- can it receive user input
- plotly can (basically) replicate Tableau
-
If you want to install plotly just run
!pip install plotlyin your jupyter notebook ORpip install plotlyin your terminal
-
Load in data
-
Make plotly visualizations
-
Show how plotly can be used in dash apps
import pandas as pd
import numpy as np
import matplotlib.pyplot as pltdf = pd.read_csv("data/covid19.csv", encoding='ISO-8859-1')df['dateRep'] = pd.to_datetime(df["dateRep"])
df.head()
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| dateRep | day | month | year | cases | deaths | countriesAndTerritories | geoId | countryterritoryCode | popData2018 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2020-03-27 | 27 | 3 | 2020 | 0 | 0 | Afghanistan | AF | AFG | 37172386.0 |
| 1 | 2020-03-26 | 26 | 3 | 2020 | 33 | 0 | Afghanistan | AF | AFG | 37172386.0 |
| 2 | 2020-03-25 | 25 | 3 | 2020 | 2 | 0 | Afghanistan | AF | AFG | 37172386.0 |
| 3 | 2020-03-24 | 24 | 3 | 2020 | 6 | 1 | Afghanistan | AF | AFG | 37172386.0 |
| 4 | 2020-03-23 | 23 | 3 | 2020 | 10 | 0 | Afghanistan | AF | AFG | 37172386.0 |
df.sort_values(by="dateRep", inplace=True)
df.reset_index(inplace=True, drop=True)
df.head()
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| dateRep | day | month | year | cases | deaths | countriesAndTerritories | geoId | countryterritoryCode | popData2018 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Singapore | SG | SGP | 5638676.0 |
| 1 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Greece | EL | GRC | 10727668.0 |
| 2 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Lithuania | LT | LTU | 2789533.0 |
| 3 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Canada | CA | CAN | 37058856.0 |
| 4 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Brazil | BR | BRA | 209469333.0 |
df.tail()
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| dateRep | day | month | year | cases | deaths | countriesAndTerritories | geoId | countryterritoryCode | popData2018 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 7120 | 2020-12-03 | 12 | 3 | 2020 | 15 | 1 | Indonesia | ID | IDN | 267663435.0 |
| 7121 | 2020-12-03 | 12 | 3 | 2020 | 1 | 0 | Cambodia | KH | KHM | 16249798.0 |
| 7122 | 2020-12-03 | 12 | 3 | 2020 | 0 | 0 | Algeria | DZ | DZA | 42228429.0 |
| 7123 | 2020-12-03 | 12 | 3 | 2020 | 15 | 0 | Iceland | IS | ISL | 353574.0 |
| 7124 | 2020-12-03 | 12 | 3 | 2020 | 15 | 0 | Russia | RU | RUS | 144478050.0 |
# in pandas with matplotlib
df_Afg = df.loc[df["countriesAndTerritories"]=="Afghanistan", ["dateRep", "cases"]]
df_Afg.head()
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| dateRep | cases | |
|---|---|---|
| 20 | 2019-12-31 | 0 |
| 131 | 2020-01-01 | 0 |
| 190 | 2020-01-02 | 0 |
| 261 | 2020-01-03 | 0 |
| 271 | 2020-01-13 | 0 |
df_Afg.set_index("dateRep").plot()<matplotlib.axes._subplots.AxesSubplot at 0x12e11a518>
import plotly.graph_objects as gofig = go.Figure()
df_Afg = df.loc[df["geoId"]=="AF"]
trace = go.Scatter(x=df_Afg["dateRep"],
y=df_Afg["cases"],
mode="lines+markers",
name="AF")
fig.add_trace(trace) <div id="c46404bc-4830-451f-a1c3-3c9aa4c7e0aa" class="plotly-graph-div" style="height:525px; width:100%;"></div>
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usa_border_buddies = ["United_States_of_America", "Mexico", "Canada"]
fig = go.Figure()
df_USA = df.loc[df["countriesAndTerritories"]=="United_States_of_America"]
trace1 = go.Scatter(x=df_USA["dateRep"], y=df_USA["cases"], mode="lines+markers", name="US")
fig.add_trace(trace1)
df_MEXICO = df.loc[df["countriesAndTerritories"]=="Mexico"]
trace2 = go.Scatter(x=df_MEXICO["dateRep"], y=df_MEXICO["cases"], mode="lines+markers", name="Mexico")
fig.add_trace(trace2)
df_CANADA = df.loc[df["countriesAndTerritories"]=="Canada"]
trace3 = go.Scatter(x=df_CANADA["dateRep"], y=df_CANADA["cases"], mode="lines+markers", name="Canada")
fig.add_trace(trace3)
