CODEWITHSUNDEEP
pythonplotlystreamlit
dummyds
dummyds

Reputation: 197

How can I make a plotly graph on streamlit grouped by period?

I'm trying to plot a line graph using Plotly + Streamlit, but grouping data by month.

I have this data:

id      date          grade
A       2020-01-01    100
B       2020-01-11    200
C       2020-01-21    500
D       2020-01-21    300
E       2020-02-01    100
F       2020-02-01    200

I want to plot the grade mean by month. This is what I got so far:

df = pd.to_datetime(df["date"], format="%m/%d/%y %I:%M%p")

df_grouped = df.groupby(
    by=[df.index.month, df.index.year]
)

df.grade.mean().plot()

The problem is that I don't know how to do the same thing (or something close to) using plotly and on streamlit.

Any ideas?

Upvotes: 2

Views: 1137

Answers (1)

vestland
vestland

Reputation: 61184

You haven't specified what kind of bar type you'd like, but I'm guessing a go.Bar() will do. But we can change that. For grouping and aggregating I would use the following approach:

df['date'] = pd.to_datetime(df["date"])
df['months'] = df['date'].dt.month_name()
df_months = df.groupby(['months']).agg('mean').reset_index()
new_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
df_months['months'] = pd.Categorical(df_months['months'], categories=new_order, ordered=True)
df_months = df_months.sort_values('months')

Why so complicated? Because I'm assuming you'd like to have month names on your x-axis. And after grouping and aggregating values by month name, the order of the months can become messed up. The somewhat laborious approach above makes sure that doesn't happen, and that you can en up with this bar chart with the months in correct order:

enter image description here

Complete code:

import pandas as pd
import plotly.graph_objects as go

df = pd.DataFrame({'id': {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F'},
                     'date': {0: '2020-01-01',
                      1: '2020-01-11',
                      2: '2020-01-21',
                      3: '2020-01-21',
                      4: '2020-02-01',
                      5: '2020-02-01'},
                     'grade': {0: 100, 1: 200, 2: 500, 3: 300, 4: 100, 5: 200}})

df['date'] = pd.to_datetime(df["date"])
df['months'] = df['date'].dt.month_name()
df_months = df.groupby(['months']).agg('mean').reset_index()
new_order = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']
df_months['months'] = pd.Categorical(df_months['months'], categories=new_order, ordered=True)
df_months = df_months.sort_values('months')

fig=go.Figure(go.Bar(x=df_months.months, y=df_months.grade))
fig.show()

Upvotes: 1

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