6.8. Sharing and Downloading#

This section covers some tools to share and download your data.

6.8.1. Datapane: Publish your Python Objects on the Web in 2 Lines of Code#

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!pip install datapane plotly

If you want to put together your pandas.DataFrame, interactive charts such as Plotly, Bokeh, Altair, or markdown into a nice report and publish it on the web, try Datapane. The code below shows how you can publish your Python objects using Datapane in a few lines of code.

import datapane as dp
import pandas as pd
import numpy as np
import plotly.express as px

# Scripts to create df and chart
df = px.data.gapminder()

chart = px.scatter(

# Once you have the df and the chart, simply use
r = dp.Report(
    dp.Text("my simple report"),  # add description
    dp.DataTable(df),  # create a table
    dp.Plot(chart),  # create a chart

# Publish your report

Uploading report and associated data - please wait…

Report successfully uploaded, click here to view and share your report

Link to Datapane

Link to my article about Datapane

6.8.2. gdown: Download a File from Google Drive in Python#

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!pip install gdown 

If you want to download a file from Google Drive in Python, use gdown. All you need to specify is the URL link.

import gdown

# Format of url: https://drive.google.com/uc?id=YOURFILEID
url = "https://drive.google.com/uc?id=1jI1cmxqnwsmC-vbl8dNY6b4aNBtBbKy3"
output = "Twitter.zip"

gdown.download(url, output, quiet=False)
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From: https://drive.google.com/uc?id=1jI1cmxqnwsmC-vbl8dNY6b4aNBtBbKy3
To: /home/khuyen/book/book/Chapter4/Twitter.zip
120MB [00:09, 12.1MB/s] 

Link to gdown.

6.8.3. pyserde: Effortless Serialization and Deserialization of Dataclass Objects#

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!pip install pyserde

Dataclasses provide a concise syntax for defining data-holding classes. Paired with pyserde, you can easily serialize and deserialize dataclass objects.

Serialization converts dataclass data into a serialized format (e.g., JSON, YAML) for easy storage and transmission. Deserialization reconstructs the dataclass object from serialized data.

from dataclasses import dataclass
from serde import serde
from serde.json import from_json, to_json
from serde.yaml import from_yaml, to_yaml

class User:
    name: str
    age: int

user1 = User(name="user1", age=20)
from_json(User, '{"name":"user1","age":20}')
User(name='user1', age=20)
age: 20
name: user1
from_yaml(User, "age: 20\nname: user1\n")
User(name='user1', age=20)

Link to pyserde.

6.8.4. ItsDangerous: Safely Pass Trusted Data to Untrusted Environments and Back#

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!pip install -U itsdangerous

When passing data between different web requests, there is a risk of malicious code injection.

To ensure the safety of passing data to untrusted environments, use ItsDangerous. ItsDangerous adds a unique signature to the data to verify that the data has not been tampered with during transmission.

from itsdangerous import URLSafeSerializer

auth_s = URLSafeSerializer("some key")
token = auth_s.dumps({"id": 5, "name": "khuyentran"})
# Get the data back from the token using the secret key
data = auth_s.loads(token)

Link to ItsDangerous.