fig.show() <div id="8971ca36-712f-4e12-8176-b65d5dd72091" class="plotly-graph-div" style="height:525px; width:100%;"></div>
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usa_border_buddies = ["United_States_of_America", "Mexico", "Canada"]
fig = go.Figure()
for country in usa_border_buddies:
df_country = df.loc[df["countriesAndTerritories"]==country]
geoId = df_country["geoId"].values[0]
trace = go.Scatter(x=df_country["dateRep"], y=df_country["cases"], mode="lines+markers", name=geoId)
fig.add_trace(trace)
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fig = go.Figure()
for country in usa_border_buddies:
df_country = df.loc[df["countriesAndTerritories"]==country]
geoId = df_country["geoId"].values[0]
trace = go.Scatter(x=df_country["dateRep"], y=df_country["cases"], mode="lines+markers", name=geoId)
fig.add_trace(trace)
# add labels
fig.update_layout(
title="Covid Cases for CA/MX/USA",
xaxis_title="Timeline",
yaxis_title="Number of Covid Cases"
)
fig.show() <div id="acc77986-5b8d-46ec-9225-fbe18cd62542" class="plotly-graph-div" style="height:525px; width:100%;"></div>
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fig = go.Figure()
for country in usa_border_buddies:
df_country = df.loc[df["countriesAndTerritories"]==country]
geoId = df_country["geoId"].values[0]
trace = go.Scatter(x=df_country["dateRep"], y=df_country["cases"], mode="lines+markers", name=geoId)
fig.add_trace(trace)
fig.update_layout(
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xaxis_title="Timeline",
yaxis_title="Number of Covid Cases"
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df.head()
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| dateRep | day | month | year | cases | deaths | countriesAndTerritories | geoId | countryterritoryCode | popData2018 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Singapore | SG | SGP | 5638676.0 |
| 1 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Greece | EL | GRC | 10727668.0 |
| 2 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Lithuania | LT | LTU | 2789533.0 |
| 3 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Canada | CA | CAN | 37058856.0 |
| 4 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Brazil | BR | BRA | 209469333.0 |
df_total_cases = df[["cases", "countryterritoryCode", "day", "month", "year"]]
df_total_cases = df_total_cases.groupby(by="countryterritoryCode").agg(sum).reset_index()
df_total_cases.head()
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| countryterritoryCode | cases | day | month | year | |
|---|---|---|---|---|---|
| 0 | ABW | 28 | 135 | 18 | 12120 |
| 1 | AFG | 75 | 1257 | 152 | 157559 |
| 2 | AGO | 3 | 147 | 18 | 12120 |
| 3 | ALB | 174 | 342 | 57 | 38380 |
| 4 | AND | 224 | 275 | 42 | 28280 |
import plotly.express as px
df_total_cases = df[["cases", "countryterritoryCode", "day", "month", "year"]]
df_total_cases = df_total_cases.groupby(by="countryterritoryCode").agg(sum).reset_index()
df_total_cases.head()
fig = px.scatter_geo(df_total_cases,
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import plotly as py
df_total_cases = df[["cases", "countryterritoryCode"]]
df_total_cases = df_total_cases.groupby(by="countryterritoryCode").agg(sum).reset_index()
df_total_cases.head()
data = dict(
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locations = df_total_cases["countryterritoryCode"],
locationmode ='ISO-3',
z=df_total_cases["cases"])
map = go.Figure(data=[data])
map.show() <div id="a032dd92-de56-42cb-981c-98d8843e5d98" class="plotly-graph-div" style="height:525px; width:100%;"></div>
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import plotly.express as px
fig = px.histogram(df, x="cases")
fig.show() <div id="c5ef8d6a-87d7-4180-bf29-2dafed277ea7" class="plotly-graph-div" style="height:600px; width:100%;"></div>
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df_Afg.head()
