DEV Community

Viper
Viper

Posted on • Edited on • Originally published at dataqoil.com

Make Awesome Data Dashboard using Streamlit and Plotly: Simple Trends

#python #datascience #beginners

Originally published on dataqoil.com.

Make Awesome Data Dashboard using Streamlit and Plotly: Simple Trends

One of the few ways we find the insights from the data is via dashboards. And for Data Analysts, there are options like tableau. But not all of them are for free. However, we can make some cool dashboards using Streamlit and in this blog, we will explore how.

This blog is just a beginning of creating simple data dashboard with Plotly in Streamlit. Here we will only plot lines in this blog. Next blog will be about plotting maps. Please Stay TUNED.

Updates

Installation

We have written a cool blog about getting started with Plotly and Cufflinks for making awesome analysis and plots in Jupyter Notebook. Please do not forget to read them.

pip install plotly cufflinks streamlit
Enter fullscreen mode Exit fullscreen mode

First Streamlit App

For making a first streamlit app:

  • We will simply create a new project folder (but it is not necessary)
  • We will create a new Python file named as main.py inside it.
  • Then inside that Python file we will add 
import streamlit as st

st.markdown("Hello world, this is my new Data Dashboard.")
Enter fullscreen mode Exit fullscreen mode
  • Now saving a file and then from the project folder, we will run streamlit: 
streamlit run main.py
Enter fullscreen mode Exit fullscreen mode
  • We could see something like below on the terminal:

  • If the link does not open to the browser by itself, open it. And we could see our markdown text on the web page.

First Plotly Plot in Streamlit

It is relatively easy to plot graphs and plots in Streamlit app than any other web apps. Lets do it how.

  • We will be making a data dashboard thus we will first prepare a real world data.
  • The data will be of COVID 19 data from this repository. The data is updated on daily level thus your results can be different than ours in this blog.

Lets put below code in our main.py file and see the changes in browser by refreshing.

import streamlit as st
import numpy as np
import pandas as pd
import cufflinks

@st.cache
def get_data(url):
    df = pd.read_csv(url)
    df["date"] = pd.to_datetime(df.date).dt.date
    df['date'] = pd.DatetimeIndex(df.date)

    return df

url = "https://covid.ourworldindata.org/data/owid-covid-data.csv"
data = get_data(url)

daily_cases = data.groupby(pd.Grouper(key="date", freq="1D")).aggregate(new_cases=("new_cases", "sum")).reset_index()
fig = daily_cases.iplot(kind="line", asFigure=True, 
                        x="date", y="new_cases")
st.plotly_chart(fig)
Enter fullscreen mode Exit fullscreen mode

In above code, we did:

  • Imported NumPy, Pandas and Cufflinks.
  • Read a csv file from a given URL inside a function along with a cache decorator. The reason to do so is that we do not want the csv file to be reloaded everytime we make small changes in a source file.
  • We made a date column with date time index.
  • We then aggregated data on daily level by finding a sum of new cases.
  • We plotted a line plot using Pandas iplot attribute. Cufflinks allowed us to use iplot with Pandas object.
  • To be able to use that figure in streamlit app, we used asFigure=True in iplot and then passed figure inside st.plotly_chart

Adding Dropdown for Location

The above plot was for entire locations and if we look carefully to all the locations, there are values like World, Asia and so on which are aggregated values and if we want to view world’s daily trend, we must either filter out rows of locations like World, Asia or we must select rows with those values. But doing filter or selection inside a code will not be much of a good idea so lets make a drop down. Just below the function, we will modify code to look like below:

url = "https://covid.ourworldindata.org/data/owid-covid-data.csv"
data = get_data(url)

locations = data.location.unique().tolist()

sidebar = st.sidebar
location_selector = sidebar.selectbox(
    "Select a Location",
    locations
)
st.markdown(f"# Currently Selected {location_selector}")

daily_cases = data.groupby(pd.Grouper(key="date", freq="1D")).aggregate(new_cases=("new_cases", "sum")).reset_index()
fig = daily_cases.iplot(kind="line", asFigure=True, 
                        x="date", y="new_cases")
st.plotly_chart(fig)
Enter fullscreen mode Exit fullscreen mode

What we did is:

  • Taken a unique list of countries or locations from above dataframe.
  • Create a sidebar object to make our drop down visible on sidebar.
  • Create a selectbox in that sidebar and give options as locations.
  • Then in markdown, show the currently selected location. Streamlit gives selected value in that selectbox.

