Here is my code for graph_objects-

go.Figure(go.Scatter(x=continent_df.date, y=continent_df.new_cases_smoothed))

Whereas my code for plotly express is this -

px.line(continent_df, x='date', y='new_cases_smoothed', color='continent')

Why does the first graph print extra straight lines for each continent? I already tried sorting the dataframe.

continent_df.sort_values(['continent','date'], inplace=True)

(Also, how can I color code each line in the first graph as it is done in the second one?)

I can't be 100% sure without a proper sample of your data. But it seems that your dataset is of a long format with multiple values in continent_df.new_cases_smoothed belonging to different contients. And you're assigning all these values to one single trace using go.Figure(go.Scatter(x=continent_df.date, y=continent_df.new_cases_smoothed)).

The straight lines are there because there's only one line that goes back and forth and covers all categories and all indexes. The straight parts of the line appear when it goes back to the beginning and starts showing a new category

However, using px.line here takes care of that by grouping the continents using color='continent'. Hence making the value categories appear as unique traces.

We can use the gapminder dataset, which has a structure similar to your real world data, to illustrate how to assign individual traces to a go.Figure using fig.add_traces(go.Scatter()). The key is to retrieve unique categories, subset your data, and add groups line by line. This gives you arguably greater flexibility compared to using px.line.

Plot

Code

import plotly.graph_objs as go
import plotly.express as px
import pandas as pd

# Data
gap = px.data.gapminder()

fig = go.Figure()
for c in gap['country'].unique()[:10]:
    df = gap[gap['country']==c]
    fig.add_traces(go.Scatter(x=df['year'], y = df['lifeExp'], name = c))
    
fig.show()