get_cumulative_change.py

import pandas as pd
import plotly.express as px


def get_updated_dataframe(sector):
    df = pd.read_json("data/cumulative/" + sector + ".json", typ="dictionary")
    df_mod = pd.DataFrame({"date": df.index, "price change": df.values})
    df_mod["sector"] = sector
    return df_mod


df_healthcare = get_updated_dataframe("Healthcare")
df_technology = get_updated_dataframe("Technology")
df_real_estate = get_updated_dataframe("Real Estate")
df_energy = get_updated_dataframe("Energy")
df_communication_services = get_updated_dataframe("Communication Services")
df_utilities = get_updated_dataframe("Utilities")
df_basic_materials = get_updated_dataframe("Basic Materials")
df_consumer_defensive = get_updated_dataframe("Consumer Defensive")
df_financial = get_updated_dataframe("Financial")
df_industrials = get_updated_dataframe("Industrials")
df_consumer_cyclical = get_updated_dataframe("Consumer Cyclical")


final_df = pd.concat(
    [
        df_healthcare,
        df_technology,
        df_real_estate,
        df_energy,
        df_communication_services,
        df_utilities,
        df_basic_materials,
        df_consumer_defensive,
        df_financial,
        df_industrials,
        df_consumer_cyclical,
    ]
)
final_df["month"] = final_df["date"].dt.strftime("%b %Y")

print(final_df)

# generate the plot
fig = px.bar(
    final_df,
    x="price change",
    y="sector",
    text="price change",
    orientation="h",
    animation_frame="month",
)

# a bit of formatting...
fig = fig.update_traces(texttemplate="%{text:4s}")

for f in fig.frames:
    for t in f["data"]:
        t["texttemplate"] = "%{text:4s}"

fig.update_layout(xaxis={"range": [-10, final_df["price change"].max() * 1.25]})
fig.update_layout(font_size=14)
# fig.update_layout(yaxis={'categoryorder':'total ascending'})
fig.show()