We can see something like below:

Adding A Checkbox to Show Data

It is even simpler. Add below code just below the markdown to show location selected.


show_data = sidebar.checkbox("Show Data")

if show_data:
    st.dataframe(data)

daily_cases = data.groupby(pd.Grouper(key="date", freq="1D")).aggregate(new_cases=("new_cases", "sum")).reset_index()
fig = daily_cases.iplot(kind="line", asFigure=True, 
                        x="date", y="new_cases")
st.plotly_chart(fig)
Enter fullscreen mode Exit fullscreen mode

We created a checkbox on sidebar and if it is clicked, we will push the data in st.dataframe. Below is the result in web app.

But the data is not much readable. So lets create a new drop down, where we will select the type of trend. But lets first create possible metrics or trend of data that we want visualize:

  1. Daily Cases : How many of cases were there on daily level?
  2. Daily Deaths : How many of the deaths were there on daily level?
  3. Daily Tests : How many of the tests were there on daily level?
  4. Daily Vaccination : How many of the daily vaccinations were there on daily level?

In above 4 metrics, we could make weekly, monthly, quarterly and yearly level aggregations easily so lets make it as a whole.

Date Level Trend Data

Just below the locations line, we will create code something like below:

sidebar = st.sidebar
location_selector = sidebar.selectbox(
    "Select a Location",
    locations
)
st.markdown(f"# Currently Selected {location_selector}")
trend_level = sidebar.selectbox("Trend Level", ["Daily", "Weekly", "Monthly", "Quarterly", "Yearly"])
st.markdown(f"### Currently Selected {trend_level}")

show_data = sidebar.checkbox("Show Data")

trend_kwds = {"Daily": "1D", "Weekly": "1W", "Monthly": "1M", "Quarterly": "1Q", "Yearly": "1Y"}
trend_data = data.query(f"location=='{location_selector}'").\
    groupby(pd.Grouper(key="date", 
    freq=trend_kwds[trend_level])).aggregate(new_cases=("new_cases", "sum"),
    new_deaths = ("new_deaths", "sum"),
    new_vaccinations = ("new_vaccinations", "sum"),
    new_tests = ("new_tests", "sum")).reset_index()

trend_data["date"] = trend_data.date.dt.date

new_cases = sidebar.checkbox("New Cases")
new_deaths = sidebar.checkbox("New Deaths")
new_vaccinations = sidebar.checkbox("New Vaccinations")
new_tests = sidebar.checkbox("New Tests")

lines = [new_cases, new_deaths, new_vaccinations, new_tests]
line_cols = ["new_cases", "new_deaths", "new_vaccinations", "new_tests"]
trends = [c[1] for c in zip(lines,line_cols) if c[0]==True]

if show_data:
    tcols = ["date"] + trends
    st.dataframe(trend_data[tcols])

daily_cases = data.groupby(pd.Grouper(key="date", freq="1D")).aggregate(new_cases=("new_cases", "sum")).reset_index()
fig = daily_cases.iplot(kind="line", asFigure=True, 
                        x="date", y="new_cases")
st.plotly_chart(fig)
Enter fullscreen mode Exit fullscreen mode

What we did in above code is:

  • Created a selectbox for selecting a trend level, daily, weekly, monthly, quarterly and yearly.
  • Then we also showed the selected level in 3rd heading level in markdown.
  • We have already made a show data checkbox.
  • We also prepared a keywords for each level. This keywords dictionary is used while taking a group on respective date level. So 1D is for daily and W is for weekly and so on. We will select a trend level as a key to this dictionary and pass the value of this dictionary as a Grouper’s frequency later.
  • We took a data of currently selected location and then grouped the filtered data according to the given trend level. Then calculated summed values of new deaths, new vaccinations, new cases and new tests on that level.
  • We also make the date column more like normalized form.
  • We made separate checkbox for each of above created trend data column.
  • We will plot a line, thus we created another list lines, holding all the checkbox variables we created on previous step.
  • We also created another list, line_cols where we kepth the name of the columns from a trend_data with respective to the lines list’s checkboxes.
  • We created another list trend and we will put those column names from lines list for which its respective checkbox is checked on.
  • If the checkbox show_data is checked on, then we will show the data but show only those columns which is checked on.