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| dateRep | day | month | year | cases | deaths | countriesAndTerritories | geoId | countryterritoryCode | popData2018 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 20 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Afghanistan | AF | AFG | 37172386.0 |
| 131 | 2020-01-01 | 1 | 1 | 2020 | 0 | 0 | Afghanistan | AF | AFG | 37172386.0 |
| 190 | 2020-01-02 | 1 | 2 | 2020 | 0 | 0 | Afghanistan | AF | AFG | 37172386.0 |
| 261 | 2020-01-03 | 1 | 3 | 2020 | 0 | 0 | Afghanistan | AF | AFG | 37172386.0 |
| 271 | 2020-01-13 | 13 | 1 | 2020 | 0 | 0 | Afghanistan | AF | AFG | 37172386.0 |
df_cumulative_sum_Afg = pd.DataFrame()df_cumulative_sum_Afg["cases"] = df_Afg["cases"].cumsum()
df_cumulative_sum_Afg["dateRep"] = df_Afg["dateRep"]
df_cumulative_sum_Afg.head()
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| cases | dateRep | |
|---|---|---|
| 20 | 0 | 2019-12-31 |
| 131 | 0 | 2020-01-01 |
| 190 | 0 | 2020-01-02 |
| 261 | 0 | 2020-01-03 |
| 271 | 0 | 2020-01-13 |
fig = go.Figure()
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df.head(2)
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| dateRep | day | month | year | cases | deaths | countriesAndTerritories | geoId | countryterritoryCode | popData2018 | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Singapore | SG | SGP | 5638676.0 |
| 1 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Greece | EL | GRC | 10727668.0 |
df["cases_sum"] = df["cases"].cumsum()df.head()
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| dateRep | day | month | year | cases | deaths | countriesAndTerritories | geoId | countryterritoryCode | popData2018 | cases_sum | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Singapore | SG | SGP | 5638676.0 | 0 |
| 1 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Greece | EL | GRC | 10727668.0 | 0 |
| 2 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Lithuania | LT | LTU | 2789533.0 | 0 |
| 3 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Canada | CA | CAN | 37058856.0 | 0 |
| 4 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Brazil | BR | BRA | 209469333.0 | 0 |
df_total_cases = df[["dateRep", "cases", "countryterritoryCode", "day", "month", "year", "cases_sum"]]
df_total_cases = df_total_cases.groupby(by="countryterritoryCode").agg(sum).reset_index()
df_total_cases.head()
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| countryterritoryCode | cases | day | month | year | cases_sum | |
|---|---|---|---|---|---|---|
| 0 | ABW | 28 | 135 | 18 | 12120 | 1717979 |
| 1 | AFG | 75 | 1257 | 152 | 157559 | 13812224 |
| 2 | AGO | 3 | 147 | 18 | 12120 | 1937816 |
| 3 | ALB | 174 | 342 | 57 | 38380 | 5076230 |
| 4 | AND | 224 | 275 | 42 | 28280 | 2936660 |
import matplotlibdates = matplotlib.dates.date2num(df["dateRep"])df["datesRepStr"] = df.dateRep.astype(str)df.head()
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| dateRep | day | month | year | cases | deaths | countriesAndTerritories | geoId | countryterritoryCode | popData2018 | cases_sum | datesRepInt | datesRepStr | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Singapore | SG | SGP | 5638676.0 | 0 | 737424.0 | 2019-12-31 |
| 1 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Greece | EL | GRC | 10727668.0 | 0 | 737424.0 | 2019-12-31 |
| 2 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Lithuania | LT | LTU | 2789533.0 | 0 | 737424.0 | 2019-12-31 |
| 3 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Canada | CA | CAN | 37058856.0 | 0 | 737424.0 | 2019-12-31 |
| 4 | 2019-12-31 | 31 | 12 | 2019 | 0 | 0 | Brazil | BR | BRA | 209469333.0 | 0 | 737424.0 | 2019-12-31 |
fig = px.scatter_geo(df,
animation_frame="datesRepStr",
animation_group="countryterritoryCode",
locations="countryterritoryCode",
locationmode="ISO-3",
size="cases_sum")
fig.update_layout(
title={
"text": "Number of Covid Cases over Time",
"x": 0.5,
"xanchor":'center'}
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