Gas Prices")

Generate a 1 month chart of February 2021 showing the price spike experinced during winter storm Uri.

df['2021-02-01':'2021-03-01'].plot( y = 'Price', figsize=(15,10), title="Natural Gas Prices - February 2021")