The result should look like below:

And if we selected all the columns with weekly trend of Afghanistan,

Date Level Trend Visualization

In above web app, our dashboard contained only a data table and a plot that we initially created. But now, lets create a visualization of that as well.

Lets put below code just below we showed our data.

subplots=sidebar.checkbox("Show Subplots", True)
if len(trends)>0:
    fig=trend_data.iplot(kind="line", asFigure=True, xTitle="Date", yTitle="Values",
                         x="date", y=trends, title=f"{trend_level} Trend of {', '.join(trends)}.", subplots=subplots)
    st.plotly_chart(fig, use_container_width=False)
Enter fullscreen mode Exit fullscreen mode

But remove the code of visualization we added earlier.

We can see something like below:

Comparison Between N Countries

In above plots, we were only plotting plots of a single location but what if we want to compare between two by viewing same on the same figure? This is not possible by default so we will tweak out code little bit.

  • Make a radio button and pass two values, Single and Multiple. If selected Single, we will do analysis on single location else on Multiple.
  • For Single selection, put everything we’ve done until now inside a if condition. 
analysis_type = sidebar.radio("Analysis Type", ["Single", "Multiple"])
st.markdown(f"Analysis Mode: {analysis_type}")

if analysis_type=="Single":
    location_selector = sidebar.selectbox(
        "Select a Location",
        locations
    )
    st.markdown(f"# Currently Selected {location_selector}")
    trend_level = sidebar.selectbox("Trend Level", ["Daily", "Weekly", "Monthly", "Quarterly", "Yearly"])
    st.markdown(f"### Currently Selected {trend_level}")

    show_data = sidebar.checkbox("Show Data")

    trend_kwds = {"Daily": "1D", "Weekly": "1W", "Monthly": "1M", "Quarterly": "1Q", "Yearly": "1Y"}
    trend_data = data.query(f"location=='{location_selector}'").\
        groupby(pd.Grouper(key="date", 
        freq=trend_kwds[trend_level])).aggregate(new_cases=("new_cases", "sum"),
        new_deaths = ("new_deaths", "sum"),
        new_vaccinations = ("new_vaccinations", "sum"),
        new_tests = ("new_tests", "sum")).reset_index()

    trend_data["date"] = trend_data.date.dt.date

    new_cases = sidebar.checkbox("New Cases")
    new_deaths = sidebar.checkbox("New Deaths")
    new_vaccinations = sidebar.checkbox("New Vaccinations")
    new_tests = sidebar.checkbox("New Tests")

    lines = [new_cases, new_deaths, new_vaccinations, new_tests]
    line_cols = ["new_cases", "new_deaths", "new_vaccinations", "new_tests"]
    trends = [c[1] for c in zip(lines,line_cols) if c[0]==True]

    if show_data:
        tcols = ["date"] + trends
        st.dataframe(trend_data[tcols])

    subplots=sidebar.checkbox("Show Subplots", True)
    if len(trends)>0:
        fig=trend_data.iplot(kind="line", asFigure=True, xTitle="Date", yTitle="Values",
                            x="date", y=trends, title=f"{trend_level} Trend of {', '.join(trends)}.", subplots=subplots)
        st.plotly_chart(fig, use_container_width=False)
Enter fullscreen mode Exit fullscreen mode
  • For multiple, we will first select few locations using multi select. Then show them in markdown. 
if analysis_type=="Multiple":
    selected = sidebar.multiselect("Select Locations ", locations)
    st.markdown(f"## Selected Locations: {', '.join(selected)}")
Enter fullscreen mode Exit fullscreen mode
  • Create a checkbox and do same as above until we created a trends list. 
    show_data = sidebar.checkbox("Show Data")
    trend_level = sidebar.selectbox("Trend Level", ["Daily", "Weekly", "Monthly", "Quarterly", "Yearly"])
    st.markdown(f"### Currently Selected {trend_level}")

    trend_kwds = {"Daily": "1D", "Weekly": "1W", "Monthly": "1M", "Quarterly": "1Q", "Yearly": "1Y"}

