Utilizing well-liked Python visualization libraries it may be comparatively simple to create domestically charts and dashboards of various types. Though, it may be way more sophisticated to share your outcomes with different individuals on the internet.
One attainable strategy to do that is utilizing libraries comparable to Streamlit, Flask, Plotly Sprint and paying for a webhosting service to cowl the server aspect and run your Python scripts to point out on the webpage. Alternatively, some suppliers like Plotly Chart or Datapane present additionally free cloud help so that you can add your Python visualizations after which embed them on the internet. In each eventualities, you’d have the ability to obtain something you want if in case you have a small price range in your undertaking, however there’s any means we might obtain comparable outcomes totally free?
As a part of this text, we’re going to discover 3 attainable approaches:
To be able to showcase every of those 3 approaches, we’re going to create a easy utility to discover historic inflation information from all around the world. So as to take action, we’re going to use The World Financial institution Gloabal Database of Inflation all details about licensing for the info will be discovered at this hyperlink [1].
As soon as downloaded the info, we are able to then use the next pre-processing operate as a way to higher form the dataset for visualization and import simply the three Excel Sheets we’re going to use as a part of the evaluation (general inflation information, inflation for meals and vitality costs).
import pandas as pddef import_data(identify):
df = pd.read_excel("Inflation-data.xlsx", sheet_name=identify)
df = df.drop(["Country Code", "IMF Country Code", "Indicator Type", "Series Name", "Unnamed: 58"], axis=1)
df = (df.soften(id_vars = ['Country', 'Note'],
var_name="Date", value_name="Inflation"))
df = df.pivot_table(index='Date', columns="Nation",
values="Inflation", aggfunc="sum")
return df
inf_df = import_data("hcpi_a")
food_df = import_data("fcpi_a")
energy_df = import_data("ecpi_a")