    trend_data = data.query(f"location in {selected}").\
        groupby(["location", pd.Grouper(key="date", 
        freq=trend_kwds[trend_level])]).aggregate(new_cases=("new_cases", "sum"),
        new_deaths = ("new_deaths", "sum"),
        new_vaccinations = ("new_vaccinations", "sum"),
        new_tests = ("new_tests", "sum")).reset_index()

    trend_data["date"] = trend_data.date.dt.date

    new_cases = sidebar.checkbox("New Cases")
    new_deaths = sidebar.checkbox("New Deaths")
    new_vaccinations = sidebar.checkbox("New Vaccinations")
    new_tests = sidebar.checkbox("New Tests")

    lines = [new_cases, new_deaths, new_vaccinations, new_tests]
    line_cols = ["new_cases", "new_deaths", "new_vaccinations", "new_tests"]
    trends = [c[1] for c in zip(lines,line_cols) if c[0]==True]
Enter fullscreen mode Exit fullscreen mode
  • Create a new data frame where we will create new columns based on each selected country. 
    ndf = pd.DataFrame(data=trend_data.date.unique(),columns=["date"])
Enter fullscreen mode Exit fullscreen mode
  • For each selected country, create new column and merge it back to ndf with key as a date. 
    for s in selected:
        new_cols = ["date"]+[f"{s}_{c}" for c in line_cols]
        tdf = trend_data.query(f"location=='{s}'")
        tdf.drop("location", axis=1, inplace=True)
        tdf.columns=new_cols
        ndf=ndf.merge(tdf,on="date",how="inner")
Enter fullscreen mode Exit fullscreen mode
  • If show_data is selected, we will show the dataframe. 
    if show_data:
        if len(ndf)>0:
            st.dataframe(ndf)
        else:
            st.markdown("Empty Dataframe")
Enter fullscreen mode Exit fullscreen mode
  • Create a new list where we will put columns related to location. 
    new_trends = []
    for c in trends:
        new_trends.extend([f"{s}_{c}" for s in selected])
Enter fullscreen mode Exit fullscreen mode
  • Create a subplots checkbox and plot a line plot with new_trends column names. 
    subplots=sidebar.checkbox("Show Subplots", True)
    if len(trends)>0:
        st.markdown("### Trend of Selected Locations")

        fig=ndf.iplot(kind="line", asFigure=True, xTitle="Date", yTitle="Values",
                            x="date", y=new_trends, title=f"{trend_level} Trend of {', '.join(trends)}.", subplots=subplots)
        st.plotly_chart(fig, use_container_width=False)
Enter fullscreen mode Exit fullscreen mode

Full Code 

import streamlit as st
import numpy as np
import pandas as pd
import cufflinks

@st.cache
def get_data(url):
    df = pd.read_csv(url)
    df["date"] = pd.to_datetime(df.date).dt.date
    df['date'] = pd.DatetimeIndex(df.date)

    return df

url = "https://covid.ourworldindata.org/data/owid-covid-data.csv"
data = get_data(url)

locations = data.location.unique().tolist()

sidebar = st.sidebar

analysis_type = sidebar.radio("Analysis Type", ["Single", "Multiple"])
st.markdown(f"Analysis Mode: {analysis_type}")

if analysis_type=="Single":
    location_selector = sidebar.selectbox(
        "Select a Location",
        locations
    )
    st.markdown(f"# Currently Selected {location_selector}")
    trend_level = sidebar.selectbox("Trend Level", ["Daily", "Weekly", "Monthly", "Quarterly", "Yearly"])
    st.markdown(f"### Currently Selected {trend_level}")

    show_data = sidebar.checkbox("Show Data")

    trend_kwds = {"Daily": "1D", "Weekly": "1W", "Monthly": "1M", "Quarterly": "1Q", "Yearly": "1Y"}
    trend_data = data.query(f"location=='{location_selector}'").\
        groupby(pd.Grouper(key="date", 
        freq=trend_kwds[trend_level])).aggregate(new_cases=("new_cases", "sum"),
        new_deaths = ("new_deaths", "sum"),
        new_vaccinations = ("new_vaccinations", "sum"),
        new_tests = ("new_tests", "sum")).reset_index()

    trend_data["date"] = trend_data.date.dt.date

    new_cases = sidebar.checkbox("New Cases")
    new_deaths = sidebar.checkbox("New Deaths")
    new_vaccinations = sidebar.checkbox("New Vaccinations")
    new_tests = sidebar.checkbox("New Tests")

    lines = [new_cases, new_deaths, new_vaccinations, new_tests]
    line_cols = ["new_cases", "new_deaths", "new_vaccinations", "new_tests"]
    trends = [c[1] for c in zip(lines,line_cols) if c[0]==True]

    if show_data:
        tcols = ["date"] + trends
        st.dataframe(trend_data[tcols])

    subplots=sidebar.checkbox("Show Subplots", True)
    if len(trends)>0:
        fig=trend_data.iplot(kind="line", asFigure=True, xTitle="Date", yTitle="Values",
                            x="date", y=trends, title=f"{trend_level} Trend of {', '.join(trends)}.", subplots=subplots)
        st.plotly_chart(fig, use_container_width=False)

if analysis_type=="Multiple":
    selected = sidebar.multiselect("Select Locations ", locations)
    st.markdown(f"## Selected Locations: {', '.join(selected)}")
    show_data = sidebar.checkbox("Show Data")
    trend_level = sidebar.selectbox("Trend Level", ["Daily", "Weekly", "Monthly", "Quarterly", "Yearly"])
    st.markdown(f"### Currently Selected {trend_level}")

    trend_kwds = {"Daily": "1D", "Weekly": "1W", "Monthly": "1M", "Quarterly": "1Q", "Yearly": "1Y"}

    trend_data = data.query(f"location in {selected}").\
        groupby(["location", pd.Grouper(key="date", 
        freq=trend_kwds[trend_level])]).aggregate(new_cases=("new_cases", "sum"),
        new_deaths = ("new_deaths", "sum"),
        new_vaccinations = ("new_vaccinations", "sum"),
        new_tests = ("new_tests", "sum")).reset_index()

    trend_data["date"] = trend_data.date.dt.date

    new_cases = sidebar.checkbox("New Cases")
    new_deaths = sidebar.checkbox("New Deaths")
    new_vaccinations = sidebar.checkbox("New Vaccinations")
    new_tests = sidebar.checkbox("New Tests")

    lines = [new_cases, new_deaths, new_vaccinations, new_tests]
    line_cols = ["new_cases", "new_deaths", "new_vaccinations", "new_tests"]
    trends = [c[1] for c in zip(lines,line_cols) if c[0]==True]

    ndf = pd.DataFrame(data=trend_data.date.unique(),columns=["date"])

    for s in selected:
        new_cols = ["date"]+[f"{s}_{c}" for c in line_cols]
        tdf = trend_data.query(f"location=='{s}'")
        tdf.drop("location", axis=1, inplace=True)
        tdf.columns=new_cols
        ndf=ndf.merge(tdf,on="date",how="inner")

    if show_data:
        if len(ndf)>0:
            st.dataframe(ndf)
        else:
            st.markdown("Empty Dataframe")

    new_trends = []
    for c in trends:
        new_trends.extend([f"{s}_{c}" for s in selected])

    subplots=sidebar.checkbox("Show Subplots", True)
    if len(trends)>0:
        st.markdown("### Trend of Selected Locations")

        fig=ndf.iplot(kind="line", asFigure=True, xTitle="Date", yTitle="Values",
                            x="date", y=new_trends, title=f"{trend_level} Trend of {', '.join(trends)}.", subplots=subplots)
        st.plotly_chart(fig, use_container_width=False)
Enter fullscreen mode Exit fullscreen mode

Output

Single

Multiple

Top comments (0)

Read next

neotechy profile image

PHP Tips and Tricks for Writing Cleaner and More Efficient Code

NeoTechy -

mfdupuis profile image

Who should be your first data hire and when should you hire them?

Marc Dupuis -

jguerrero-voxel51 profile image

ECCV 2024 Redux: Fast and Photo-realistic Novel View Synthesis from Sparse Images

Jimmy Guerrero -

slotbite profile image

Installing Python Dependencies on AWS Lambda Using EFS

Cristián Andrés -